Relevant bibliographies by topics / Robust state estimation

Academic literature on the topic 'Robust state estimation'

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Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Robust state estimation"

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Khan, Zahid, Katrina Lane Krebs, Sarfaraz Ahmad, and Misbah Munawar. "POWER SYSTEM STATE ESTIMATION USING A ROBUST ESTIMATOR." NED University Journal of Research XVI, no. 4 (August 30, 2019): 53–65. http://dx.doi.org/10.35453/nedjr-ascn-2018-0038.

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State estimation (SE) is a primary data processing algorithm which is utilised by the control centres of advanced power systems. The most generally utilised state estimator is based on the weighted least squares (WLS) approach which is ineffective in addressing gross errors of input data of state estimator. This paper presents an innovative robust estimator for SE environments to overcome the non-robustness of the WLS estimator. The suggested approach not only includes the similar functioning of the customary loss function of WLS but also reflects loss function built on the modified WLS (MWLS) estimator. The performance of the proposed estimator was assessed based on its ability to decrease the impacts of gross errors on the estimation results. The properties of the suggested state estimator were investigated and robustness of the estimator was studied considering the influence function. The effectiveness of the proposed estimator was demonstrated with the help of examples which also indicated non-robustness of MWLS estimator in SE algorithm.
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De Palma, Daniela, and Giovanni Indiveri. "Output outlier robust state estimation." International Journal of Adaptive Control and Signal Processing 31, no. 4 (February 9, 2016): 581–607. http://dx.doi.org/10.1002/acs.2673.

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Zhang, Zhenglei, Jirong Wang, Junwei Gao, and Huabo Liu. "Robust State Estimation for T–S Fuzzy Markov Jump Systems." Mathematics 11, no. 2 (January 16, 2023): 487. http://dx.doi.org/10.3390/math11020487.

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The problem of robust state estimation for a class of uncertain nonlinear systems with Markov jump is investigated. The uncertain nonlinear system under consideration is represented by the Takagi–Sugeno (T–S) fuzzy model because it is difficult to describe. Firstly, different from the traditional T–S fuzzy modeling method, the deviation of the linear system approaching a nonlinear system is considered, which is represented as a model error in system modeling. Secondly, through a robust state estimation method based on the sensitivity penalty, we develop a robust state estimator for linear subsystems, and the fuzzy robust state estimator is obtained by fuzzy rules. Thirdly, the stability and boundedness of the fuzzy robust state estimator are proved under the assumption conditions to ensure the reliability of the obtained estimator. Finally, some numerical examples are given to verify the effectiveness of the fuzzy robust state estimator.
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Wang, Min, and Huabo Liu. "Event-Triggered Robust State Estimation for Nonlinear Networked Systems with Measurement Delays against DoS Attacks." Sensors 23, no. 14 (July 20, 2023): 6553. http://dx.doi.org/10.3390/s23146553.

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In this paper, we focus on the event-triggered robust state estimation problems for nonlinear networked systems with constant measurement delays against denial-of-service (DoS) attacks. The computation of the extended Kalman filter (EKF) generates errors of linearization approximations, which can result in increased state estimation errors, and subsequently amplifies the linearization errors. DoS attacks interfere with the transmission of measurements sent to the remote robust state estimator by overloading the communication networks, while the communication rate of the communication channel is constrained. Therefore, an event-triggered robust state estimation algorithm based on sensitivity penalization with an explicit packet arrival parameter is derived to defend against DoS attacks and linearization errors. Meanwhile, the presence of measurement delays precludes the direct use of conventional state estimation algorithms, prompting us to devise an innovative state augmentation method. The results of the numerical simulations show that the proposed robust state estimator can appreciably improve the accuracy of state estimation.
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Chen, Yung Yue, Shyang Jye Chang, Sheng Chih Shen, and Yung Hsiang Chen. "Robust Estimation Design of a Class of Systems with Noise Coupling Input Saturation." Materials Science Forum 594 (August 2008): 494–99. http://dx.doi.org/10.4028/www.scientific.net/msf.594.494.

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An investigation about the robust estimation of a class of systems with noise coupling input saturation is presented in this study. In general, the existed estimation algorithm is based on the exactly known of the input saturation, but in fact, this is not always true in some practical cases because of the coupling of input saturations and input noises. For treating these kinds of coupling problems in the state estimations of systems, in this study, one fuzzy-based robust estimation algorithm is proposed. The proposed robust estimator includes two parts: firstly, a regression form fuzzy system that is adopted to approximate the unknown input saturation, and then, a robust estimator that combines the above fuzzy system by robust filter design concept for eliminating the effects of noises and modeling uncertainties is proposed. This combination of fuzzy approach and robust filtering technologies successfully offers one a more simple and practical method for treating the estimation problem of a class of systems that input saturations and input noises couple together.
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Graham, Matthew C., Jonathan P. How, and Donald E. Gustafson. "Robust State Estimation with Sparse Outliers." Journal of Guidance, Control, and Dynamics 38, no. 7 (July 2015): 1229–40. http://dx.doi.org/10.2514/1.g000350.

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Garimella, S. S., and K. Srinivasan. "Robust State Estimation for Linear Systems." Journal of Dynamic Systems, Measurement, and Control 115, no. 1 (March 1, 1993): 193–96. http://dx.doi.org/10.1115/1.2897397.

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Real-time state estimation of a linear dynamic system using an observer, in the presence of modeling errors in the system model used by the observer and uncertainty in the initial system states, is considered here. A guideline for designing observers for multioutput systems is established, based on an expression for an upper bound on the norm of the state estimation error derived in this paper. An example is presented to illustrate the usefulness of this guideline.
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Irving, M. R. "Robust Algorithm for Generalized State Estimation." IEEE Transactions on Power Systems 24, no. 4 (November 2009): 1886–87. http://dx.doi.org/10.1109/tpwrs.2009.2030116.

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Kekatos, Vassilis, and Georgios B. Giannakis. "Distributed Robust Power System State Estimation." IEEE Transactions on Power Systems 28, no. 2 (May 2013): 1617–26. http://dx.doi.org/10.1109/tpwrs.2012.2219629.

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Alvarez, Jesús. "Nonlinear state estimation with robust convergence." Journal of Process Control 10, no. 1 (February 2000): 59–71. http://dx.doi.org/10.1016/s0959-1524(99)00018-9.

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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 &amp 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 &amp 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 efficacité
This 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 &amp 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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Books on the topic "Robust state estimation"

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Kyun, Chung Wan, ed. Perturbation compensator based robust tracking control and state estimation of mechanical systems. Berlin: Springer, 2004.

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Kwon, Sang Joo, and Wan Kyun Chung. Perturbation Compensator based Robust Tracking Control and State Estimation of Mechanical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/bfb0121383.

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Luc, Jaulin, ed. Applied interval analysis: With examples in parameter and state estimation, robust control and robotics. London: Springer, 2001.

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Jaulin, Luc. Applied Interval Analysis: With Examples in Parameter and State Estimation, Robust Control and Robotics. London: Springer London, 2001.

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Leigh, J. R. Control Theory. 2nd ed. Stevenage: IET, 2004.

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Zhao, Junbo, Marcos Netto, and Lamine Mili. Robust Dynamic State Estimation of Power Systems. Elsevier, 2023.

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Zhao, Junbo, Marcos Netto, and Lamine Mili. Robust Dynamic State Estimation of Power Systems. Elsevier, 2023.

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Kohan, Rashid R. Robust state estimation and control of highway traffic systems. 2001.

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Shmaliy, Yuriy S., and Shunyi Zhao. Optimal and Robust State Estimation: Finite Impulse Response and Kalman Approaches. Wiley & Sons, Incorporated, John, 2022.

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Shmaliy, Yuriy S., and Shunyi Zhao. Optimal and Robust State Estimation: Finite Impulse Response and Kalman Approaches. Wiley & Sons, Limited, John, 2022.

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Book chapters on the topic "Robust state estimation"

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Monticelli, A. "Numerically Robust State Estimators." In State Estimation in Electric Power Systems, 343–68. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4999-4_13.

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Hauth, Jan, Patrick Lang, and Andreas Wirsen. "Robust State Estimation of Complex Systems." In Currents in Industrial Mathematics, 291–350. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48258-2_9.

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Matveev, Alexey S., and Andrey V. Savkin. "Robust Kalman State Estimation with Switched Sensors." In Estimation and Control over Communication Networks, 1–11. Boston: Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4607-3_15.

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Savkin, Andrey V., and Robin J. Evans. "Optimal Robust State Estimation via Sensor Switching." In Hybrid Dynamical Systems, 107–20. Boston, MA: Birkhäuser Boston, 2002. http://dx.doi.org/10.1007/978-1-4612-0107-6_7.

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Mseddi, Amina, Omar Naifar, and Ahmed Abid. "Robust EV's Speed Tracking Using Fractional Order Controller." In State Estimation and Stabilization of Nonlinear Systems, 405–15. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37970-3_21.

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Petersen, Ian R., and Andrey V. Savkin. "Discrete-Time Set-Valued State Estimation." In Robust Kalman Filtering for Signals and Systems with Large Uncertainties, 71–87. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1594-3_5.

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Petersen, Ian R., and Andrey V. Savkin. "Robust State Estimation with Discrete and Continuous Measurements." In Robust Kalman Filtering for Signals and Systems with Large Uncertainties, 89–105. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1594-3_6.

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Fakoorian, Seyed, Kyohei Otsu, Shehryar Khattak, Matteo Palieri, and Ali-akbar Agha-mohammadi. "ROSE: Robust State Estimation via Online Covariance Adaption." In Springer Proceedings in Advanced Robotics, 452–67. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25555-7_31.

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Matveev, Alexey S., and Andrey V. Savkin. "Robust Set-Valued State Estimation via Limited Capacity Communication Channels." In Estimation and Control over Communication Networks, 1–16. Boston: Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4607-3_5.

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Talebi, Heidar A., Farzaneh Abdollahi, Rajni V. Patel, and Khashayar Khorasani. "A Robust Actuator Gain Fault Detection and Isolation Scheme." In Neural Network-Based State Estimation of Nonlinear Systems, 83–98. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1438-5_5.

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Conference papers on the topic "Robust state estimation"

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Rollinson, David, Howie Choset, and Stephen Tully. "Robust State Estimation With Redundant Proprioceptive Sensors." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3873.

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We present a framework for robust estimation of the configuration of an articulated robot using a large number of redundant proprioceptive sensors (encoders, gyros, accelerometers) distributed throughout the robot. Our method uses an Unscented Kalman Filter (UKF) to fuse the robot’s sensor measurements. The filter estimates the angle of each joint of the robot, enabling the accurate estimation of the robot’s kinematics even if not all modules report sensor readings. Additionally, a novel outlier detection method allows the the filter to be robust to corrupted accelerometer and gyro data.
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Piperakis, Stylianos, Dimitrios Kanoulas, Nikos G. Tsagarakis, and Panos Trahanias. "Outlier-Robust State Estimation for Humanoid Robots*." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8968152.

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Halbe, Omkar, Manfred Hajek, and Florian Holzapfel. "Observers for Robust Rotor State Estimation." In Vertical Flight Society 78th Annual Forum & Technology Display. The Vertical Flight Society, 2022. http://dx.doi.org/10.4050/f-0078-2022-17497.

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The paper discusses the synthesis of linear and nonlinear observers to estimate rotor states from fuselage state measurements alone. First, the paper reviews two forms of the classical Luenberger linear observer applied to the rotor state estimation problem and identifies some limitations thereof. Thereafter, the paper proposes a new robust nonlinear discontinuous observer based on the sliding mode theory to simultaneously estimate rotor flapping and lead-lagging states from fuselage state measurements. For this new nonlinear observer, the paper presents stability analyses to determine conditions that guarantee rotor state estimation accuracy despite unknown but bounded turbulence input. The nonlinear observer also lends itself to the online estimation of the unknown turbulence input. Simulation results in calm and turbulent air conditions highlight the efficacy and performance of the nonlinear discontinuous observer. Such rotor state observers could provide an independent source of online rotor states estimates to complement or supplement in situ rotor state measurement apparatus for various flight control and health monitoring applications.
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Gallego-Mejia, Joseph, and Fabio Gonzalez. "Robust Estimation in Reproducing Kernel Hilbert Space." In LatinX in AI at Neural Information Processing Systems Conference 2019. Journal of LatinX in AI Research, 2019. http://dx.doi.org/10.52591/lxai2019120829.

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Our work shows that estimating the mean in a feature space induced by certain kinds of kernels is the same as doing a robust mean estimation using an M-estimator in the original problem space. In particular, we show that calculating the average on a feature space induced by a Gaussian kernel is equivalent to perform robust mean estimation with the Welsch M-estimator. Besides, a new framework is proposed that was used to build four new robust kernels: Tukey’s, Andrews’, Huber’s and Cauchy’s robust kernels. The new robust kernels, combined with kernel matrix factorization clustering algorithm, were compared to state-of-the-art algorithms in clustering tasks. The result shows that some of the new robust kernels perform in a par with state-of-the-art algorithms.
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Hashemi, Ehsan, Mohammad Pirani, Baris Fidan, Amir Khajepour, Shih-ken Chen, and Bakhtiar Litkouhi. "Distributed robust vehicle state estimation." In 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2017. http://dx.doi.org/10.1109/ivs.2017.7995798.

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Roy, Shibdas, Ian R. Petersen, and Elanor H. Huntington. "Robust Phase Estimation of Squeezed State." In CLEO: QELS_Fundamental Science. Washington, D.C.: OSA, 2013. http://dx.doi.org/10.1364/cleo_qels.2013.jth2a.88.

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Brinkmann, Bernd, and Michael Negnevisky. "Robust state estimation in distribution networks." In 2016 Australasian Universities Power Engineering Conference (AUPEC). IEEE, 2016. http://dx.doi.org/10.1109/aupec.2016.7749306.

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Gol, Murat, and Ali Abur. "PMU placement for robust state estimation." In 2013 North American Power Symposium (NAPS). IEEE, 2013. http://dx.doi.org/10.1109/naps.2013.6666868.

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Xu, Chenxi, and Ali Abur. "Robust state estimation via network partitioning." In 2017 North American Power Symposium (NAPS). IEEE, 2017. http://dx.doi.org/10.1109/naps.2017.8107392.

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Zhang, Thomas T. C. K., Bruce D. Lee, Hamed Hassani, and Nikolai Matni. "Adversarial Tradeoffs in Robust State Estimation." In 2023 American Control Conference (ACC). IEEE, 2023. http://dx.doi.org/10.23919/acc55779.2023.10156358.

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Reports on the topic "Robust state estimation"

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Vogl, C. J., S. J. Chapin, and C. V. Ponce. Robust Decentralized State Estimation. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1558336.

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Dejene Mamo, Bekana. The Impact of Intergovernmental Transfers on Fiscal Behaviour of Local Governments in Ethiopia. Institute of Development Studies (IDS), November 2020. http://dx.doi.org/10.19088/ictd.2020.001.

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This paper examines the effect of intergovernmental fiscal transfers on the fiscal behaviour of local governments in Ethiopia for the period 2004-2018. The empirical findings suggest that central government grants bolster state-level employment and expenditure. However, grants from the central government to states do not crowd out state-level revenue collection. Hence, this paper argues that fiscal decentralisation in Ethiopia has mostly, at least in theory, taken the form of devolution of the power to tax and spend public money. However, on average state-level revenue can only finance up to 26 per cent of their annual expenditure. As a result, fiscal federalism in Ethiopia appears to be a form of delegation of spending responsibilities. It has to be considered in the context of a decentralised tax system, but with a transfer scheme and political hierarchy. The results are found to be robust to alternative econometric estimation techniques.
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Abur, Ali, Jianzhong Tong, David Kelle, Andre Langner, and Ramtin Khalili. Robust Distributed State Estimator for Interconnected Transmission and Distribution Networks (Final Report RPPR-1). Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1772562.

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Benavente, José Miguel, and Pluvia Zuñiga. How Does Market Competition Affect Firm Innovation Incentives in Emerging Countries? Evidence from Chile and Colombia. Inter-American Development Bank, May 2022. http://dx.doi.org/10.18235/0004235.

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The effect of market competition on firm innovation remains controversial, especially in the context of developing countries. This paper presents new empirical evidence about the causal impact of competition on firm innovation for Chilean and Colombian manufacturing firms. Using instrumental-variable estimation, our results show that market competition increases firm propensity to invest in innovation, but this relationship manifests differently in the two countries. While this relationship is linear in Chilean firms, an inversed-U shaped relation prevails in Colombian firms. In both countries, however, innovation incentives are mostly concentrated in the medium range of the firm productivity distribution. These findings are robust to including past innovation engagement, import competition, and business dynamics. In addition, first- stage estimations show that competition law interventions improved market competition in sanctioned sectors while business entry reforms significantly leveraged competition across industries. These findings stress the importance of pro-competition regulations and competition policy, not only to benefit consumers welfare but also to support firm innovation.
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Baltagi, Badi H., Georges Bresson, Anoop Chaturvedi, and Guy Lacroix. Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change. CIRANO, January 2023. http://dx.doi.org/10.54932/ufyn4045.

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This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the dynamic space-time panel model with random effects, with cross-correlated effects `a la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using an extensive Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. We illustrate our robust Bayesian estimator using the same data as in Keane and Neal (2020). We obtain short run as well as long run effects of climate change on corn producers in the United States.
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