Literatura científica selecionada sobre o tema "Constrained state estimation"
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Artigos de revistas sobre o assunto "Constrained state estimation"
Gomez-Quiles, Catalina, Hugo A. Gil, Antonio de la Villa Jaen e Antonio Gomez-Exposito. "Equality-constrained bilinear state estimation". IEEE Transactions on Power Systems 28, n.º 2 (maio de 2013): 902–10. http://dx.doi.org/10.1109/tpwrs.2012.2215058.
Texto completo da fonteQuintana, V. H., B. W. Scott e A. Y. Chikhani. "Constrained Power System State Estimation". IFAC Proceedings Volumes 20, n.º 5 (julho de 1987): 7–12. http://dx.doi.org/10.1016/s1474-6670(17)55409-9.
Texto completo da fonteHu, Yudong, Changsheng Gao e Wuxing Jing. "Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization". Aerospace 9, n.º 4 (14 de abril de 2022): 217. http://dx.doi.org/10.3390/aerospace9040217.
Texto completo da fonteMare, José B., e José A. De Doná. "Symmetry between constrained reference tracking and constrained state estimation". Automatica 45, n.º 1 (janeiro de 2009): 207–11. http://dx.doi.org/10.1016/j.automatica.2008.06.020.
Texto completo da fonteLiu, Yuanyuan, Yaqiong Fu, Huipin Lin, Jingbiao Liu, Mingyu Gao e Zhiwei He. "A New Constrained State Estimation Method Based on Unscented H∞ Filtering". Applied Sciences 10, n.º 23 (27 de novembro de 2020): 8484. http://dx.doi.org/10.3390/app10238484.
Texto completo da fontePrakash, J., Sachin C. Patwardhan e Sirish L. Shah. "Constrained State Estimation Using Particle Filters". IFAC Proceedings Volumes 41, n.º 2 (2008): 6472–77. http://dx.doi.org/10.3182/20080706-5-kr-1001.01091.
Texto completo da fonteDasgupta, Kalyan, e K. S. Swarup. "Tie-line constrained distributed state estimation". International Journal of Electrical Power & Energy Systems 33, n.º 3 (março de 2011): 569–76. http://dx.doi.org/10.1016/j.ijepes.2010.12.010.
Texto completo da fonteNie, S., J. Zhu e 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, n.º 8 (3 de agosto de 2011): 2437–57. http://dx.doi.org/10.5194/hess-15-2437-2011.
Texto completo da fonteKorres, George N., e Theodore A. Alexopoulos. "A Constrained Ordering for Solving the Equality Constrained State Estimation". IEEE Transactions on Power Systems 27, n.º 4 (novembro de 2012): 1998–2005. http://dx.doi.org/10.1109/tpwrs.2012.2194745.
Texto completo da fonteWang, Yanyan, e 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.
Texto completo da fonteTeses / dissertações sobre o assunto "Constrained state estimation"
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.
Texto completo da fonteTitle from first page of PDF file (viewed May 3, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 106-110).
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.
Texto completo da fonteVenturino, 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.
Texto completo da fonteDistributed 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
Duan, Zhansheng. "State Estimation with Unconventional and Networked Measurements". ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1133.
Texto completo da fonteMook, 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.
Texto completo da fontePajic, 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.
Texto completo da fonteMerlinge, Nicolas. "State estimation and trajectory planning using box particle kernels". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS425/document.
Texto completo da fonteState 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
Steinig, Simeon [Verfasser], e 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.
Texto completo da fonteSircoulomb, 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.
Texto completo da fonteTo 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
Healey, Christopher M. "Advances in ranking and selection: variance estimation and constraints". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34768.
Texto completo da fonteLivros sobre o assunto "Constrained state estimation"
Ariyur, Kartik. Navigation with Signals and Constraints of Opportunity: Exploiting Unstructured Environments for State Estimation. Elsevier Science & Technology Books, 2019.
Encontre o texto completo da fonteBeenakker, Carlo W. J. Extreme eigenvalues of Wishart matrices: application to entangled bipartite system. Editado por Gernot Akemann, Jinho Baik e Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.37.
Texto completo da fonteCapítulos de livros sobre o assunto "Constrained state estimation"
Shi, Dawei, Ling Shi e 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.
Texto completo da fonteKurzhanski, Alexander B., e 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.
Texto completo da fonteBergounioux, M., e 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.
Texto completo da fonteWang, Yudong, Jingchun Wang e 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.
Texto completo da fonteKurzhanski, 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.
Texto completo da fontePark, Ju H., Hao Shen, Xiao-Heng Chang e 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.
Texto completo da fonteMatei, Alexander, e 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.
Texto completo da fonteMordukhovich, Boris S., e 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.
Texto completo da fonteCasas, E., J. P. Raymond e 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.
Texto completo da fonteFattorini, 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Constrained state estimation"
Gomez-Quiles, Catalina, Hugo A. Gil, Antonio de la Villa Jaen e 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.
Texto completo da fonteGoel, Ankit, e 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.
Texto completo da fonteEbinger, Bradley, Nidhal Bouaynaya, Robi Polikar e 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.
Texto completo da fonteKuklišová 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.
Texto completo da fonteTeixeira, B. O. S., J. Chandrasekar, L. A. B. Torres, L. A. Aguirre e 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.
Texto completo da fonteSurana, Amit, Matthew O. Williams, Manfred Morari e 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.
Texto completo da fonteSodhi, Paloma, Sanjiban Choudhury, Joshua G. Mangelson e 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.
Texto completo da fontePatel, Rahul, Sharad Bhartiya e 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.
Texto completo da fonteNorman-Tenazas, Raphael, Brian S. Robinson, Justin Joyce, Isaac Western, Erik C. Johnson, William Gray-Roncal e 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.
Texto completo da fonteVon Einem, Cornelius, Andrei Cramariuc, Roland Siegwart, Cesar Cadena e 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "Constrained state estimation"
Zeller, Lucas, Daniel McGrath, Louis Sass, Shad O’Neel, Christopher McNeil e 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.), maio de 2024. http://dx.doi.org/10.21079/11681/48497.
Texto completo da fonteEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak e Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, julho de 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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