Relevant bibliographies by topics / Stochastic robust control

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Stochastic robust control'

Author: Grafiati
Published: 30 May 2022
Last updated: 26 July 2025

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Journal articles on the topic "Stochastic robust control"

1

Pun, Chi Seng. "Robust time-inconsistent stochastic control problems." Automatica 94 (August 2018): 249–57. http://dx.doi.org/10.1016/j.automatica.2018.04.038.

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Zhang, Tianliang, Yu-Hong Wang, Xiushan Jiang, and Weihai Zhang. "Robust Stability, Stabilization, andH∞Control of a Class of Nonlinear Discrete Time Stochastic Systems." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/5185784.

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This paper studies robust stability, stabilization, andH∞control for a class of nonlinear discrete time stochastic systems. Firstly, the easily testing criteria for stochastic stability and stochastic stabilizability are obtained via linear matrix inequalities (LMIs). Then a robustH∞state feedback controller is designed such that the concerned system not only is internally stochastically stabilizable but also satisfies robustH∞performance. Moreover, the previous results of the nonlinearly perturbed discrete stochastic system are generalized to the system with state, control, and external distu
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Chen, Guici, and Yi Shen. "Robust ReliableH∞Control for Nonlinear Stochastic Markovian Jump Systems." Mathematical Problems in Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/431576.

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The robust reliableH∞control problem for a class of nonlinear stochastic Markovian jump systems (NSMJSs) is investigated. The system under consideration includes Itô-type stochastic disturbance, Markovian jumps, as well as sector-bounded nonlinearities and norm-bounded stochastic nonlinearities. Our aim is to design a controller such that, for possible actuator failures, the closed-loop stochastic Markovian jump system is exponential mean-square stable with convergence rateαand disturbance attenuationγ. Based on the Lyapunov stability theory and Itô differential rule, together with LMIs techni
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Benavoli, A., and L. Chisci. "Robust stochastic control based on imprecise probabilities*." IFAC Proceedings Volumes 44, no. 1 (2011): 4606–13. http://dx.doi.org/10.3182/20110828-6-it-1002.02081.

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Glasserman, Paul, and Xingbo Xu. "Robust Portfolio Control with Stochastic Factor Dynamics." Operations Research 61, no. 4 (2013): 874–93. http://dx.doi.org/10.1287/opre.2013.1180.

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Hassan, Ali, Robert Mieth, Deepjyoti Deka, and Yury Dvorkin. "Stochastic and Distributionally Robust Load Ensemble Control." IEEE Transactions on Power Systems 35, no. 6 (2020): 4678–88. http://dx.doi.org/10.1109/tpwrs.2020.2992268.

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Martínez-Frutos, J., R. Ortigosa, P. Pedregal, and F. Periago. "Robust optimal control of stochastic hyperelastic materials." Applied Mathematical Modelling 88 (December 2020): 888–904. http://dx.doi.org/10.1016/j.apm.2020.07.012.

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Steinbach, Marc C. "Robust Process Control by Dynamic Stochastic Programming." PAMM 4, no. 1 (2004): 11–14. http://dx.doi.org/10.1002/pamm.200410003.

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Violino, Elia, and Kouamana Bousson. "Robust predictive attitude control with stochastic dynamics." Journal of Applied Mathematics and Computational Mechanics 21, no. 4 (2022): 86–97. http://dx.doi.org/10.17512/jamcm.2022.4.08.

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Wang, Cheng, and Cong Jun Rao. "Study on Stability and Robust Control of Stochastic Network Control System." Applied Mechanics and Materials 602-605 (August 2014): 1023–26. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1023.

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Network control system is a feedback control system of realizing the exchange of information control system in different regional components by using the digital communication. Focusing on the stability and robust control of stochastic network control system, this paper introduces the research history and the newest research trends, and presents many widespread theoretical and applications problems. Moreover, assumptions, main ideas, and conclusions of literature related to stochastic network control system are reviewed and commented.
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Dissertations / Theses on the topic "Stochastic robust control"

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Cheng, Qifeng. "Robust & stochastic model predictive control." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:89da4934-9de7-4142-958e-513065189518.

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In the thesis, two different model predictive control (MPC) strategies are investigated for linear systems with uncertainty in the presence of constraints: namely robust MPC and stochastic MPC. Firstly, a Youla Parameter is integrated into an efficient robust MPC algorithm. It is demonstrated that even in the constrained cases, the use of the Youla Parameter can desensitize the costs to the effect of uncertainty while not affecting the nominal performance, and hence it strengthens the robustness of the MPC strategy. Since the controller u = K x + c can offer many advantages and is used across
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Munoz, Carpintero Diego Alejandro. "Strategies in robust and stochastic model predictive control." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f6bce71-f91f-4d5a-998f-295eff5b089a.

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The presence of uncertainty in model predictive control (MPC) has been accounted for using two types of approaches: robust MPC (RMPC) and stochastic MPC (SMPC). Ideal RMPC and SMPC formulations consider closed-loop optimal control problems whose exact solution, via dynamic programming, is intractable for most systems. Much effort then has been devoted to find good compromises between the degree of optimality and computational tractability. This thesis expands on this effort and presents robust and stochastic MPC strategies with reduced online computational requirements where the conservativene
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Fleming, James. "Robust and stochastic MPC of uncertain-parameter systems." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310.

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Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions (LDIs) and linear parameter varying (LPV) systems. The designer is faced with a choice of using conservative bounds that may give poor performance, or accurate ones that require heavy online computation. This thesis presents a framework to achieve a more flexible trade-off between these two extremes by using a state tube, a sequence of parametrised polyhedra that is guaranteed to contain the future state. To define controllers using a tube, one must ensure that the polyhedra are a sub-set of th
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Evans, Martin A. "Multiplicative robust and stochastic MPC with application to wind turbine control." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:0ad9b878-00f3-4cfa-a683-148765e3ae39.

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A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of sa
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Hua, H. "Optimal and robust control for a class of nonlinear stochastic systems." Thesis, University of Liverpool, 2016. http://livrepository.liverpool.ac.uk/3001023/.

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This thesis focuses on theoretical research of optimal and robust control theory for a class of nonlinear stochastic systems. The nonlinearities that appear in the diffusion terms are of a square-root type. Under such systems the following problems are investigated: optimal stochastic control in both finite and infinite horizon; robust stabilization and robust H∞ control; H₂/H∞ control in both finite and infinite horizon; and risk-sensitive control. The importance of this work is that explicit optimal linear controls are obtained, which is a very rare case in the nonlinear system. This is rega
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Kim, Dongwook Sawan Edwin M. "Application of stochastic control and robust stability of singularly perturbed unified systems." Diss., Click here for available full-text of this thesis, 2006. http://library.wichita.edu/digitallibrary/etd/2006/t026.pdf.

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Thesis (M.S.)--Wichita State University, Dept. of Electrical Engineering.<br>"August 2006." Title from PDF title page (viewed on October 2, 2006). Thesis adviser: Edwin M. Sawan. Includes bibliographic references (leaves 50-53).
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Song, Miao Ph D. Massachusetts Institute of Technology. "Applications of stochastic inventory control in market-making and robust supply chains." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62049.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 169-172).<br>This dissertation extends the classical inventory control model to address stochastic inventory control problems raised in market-making and robust supply chains. In the financial market, market-makers assume the role of a counterpart so that investors can trade any fixed amounts of assets at quoted bid or ask prices at any time. Market-makers benefit from the spread between the bid and ask p
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Yang, Lin. "Linear robust H-infinity stochastic control theory on the insurance premium-reserve processes." Thesis, University of Liverpool, 2015. http://livrepository.liverpool.ac.uk/2037227/.

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This thesis deals with the stability analysis of linear discrete-time premium-reserve (P-R) systems in a stochastic framework. Such systems are characterised by a mixture of the premium pricing process and the medium- and long- term stability in the accumulated reserve (surplus) policy, and they play a key role in the modern actuarial literature. Although the mathematical and practical analysis of P-R systems is well studied and motivated, their stability properties have not been studied thoughtfully and they are restricted in a deterministic framework. In Engineering, during the last three de
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Paul, Anand Abraham. "Stochastic models in the analysis of project subcontracting and robustness to variability in project networks." Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p9992885.

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Azad, Saeed. "Combined Design and Control Optimization of Stochastic Dynamic Systems." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1602153122063302.

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Books on the topic "Stochastic robust control"

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Weinmann, A. Uncertain models and robust control. Springer-Verlag, 1991.

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Drăgan, Vasile, Samir Aberkane, and Ioan Lucian Popa. Robust Control of Jump Linear Stochastic Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84070-8.

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Dragan, Vasile, Toader Morozan, and Adrian-Mihail Stoica. Mathematical Methods in Robust Control of Linear Stochastic Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8663-3.

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Lihua, Xie, Popa Dan 1969-, and Lewis Frank L, eds. Optimal and robust estimation: With an introduction to stochastic control theory. 2nd ed. CRC Press, 2008.

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Dragan, Vasile, Toader Morozan, and Adrian-Mihail Stoica. Mathematical Methods in Robust Control of Discrete-Time Linear Stochastic Systems. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-0630-4.

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Lewis, Frank L. Optimal and robust estimation: With an introduction to stochastic control theory. 2nd ed. CRC Press/Taylor & Francis, 2007.

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Toader, Morozan, Stoica Adrian, and SpringerLink (Online service), eds. Mathematical Methods in Robust Control of Discrete-Time Linear Stochastic Systems. Springer Science+Business Media, LLC, 2010.

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T, Leondes Cornelius, ed. Robust control system techniques and applications. Academic Press, 1992.

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T, Leondes Cornelius, ed. Stochastic techniques in digital signal processing systems. Academic Press, 1994.

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T, Leondes Cornelius, ed. System identification and adaptive control. Academic Press, 1987.

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Book chapters on the topic "Stochastic robust control"

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Inaba, Hiroshi, and Naohisa Otsuka. "Stochastic Disturbance Decoupling." In Robust Control of Linear Systems and Nonlinear Control. Birkhäuser Boston, 1990. http://dx.doi.org/10.1007/978-1-4612-4484-4_38.

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Petersen, Ian R., Valery A. Ugrinovskii, and Andrey V. Savkin. "Robust control of stochastic uncertain systems." In Communications and Control Engineering. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0447-6_8.

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Boukas, El-Kébir, and Zi-Kuan Liu. "Robust ‘H∞Control, Filtering, and Guaranteed Cost Control." In Deterministic and Stochastic Time-Delay Systems. Birkhäuser Boston, 2002. http://dx.doi.org/10.1007/978-1-4612-0077-2_6.

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Øksendal, Bernt, and Agnès Sulem. "Robust Stochastic Control and Equivalent Martingale Measures." In Stochastic Analysis with Financial Applications. Springer Basel, 2011. http://dx.doi.org/10.1007/978-3-0348-0097-6_12.

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Dragan, Vasile, Toader Morozan, and Adrian-Mihail Stoica. "Stochastic H 2 Optimal Control." In Mathematical Methods in Robust Control of Linear Stochastic Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8663-3_7.

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Hinrichsen, D., and A. J. Pritchard. "Robust stability of linear stochastic systems." In Open Problems in Mathematical Systems and Control Theory. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0807-8_26.

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Dragan, Vasile, Toader Morozan, and Adrian-Mihail Stoica. "Robust Stabilization of Linear Stochastic Systems." In Mathematical Methods in Robust Control of Linear Stochastic Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8663-3_9.

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Yao, Xiuming, Ligang Wu, and Wei Xing Zheng. "Robust Filtering of Markovian Jump Stochastic Systems." In Studies in Systems, Decision and Control. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31915-5_2.

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Marti, Kurt. "Stochastic Optimization Methods in Robust Adaptive Control of Robots." In Online Optimization of Large Scale Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04331-8_28.

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Drăgan, Vasile, Samir Aberkane, and Ioan Lucian Popa. "Robust Stability and Robust Stabilization of Jump Linear Stochastic Systems Under a Class of Stochastic Uncertainties." In Lecture Notes in Control and Information Sciences. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84070-8_9.

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Conference papers on the topic "Stochastic robust control"

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Zhang, Yihuai, Jean Auriol, and Huan Yu. "Robust Boundary Stabilization of Stochastic Hyperbolic PDEs." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644228.

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Pfefferkorn, Maik, Venkatraman Renganathan, and Rolf Findeisen. "Regret and Conservatism of Distributionally Robust Constrained Stochastic Model Predictive Control." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644448.

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Enwerem, Clinton, Erfaun Noorani, John S. Baras, and Brian M. Sadler. "Robust Stochastic Shortest-Path Planning via Risk-Sensitive Incremental Sampling*." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886577.

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HomChaudhuri, Baisravan. "Distributionally Robust Stochastic Model Predictive Control for Collision Avoidance." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9160.

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Abstract This paper focuses on distributionally robust controller design for avoiding dynamic and stochastic obstacles whose exact probability distribution is unknown. The true probability distribution of the disturbance associated with an obstacle, although unknown, is considered to belong to an ambiguity set that includes all the probability distributions that share the same first two moment. The controller thus focuses on ensuring the satisfaction of the probabilistic collision avoidance constraints for all probability distributions in the ambiguity set, hence making the solution robust to
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Sun, Haiyi, Qingling Zhang, and Ning Li. "Robust passive control for stochastic networked control systems." In 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5194987.

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George, Jemin, and Khanh Pham. "Robust Statistical Controller for Stochastic Systems." In AIAA Guidance, Navigation, and Control Conference. American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-5630.

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Rueckert, Elmar, Max Mindt, Jan Peters, and Gerhard Neumann. "Robust policy updates for stochastic optimal control." In 2014 IEEE-RAS 14th International Conference on Humanoid Robots (Humanoids 2014). IEEE, 2014. http://dx.doi.org/10.1109/humanoids.2014.7041389.

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Taylor, C. J. "Robust PIP control of multivariable stochastic systems." In IEE Colloquium on Robust Control: Theory, Software and Applications. IEE, 1997. http://dx.doi.org/10.1049/ic:19971286.

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George, Jemin, and Richard Linares. "Robust estimator for uncertain stochastic systems." In 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5716963.

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Xu, Yunjun. "Nonlinear robust stochastic control for unmanned aerial vehicles." In 2009 American Control Conference. IEEE, 2009. http://dx.doi.org/10.1109/acc.2009.5159978.

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