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Academic literature on the topic 'Constrained state estimation'

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Published: 25 May 2024

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Journal articles on the topic "Constrained state estimation"

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Gomez-Quiles, Catalina, Hugo A. Gil, Antonio de la Villa Jaen, and Antonio Gomez-Exposito. "Equality-constrained bilinear state estimation." IEEE Transactions on Power Systems 28, no. 2 (May 2013): 902–10. http://dx.doi.org/10.1109/tpwrs.2012.2215058.

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Quintana, V. H., B. W. Scott, and A. Y. Chikhani. "Constrained Power System State Estimation." IFAC Proceedings Volumes 20, no. 5 (July 1987): 7–12. http://dx.doi.org/10.1016/s1474-6670(17)55409-9.

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Hu, Yudong, Changsheng Gao, and Wuxing Jing. "Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization." Aerospace 9, no. 4 (April 14, 2022): 217. http://dx.doi.org/10.3390/aerospace9040217.

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Aimed at joint state and parameter estimation problems in hypersonic glide vehicle defense, a novel moving horizon estimation algorithm via Carleman linearization is developed in this paper. First, the maneuver characteristic parameters that reflect the target maneuver law are extended into the state vector, and a dynamic tracking model applicable to various hypersonic glide vehicles is constructed. To improve the estimation accuracy, constraints such as path and parameter change amplitude constraints in flight are taken into account, and the estimation problem is transformed into a nonlinear constrained optimal estimation problem. Then, to solve the problem of high time cost for solving a nonlinear constrained optimal estimation problem, in the framework of moving horizon estimation, nonlinear constrained optimization problems are transformed into bilinear constrained optimization problems by linearizing the nonlinear system via Carleman linearization. For ensuring the consistency of the linearized system with the original nonlinear system, the linearized model is continuously updated as the window slides forward. Moreover, a CKF-based arrival cost update algorithm is also provided to improve the estimation accuracy. Simulation results demonstrate that the proposed joint state and parameter estimation algorithm greatly improves the estimation accuracy while reducing the time cost significantly.
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Mare, José B., and José A. De Doná. "Symmetry between constrained reference tracking and constrained state estimation." Automatica 45, no. 1 (January 2009): 207–11. http://dx.doi.org/10.1016/j.automatica.2008.06.020.

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Liu, Yuanyuan, Yaqiong Fu, Huipin Lin, Jingbiao Liu, Mingyu Gao, and Zhiwei He. "A New Constrained State Estimation Method Based on Unscented H∞ Filtering." Applied Sciences 10, no. 23 (November 27, 2020): 8484. http://dx.doi.org/10.3390/app10238484.

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The unscented Kalman filter (UKF) is widely used in many fields. When the unscented Kalman filter is combined with the H∞ filter (HF), the obtained unscented H∞ filtering (UHF) is very suitable for state estimation of nonlinear non-Gaussian systems. However, the application of state estimation is often limited by physical laws and mathematical models on some occasions. The standard unscented H∞ filtering always performs poorly under this situation. To solve this problem, this paper improves the UHF algorithm based on state constraints and studies the UHF algorithm based on the projection method. The standard UHF sigma points that violate the state constraints are projected onto the constraint boundary. Firstly, the paper gives a broad overview of H∞ filtering and unscented H∞ filtering, then addresses the issue of how to add constraints using the UHF approach, and finally, the new method is tested and evaluated by the gas-phase reversible reaction and the State of Charge (SOC) estimation examples. Simulation results show the validity and feasibility of the state-constrained UHF algorithm.
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Prakash, J., Sachin C. Patwardhan, and Sirish L. Shah. "Constrained State Estimation Using Particle Filters." IFAC Proceedings Volumes 41, no. 2 (2008): 6472–77. http://dx.doi.org/10.3182/20080706-5-kr-1001.01091.

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Dasgupta, Kalyan, and K. S. Swarup. "Tie-line constrained distributed state estimation." International Journal of Electrical Power & Energy Systems 33, no. 3 (March 2011): 569–76. http://dx.doi.org/10.1016/j.ijepes.2010.12.010.

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Nie, S., J. Zhu, and Y. Luo. "Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments." Hydrology and Earth System Sciences 15, no. 8 (August 3, 2011): 2437–57. http://dx.doi.org/10.5194/hess-15-2437-2011.

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Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.
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Korres, George N., and Theodore A. Alexopoulos. "A Constrained Ordering for Solving the Equality Constrained State Estimation." IEEE Transactions on Power Systems 27, no. 4 (November 2012): 1998–2005. http://dx.doi.org/10.1109/tpwrs.2012.2194745.

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Wang, Yanyan, and Yingsong Li. "Sparse Multipath Channel Estimation Using Norm Combination Constrained Set-Membership NLMS Algorithms." Wireless Communications and Mobile Computing 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8140702.

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A norm combination penalized set-membership NLMS algorithm with l0 and l1 independently constrained, which is denoted as l0 and l1 independently constrained set-membership (SM) NLMS (L0L1SM-NLMS) algorithm, is presented for sparse adaptive multipath channel estimations. The L0L1SM-NLMS algorithm with fast convergence and small estimation error is implemented by independently exerting penalties on the channel coefficients via controlling the large group and small group channel coefficients which are implemented by l0 and l1 norm constraints, respectively. Additionally, a further improved L0L1SM-NLMS algorithm denoted as reweighted L0L1SM-NLMS (RL0L1SM-NLMS) algorithm is presented via integrating a reweighting factor into our L0L1SM-NLMS algorithm to properly adjust the zero-attracting capabilities. Our developed RL0L1SM-NLMS algorithm provides a better estimation behavior than the presented L0L1SM-NLMS algorithm for implementing an estimation on sparse channels. The estimation performance of the L0L1SM-NLMS and RL0L1SM-NLMS algorithms is obtained for estimating sparse channels. The achieved simulation results show that our L0L1SM- and RL0L1SM-NLMS algorithms are superior to the traditional LMS, NLMS, SM-NLMS, ZA-LMS, RZA-LMS, and ZA-, RZA-, ZASM-, and RZASM-NLMS algorithms in terms of the convergence speed and steady-state performance.
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Dissertations / Theses on the topic "Constrained state estimation"

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Yan, Jun. "Constrained model predictive control, state estimation and coordination." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3206875.

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Thesis (Ph. D.)--University of California, San Diego, 2006.
Title from first page of PDF file (viewed May 3, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 106-110).
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Lopez, Negrete de la Fuente Rodrigo. "Nonlinear Programming Sensitivity Based Methods for Constrained State Estimation." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/174.

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Model based control schemes, such as nonlinear model predictive control, assume that the full state vector of the plant is known for feedback control. However, in reality this is not always true. Most times only a set of noisy measurements are available, and thus, the unmeasured states need to be inferred from these measurements. This is done in combination with a detailed model of the system. The most common nonlinear state estimation methods do not have a means to deal with bounds or constraints on the states in an efficient or systematical way. These bounds and constraints are important in chemical engineering processes since states usually have physical meaning, for example, concentrations, molecular weights, and conversions are always positive. Therefore, state estimates must be physically feasible. Since Moving Horizon Estimation (MHE) is optimization based it has become a superior strategy for constrained state estimation because bounds are handled optimally by theNonlinear Programming (NLP) solver. In the presentworkwe develop strategies for MHE based on NLP sensitivity to reduce the on-line computational expense of solving these problems. These formulations are intended to make the on-line application of MHE feasible, by reducing the potential of delays due to the computational expense of solving the associated NLP. Here we discuss two approaches to update certain tuning parameters in MHE: one of them allows us to reduce the size of the NLP that is being solved, while the other provides a fast approximation of the covariance of the initial condition. The former method is only suitable for small and medium sized problems, while the latter one is better suited for large-scale systems. Additionally, we also discuss NLP sensitivity theory and extensions that apply to the Interior Point solver we use (i.e., IPOPT). With these extensions we are able to develop fast on-line strategies for NMPC and MHE. However, in this work we focus only in the application of these developments to the latter. To reduce the horizon window we propose methods to approximate the initial condition parameters based on particle filters and sample based statistics to approximate the conditional probability density function (or its parameters) of the initial condition of the states in the MHE horizon window (i.e., the so-called arrival cost). As mentioned above, this approach is suitable mostly for only certain classes of systems that have few states or almost Gaussian behaviors. Therefore,we also develop othermethods for on-lineMHE suitable for large-scale systems that usesmoremeasurements to reduce any effects of the simplifications done to approximate the initial condition terms. Thus, using NLP sensitivity we develop strategies that leverage the parametric properties of the MHE problem to formulate fast on-line methods applicable to large-scale systems. Moreover, using NLP sensitivity also allows us to relate the optimality conditions of the associated NLP problem to the stochastic origin of MHE. For example, we show the relationship of the covariance of the state estimates with the reduced Hessian matrix of the NLP. This information can also be used to update the parameters if the initial condition penalty term. Moreover, we also discuss the use of Robust M-Estimators to reduce the effects of outliers or gross errors in the measurements. Finally, we illustrate the use and benefits of these strategies through several small and large-scale examples taken from the literature.
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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 estimées
Distributed 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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Duan, Zhansheng. "State Estimation with Unconventional and Networked Measurements." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1133.

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This dissertation consists of two main parts. One is about state estimation with two types of unconventional measurements and the other is about two types of network-induced state estimation problems. The two types of unconventional measurements considered are noise-free measurements and set measurements. State estimation with them has numerous real supports. For state estimation with noisy and noise-free measurements, two sequential forms of the batch linear minimum mean-squared error (LMMSE) estimator are obtained to reduce the computational complexity. Inspired by the estimation with quantized measurements developed by Curry [28], under a Gaussian assumption, the minimum mean-squared error (MMSE) state estimator with point measurements and set measurements of any shape is proposed by discretizing continuous set measurements. State estimation under constraints, which are special cases of the more general framework, has some interesting properties. It is found that under certain conditions, although constraints are indispensable in the evolution of the state, update by treating them as measurements is redundant in filtering. The two types of network-induced estimation problems considered are optimal state estimation in the presence of multiple packet dropouts and optimal distributed estimation fusion with transformed data. An alternative form of LMMSE estimation in the presence of multiple packet dropouts, which can overcome the shortcomings of two existing ones, is proposed first. Then under a Gaussian assumption, the MMSE estimation is also obtained based on a hard decision by comparing the measurements at two consecutive time instants. It is pointed out that if this comparison is legitimate, our simple MMSE solution largely nullifies existing work on this problem. By taking linear transformation of the raw measurements received by each sensor, two optimal distributed fusion algorithms are proposed. In terms of optimality, communication and computational requirements, three nice properties make them attractive.
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Mook, Daniel Joseph. "Measurement covariance-constrained estimation for poorly modeled dynamic systems." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/49776.

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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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Merlinge, Nicolas. "State estimation and trajectory planning using box particle kernels." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS425/document.

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L'autonomie d'un engin aérospatial requière de disposer d'une boucle de navigation-guidage-pilotage efficace et sûre. Cette boucle intègre des filtres estimateurs et des lois de commande qui doivent dans certains cas s'accommoder de non-linéarités sévères et être capables d'exploiter des mesures ambiguës. De nombreuses approches ont été développées à cet effet et parmi celles-ci, les approches particulaires présentent l'avantage de pouvoir traiter de façon unifiée des problèmes dans lesquels les incertitudes d’évolution du système et d’observation peuvent être soumises à des lois statistiques quelconques. Cependant, ces approches ne sont pas exemptes de défauts dont le plus important est celui du coût de calcul élevé. D'autre part, dans certains cas, ces méthodes ne permettent pas non plus de converger vers une solution acceptable. Des adaptations récentes de ces approches, combinant les avantages du particulaire tel que la possibilité d'extraire la recherche d'une solution d'un domaine local de description et la robustesse des approches ensemblistes, ont été à l'origine du travail présenté dans cette thèse.Cette thèse présente le développement d’un algorithme d’estimation d’état, nommé le Box Regularised Particle Filter (BRPF), ainsi qu’un algorithme de commande, le Box Particle Control (BPC). Ces algorithmes se basent tous deux sur l’utilisation de mixtures de noyaux bornés par des boites (i.e., des vecteurs d’intervalles) pour décrire l’état du système sous la forme d’une densité de probabilité multimodale. Cette modélisation permet un meilleur recouvrement de l'espace d'état et apporte une meilleure cohérence entre la prédite et la vraisemblance. L’hypothèse est faite que les incertitudes incriminées sont bornées. L'exemple d'application choisi est la navigation par corrélation de terrain qui constitue une application exigeante en termes d'estimation d'état.Pour traiter des problèmes d’estimation ambiguë, c’est-à-dire lorsqu’une valeur de mesure peut correspondre à plusieurs valeurs possibles de l’état, le Box Regularised Particle Filter (BRPF) est introduit. Le BRPF est une évolution de l’algorithme de Box Particle Filter (BPF) et est doté d’une étape de ré-échantillonnage garantie et d’une stratégie de lissage par noyau (Kernel Regularisation). Le BRPF assure théoriquement une meilleure estimation que le BPF en termes de Mean Integrated Square Error (MISE). L’algorithme permet une réduction significative du coût de calcul par rapport aux approches précédentes (BPF, PF). Le BRPF est également étudié dans le cadre d’une intégration dans des architectures fédérées et distribuées, ce qui démontre son efficacité dans des cas multi-capteurs et multi-agents.Un autre aspect de la boucle de navigation–guidage-pilotage est le guidage qui nécessite de planifier la future trajectoire du système. Pour tenir compte de l'incertitude sur l'état et des contraintes potentielles de façon versatile, une approche nommé Box Particle Control (BPC) est introduite. Comme pour le BRPF, le BPC se base sur des mixtures de noyaux bornés par des boites et consiste en la propagation de la densité d’état sur une trajectoire jusqu’à un certain horizon de prédiction. Ceci permet d’estimer la probabilité de satisfaire les contraintes d’état au cours de la trajectoire et de déterminer la séquence de futures commandes qui maintient cette probabilité au-delà d’un certain seuil, tout en minimisant un coût. Le BPC permet de réduire significativement la charge de calcul
State estimation and trajectory planning are two crucial functions for autonomous systems, and in particular for aerospace vehicles.Particle filters and sample-based trajectory planning have been widely considered to tackle non-linearities and non-Gaussian uncertainties.However, these approaches may produce erratic results due to the sampled approximation of the state density.In addition, they have a high computational cost which limits their practical interest.This thesis investigates the use of box kernel mixtures to describe multimodal probability density functions.A box kernel mixture is a weighted sum of basic functions (e.g., uniform kernels) that integrate to unity and whose supports are bounded by boxes, i.e., vectors of intervals.This modelling yields a more extensive description of the state density while requiring a lower computational load.New algorithms are developed, based on a derivation of the Box Particle Filter (BPF) for state estimation, and of a particle based chance constrained optimisation (Particle Control) for trajectory planning under uncertainty.In order to tackle ambiguous state estimation problems, a Box Regularised Particle Filter (BRPF) is introduced.The BRPF consists of an improved BPF with a guaranteed resampling step and a smoothing strategy based on kernel regularisation.The proposed strategy is theoretically proved to outperform the original BPF in terms of Mean Integrated Square Error (MISE), and empirically shown to reduce the Root Mean Square Error (RMSE) of estimation.BRPF reduces the computation load in a significant way and is robust to measurement ambiguity.BRPF is also integrated to federated and distributed architectures to demonstrate its efficiency in multi-sensors and multi-agents systems.In order to tackle constrained trajectory planning under non-Gaussian uncertainty, a Box Particle Control (BPC) is introduced.BPC relies on an interval bounded kernel mixture state density description, and consists of propagating the state density along a state trajectory at a given horizon.It yields a more accurate description of the state uncertainty than previous particle based algorithms.A chance constrained optimisation is performed, which consists of finding the sequence of future control inputs that minimises a cost function while ensuring that the probability of constraint violation (failure probability) remains below a given threshold.For similar performance, BPC yields a significant computation load reduction with respect to previous approaches
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Steinig, Simeon [Verfasser], and Kunibert G. [Akademischer Betreuer] Siebert. "Adaptive finite elements for state-constrained optimal control problems - convergence analysis and a posteriori error estimation / Simeon Steinig. Betreuer: Kunibert G. Siebert." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2014. http://d-nb.info/106430897X/34.

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Sircoulomb, Vincent. "Étude des concepts de filtrage robuste aux méconnaissances de modèles et aux pertes de mesures. Application aux systèmes de navigation." Thesis, Vandoeuvre-les-Nancy, INPL, 2008. http://www.theses.fr/2008INPL093N/document.

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La résolution d'un problème d'estimation de l'état d'un système nécessite de disposer d'un modèle régissant l'évolution des variables d'état et de mesurer de manière directe ou indirecte l'ensemble ou une partie de ces variables d'état. Les travaux exposés dans ce mémoire de thèse portent sur la problématique d'estimation en présence de méconnaissances de modèle et de pertes de capteurs. La première partie de ce travail constitue la synthèse d'un dispositif d'estimation d'état pour systèmes non linéaires. Cela consiste à sélectionner un estimateur d'état et convenablement le régler, puis à concevoir algorithmiquement, à partir d'un critère introduit pour la circonstance, une redondance matérielle visant à compenser la perte de certains capteurs. La seconde partie de ce travail porte sur la conception, à l'aide de la variance d'Allan, d'un sous-modèle permettant de compenser les incertitudes d'un modèle d'état, ce sous-modèle étant utilisable par un filtre de Kalman. Ce travail a été exploité pour tenir compte de dérives gyroscopiques dans le cadre d'une navigation inertielle hybridée avec des mesures GPS par un filtre de Kalman contraint. Les résultats obtenus, issus d'expériences sur deux trajectoires d'avion, ont montré un comportement sain et robuste de l'approche proposée
To solve the problem of estimating the state of a system, it is necessary to have at one's disposal a model governing the dynamic of the state variables and to measure directly or indirectly all or a part of these variables. The work presented in this thesis deals with the estimation issue in the presence of model uncertainties and sensor losses. The first part of this work represents the synthesis of a state estimation device for nonlinear systems. It consists in selecting a state estimator and properly tuning it. Then, thanks to a criterion introduced for the occasion, it consists in algorithmically designing a hardware redundancy aiming at compensating for some sensor losses. The second part of this work deals with the conception of a sub-model compensating for some model uncertainties. This sub-model, designed by using the Allan variance, is usable by a Kalman filter. This work has been used to take into account some gyroscopical drifts in a GPS-INS integrated navigation based on a constrained Kalman filter. The results obtained, coming from experiments on two plane trajectories, showed a safe and robust behaviour of the proposed method
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Healey, Christopher M. "Advances in ranking and selection: variance estimation and constraints." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34768.

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In this thesis, we first show that the performance of ranking and selection (R&S) procedures in steady-state simulations depends highly on the quality of the variance estimates that are used. We study the performance of R&S procedures using three variance estimators --- overlapping area, overlapping Cramer--von Mises, and overlapping modified jackknifed Durbin--Watson estimators --- that show better long-run performance than other estimators previously used in conjunction with R&S procedures for steady-state simulations. We devote additional study to the development of the new overlapping modified jackknifed Durbin--Watson estimator and demonstrate some of its useful properties. Next, we consider the problem of finding the best simulated system under a primary performance measure, while also satisfying stochastic constraints on secondary performance measures, known as constrained ranking and selection. We first present a new framework that allows certain systems to become dormant, halting sampling for those systems as the procedure continues. We also develop general procedures for constrained R&S that guarantee a nominal probability of correct selection, under any number of constraints and correlation across systems. In addition, we address new topics critical to efficiency of the these procedures, namely the allocation of error between feasibility check and selection, the use of common random numbers, and the cost of switching between simulated systems.
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Books on the topic "Constrained state estimation"

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Ariyur, Kartik. Navigation with Signals and Constraints of Opportunity: Exploiting Unstructured Environments for State Estimation. Elsevier Science & Technology Books, 2019.

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Beenakker, Carlo W. J. Extreme eigenvalues of Wishart matrices: application to entangled bipartite system. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.37.

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This article describes the application of random matrix theory (RMT) to the estimation of the bipartite entanglement of a quantum system, with particular emphasis on the extreme eigenvalues of Wishart matrices. It first provides an overview of some spectral properties of unconstrained Wishart matrices before introducing the problem of the random pure state of an entangled quantum bipartite system consisting of two subsystems whose Hilbert spaces have dimensions M and N respectively with N ≤ M. The focus is on the smallest eigenvalue which serves as an important measure of entanglement between the two subsystems. The minimum eigenvalue distribution for quadratic matrices is also considered. The article shows that the N eigenvalues of the reduced density matrix of the smaller subsystem are distributed exactly as the eigenvalues of a Wishart matrix, except that the eigenvalues satisfy a global constraint: the trace is fixed to be unity.
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Book chapters on the topic "Constrained state estimation"

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Shi, Dawei, Ling Shi, and Tongwen Chen. "A Constrained Optimization Approach." In Event-Based State Estimation, 77–108. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26606-0_5.

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Kurzhanski, Alexander B., and Alexander N. Daryin. "State Estimation and State Constrained Control." In Dynamic Programming for Impulse Feedback and Fast Controls, 193–209. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7437-0_9.

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Bergounioux, M., and K. Kunisch. "Augmented Lagrangian Algorithms for State Constrained Optimal Control Problems." In Control and Estimation of Distributed Parameter Systems, 33–48. Basel: Birkhäuser Basel, 1998. http://dx.doi.org/10.1007/978-3-0348-8849-3_3.

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Wang, Yudong, Jingchun Wang, and Bo Liu. "Constrained Nonlinear State Estimation – A Differential Evolution Based Moving Horizon Approach." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 1184–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74205-0_122.

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Kurzhanski, Alexander B. "On the Generalized Duality Principle for State-Constrained Control and State Estimation Under Impulsive Inputs." In Lecture Notes in Economics and Mathematical Systems, 119–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75169-6_7.

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Park, Ju H., Hao Shen, Xiao-Heng Chang, and Tae H. Lee. "Network-Based $$\mathscr {H}_{\infty }$$H∞ State Estimation for Neural Networks Using Limited Measurement." In Recent Advances in Control and Filtering of Dynamic Systems with Constrained Signals, 193–210. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96202-3_10.

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Matei, Alexander, and Stefan Ulbrich. "Detection of Model Uncertainty in the Dynamic Linear-Elastic Model of Vibrations in a Truss." In Lecture Notes in Mechanical Engineering, 281–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_22.

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AbstractDynamic processes have always been of profound interest for scientists and engineers alike. Often, the mathematical models used to describe and predict time-variant phenomena are uncertain in the sense that governing relations between model parameters, state variables and the time domain are incomplete. In this paper we adopt a recently proposed algorithm for the detection of model uncertainty and apply it to dynamic models. This algorithm combines parameter estimation, optimum experimental design and classical hypothesis testing within a probabilistic frequentist framework. The best setup of an experiment is defined by optimal sensor positions and optimal input configurations which both are the solution of a PDE-constrained optimization problem. The data collected by this optimized experiment then leads to variance-minimal parameter estimates. We develop efficient adjoint-based methods to solve this optimization problem with SQP-type solvers. The crucial test which a model has to pass is conducted over the claimed true values of the model parameters which are estimated from pairwise distinct data sets. For this hypothesis test, we divide the data into k equally-sized parts and follow a k-fold cross-validation procedure. We demonstrate the usefulness of our approach in simulated experiments with a vibrating linear-elastic truss.
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Mordukhovich, Boris S., and Kaixia Zhang. "Dirichlet Boundary Control of Parabolic Systems with Pointwise State Constraints." In Control and Estimation of Distributed Parameter Systems, 223–36. Basel: Birkhäuser Basel, 1998. http://dx.doi.org/10.1007/978-3-0348-8849-3_17.

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Casas, E., J. P. Raymond, and H. Zidani. "Optimal Control Problem Governed by Semilinear Elliptic Equations with Integral Control Constraints and Pointwise State Constraints." In Control and Estimation of Distributed Parameter Systems, 89–102. Basel: Birkhäuser Basel, 1998. http://dx.doi.org/10.1007/978-3-0348-8849-3_7.

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Fattorini, H. O. "Control Problems for Parabolic Equations with State Constraints and Unbounded Control Sets." In Control and Estimation of Distributed Parameter Systems, 129–40. Basel: Birkhäuser Basel, 1998. http://dx.doi.org/10.1007/978-3-0348-8849-3_10.

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Conference papers on the topic "Constrained state estimation"

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Gomez-Quiles, Catalina, Hugo A. Gil, Antonio de la Villa Jaen, and Antonio Gomez-Exposito. "Equality-constrained bilinear state estimation." In 2013 IEEE Power & Energy Society General Meeting. IEEE, 2013. http://dx.doi.org/10.1109/pesmg.2013.6672832.

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Goel, Ankit, and Dennis S. Bernstein. "Adaptive State Estimation with Subspace-Constrained State Correction." In 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9147916.

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Ebinger, Bradley, Nidhal Bouaynaya, Robi Polikar, and Roman Shterenberg. "Constrained state estimation in particle filters." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178732.

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Kuklišová Pavelková, Lenka. "Bayesian State Estimation Using Constrained Zonotopes." In 20th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0012230900003543.

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Teixeira, B. O. S., J. Chandrasekar, L. A. B. Torres, L. A. Aguirre, and D. S. Bernstein. "State estimation for equality-constrained linear systems." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434800.

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Surana, Amit, Matthew O. Williams, Manfred Morari, and Andrzej Banaszuk. "Koopman operator framework for constrained state estimation." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8263649.

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Sodhi, Paloma, Sanjiban Choudhury, Joshua G. Mangelson, and Michael Kaess. "ICS: Incremental Constrained Smoothing for State Estimation." In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. http://dx.doi.org/10.1109/icra40945.2020.9196649.

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Patel, Rahul, Sharad Bhartiya, and Ravindra D. Gudi. "State Estimation Using Physics Constrained Neural Networks." In 2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP). IEEE, 2022. http://dx.doi.org/10.1109/adconip55568.2022.9894188.

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Norman-Tenazas, Raphael, Brian S. Robinson, Justin Joyce, Isaac Western, Erik C. Johnson, William Gray-Roncal, and Joan A. Hoffmann. "Continuous State Estimation With Synapse-constrained Connectivity." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892549.

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Von Einem, Cornelius, Andrei Cramariuc, Roland Siegwart, Cesar Cadena, and Florian Tschopp. "Path-Constrained State Estimation for Rail Vehicles." In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2023. http://dx.doi.org/10.1109/itsc57777.2023.10422075.

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Reports on the topic "Constrained state estimation"

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Zeller, Lucas, Daniel McGrath, Louis Sass, Shad O’Neel, Christopher McNeil, and Emily Baker. Beyond glacier-wide mass balances : parsing seasonal elevation change into spatially resolved patterns of accumulation and ablation at Wolverine Glacier, Alaska. Engineer Research and Development Center (U.S.), May 2024. http://dx.doi.org/10.21079/11681/48497.

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We present spatially distributed seasonal and annual surface mass balances of Wolverine Glacier, Alaska, from 2016 to 2020. Our approach accounts for the effects of ice emergence and firn compaction on surface elevation changes to resolve the spatial patterns in mass balance at 10 m scale. We present and compare three methods for estimating emergence velocities. Firn compaction was constrained by optimizing a firn model to fit three firn cores. Distributed mass balances showed good agreement with mass-balance stakes (RMSE = 0.67 m w.e., r = 0.99, n = 41) and ground-penetrating radar surveys (RMSE = 0.36 m w.e., r = 0.85, n = 9024). Fundamental differences in the distributions of seasonal balances highlight the importance of disparate physical processes, with anomalously high ablation rates observed in icefalls. Winter balances were found to be positively skewed when controlling for elevation, while summer and annual balances were negatively skewed. We show that only a small percent of the glacier surface represents ideal locations for mass-balance stake placement. Importantly, no suitable areas are found near the terminus or in elevation bands dominated by icefalls. These findings offer explanations for the often needed geodetic calibrations of glaciological time series.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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