Dissertations / Theses on the topic 'Robust state estimation'
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Graham, Matthew Corwin 1986. "Robust Bayesian state estimation and mapping." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98678.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 135-146).
Virtually all robotic and autonomous systems rely on navigation and mapping algorithms (e.g. the Kalman filter or simultaneous localization and mapping (SLAM)) to determine their location in the world. Unfortunately, these algorithms are not robust to outliers and even a single faulty measurement can cause a catastrophic failure of the navigation system. This thesis proposes several novel robust navigation and SLAM algorithms that produce accurate results when outliers and faulty measurements occur. The new algorithms address the robustness problem by augmenting the standard models used by filtering and SLAM algorithms with additional latent variables that can be used to infer when outliers have occurred. Solving the augmented problems leads to algorithms that are naturally robust to outliers and are nearly as efficient as their non-robust counterparts. The first major contribution of this thesis is a novel robust filtering algorithm that can compensate for both measurement outliers and state prediction errors using a set of sparse latent variables that can be inferred using an efficient convex optimization. Next the thesis proposes a batch robust SLAM algorithm that uses the Expectation- Maximization algorithm to infer both the navigation solution and the measurement information matrices. Inferring the information matrices allows the algorithm to reduce the impact of outliers on the SLAM solution while the Expectation-Maximization procedure produces computationally efficient calculations of the information matrix estimates. While several SLAM algorithms have been proposed that are robust to loop closure errors, to date no SLAM algorithms have been developed that are robust to landmark errors. The final contribution of this thesis is the first SLAM algorithm that is robust to both loop closure and landmark errors (incremental SLAM with consistency checking (ISCC)). ISCC adds integer variables to the SLAM optimization that indicate whether each measurement should be included in the SLAM solution. ISCC then uses an incremental greedy strategy to efficiently determine which measurements should be used to compute the SLAM solution. Evaluation on standard benchmark datasets as well as visual SLAM experiments demonstrate that ISCC is robust to a large number of loop closure and landmark outliers and that it can provide significantly more accurate solutions than state-of-the-art robust SLAM algorithms when landmark errors occur.
by Matthew C. Graham.
Ph. D.
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Phaniraj, Viruru. "Robust state estimation in power systems." Diss., Virginia Tech, 1991. http://hdl.handle.net/10919/39776.
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Vichare, Nitin Shrikrishna. "Robust Mahalanobis distance in power systems state estimation." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/40024.
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Al-Takrouri, Saleh Othman Saleh Electrical Engineering & Telecommunications Faculty of Engineering UNSW. "Robust state estimation and model validation techniques in computer vision." Publisher:University of New South Wales. Electrical Engineering & Telecommunications, 2008. http://handle.unsw.edu.au/1959.4/41002.
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The main objective of this thesis is to apply ideas and techniques from modern control theory, especially from robust state estimation and model validation, to various important problems in computer vision. Robust model validation is used in texture recognition where new approaches for classifying texture samples and segmenting textured images are developed. Also, a new model validation approach to motion primitive recognition is demonstrated by considering the motion segmentation problem for a mobile wheeled robot. A new approach to image inpainting based on robust state estimation is proposed where the implementation presented here concerns with recovering corrupted frames in video sequences. Another application addressed in this thesis based on robust state estimation is video-based tracking. A new tracking system is proposed to follow connected regions in video frames representing the objects in consideration. The system accommodates tracking multiple objects and is designed to be robust towards occlusions. To demonstrate the performance of the proposed solutions, examples are provided where the developed methods are applied to various gray-scale images, colored images, gray-scale videos and colored videos. In addition, a new algorithm is introduced for motion estimation via inverse polynomial interpolation. Motion estimation plays a primary role within the video-based tracking system proposed in this thesis. The proposed motion estimation algorithm is also applied to medical image sequences. Motion estimation results presented in this thesis include pairs of images from a echocardiography video and a robot-assisted surgery video.
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Remund, Todd Gordon. "A Naive, Robust and Stable State Estimate." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1424.
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A naive approach to filtering for feedback control of dynamic systems that is robust and stable is proposed. Simulations are run on the filters presented to investigate the robustness properties of each filter. Each simulation with the comparison of the filters is carried out using the usual mean squared error. The filters to be included are the classic Kalman filter, Krein space Kalman, two adjustments to the Krein filter with input modeling and a second uncertainty parameter, a newly developed filter called the Naive filter, bias corrected Naive, exponentially weighted moving average (EWMA) Naive, and bias corrected EWMA Naive filter.
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Kohan, Rashid Rahmati. "Robust state estimation and control of highway traffic systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ63642.pdf.
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Malyavej, Veerachai Electrical Engineering & Telecommunications Faculty of Engineering UNSW. "Robust control and state estimation via limited capacity communication networks." Awarded by:University of New South Wales. Electrical Engineering and Telecommunications, 2006. http://handle.unsw.edu.au/1959.4/23981.
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Telecommunication networks become major parts in modern complex control systems recently. They provide many advantages over conventional point-to-point connections, such as the simplification on installation and maintenance with comparatively low cost and the nature requirement of wireless communication in remote control systems. In practice, limited resource networks are shared by multiple controllers, sensors and actuators, and they may need to serve some other information unrelated to control purpose. Consequently, the control system design in networked control systems should be revised by taking communication constraints, for example, finite precision data, time delay and noise in transmission, into account. This thesis studies the robust control and state estimation of uncertain systems, when feedback information is sent via limited capacity communication channels. It focuses on the problem of finite precision data due to the communication constraints. The proposed schemes are based on the robust set-valued state estimation and the optimal control techniques. A state estimation problem of linear uncertain system is studied first. In this problem, we propose an algorithm called coder-decoder for uncertain systems. The coder encodes the observed output into a finite-length codeword and sends it to the decoder that generates the estimated state based on the received codeword. As an illustration, we apply the results in state estimation problem to a precision missile guidance problem using sensor fusion. In this problem, the information obtained from remote sensors is transmitted through limited capacity communication networks to the guided missile. Next, we study a stabilization problem of linear uncertain systems with state feedback. In this problem, the coder-controller scheme is developed to asymptotically stabilize the uncertain systems via limited capacity communication channels. The coder encodes the full state variable into a finite-length codeword and sends it to the controller that drives the system state to the origin. To achieve the asymptotic stability, we use a dynamic quantizer so that quantization noise converges to zero. The results in both state estimation and stabilization problems can handle the problem of finite data rate communication networks in control systems.
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Post, Brian Karl. "Robust state estimation for the control of flexible robotic manipulators." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52193.
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In this thesis, a novel robust estimation strategy for observing the system state variables of robotic manipulators with distributed flexibility is established. Motivation for the derived approach stems from the observation that lightweight, high speed, and large workspace robotic manipulators often suffer performance degradation because of inherent structural compliance. This flexibility often results in persistent residual vibration, which must be damped before useful work can resume. Inherent flexibility in robotic manipulators, then, increases cycle times and shortens the operational lives of the robots. Traditional compensation techniques, those which are commonly used for the control of rigid manipulators, can only approach a fraction of the open-loop system bandwidth without inducing significant excitation of the resonant dynamics. To improve the performance of these systems, the structural flexibility cannot simply be ignored, as it is when the links are significantly stiff and approximate rigid bodies. One thus needs a model to design a suitable compensator for the vibration, but any model developed to correct this problem will contain parametric error. And in the case of very lightly damped systems, like flexible robotic manipulators, this error can lead to instability of the control system for even small errors in system parameters.
This work presents a systematic solution for the problem of robust state estimation for flexible manipulators in the presence of parametric modeling error. The solution includes: 1) a modeling strategy, 2) sensor selection and placement, and 3) a novel, multiple model estimator. Modeling of the FLASHMan flexible gantry manipulator is accomplished using a developed hybrid transfer matrix / assumed modes method (TMM/AMM) approach to determine an accurate low-order state space representation of the system dynamics. This model is utilized in a genetic algorithm optimization in determining the placement of MEMs accelerometers for robust estimation and observability of the system’s flexible state variables. The initial estimation method applied to the task of determining robust state estimates under conditions of parametric modeling error was of a sliding mode observer type. Evaluation of the method through analysis, simulations and experiments showed that the state estimates produced were inadequate. This led to the development of a novel, multiple model adaptive estimator. This estimator utilizes a bank of similarly designed sub-estimators and a selection algorithm to choose the true value from a given set of possible system parameter values as well as the correct state vector estimate. Simulation and experimental results are presented which demonstrate the applicability and effectiveness of the derived method for the task of state variable estimation for flexible robotic manipulators.
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Zammali, Chaima. "Robust state estimation for switched systems : application to fault detection." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS124.
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Cette thèse s’intéresse à l’estimation d’état et à la détection de défauts de systèmes linéaires à commutations. Deux approches d’estimation par intervalles sont développées. La première consiste à proposer une estimation d’état pour des systèmes linéaires à commutations à paramètres variants en temps continu et en temps discret. La deuxième approche consiste à proposer une nouvelle logique d’estimation du signal de commutations d’un système linéaire à commutations à entrée inconnue en combinant la technique par modes glissants et l’approche par intervalles. Le problème d’estimation d’état constitue une des étapes fondamentales pour traiter le problème de détection de défauts. Par conséquent, des solutions robustes pour la détection de défauts sont développées en utilisant la théorie des ensembles. Deux méthodologies ont été employées pour détecter les défauts : un observateur classique par intervalle et une nouvelle structure TNL d’observateur par intervalle. Les performances de détection de défauts sont améliorées en se basant sur un critère L∞. De plus, une stratégie robuste de détection de défauts est introduite en utilisant des techniques zonotopiques et ellipsoïdales. En se basant sur des critères d’optimisation, ces techniques sont utilisées pour fournir des seuils dynamiques pour l’évaluation du résidu et pour améliorer la précision des résultats de détection de défauts sans tenir compte de l’hypothèse de coopérativité. Les méthodes développées dans cette thèse sont illustrées par des exemples académiques et les résultats obtenus montrent leur efficacitéThis thesis deals with state estimation and fault detection for a class of switched linear systems. Two interval state estimation approaches are proposed. The first one is investigated for both continuous and discrete-time linear parameter varying switched systems subject to measured polytopic parameters. The second approach is concerned with a new switching signal observer, combining sliding mode and interval techniques, for a class of switched linear systems with unknown input. State estimation remains one of the fundamental steps to deal with fault detection. Hence, robust solutions for fault detection are considered using set-membership theory. Two interval techniques are achieved to deal with fault detection for discrete-time switched systems. First, a commonly used interval observer is designed based on an L∞ criterion to obtain accurate fault detection results. Second, a new interval observer structure (TNL structure) is investigated to relax the cooperativity constraint. In addition, a robust fault detection strategy is considered using zonotopic and ellipsoidal analysis. Based on optimization criteria, the zonotopic and ellipsoidal techniques are used to provide a systematic and effective way to improve the accuracy of the residual boundaries without considering the nonnegativity assumption. The developed techniques in this thesis are illustrated using academic examples and the results show their effectiveness
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Xie, Li Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "Finite horizon robust state estimation for uncertain finite-alphabet hidden Markov models." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2004. http://handle.unsw.edu.au/1959.4/38664.
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In this thesis, we consider a robust state estimation problem for discrete-time, homogeneous, first-order, finite-state finite-alphabet hidden Markov models (HMMs). Based on Kolmogorov's Theorem on the existence of a process, we first present the Kolmogorov model for the HMMs under consideration. A new change of measure is introduced. The statistical properties of the Kolmogorov representation of an HMM are discussed on the canonical probability space. A special Kolmogorov measure is constructed. Meanwhile, the ergodicity of two expanded Markov chains is investigated. In order to describe the uncertainty of HMMs, we study probability distance problems based on the Kolmogorov model of HMMs. Using a change of measure technique, the relative entropy and the relative entropy rate as probability distances between HMMs, are given in terms of the HMM parameters. Also, we obtain a new expression for a probability distance considered in the existing literature such that we can use an information state method to calculate it. Furthermore, we introduce regular conditional relative entropy as an a posteriori probability distance to measure the discrepancy between HMMs when a realized observation sequence is given. A representation of the regular conditional relative entropy is derived based on the Radon-Nikodym derivative. Then a recursion for the regular conditional relative entropy is obtained using an information state method. Meanwhile, the well-known duality relationship between free energy and relative entropy is extended to the case of regular conditional relative entropy given a sub-[special character]-algebra. Finally, regular conditional relative entropy constraints are defined based on the study of the probability distance problem. Using a Lagrange multiplier technique and the duality relationship for regular conditional relative entropy, a finite horizon robust state estimator for HMMs with regular conditional relative entropy constraints is derived. A complete characterization of the solution to the robust state estimation problem is also presented.
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Chapman, Michael Addison. "Adaptation and Installation of a Robust State Estimation Package in the Eef Utility." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/31432.
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Robust estimation methods have been successfully applied to the problem of power system state estimation in a real-time environment. The Schweppe-type GM-estimator with the Huber psi-function (SHGM) has been fully installed in conjunction with a topology processor in the EEF utility, headquartered in Fribourg, Switzerland.
Some basic concepts of maximum likelihood estimation and robust analysis are reviewed, and applied to the development of the SHGM-estimator. The algorithms used by the topology processor and state estimator are presented, and the superior performance of the SHGM-estimator over the classic weighted least squares estimator is demonstrated on the EEF network. The measurement configuration of the EEF network has been evaluated, and suggestions for its reinforcement have been proposed.Master of Science
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Adduri, Phani R. "ROBUST ESTIMATION OF RELIABILITY IN THE PRESENCE OF MULTIPLE FAILURE MODES." Wright State University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=wright1166045748.
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Tsitsimelis, Achilleas. "Advanced signal processing techniques for robust state estimation applications in smart grids." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/670010.
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Since their inception, more than one century ago, electrical grids have played the role of a critical infrastructure. During the majority of this time, power systems have not faced radical changes. In contrast, over the last two decades this paradigm has rapidly changed. On the one hand, the environmental need for de-carbonization has stimulated the introduction of (i) green energy through renewable energy sources (RES); and (ii) Distributed Energy Resources (DER). On the other, the de-regulation of energy market has raised the necessity for substantial cooperation between the energy utilities.
All the above implies that, to start with, power grids must be able to support bi-directional power flows. And, further, that variations in power generation and consumption must be timely and accurately monitored. To that aim, engineers and researchers can exploit recent innovations in measurement technology; advanced signal processing algorithms and optimization tools; and a widespread use of wired and wireless communication technologies. This, clearly, brings the notion of Smart Grids (SG) into play. The accomplishment of this modern paradigm requires the re-design of a number of classical management and control strategies running in the operation centers of traditional grids.
Specifically, the main objective of this PhD dissertation is the re-formulation of a key functionality for the efficient monitoring, control and optimization of electrical networks: State Estimation (SE). Our research has been divided in two parts. In the first, the study is focused on the Transmission Grids (TG). The second, is dedicated to the (medium voltage) Distribution Grid (DG).
With respect to TGs, we propose a hybrid SE scheme exploiting both PMU and legacy measurements. The problem suffers from an inherent non-convexity and, thus, we adopt a successive convex approximation framework (SCA-SE) to iteratively solve it. Our goal is to attain increased accuracy and faster convergence rate. Going one step beyond, we pose the SCA-SE problem in a decentralized setting. For the solution, we resort to the so-called Alternating Direction Method of Multipliers (ADMM). Finally, we take into consideration the presence of bad data in the measurement sets. In this case, we reformulate the problem in a Least Absolute Shrinkage and Selection Operator (LASSO) optimization framework and, we provide joint state estimation and bad data detection.
In the second part of this dissertation, we address the problem of SE for the distribution grid. Our aim is to propose an algorithm capable of tracking the rapid variations over the voltage profile. To do so, we leverage on the recently introduced micro-PMUs (µPMUs) for distribution grids. Specifically, we present a regularized SE scheme operating at two different time-scales: (i) a robust state estimator that operates at the main time instants; and (ii) a regularized SE scheme for a number of intermediate time instants. For the former, we formulate the estimator as a regularized version of the Normal-Equations based SE solution (R-NESE). As for the latter, we present a Decomposed Weighted Total Variation State Estimation (D-WTVSE) scheme. In order to solve the D-WTVSE problem, we resort to the ADMM. Besides, we study the problem of µPMU placement (µPP). The problem is posed as a mixed integer semidefinite programming (MISDP) model and, thus, it can be efficiently solved.Desde hace más de un siglo de su creación, las redes eléctricas han desempeñado el papel de una infraestructura critica. Durante la mayor parte de este tiempo, los sistemas de potencia electrica no han tenido cambios radicales. En contraste, este paradigma ha cambiado rápidamente en las últimas dos décadas. Por un lado, la necesidad ambientes de descarbonización ha estimulado la introducción de (i) energía verde a través de fuentes de energía renovables; (ii) Recursos energéticos distribuidos. Por otro lado, la desregulación del mercado energético ha planteado la necesidad de una cooperación sustancias entre las empresas de energía. Todo lo anterior implica que, para empezar, las redes eléctricas deben ser capaces de soportar flujos de energía bidireccionales. Y, ademas, que las variaciones en la generación y el consumo de energía deben ser monitoreadas de manera oportuna y precisa. Con este objetivo, los ingenieros e investigadores pueden explotar innovaciones recientes en tecnología de medición; algoritmos avanzados en procesamiento de señales y herramientas de optimización; y un uso generalizado de tecnologías de comunicación por cable e inalámbricas. Esto, claramente, implica a las redes eléctricas inteligentes (smart grids en inglés). La realización de este paradigma moderno, requiere el diseño de una serie de estrategias clásicas de gestión y control que se ejecutan en los centros de operación de las redes tradicionales. Específicamente, el objetivo de esta tesis doctoral es la formulación de una funcionalidad clave para el monitoreo, control y optimización eficiente de las redes eléctricas: Estimación de Estado (SE en sus siglas en inglés). Nuestra investigación se ha dividido en dos partes. En la primera, el estudio se centra en las Redes de Transmisión (TG). La segunda parte esta dedicada a la Redes de Distribución (DG) de media tensión. Con respecto a las TG, proponemos un esquema de SE hibrido que explota tanto las mediciones de PMU como las heredadas. El problema no es convexo y, así mismo, adoptamos un marco de aproximación convexo sucesivo (SCA-SE) para resolverlo de forma iterativa. Nuestro objetivo es lograr una mayo precisión y una tasa de convergencia más rápida. Yendo un paso adelante, planteamos el problema SCA-SE en un entorno descentralizado. Para solucionar esto, recurrimos al llamado Método de dirección Alterna de Multiplicadores (ADMM en sus siglas en inglés). Finalmente, tomamos en cuenta la presencia de datos incorrectos en los conjuntos de medidas. En este caso, reformulamos el problema en un marco optimización del método LASSO (Shrinkage and Selection Operator, por sus siglas en inglés) y, proporcionamos una estimación del estado conjunto y detección de datos incorrectos. En la segunda parte de esta disertación, abordamos el problema SE para la red de distribución. Nuestro objetivo es proponer un algoritmo capas de rastrear las variaciones rápidas sobre el perfil de voltaje. Para hacer esto, aprovechamos las micro-PMU (µPMUs) introducidas recientemente en las redes de distribución. Específicamente, presentamos un esquema SE regularizado que opera en dos escalas de tiempo diferentes: (i) un robusto estimador de estado que opera en los instantes de tiempo principales; y (ii) un esquema SE regularizado para varios instantes de tiempo intermedios. Para el primero, formulamos el estimador como la versión regularizada de ecuaciones normales basadas en solución SE (R-NESE). En cuanto a este ultimo, presentamos un esquema D-WTVSE (Decomposed Weighted Total Variation State Estimation, por sus siglas en inglés). Para resolver el problema de D-WTVSE, recurrimos al ADMM. Ademas, estudiamos el problema µPP (µPMU placement, por sus siglas en inglés). El problema se plantea como un modelo MISDP (Mixed Integer Semidefinite Programming, por sus siglas en inglés) y, por lo tanto, el problema puede ser resulto de manera eficiente.
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Ravuri, Muralidhar 1973. "Asymmetric order-doubling and robust state estimation for uncertain nonminimum phase systems." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80517.
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Wittmeyer, Gordon William. "Robust estimation of parameters in nonlinear subsurface flow models using adjoint state methods." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185329.
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Estimating the parameters of groundwater flow models by automatic calibration methods is an extremely difficult problem, but one which must be solved in order to produce reliable model predictions. The data upon which the model is calibrated are usually corrupted by measurement and model structure errors which can unduly affect the values of the parameter estimates. In this dissertation the statistically robust M-estimator of Huber is used to reduce the influence of large, outlying errors in the measured head data on the values of the estimated model parameters. The robust estimation procedure is implemented in a computer program which models unconfined, steady-state and transient flow as described by the Boussinesq equation for Dupuit-type flow. The program allows the user to estimate hydraulic conductivity, specific yield, specific storage, recharge rates, leakances, boundary heads and boundary fluxes. The nonlinear error criterion is minimized using conjugate gradient and quasi-Newton methods coupled with both accurate and innaccurate line search algorithms. The gradient of the error criterion is efficiently computed by using the adjoint state finite element method. Monte Carlo studies of a synthetic aquifer model are used to demonstrate the superior efficiency of the Huber M-estimator to that of ordinary least squares. The method is also applied to a large scale inverse modeling study of the Tucson basin regional aquifer.
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Zhao, Junbo. "A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83423.
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The enhancement of the reliability, security, and resiliency of electric power systems depends on the availability of fast, accurate, and robust dynamic state estimators. These estimators should be robust to gross errors on the measurements and the model parameter values while providing good state estimates even in the presence of large dynamical system model uncertainties and non-Gaussian thick-tailed process and observation noises. It turns out that the current Kalman filter-based dynamic state estimators given in the literature suffer from several important shortcomings, precluding them from being adopted by power utilities for practical applications. To be specific, they cannot handle (i) dynamic model uncertainty and parameter errors; (ii) non-Gaussian process and observation noise of the system nonlinear dynamic models; (iii) three types of outliers; and (iv) all types of cyber attacks. The three types of outliers, including observation, innovation, and structural outliers are caused by either an unreliable dynamical model or real-time synchrophasor measurements with data quality issues, which are commonly seen in the power system.
To address these challenges, we have pioneered a general theoretical framework that advances both robust statistics and robust control theory for robust dynamic state and parameter estimation of a cyber-physical system. Specifically, the generalized maximum-likelihood-type (GM)-estimator, the unscented Kalman filter (UKF), and the H-infinity filter are integrated into a unified framework to yield various centralized and decentralized robust dynamic state estimators. These new estimators include the GM-iterated extended Kalman filter (GM-IEKF), the GM-UKF, the H-infinity UKF and the robust H-infinity UKF. The GM-IEKF is able to handle observation and innovation outliers but its statistical efficiency is low in the presence of non-Gaussian system process and measurement noise. The GM-UKF addresses this issue and achieves a high statistical efficiency under a broad range of non-Gaussian process and observation noise while maintaining the robustness to observation and innovation outliers. A reformulation of the GM-UKF with multiple hypothesis testing further enables it to handle structural outliers. However, the GM-UKF may yield biased state estimates in presence of large system uncertainties. To this end, the H-infinity UKF that relies on robust control theory is proposed. It is shown that H-infinity is able to bound the system uncertainties but lacks of robustness to outliers and non-Gaussian noise. Finally, the robust H-infinity filter framework is proposed that leverages the H-infinity criterion to bound system uncertainties while relying on the robustness of GM-estimator to filter out non-Gaussian noise and suppress outliers. Furthermore, these new robust estimators are applied for system bus frequency monitoring and control and synchronous generator model parameter calibration. Case studies of several different IEEE standard systems show the efficiency and robustness of the proposed estimators.Ph. D.
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Pajic, Slobodan. "Power System State Estimation and Contingency Constrained Optimal Power Flow - A Numerically Robust Implementation." Digital WPI, 2007. https://digitalcommons.wpi.edu/etd-dissertations/240.
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The research conducted in this dissertation is divided into two main parts. The first part provides further improvements in power system state estimation and the second part implements Contingency Constrained Optimal Power Flow (CCOPF) in a stochastic multiple contingency framework. As a real-time application in modern power systems, the existing Newton-QR state estimation algorithms are too slow and too fragile numerically. This dissertation presents a new and more robust method that is based on trust region techniques. A faster method was found among the class of Krylov subspace iterative methods, a robust implementation of the conjugate gradient method, called the LSQR method. Both algorithms have been tested against the widely used Newton-QR state estimator on the standard IEEE test networks. The trust region method-based state estimator was found to be very reliable under severe conditions (bad data, topological and parameter errors). This enhanced reliability justifies the additional time and computational effort required for its execution. The numerical simulations indicate that the iterative Newton-LSQR method is competitive in robustness with classical direct Newton-QR. The gain in computational efficiency has not come at the cost of solution reliability. The second part of the dissertation combines Sequential Quadratic Programming (SQP)-based CCOPF with Monte Carlo importance sampling to estimate the operating cost of multiple contingencies. We also developed an LP-based formulation for the CCOPF that can efficiently calculate Locational Marginal Prices (LMPs) under multiple contingencies. Based on Monte Carlo importance sampling idea, the proposed algorithm can stochastically assess the impact of multiple contingencies on LMP-congestion prices.
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Hu, Nan. "A unified discrepancy-based approach for balancing efficiency and robustness in state-space modeling estimation, selection, and diagnosis." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2224.
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Due to its generality and flexibility, the state-space model has become one of the most popular models in modern time domain analysis for the description and prediction of time series data. The model is often used to characterize processes that can be conceptualized as "signal plus noise," where the realized series is viewed as the manifestation of a latent signal that has been corrupted by observation noise.
In the state-space framework, parameter estimation is generally accomplished by maximizing the innovations Gaussian log-likelihood. The maximum likelihood estimator (MLE) is efficient when the normality assumption is satisfied. However, in the presence of contamination, the MLE suffers from a lack of robustness. Basu, Harris, Hjort, and Jones (1998) introduced a discrepancy measure (BHHJ) with a non-negative tuning parameter that regulates the trade-off between robustness and efficiency. In this manuscript, we propose a new parameter estimation procedure based on the BHHJ discrepancy for fitting state-space models. As the tuning parameter is increased, the estimation procedure becomes more robust but less efficient. We investigate the performance of the procedure in an illustrative simulation study. In addition, we propose a numerical method to approximate the asymptotic variance of the estimator, and we provide an approach for choosing an appropriate tuning parameter in practice. We justify these procedures theoretically and investigate their efficacy in simulation studies.
Based on the proposed parameter estimation procedure, we then develop a new model selection criterion in the state-space framework. The traditional Akaike information criterion (AIC), where the goodness-of-fit is assessed by the empirical log-likelihood, is not robust to outliers. Our new criterion is comprised of a goodness-of-fit term based on the empirical BHHJ discrepancy, and a penalty term based on both the tuning parameter and the dimension of the candidate model. We present a comprehensive simulation study to investigate the performance of the new criterion. In instances where the time series data is contaminated, our proposed model selection criterion is shown to perform favorably relative to AIC.
Lastly, using the BHHJ discrepancy based on the chosen tuning parameter, we propose two versions of an influence diagnostic in the state-space framework. Specifically, our diagnostics help to identify cases that influence the recovery of the latent signal, thereby providing initial guidance and insight for further exploration. We illustrate the behavior of these measures in a simulation study.
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Ablay, Gunyaz. "Sliding Mode Approaches for Robust Control, State Estimation, Secure Communication, and Fault Diagnosis in Nuclear Systems." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1354551858.
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Dando, Aaron John. "Robust adaptive control of rigid spacecraft attitude maneuvers." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/16695/1/Aaron_John_Dando_Thesis.pdf.
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In this thesis novel feedback attitude control algorithms and attitude estimation algorithms are developed for a three-axis stabilised spacecraft attitude control system. The spacecraft models considered include a rigid-body spacecraft equipped with (i) external control torque devices, and (ii) a redundant reaction wheel configuration. The attitude sensor suite comprises a three-axis magnetometer and three-axis rate gyroscope assembly. The quaternion parameters (also called Euler symmetric parameters), which globally avoid singularities but are subject to a unity-norm constraint, are selected as the primary attitude coordinates. There are four novel contributions presented in this thesis. The first novel contribution is the development of a robust control strategy for spacecraft attitude tracking maneuvers, in the presence of dynamic model uncertainty in the spacecraft inertia matrix, actuator magnitude constraints, bounded persistent external disturbances, and state estimation error. The novel component of this algorithm is the incorporation of state estimation error into the stability analysis. The proposed control law contains a parameter which is dynamically adjusted to ensure global asymptotic stability of the overall closedloop system, in the presence of these specific system non-idealities. A stability proof is presented which is based on Lyapunov's direct method, in conjunction with Barbalat's lemma. The control design approach also ensures minimum angular path maneuvers, since the attitude quaternion parameters are not unique. The second novel contribution is the development of a robust direct adaptive control strategy for spacecraft attitude tracking maneuvers, in the presence of dynamic model uncertainty in the spacecraft inertia matrix. The novel aspect of this algorithm is the incorporation of a composite parameter update strategy, which ensures global exponential convergence of the closed-loop system. A stability proof is presented which is based on Lyapunov's direct method, in conjunction with Barbalat's lemma. The exponential convergence results provided by this control strategy require persistently exciting reference trajectory commands. The control design approach also ensures minimum angular path maneuvers. The third novel contribution is the development of an optimal control strategy for spacecraft attitude maneuvers, based on a rigid body spacecraft model including a redundant reaction wheel assembly. The novel component of this strategy is the proposal of a performance index which represents the total electrical energy consumed by the reaction wheel over the maneuver interval. Pontraygin's minimum principle is applied to formulate the necessary conditions for optimality, in which the control torques are subject to timevarying magnitude constraints. The presence of singular sub-arcs in the statespace and their associated singular controls are investigated using Kelley's necessary condition. The two-point boundary-value problem (TPBVP) is formulated using Pontrayagin's minimum principle. The fourth novel contribution is an attitude estimation algorithm which estimates the spacecraft attitude parameters and sensor bias parameters from three-axis magnetometer and three-axis rate gyroscope measurement data. The novel aspect of this algorithm is the assumption that the state filtering probability density function (PDF) is Gaussian distributed. This Gaussian PDF assumption is also applied to the magnetometer measurement model. Propagation of the filtering PDF between sensor measurements is performed using the Fokker-Planck equation, and Bayes theorem incorporates measurement update information. The use of direction cosine matrix elements as the attitude coordinates avoids any singularity issues associated with the measurement update and estimation error covariance representation.
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Dando, Aaron John. "Robust adaptive control of rigid spacecraft attitude maneuvers." Queensland University of Technology, 2008. http://eprints.qut.edu.au/16695/.
Full textAbstract:
In this thesis novel feedback attitude control algorithms and attitude estimation algorithms are developed for a three-axis stabilised spacecraft attitude control system. The spacecraft models considered include a rigid-body spacecraft equipped with (i) external control torque devices, and (ii) a redundant reaction wheel configuration. The attitude sensor suite comprises a three-axis magnetometer and three-axis rate gyroscope assembly. The quaternion parameters (also called Euler symmetric parameters), which globally avoid singularities but are subject to a unity-norm constraint, are selected as the primary attitude coordinates. There are four novel contributions presented in this thesis. The first novel contribution is the development of a robust control strategy for spacecraft attitude tracking maneuvers, in the presence of dynamic model uncertainty in the spacecraft inertia matrix, actuator magnitude constraints, bounded persistent external disturbances, and state estimation error. The novel component of this algorithm is the incorporation of state estimation error into the stability analysis. The proposed control law contains a parameter which is dynamically adjusted to ensure global asymptotic stability of the overall closedloop system, in the presence of these specific system non-idealities. A stability proof is presented which is based on Lyapunov's direct method, in conjunction with Barbalat's lemma. The control design approach also ensures minimum angular path maneuvers, since the attitude quaternion parameters are not unique. The second novel contribution is the development of a robust direct adaptive control strategy for spacecraft attitude tracking maneuvers, in the presence of dynamic model uncertainty in the spacecraft inertia matrix. The novel aspect of this algorithm is the incorporation of a composite parameter update strategy, which ensures global exponential convergence of the closed-loop system. A stability proof is presented which is based on Lyapunov's direct method, in conjunction with Barbalat's lemma. The exponential convergence results provided by this control strategy require persistently exciting reference trajectory commands. The control design approach also ensures minimum angular path maneuvers. The third novel contribution is the development of an optimal control strategy for spacecraft attitude maneuvers, based on a rigid body spacecraft model including a redundant reaction wheel assembly. The novel component of this strategy is the proposal of a performance index which represents the total electrical energy consumed by the reaction wheel over the maneuver interval. Pontraygin's minimum principle is applied to formulate the necessary conditions for optimality, in which the control torques are subject to timevarying magnitude constraints. The presence of singular sub-arcs in the statespace and their associated singular controls are investigated using Kelley's necessary condition. The two-point boundary-value problem (TPBVP) is formulated using Pontrayagin's minimum principle. The fourth novel contribution is an attitude estimation algorithm which estimates the spacecraft attitude parameters and sensor bias parameters from three-axis magnetometer and three-axis rate gyroscope measurement data. The novel aspect of this algorithm is the assumption that the state filtering probability density function (PDF) is Gaussian distributed. This Gaussian PDF assumption is also applied to the magnetometer measurement model. Propagation of the filtering PDF between sensor measurements is performed using the Fokker-Planck equation, and Bayes theorem incorporates measurement update information. The use of direction cosine matrix elements as the attitude coordinates avoids any singularity issues associated with the measurement update and estimation error covariance representation.
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Wittmann, Robert [Verfasser], Heinz [Akademischer Betreuer] [Gutachter] Ulbrich, and Boris [Gutachter] Lohmann. "Robust Walking Robots in Unknown Environments : Dynamic Models, State Estimation and Real-Time Trajectory Optimization / Robert Wittmann ; Gutachter: Boris Lohmann, Heinz Ulbrich ; Betreuer: Heinz Ulbrich." München : Universitätsbibliothek der TU München, 2017. http://d-nb.info/1145141412/34.
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Steckenrider, John Josiah. "Simultaneous Estimation and Modeling of State-Space Systems Using Multi-Gaussian Belief Fusion." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97583.
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This work describes a framework for simultaneous estimation and modeling (SEAM) of dynamic systems using non-Gaussian belief fusion by first presenting the relevant fundamental formulations, then building upon these formulations incrementally towards a more general and ubiquitous framework. Multi-Gaussian belief fusion (MBF) is introduced as a natural and effective method of fusing non-Gaussian probability distribution functions (PDFs) in arbitrary dimensions efficiently and with no loss of accuracy. Construction of some multi-Gaussian structures for potential use in MBF is addressed. Furthermore, recursive Bayesian estimation (RBE) is developed for linearized systems with uncertainty in model parameters, and a rudimentary motion model correction stage is introduced. A subsequent improvement to motion model correction for arbitrarily non-Gaussian belief is developed, followed by application to observation models. Finally, SEAM is generalized to fully nonlinear and non-Gaussian systems. Several parametric studies were performed on simulated experiments in order to assess the various dependencies of the SEAM framework and validate its effectiveness in both estimation and modeling. The results of these studies show that SEAM is capable of improving estimation when uncertainty is present in motion and observation models as compared to existing methods. Furthermore, uncertainty in model parameters is consistently reduced as these parameters are updated throughout the estimation process. SEAM and its constituents have potential uses in robotics, target tracking and localization, state estimation, and more.Doctor of Philosophy
The simultaneous estimation and modeling (SEAM) framework and its constituents described in this dissertation aim to improve estimation of signals where significant uncertainty would normally introduce error. Such signals could be electrical (e.g. voltages, currents, etc.), mechanical (e.g. accelerations, forces, etc.), or the like. Estimation is accomplished by addressing the problem probabilistically through information fusion. The proposed techniques not only improve state estimation, but also effectively "learn" about the system of interest in order to further refine estimation. Potential uses of such methods could be found in search-and-rescue robotics, robust control algorithms, and the like. The proposed framework is well-suited for any context where traditional estimation methods have difficulty handling heightened uncertainty.
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Steeno, Gregory Sean. "Robust and Nonparametric Methods for Topology Error Identification and Voltage Calibration in Power Systems Engineering." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/39305.
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There is a growing interest in robust and nonparametric methods with engineering applications, due to the nature of the data. Here, we study two power systems engineering applications that employ or recommend robust and nonparametric methods; topology error identification and voltage calibration.
Topology errors are a well-known, well-documented problem for utility companies. A topology error occurs when a line's status in a power network, whether active or deactive, is misclassified. This will lead to an incorrect Jacobian matrix used to estimate the unknown parameters of a network in a nonlinear regression model. We propose a solution using nonlinear regression techniques to identify the correct status of every line in the network by deriving a statistical model of the power flows and injections while employing Kirchhoff's Current Law. Simulation results on the IEEE-118 bus system showed that the methodology was able to detect where topology errors occurred as well as identify gross measurement errors.
The Friedman Two-Way Analysis of Variance by Ranks test is advocated to calibrate voltage measurements at a bus in a power network. However, it was found that the Friedman test was only slightly more robust or resistant in the presence of discordant measurements than the classical F-test. The resistance of a statistical test is defined as the fraction of bad data necessary to switch a statistical conclusion. We mathematically derive the maximum resistance to rejection and to acceptance of the Friedman test, as well as the Brown-Mood test, and show that the Brown-Mood test has a higher maximum resistance to rejection and to acceptance than the Friedman test. In addition, we simulate the expected resistance to rejection and to acceptance of both tests and show that on average the Brown-Mood test is slightly more robust to rejection while on average the Friedman test is more robust to acceptance.Ph. D.
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Bianco, Aline Fernanda. "Filtros de Kalman robustos para sistemas dinâmicos singulares em tempo discreto." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-12082009-110152/.
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Esta tese trata do problema de estimativa robusta ótima para sistemas dinâmicos regulares discretos no tempo. Novos algoritmos recursivos são formulados para as estimativas filtradas e preditoras com as correspondentes equações de Riccati. O filtro robusto tipo Kalman e a equação de Riccati correspondente são obtidos numa formulação mais geral, estendendo os resultados apresentados na literatura. O funcional quadrático proposto para deduzir este filtro faz a combinação das técnicas mínimos quadrados regularizados e funções penalidade. O sistema considerado para obtenção de tais estimativas é singular, discreto, variante no tempo, com ruídos correlacionados e todos os parâmetros do modelo linear estão sujeitos a incertezas. As incertezas paramétricas são limitadas por norma. As propriedades de estabilidade e convergência do filtro de Kalman para sistemas nominais e incertos são provadas, mostrando-se que o filtro em estado permanente é estável e a recursão de Riccati associada a ele é uma sequência monótona não decrescente, limitada superiormente pela solução da equação algébrica de Riccati.This thesis considers the optimal robust estimates problem for discrete-time singular dymanic systems. New recursive algorithms are developed for the Kalman filtered and predicted estimated recursions with the corresponding Riccati equations. The singular robust Kalman type filter and the corresponding recursive Riccati equation arer obtained in their most general formulation, extending the results presented in the literature. The quadratic functional developed to deduce this filter combines regularized least squares and penalty functions approaches. The system considered to obtain the estimates is singular, time varying with correlated noises and all parameter matrices of the underlying linear model are subject to uncertainties. The parametric uncertainty is assumed to be norm bounded. The properties of stability and convergence of the Kalman filter for nominal and uncertain system models are proved, where we show that steady state filter is stable and the Riccati recursion associated with this is a nondecreasing monotone sequence with upper bound.
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Senkel, Luise [Verfasser]. "Sliding Mode Techniques for Robust Control, State Estimation and Parameter Identification of Uncertain Dynamic Systems / Luise Senkel." Aachen : Shaker, 2018. http://d-nb.info/1159833230/34.
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Wang, Ye. "Advances in state estimation, diagnosis and control of complex systems." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/669680.
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This dissertation intends to provide theoretical and practical contributions on estimation, diagnosis and control of complex systems, especially in the mathematical form of descriptor systems. The research is motivated by real applications, such as water networks and power systems, which require a control system to provide a proper management able to take into account their specific features and operating limits in presence of uncertainties related to their operation and failures from component malfunctions. Such a control system is expected to provide an optimal operation to obtain efficient and reliable performance.
State estimation is an essential tool, which can be used not only for fault diagnosis but also for the controller design. To achieve a satisfactory robust performance, set theory is chosen to build a general framework for descriptor systems subject to uncertainties. Under certain assumptions, these uncertainties are propagated and bounded by deterministic sets that can be explicitly characterized at each iteration step. Moreover, set-invariance characterizations for descriptor systems are also of interest to describe the steady performance, which can also be used for active mode detection.
For the controller design for complex systems, new developments of economic model predictive control (EMPC) are studied taking into account the case of underlying periodic behaviors. The EMPC controller is designed to be recursively feasible even with sudden changes in the economic cost function and the closed-loop convergence is guaranteed. Besides, a robust technique is plugged into the EMPC controller design to maintain these closed-loop properties in presence of uncertainties.
Engineering applications modeled as descriptor systems are presented to illustrate these control strategies. From the real applications, some additional difficulties are solved, such as using a two-layer control strategy to avoid binary variables in real-time optimizations and using nonlinear constraint relaxation to deal with nonlinear algebraic equations in the descriptor model. Furthermore, the fault-tolerant capability is also included in the controller design for descriptor systems by means of the designed virtual actuator and virtual sensor together with an observer-based delayed controller.Esta tesis propone contribuciones de carácter teórico y aplicado para la estimación del estado, el diagnóstico y el control óptimo de sistemas dinámicos complejos en particular, para los sistemas descriptores, incluyendo la capacidad de tolerancia a fallos. La motivación de la tesis proviene de aplicaciones reales, como redes de agua y sistemas de energía, cuya naturaleza crítica requiere necesariamente un sistema de control para una gestión capaz de tener en cuenta sus características específicas y límites operativos en presencia de incertidumbres relacionadas con su funcionamiento, así como fallos de funcionamiento de los componentes. El objetivo es conseguir controladores que mejoren tanto la eficiencia como la fiabilidad de dichos sistemas. La estimación del estado es una herramienta esencial que puede usarse no solo para el diagnóstico de fallos sino también para el diseño del control. Con este fin, se ha decidido utilizar metodologías intervalares, o basadas en conjuntos, para construir un marco general para los sistemas de descriptores sujetos a incertidumbres desconocidas pero acotadas. Estas incertidumbres se propagan y delimitan mediante conjuntos que se pueden caracterizar explícitamente en cada instante. Por otra parte, también se proponen caracterizaciones basadas en conjuntos invariantes para sistemas de descriptores que permiten describir comportamientos estacionarios y resultan útiles para la detección de modos activos. Se estudian también nuevos desarrollos del control predictivo económico basado en modelos (EMPC) para tener en cuenta posibles comportamientos periódicos en la variación de parámetros o en las perturbaciones que afectan a estos sistemas. Además, se demuestra que el control EMPC propuesto garantiza la factibilidad recursiva, incluso frente a cambios repentinos en la función de coste económico y se garantiza la convergencia en lazo cerrado. Por otra parte, se utilizan técnicas de control robusto pata garantizar que las estrategias de control predictivo económico mantengan las prestaciones en lazo cerrado, incluso en presencia de incertidumbre. Los desarrollos de la tesis se ilustran con casos de estudio realistas. Para algunas de aplicaciones reales, se resuelven dificultades adicionales, como el uso de una estrategia de control de dos niveles para evitar incluir variables binarias en la optimización y el uso de la relajación de restricciones no lineales para tratar las ecuaciones algebraicas no lineales en el modelo descriptor en las redes de agua. Finalmente, se incluye también una contribución al diseño de estrategias de control con tolerancia a fallos para sistemas descriptores.
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PILLONI, ALESSANDRO. "Robust Observation and Control of Complex Networks." Doctoral thesis, Università degli Studi di Cagliari, 2014. http://hdl.handle.net/11584/266472.
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The problem of understanding when individual actions of interacting agents display to a coordinated collective behavior has receiving a considerable attention in many research fields. Especially in control engineering, distributed applications in cooperative environments
are achieving resounding success, due to the large number of relevant applications, such as formation control, attitude synchronization tasks and cooperative applications in large-scale systems.
Although those problems have been extensively studied in Literature, themost of classic approaches use to consider the unrealistic scenario in which networks always consist of
identical, linear, time-invariant entities. It’s clear that this assumption strongly approximates the effective behavior of a network. In fact agents can be subjected to parameter uncertainties,
unmodeled dynamics or simply characterized by proper nonlinear dynamics.
Therefore, motivated by those practical problems, the present Thesis proposes various approaches for dealing with the problem of observation and control in both the framework
of multi-agents and complex interconnected systems. The main contributions of this Thesis consist on the development of several algorithms based on concepts of discontinuous slidingmode control. This techniques can be employed for solving in finite-time problems of robust
state estimation and consensus-based synchronization in network of heterogenous nonlinear systems subjected to unknown but bounded disturbances and sudden topological changes.
Both directed and undirected topologies have been taken into account. It is worth to mention also the extension of the consensus problem to networks of agents governed by a class parabolic partial differential equation, for which, for the first time, a boundary-based robust local interaction protocol has been presented.
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Kersten, Julia [Verfasser]. "Cooperativity and its Use in Robust Control and State Estimation for Uncertain Dynamic Systems with Engineering Applications / Julia Kersten." Düren : Shaker, 2020. http://d-nb.info/1220610356/34.
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Schick, Ä°rvin C. (Ä°rvin Cemil). "Robust recursive estimation of the state of a discrete-time stochastic linear dynamic system in the presence of heavy-tailed observation noise." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14323.
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Alatorre, Vazquez Angel Gabriel. "Robust estimation of dynamics behavior and driving diagnosis applied to an intelligent MAGV." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2554.
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Cette thèse présente une série de stratégies pour estimer la dynamique des véhicules. Le but de ce travail est de développer une stratégie d'observation qui peut être appliquée aux véhicules de série. L'idée d'avoir un algorithme dans une voiture produite en série pose un grand nombre de défis,' ceux que nous considérons dans ce travail sont la robustesse et le coût. Nous avons proposé des modèles et des stratégies d'observateurs capables de faire face à des niveaux d'excitation élevés et faibles. Nous avons validé la robustesse de l'algorithme avec de nombreux tests, des petites pistes à faible vitesse aux manœuvres de changement de voie. Nos algorithmes ont été retravaillés plusieurs fois pour atteindre un degré de précision qui peut être utile pour l'intégration dans ADAS. Nous avons également proposé des stratégies d'observateurs qui permettent ce degré de robustesse tout en conservant une grille de capteurs à faible coût. Les principales contributions sont au nombre de trois : 1 - Estimation de la vitesse latérale et longitudinale,' ces variables sont essentielles pour une bonne rétroaction des régulateurs de stabilisation et de vitesse de croisière. Notre proposition utilise comme base un modèle cinématique pour éviter d'utiliser des paramètres liés à la masse dans notre modèle; cela est possible puisque notre grille de capteurs comprend des accéléromètres et des gyroscopes. L'une des principales contributions de cette section est la compensation de la gravité,' une équation différentielle de quaternions définit l'attitude de notre système. Plus de 100 tests valident la robustesse de l'algorithme, et nous obtenons des résultats cohérents dans chacun d'eux. 2- Estimation de l'estimation de la force pneu-sol normale. Cette variable est, à notre avis, la plus difficile à estimer car la grille de capteurs des véhicules de série ne contient pas beaucoup de capteurs mesurant la dynamique verticale. Cette section doit étendre notre solution aux véhicules de série avec des systèmes de suspension améliorés, y compris des capteurs de déflexion. Nous pouvons estimer la masse, la distribution de masse et le centre de gravité avec ces capteurs en place et transmettre l'estimation normale de la force pneu-sol en utilisant la fusion de modèles et le filtre de Kalman. 3 - Stratégies d'estimation des forces longitudinales et latérales pneu-sol. La première méthode utilise les modèles bicycle et hoverboard connus et les filtres de Kalman pour estimer les TGFs, et d'autres modèles sont introduits pour répartir ces forces sur le pneu adéquat. Cette méthode doit gérer la saturation des pneus, pour séparer correctement les TGF virtuels. La deuxième méthode utilise les lois de Newton du mouvement; ici, nous calculons les accélérations locales en utilisant l'accélération et les rotations d'un corps rigide. Étant donné que nous connaissons déjà les TGF normaux à chaque pneu, nous pouvons calculer les TGFs latéraux et longitudinaux avec précision. Cette dernière méthode est plus précise et robuste que la première méthode. Enfin, au final, nous proposons une série de systèmes qui bénéficieront des estimations antérieuresThe context of this thesis is the improvement of road safety through the development of active safety systems. One challenge in the development of active safety systems is obtaining accurate information about unmeasurable vehicle dynamic states. Specifically, the necessity to estimate the vertical load, frictional forces at each wheel (longitudinal and lateral), and also the sideslip angle at the center of gravity. These states are the critical parameters for optimizing the control of a vehicle’s stability. If the vertical load on each tire can be estimated, then the risk of rollover can be evaluated. Estimating tire lateral forces can help to reduce lateral slip and prevent dangerous situations like spinning and drifting out the road. Tire longitudinal forces influence the performance of a vehicle. Sideslip angle is one of the essential parameters for controlling the lateral dynamics of a vehicle. However, the different technologies that the market offers, are not based on tire-ground forces due to the lack of cost-effective methods for obtaining the required information. For the above mentioned reasons, we want to develop a system that monitors these dynamic vehicle states using only low-cost sensors. To accomplish our endeavor, we propose developing novel observers to estimate unmeasured states. Constructing an observer that met the reliability, robustness and accuracy requirements is not an easy task. It requires one the one hand, accurate and efficient models, and on the other hand, robust estimation algorithms that take into account variations in parameters and measurement errors. The present thesis has consequently been structured around the following two aspects: modeling of vehicle dynamics, and design of observers. Under the heading of modeling, we propose new models to describe vehicle dynamics. Current models simplify the vehicle motion as a planar motion. In our proposal, our models describe vehicle motion as a 3D motion, including the effects of road inclination. Regarding vertical dynamics, we propose incorporating the suspension deflection to calculate the transfer of vertical load. Regarding lateral dynamics, we propose a model for the lateral forces transfer to describe the interaction between the left wheel and the right wheel. With this relationship, the lateral force on each tire is computed without using the sideslip angle. Similarly, for longitudinal dynamics, we also propose a model for the transfer of longitudinal forces to calculate the longitudinal force at each tire. Under the heading of observer design, we propose a novel observation system consisting of four individual observers connected in cascade. The four observers are developed for estimating vertical tire force, lateral tire force, longitudinal tire force, and sideslip angle, respectively. For the linear system, the Kalman filter is employed, while for the nonlinear system, the EKF applied to reduce estimation errors. Finally, we implement our algorithm in an experimental vehicle to perform estimation in real-time, and we validate our proposed algorithm using experimental data
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Waqar, Mohsin. "Robust nonlinear observer for a non-collocated flexible motion system." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22696.
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Rauh, Andreas [Verfasser]. "Sensitivity Methods for Analysis and Design of Dynamic Systems with Applications in Control Engineering : Feedforward Control – Feedback Control – Robust Control – State Estimation / Andreas Rauh." Aachen : Shaker, 2017. http://d-nb.info/1149278722/34.
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Nielsen, Jerel Bendt. "Robust Visual-Inertial Navigation and Control of Fixed-Wing and Multirotor Aircraft." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7584.
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With the increased performance and reduced cost of cameras, the robotics community has taken great interest in estimation and control algorithms that fuse camera data with other sensor data.In response to this interest, this dissertation investigates the algorithms needed for robust guidance, navigation, and control of fixed-wing and multirotor aircraft applied to target estimation and circumnavigation.This work begins with the development of a method to estimate target position relative to static landmarks, deriving and using a state-of-the-art EKF that estimates static landmarks in its state.Following this estimator, improvements are made to a nonlinear observer solving part of the SLAM problem.These improvements include a moving origin process to keep the coordinate origin within the camera field of view and a sliding window iteration algorithm to drastically improve convergence speed of the observer.Next, observers to directly estimate relative target position are created with a circumnavigation guidance law for a multirotor aircraft.Taking a look at fixed-wing aircraft, a state-dependent LQR controller with inputs based on vector fields is developed, in addition to an EKF derived from error state and Lie group theory to estimate aircraft state and inertial wind velocity.The robustness of this controller/estimator combination is demonstrated through Monte Carlo simulations.Next, the accuracy, robustness, and consistency of a state-of-the-art EKF are improved for multirotors by augmenting the filter with a drag coefficient, partial updates, and keyframe resets.Monte Carlo simulations demonstrate the improved accuracy and consistency of the augmented filter.Lastly, a visual-inertial EKF using image coordinates is derived, as well as an offline calibration tool to estimate the transforms needed for accurate, visual-inertial estimation algorithms.The imaged-based EKF and calibrator are also shown to be robust under various conditions through numerical simulation.
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Ichalal, Dalil. "Estimation et diagnostic de systèmes non linéaires décrits par un modèle de Takagi-Sugeno." Phd thesis, Institut National Polytechnique de Lorraine - INPL, 2009. http://tel.archives-ouvertes.fr/tel-00454793.
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Cette thèse traite le problème de l'estimation d'état, du diagnostic et de commande tolérante aux défauts des systèmes non linéaires représentés par un modèle de Takagi-Sugeno (T-S) à variables de prémisse non mesurables. De nombreux algorithmes pour la synthèse d'observateurs robustes vis-à-vis des perturbations, des imperfections de modélisation et des entrées inconnues sont présentés en se basant sur quatre types d'observateurs : les observateurs proportionnels, les observateurs à entrées inconnues, les observateurs proportionnel intégral (PI) et multi-intégral (PMI). Par la suite, ces derniers sont utilisés pour le diagnostic de fautes des systèmes non linéaires. Ceci est réalisé au moyen de trois stratégies. La première utilise l'observateur à entrée inconnue par découplage afin de rendre l'observateur insensible à certains défauts et permettre de détecter et d'isoler les défauts en construisant des bancs d'observateurs. En raison des conditions structurelles souvent insatisfaites, le découplage total des défauts de l'erreur d'estimation d'état n'est pas réalisable. Afin de s'affranchir de ces contraintes, la seconde stratégie utilise les observateurs PI et PMI pour estimer simultanément l'état et les défauts du système. La troisième stratégie qui utilise le formalisme H_inf vise à concevoir un générateur de résidus minimisant l'influence des perturbations et maximisant l'influence des défauts. Un choix adéquat des paramètres du générateur de résidus permet la détection, la localisation et l'estimation des défauts. Enfin, une loi de commande tolérante aux défauts par poursuite de trajectoire d'un modèle de référence est proposée en exploitant les observateurs PI et PMI.
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Ichalal, Dalil. "Estimation et diagnostic de systèmes non linéaires décrits par un modèle de Takagi-Sugeno." Electronic Thesis or Diss., Vandoeuvre-les-Nancy, INPL, 2009. http://www.theses.fr/2009INPL088N.
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Cette thèse traite le problème de l'estimation d'état, du diagnostic et de commande tolérante aux défauts des systèmes non linéaires représentés par un modèle de Takagi-Sugeno (T-S) à variables de prémisse non mesurables. De nombreux algorithmes pour la synthèse d'observateurs robustes vis-à-vis des perturbations, des imperfections de modélisation et des entrées inconnues sont présentés en se basant sur quatre types d'observateurs : les observateursproportionnels, les observateurs à entrées inconnues, les observateurs proportionnel intégral (PI) et multi-intégral (PMI). Par la suite, ces derniers sont utilisés pour le diagnostic de fautes affectant des systèmes non linéaires. Ceci est réalisé au moyen de trois stratégies. La première utilise l'observateur à entrée inconnue par découplage afin de rendre l'observateur insensible à certains défauts et permettre de détecter et d'isoler les défauts en construisant des bancs d'observateurs. En raison des conditions structurelles souvent insatisfaites, le découplage total des défauts de l'erreur d'estimation d'état n'est pas réalisable. Afin de s'affranchir de ces contraintes, la seconde stratégie utilise les observateurs PI et PMI pour estimer simultanément l'état et les défauts du système. La troisième stratégie qui utilise le formalisme H8 vise à concevoir un générateur de résidus minimisant l'influence des perturbations et maximisant l'influence des défauts. Un choix adéquat des paramètres du générateur de résidus permet la détection, la localisation et l'estimation des défauts. Enfin, une loi de commande tolérante aux défauts par poursuite de trajectoire d'un modèle de référence estproposée en exploitant les observateurs PI et PMIThis thesis deals with state estimation, fault diagnosis and fault tolerant control of nonlinear systems represented by a Takagi-Sugeno model with unmeasurable premise variables. The problem of state estimation of nonlinear systems with T-S model with unmeasurable premise variable is explored. Algorithms for robust observers synthesis with respect to perturbations, modeling uncertainties and unknown inputs are afterward presented. These algorithms are based on four kinds of observers called proportional, unknown input observers (UIOs), proportional-integral (PI) and multiple-integral (PMI) . The application on model-based diagnosis is studied based on three strategies. The first one uses unknown input observer to decouple some faults and makes the observers insensitive to certain faults. This allows to detect and isolate faults by constructing observers banks. Due to strong structural conditions on designing UIOs decoupling the faults on the state estimation error is not possible. To avoid this problem, the second strategy uses PI and PMI observers in order to estimate simultaneously the state and the faults of the system. The third strategy uses the H8 formalism. This aims to minimize the influence of perturbations and to maximize the effects of faults on the residual signal. An adequate choice of the residual generator parameters allows to detect, to isolate and to estimate the faults affecting the system. Lastly, a fault tolerant control law is proposed by reference trajectory tracking based on the use of PI and PMI observers
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Jönsson, Jack. "Belief-aided Robust Control for Remote Electrical Tilt Optimization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301028.
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Remote Electrical Tilt (RET) is a method for configuring antenna downtilt in base stations to optimize mobile network performance. Reinforcement Learning (RL) is an approach to automating the process by letting an agent learn an optimal control strategy and adapt to the dynamic environment. Applying RL in real world comes with challenges, for the RET problem there are performance requirements and partial observability of the system through exogenous factors inducing noise in observations. This thesis proposes a solution method through modeling the problem by a Partially Observable Markov Decision Process (POMDP). The set of hidden states are modeled as a high- level representation of situations requiring one of the possible actions uptilt, downtilt, no change. From this model, a Bayesian Neural Network (BNN) is trained to predict an observation model, relating observed Key Performance Indicators (KPIs) to the hidden states. The observation model is used for estimating belief state probabilities of each hidden state, from which decision of control action is made through a restrictive threshold policy. Experiments comparing the method to a baseline Deep Q- network (DQN) agent shows the method able to reach the same average performance increase as the baseline while outperforming the baseline in two metrics important for robust and safe control behaviour, the worst- case minimum reward increase and the average reward increase per number of tilt actions.Fjärrstyrning av Elektrisk Lutning (FEL) är en metod för att reglera lutningen av antenner i basstationer för att optimera presentandan i ett mobilnätverk. Förstärkande Inlärning (FI) används som metod för att automatisera processen genom att låta en agent lära sig en optimal strategi för reglering och anpassa sig till den dynamiska miljön. Att tillämpa FI i ett verkligt scenario innebär utmaningar, för FEL specifikt finns det krav på en viss nivå av prestanda samt endast en delvis observerbarhet av systemet på grund av externa faktorer som orsakar brus i observationerna. I detta arbete föreslås en metod för att hantera detta genom att modellera problemet som en Delvis Observerbar Markovprocess (DOM). De dolda tillstånden modelleras för att representera situationer där var och en av de möjliga aktionerna behövs, det vill säga att luta antennen upp, ner eller inte ändra på lutningen. Utifrån denna modellering så tränas ett Bayesiskt Neuralt Nätverk (BNN) för att estimera en observationsmodel som kopplar observerade nyckeltal till de dolda tillstånden. Denna observationsmodel används för att estimera sannolikheten att vardera dolt tillstånd är det rätta. Utifrån dessa sannolikheter så görs valet av aktion genom ett tröskelvärde på sannolikheterna. Genom experiment som jämför metoden med en standardimplementering av en agent baserad på ett Djupt Qnätverk (DQN) visas att metoden har samma prestation när det kommer till en medelnivå på prestandaökning i nätverket. Metoden överträffar dock standardmetoden i två andra mätvärden som är viktiga ur aspekten säker och robust reglering, minimumvärdet på prestandaökningen samt medelökningen av prestandan per antal up- och nerlutningar som används.
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Popescu, Andrei. "Approches de commande pour des objectifs d'estimation : application au courant tunnel et aux processus de lévitation magnétique." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT062.
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Cette thèse de doctorat regroupe ses principales contributions dans le domaine des observateurs de systèmes dynamiques, motivés à l'origine par des applications en systèmes MEMS ou NEMS (systèmes micro ou nano électromécaniques), avec un cas plus particulier lié au courant tunnel. Il est également arrivé d’envisager des expériences avec un système de lévitation magnétique.Les contributions de cette thèse sont de deux types, en fonction de ses deux parties principales:1. Partie méthodologique: concevoir différentes stratégies de contrôle pour obtenir des observateurs en utilisant le paradigme basé sur le contrôle. En particulier, nous nous sommes concentrés sur la non-optimale approches (comme Proportionnelle et Proportionnelle-Intégrale), optimale (LinéaireRégulateur Quadratique et Linéaire Quadratique Intégrateur) et méthodes sous-optimales (Contrôleur Hinf). De plus, nous nous concentrons sur les deux principaux moyens de formuler un problème de contrôle (poursuite). C'est-à-dire Le problème de régulation du retour d’erreur et Le problème de régulation en utilisant l'information complète d’état.2. Partie expérimentale: application des méthodes obtenues pour améliorer l’imagerie topographique à l’aide d’un microscope basé sur l’effet tunnel et à l’amélioration de l’estimation de perturbation sur les entres pour un processus de lévitation magnétiquePlus précisément, chaque partie prendra la forme de deux chapitres:1. Chapitre II, consacré à une introduction formelle et à une discussion contributive sur l’approche "observateur basée sur le contrôle" que cette thèse étudie, et le chapitre III, qui porte sur l’utilisation de cette approche pour la conception d’un nouveau concept d’observateur robuste, en particulier dans un cadre Hinf2. Chapitre IV, relatif à l’application STM, et Chapitre V, présentant l’affaire MAGLEV.Un dernier chapitre VI résume les principales conclusions de ce travail ainsi que certaines perspectivesThis PhD thesis gathers its main contributions in the field of observers for dynamical systems, originally motivated by applications in MEMS or NEMS (Micro or Nano Electromechanical Systems), with a more particular case related to tunneling current. It also happened to consider experiments with a magnetic levitation system.Contributions of this PhD thesis are of two types, according to its two main parts:1. Methodological part: designing different control strategies to obtain observers using the control-based paradigm. In particular, we focused on non-optimal approaches (like Proportional and Proportional-Integral), optimal ones (Linear Quadratic Regulator and Linear Quadratic Integrator) and sub-optimal methods (Hinf controller). Moreover, we focus on the main two ways to formulate a control (tracking) problem, namely Error feedback regulation problem and Full information regulation problem.2. Experimental part: Applying the obtained methods for improving the topographic imaging using a Scanning-Tunneling Microscope as well as to improve the disturbance estimation for a magnetic levitation process.More precisely, each part will take the form of two chapters:1. Chapter II, dedicated to a formal introduction and contributive discussion about the ’control based observer’ approach this PhD investigates, and Chapter III, focusing on the use of such an approach for the purpose of new robust observer design in particular within an Hinf framework.2. Chapter IV, related to STM application, and chapter V, presenting the MAGLEV case.A final chapter VI summarizes the main conclusions of this work as well as some perspectives
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Orjuela, Rodolfo. "Contribution à l'estimation d'état et au diagnostic des systèmes représentés par des multimodèles." Phd thesis, Institut National Polytechnique de Lorraine - INPL, 2008. http://tel.archives-ouvertes.fr/tel-00359631.
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Nombreux sont les problèmes classiquement rencontrés dans les sciences de l'ingénieur dont la résolution fait appel à l'estimation d'état d'un système par le biais d'un observateur. La synthèse d'un observateur n'est envisageable qu'à la condition de disposer d'un modèle à la fois exploitable et représentatif du comportement dynamique du système. Or, la modélisation du système et la synthèse de l'observateur deviennent des tâches difficiles à accomplir dès lors que le comportement dynamique du système doit être représenté par un modèle de nature non linéaire. Face à ces difficultés, l'approche multimodèle peut être mise à profit.Les travaux présentés dans cette thèse portent sur les problèmes soulevés par l'identification, l'estimation d'état et le diagnostic de systèmes non linéaires représentés à l'aide d'un multimodèle découplé. Ce dernier, composé de sous-modèles qui peuvent être de dimensions différentes, est doté d'un haut degré de généralité et de flexibilité et s'adapte particulièrement bien à la modélisation des systèmes complexes à structure variable. Cette caractéristique le démarque des approches multimodèles plus conventionnelles qui ont recours à des sous-modèles de même dimension.
Après une brève introduction à l'approche multimodèle, le problème de l'estimation paramétrique du multimodèle découplé est abordé. Puis sont présentés des algorithmes de synthèse d'observateurs d'état robustes vis-à-vis des perturbations, des incertitudes paramétriques et des entrées inconnues affectant le système. Ces algorithmes sont élaborés à partir de trois types d'observateurs dits à gain proportionnel, à gain proportionnel-intégral et à gain multi-intégral. Enfin, les différentes phases d'identification, de synthèse d'observateurs et de génération d'indicateurs de défauts sont illustrées au moyen d'un exemple académique de diagnostic du fonctionnement d'un bioréacteur.
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Orjuela, Rodolfo. "Contribution à l'estimation d'état et au diagnostic des systèmes représentés par des multimodèles." Electronic Thesis or Diss., Vandoeuvre-les-Nancy, INPL, 2008. http://www.theses.fr/2008INPL060N.
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Nombreux sont les problèmes classiquement rencontrés dans les sciences de l'ingénieur dont la résolution fait appel à l'estimation d'état d'un système par le biais d'un observateur. La synthèse d'un observateur n'est envisageable qu'à la condition de disposer d'un modèle à la fois exploitable et représentatif du comportement dynamique du système. Or, la modélisation du système et la synthèse de l'observateur deviennent des tâches difficiles à accomplir dès lors que le comportement dynamique du système doit être représenté par un modèle de nature non linéaire. Face à ces difficultés, l'approche multimodèle peut être mise à profit. Les travaux présentés dans cette thèse portent sur les problèmes soulevés par l'identification, l'estimation d'état et le diagnostic de systèmes non linéaires représentés à l'aide d'un multimodèle découplé. Ce dernier, composé de sous-modèles qui peuvent être de dimensions différentes, est doté d'un haut degré de généralité et de flexibilité et s'adapte particulièrement bien à la modélisation des systèmes complexes à structure variable. Cette caractéristique le démarque des approches multimodèles plus conventionnelles qui ont recours à des sous-modèles de même dimension. Après une brève introduction à l'approche multimodèle, le problème de l'estimation paramétrique du multimodèle découplé est abordé. Puis sont présentés des algorithmes de synthèse d'observateurs d'état robustes vis-à-vis des perturbations, des incertitudes paramétriques et des entrées inconnues affectant le système. Ces algorithmes sont élaborés à partir de trois types d'observateurs dits à gain proportionnel, à gain proportionnel-intégral et à gain multi-intégral. Enfin, les différentes phases d'identification, de synthèse d'observateurs et de génération d'indicateurs de défauts sont illustrées au moyen d'un exemple académique de diagnostic du fonctionnement d'un bioréacteurThe state estimation of a system, with the help of an observer, is largely used in many practical situations in order to cope with many classic problems arising in control engineering. The observer design needs an exploitable model able to give an accurate description of the dynamic behaviour of the system. However, system modelling and observer design can not easily be accomplished when the dynamic behaviour of the system must be described by non linear models. The multiple model approach can be used to tackle these difficulties. This thesis deals with black box modelling, state estimation and fault diagnosis of nonlinear systems represented by a decoupled multiple model. This kind of multiple model provides a high degree of generality and flexibility in the modelling stage. Indeed, the decoupled multiple model is composed of submodels which dimensions can be different. Thus, this feature is a significant difference between the decoupled multiple model and the classical used multiple model where all the submodels have the same dimension. After a brief introduction to the multiple model approach, the parametric identification problem of a decoupled multiple model is explored. Algorithms for robust observers synthesis with respect to perturbations, modelling uncertainties and unknown inputs are afterwards presented. These algorithms are based on three kinds of observers called proportional, proportional-integral and multiple-integral. Lastly, identification, observers synthesis and fault sensitivity signals generation are illustrated via a simulation example of a bioreactor
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Campos, José Carlos Teles. "Filtragem robusta para sistemas singulares discretos no tempo." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-07102015-150651/.
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Esta tese apresenta novos algoritmos que resolvem problemas de estimativas filtrada, suavizadora e preditora para sistemas singulares no tempo discreto usando apenas argumentos determinísticos. Cada capítulo aborda inicialmente as estimativas para o sistema nominal e em seguida, as versões robustas para o sistema com incertezas limitadas. Os resultados encontrados podem ser aplicados tanto em sistemas invariantes como variantes no tempo discreto, utilizando a mesma estrutura do filtro de Kalman. Nos últimos anos, uma quantidade significativa de trabalhos envolvendo estimativas singulares foi publicada enfocando apenas a estimativa filtrada sob a justificativa de que a estimativa preditora era de significativa complexidade quando modelada pelo método dos mínimos quadrados. Por este motivo, poucos trabalhos, como NIKOUKHAH et al. (1992) e ZHANG et al. (1998), deduziram a estimativa preditora. Este último artigo apresentou também um algoritmo para a estimativa suavizadora, mas usando o modelo de inovação ARMA. No entanto, até onde foi possível identificar, nenhum trabalho até agora resolveu o problema de estimativa robusta, considerando incertezas nos parâmetros, para sistemas singulares. Para a dedução das estimativas singulares robustas, esta tese tomou como base SAYED (2001), que deduz o filtro de Kalman robusto com incertezas limitadas utilizando uma abordagem determinística, o chamado filtro BDU. Os filtros robustos para sistemas singulares apresentados nesta tese, são mais abrangentes que os apresentados em SAYED (2001). Quando particularizados para o espaço de estados sem incertezas, todos os filtros se assemelham ao filtro de Kalman.New algorithms to optimal recursive filtering, smoothed and prediction for general time-invariant or time-variant descriptor systems are proposed in this thesis. The estimation problem is addressed as an optimal deterministic trajectory fitting. This problem is solved using exclusively deterministic arguments for systems with or without uncertainties. Kalman type recursive algorithms for robust filtered, predicted and smoothed estimations are derived. In the last years, many papers have paid attention to the estimation problems of linear singular systems. Unfortunately, all those works were concentrated only on the study of filtering problems, for nominal systems. The predicted and smoothed filters are more involved and were considered only by few works : NIKOUKHAH et al. (1992) and ZHANG et al. (1998) had proposed a unified approach for filtering, prediction and smoothing problems which were derived by using the projection formula and were calculated based on the ARMA innovation model, but they had not considered the uncertainties. In this thesis its applied for descriptor systems a robust procedure for usual state space systems developed by SAYED (2001), called BDU filter. It is obtained a robust descriptor Kalman type recursions for filtered, predicted and smoothed estimates. Considering the nominal state space, all descriptor filters developed in this work collapse to the Kalman filter.
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Hu, Humphrey. "Adapting to Context in Robot State Estimation." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1214.
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The promised future filled with robots sensing and acting intelligently in the world is near fruition, thanks in part to continuous progress in robotic perception and state estimation. However, a number of challenges remain before state estimation systems and the robots that rely on them can be considered truly reliable. In particular, we must consider what happens when highly complex hardware and software systems designed and validated in laboratory environments enter the unbounded variety of reality. Will these systems fail innocuously or catastrophically? If so, how can we avoid or eliminate these failures to achieve reliable, robust behavior? The premise of this thesis is that engineering constraints and human finitude result in fallible systems that cannot compensate for all possible factors and situations. We refer to the collection of uncompensated factors as the con- text of a system, and propose that variations in context can explain why it is difficult to make state estimation reliable at scale. Vexingly, since context is, by nature, unknowable and unmodeled, we cannot rely on prediction and foresight to compensate for it. Instead, this thesis proposes that state estimation systems can adapt their behavior after deployment to the operating site to correct for unknown contextual effects. An example of this is the widespread and common practice of \parameter tuning", typically performed by a human expert to specialize a system to each deployment. To generalize this and other mechanisms of adaptation, we first develop a general theory of context in estimation and establish a statistical definition for estimation performance. We then develop a practical method for evaluating performance on-site without supervision, enabling estimation systems to observe the effects of context during operation. Finally, we explore automatic parameter tuning and experience-driven failure prediction as two methods of general adaptation. We demonstrate and validate this work on state estimation systems using online data from an instrumented automobile as well as online an indoor ground robot.
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Monge, Thierry. "Modélisation et commande multivariable non linéaire robuste des réacteurs chimiques discontinus - application à un procédé industriel." Rouen, 1996. http://www.theses.fr/1996ROUES069.
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Le présent travail concerne, dans une première partie, la modélisation d'un réacteur chimique semi-continu industriel, celle-ci étant ensuite appliquée à la commande multivariable non linéaire. Les bilans instantanés de matière et d'énergie ont d'abord été établis. Une méthodologie a été mise au point pour décrire le système réactionnel correspondant, ceci à l'aide d'une approche hybride molécules/groupements fonctionnels. L'étude de stabilité menée sur le procédé en a montré le caractère instable. A l'aide d'un certain nombre d'expériences, les paramètres cinétiques des réactions principales de la synthèse étudiée ont pu, à l'aide de méthodes spécifiques, être estimés par le logiciel Simulbatch® précédemment développé. Avec ces données, le logiciel est ensuite capable de reproduire correctement l'évolution dynamique des températures et des concentrations du milieu réactionnel. Le modèle a été validé pour des chauffages-refroidissements d'inertes ainsi qu'avec différents systèmes réactionnels. L'intégration en temps réel d'un système d'équations différentielles représentatives du procédé, recalé en ligne, permet d'effectuer une linéarisation et un découplage dynamique par rebouclage du système à réguler. Ce nouveau système est alors régulé par une technique de commande prédictive robuste. Cette approche feedforward/feedback permet de prendre en compte l'instationnarité et les non linéarités inhérentes à ce type de procédé. Le module de commande et de communication a été écrit en C++ dans l'environnement Windows™, ce qui a permis de développer un outil convivial pour l'utilisateur et pour le programmeur et a donné lieu au logiciel Sisobatch®. L'association de ce module à Simulbatch® a donné lieu au logiciel de commande de réacteurs chimiques discontinus, Commandbatch®
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Schmitt, Thorsten. "Vision-based probabilistic state estimation for cooperating autonomous robots." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=97442997X.
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Soobhug, Divij. "Optimal state estimation for a power line inspection robot." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29474.
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Following a paper published by E. Boje[1], this thesis discusses the design and off-line testing of different types of Kalman filters to estimate the attitude, position and velocity of a robotic platform moving along a power line. The nature of this problem limits the use of magnetometers. Magnetic field interference from the steel pylons and steel cored conductors will affect the local magnetic field. Moreover, high frequency signals from on-board power electronic drives and induced magnetic fields due to ferromagnetic components of the robot along with aliasing, quantization effects and a low signal to noise ratio make notch filtering at 50 Hz impractical. Thus, a GPS/IMU filter solution, which uses the power line curvature and horizontal direction in measurements, to constrain the robot to the line was designed. Different types of filters were implemented; The Extended Kalman filter (EKF), the Unscented Kalman filter (UKF) and the Error State Kalman filter (ErKF). Measurements were recorded and the filters were tested offline. While all the filters tracked properly, it was found that the EKF was better in computational speed completing an iteration in 87 µs, the ErKF was second best with an average time of 120 µs for one iteration and the UKF was last with an average time of 1040 µs for one iteration. Errors between the true state and estimated state for the simulation were quantified using root mean square values (RMS). The RMS values were almost the same for the EKF and ErKF with the error for the x position at 0.81 m and z position at 0.038 m. The UKF produced RMS errors of 0.79 m for x position and 0.11 m for z position. It can be seen that the UKF is slightly better for the x position but is much worse for the z position. Overall, the GPS measurement RMS values used were 4 m and 20 m for the horizontal and vertical positions respectively. Thus, the filters brought a big improvement. However, the recommended filter is the EKF as is produced comparable or better results as compared to other filters and expends the least computational effort. A state estimator was also developed for a J.Patel’s PLIR project [2], where a brachiating version of a power line robot was modeled. The brachiation mechanism was approximated to a double pendulum and kinematics based Kalman filter was designed. Simulations of EKF and UKF were made. The EKF is still recommended as its estimates are closer to the true values and its computation time is about five times faster.
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Wirnshofer, Florian [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "State estimation and planning under uncertainty for robot manipulation." Freiburg : Universität, 2021. http://d-nb.info/1238016251/34.
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Chamberlain, Caleb H. "System Identification, State Estimation, and Control of Unmanned Aerial Robots." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2605.
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This thesis describes work in a variety of topics related to aerial robotics, including system identification, state estimation, control, and path planning. The path planners described in this thesis are used to guide a fixed-wing UAV along paths that optimize the aircraft's ability to track a ground target. Existing path planners in the literature either ignore occlusions entirely, or they have limited capability to handle different types of paths. The planners described in this thesis are novel in that they specifically account for the effect of occlusions in urban environments, and they can produce a much richer set of paths than existing planners that account for occlusions. A 3D camera positioning system from Motion Analysis is also described in the context of state estimation, system identification, and control of small unmanned rotorcraft. Specifically, the camera positioning system is integrated inside a control architecture that allows a quadrotor helicopter to fly autonomously using truth data from the positioning system. This thesis describes the system architecture in addition to experiments in state estimation, control, and system identification. There are subtleties involved in using accelerometers for state estimation onboard flying rotorcraft that are often ignored even by researchers well-acquainted with the UAV field. In this thesis, accelerometer-rotorcraft behavior is described in detail. The consequences of ignoring accelerometer-rotorcraft behavior are evaluated, and an observer is presented that achieves better performance by specifically modeling actual accelerometer behavior. The observer is implemented in hardware and results are presented.
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Venturino, Antonello. "Constrained distributed state estimation for surveillance missions using multi-sensor multi-robot systems." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST118.
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Les algorithmes distribués sont dorénavant présents dans de nombreux aspects de l'Automatique avec des applications pour des systèmes multi-robots, des réseaux de capteurs, couvrant des sujets tels que la commande, l'estimation d'état, la détection de défauts, la détection et l'atténuation des cyberattaques sur les systèmes cyber-physiques, etc. En effet, les systèmes distribués sont confrontés à des problèmes tels que l'extensibilité à un grand nombre d'agents et la communication entre eux. Dans les applications de systèmes multi-agents (par exemple, flotte de robots mobiles, réseaux de capteurs), il est désormais courant de concevoir des algorithmes d'estimation d'état de manière distribuée afin que les agents puissent accomplir leurs tâches sur la base de certaines informations partagées au sein de leur voisinage. Dans le cas de missions de surveillance, un réseau de capteurs statique et à faible coût (par exemple, caméras) pourrait ainsi être déployé pour localiser de manière distribuée des intrus dans une zone donnée. Dans ce contexte, l'objectif principal de cette thèse est de concevoir des observateurs distribués pour estimer l'état d'un système dynamique (par exemple, flotte de robots intrus) avec une charge de calcul réduite tout en gérant efficacement les contraintes et les incertitudes. Cette thèse propose de nouveaux algorithmes d'estimation distribuée à horizon glissant avec une pré-estimation de type Luenberger dans la formulation du problème local résolu par chaque capteur, entraînant une réduction significative du temps de calcul, tout en préservant la précision de l'estimation. En outre, ce manuscrit propose une stratégie de consensus pour améliorer le temps de convergence des estimations entre les capteurs sous des conditions de faible observabilité (par exemple, des véhicules intrus non visibles par certaines caméras). Une autre contribution concerne l'amélioration de la convergence de l'erreur d'estimation en atténuant les problèmes de non observabilité à l'aide d'un mécanisme de diffusion de l'information sur plusieurs pas (appelé "l-step") entre voisinages. L'estimation distribuée proposée est conçue pour des scénarios réalistes de systèmes à grande échelle impliquant des mesures sporadiques (c'est-à-dire disponibles à des instants a priori inconnus). À cette fin, les contraintes sur les mesures (par exemple, le champ de vision de caméras) sont incorporées dans le problème d'optimisation à l'aide de paramètres binaires variant dans le temps. L'algorithme développé est implémenté sous le middleware ROS (Robot Operating System) et des simulations réalistes sont faites à l'aide de l'environnement Gazebo. Une validation expérimentale de la technique de localisation proposée est également réalisée pour un système multi-véhicules (SMV) à l'aide d'un réseau de capteurs statiques composé de caméras à faible coût qui fournissent des mesures sur les positions d'une flotte de robots mobiles composant le SMV. Les algorithmes proposés sont également comparés à des résultats de la littérature en considérant diverses métriques telles que le temps de calcul et la précision des estiméesDistributed algorithms have pervaded many aspects of control engineering with applications for multi-robot systems, sensor networks, covering topics such as control, state estimation, fault detection, cyber-attack detection and mitigation on cyber-physical systems, etc. Indeed, distributed schemes face problems like scalability and communication between agents. In multi-agent systems applications (e.g. fleet of mobile robots, sensor networks) it is now common to design state estimation algorithms in a distributed way so that the agents can accomplish their tasks based on some shared information within their neighborhoods. In surveillance missions, a low-cost static Sensor Network (e.g. with cameras) could be deployed to localize in a distributed way intruders in a given area. In this context, the main objective of this work is to design distributed observers to estimate the state of a dynamic system (e.g. a multi-robot system) that efficiently handle constraints and uncertainties but with reduced computation load. This PhD thesis proposes new Distributed Moving Horizon Estimation (DMHE) algorithms with a Luenberger pre-estimation in the formulation of the local problem solved by each sensor, resulting in a significant reduction of the computation time, while preserving the estimation accuracy. Moreover, this manuscript proposes a consensus strategy to enhance the convergence time of the estimates among sensors while dealing with weak unobservability conditions (e.g. vehicles not visible by some cameras). Another contribution concerns the improvement of the convergence of the estimation error by mitigating unobservability issues by using a l-step neighborhood information spreading mechanism. The proposed distributed estimation is designed for realistic large-scale systems scenarios involving sporadic measurements (i.e. available at time instants a priori unknown). To this aim, constraints on measurements (e.g. camera field of view) are embodied using time-varying binary parameters in the optimization problem. Both realistic simulations within the Robot Operating System (ROS) framework and Gazebo environment, as well as experimental validation of the proposed DMHE localization technique of a Multi-Vehicle System (MVS) with ground mobile robots are performed, using a static Sensor Network composed of low-cost cameras which provide measurements on the positions of the robots of the MVS. The proposed algorithms are compared to previous results from the literature, considering several metrics such as computation time and accuracy of the estimates
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Shkurti, Florian. "State estimation for an underwater robot using visual and inertial cues." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106603.
Full textAbstract:
This thesis addresses the problem of 3D position and orientation (pose) estimation using measurementsfrom a monocular camera and an inertial measurement unit (IMU). While the algorithmic formulation of the problem is generic enough to be applied to any intelligent agent that moves in 3D and possessesthe sensor modalities mentioned above, our implementation of the solution is particularly targeted to robots operating in underwater environments. The algorithmic approach used in this work is based on statistical estimators, and in particular the extended Kalman filter (EKF) formulation, which combines measurements from the camera and the IMU into a unique position and orientation estimate, relative to thestarting pose of the robot. Aside from estimating the relative 3D trajectory of the robot, the algorithm estimatesthe 3D structure of the environment. We present implementation trade-offs that affect estimation accuracy versus real-time operation of the system, and we also present an error analysis that describes how errors induced from any component of the system affect the remaining parts. To validate the approach we present extensive experimental results, both in simulation and in datasets of real-world underwater environments accompanied by ground truth, which confirm that this is a viable approach in terms of accuracy.Cette thèse aborde le problème d'estimation de la position et de l'orientation 3D (pose) en utilisant des mesuresprovenant d'une caméra monoculaire et d'une unité de mesure inertielle (IMU). Tandis que la formulation algorithmique de ce problème est suffisamment générique pour être appliquée aux tous les agents intelligents qui se déplacent en 3D et possèdent les mêmes capteurs mentionnés ci-dessus,notre implémentation s'addresse en particulier des robots fonctionnant dans des environnements sous-marins. L'approche algorithmique utilisée dans ce thèse est basée sur des estimateurs statistiqueset en particulier le Extended Kalman Filter (EKF), qui combine les mesures provenant de la caméra et de l'IMU dans une estimation de position et d'orientation unique, relative à la pose de départ du robot. En plus de l'estimation de la trajectoire relative du robot en 3D, l'algorithme estime la structure 3D de l'environnement. Nous présentons des compromis d'implementation qui affectent la précision d'estimation en fonction de l'utilisationdu système en temps réel, et nous présentons aussi une analyse des erreurs qui décrit comment les erreurs introduites par un composant du système affectent les parties restantes. Pour valider l'approche, nous présentons des nombreux résultats expérimentaux, tant en matière de simulation et de banque de données des environnements sous-marins accompagnée de réalité de terrain, qui confirme que cette approche est viable en termes de précision.
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Eng, Donald S. "State estimation for a holonomic omniwheel robot using a particle filter." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61159.
Full textAbstract:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 62).
The holonomic robot platform designed for the Opera of the Future must perform continuously on stage in a 10 meter by 20 meter world for one hour. The robot interacts with twelve other robots, stage elements, and human performers. Fast, accurate, and continuous state estimation for robot pose is a critical component for robots to safely perform on stage in front of a live audience. A custom robot platform was designed to use a Particle Filter to estimate state. The motor controller was developed to control robot vectoring and report odometry, and noise analysis on an absolute positioning system, Ubisense, was performed to characterize the system. High frequency noise confounds the Ubisense measurement of 0, but the Particle Filter acts as a low pass filter on the absolute positions and mixes the high frequency components of the odometry to determine an accurate estimate of the robot pose.
by Donald S. Eng.
M.Eng.