Journal articles: '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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Benavoli, A., and L. Chisci. "Robust stochastic control based on imprecise probabilities*." IFAC Proceedings Volumes 44, no. 1 (January 2011): 4606–13. http://dx.doi.org/10.3182/20110828-6-it-1002.02081.

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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 (November 2020): 4678–88. http://dx.doi.org/10.1109/tpwrs.2020.2992268.

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

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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 (December 2004): 11–14. http://dx.doi.org/10.1002/pamm.200410003.

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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 disturbance dependent noise simultaneously. Two numerical examples are given to illustrate the effectiveness of the proposed results.

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8

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 techniques, a sufficient condition for stochastic systems is first established in Lemma 3. Then, using the lemma, the sufficient conditions of the solvability of the robust reliableH∞controller for linear SMJSs and NSMJSs are given. Finally, a numerical example is exploited to show the usefulness of the derived results.

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9

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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10

Schacher, Michael. "Optimal control of robots under stochastic uncertainty: robust feedback control." PAMM 7, no. 1 (December 2007): 1061801–2. http://dx.doi.org/10.1002/pamm.200700037.

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11

Kolansky, Jeremy, Amandeep Singh, and Jill Goryca. "Robust Semi-Active Ride Control under Stochastic Excitation." SAE International Journal of Passenger Cars - Mechanical Systems 7, no. 1 (April 1, 2014): 96–104. http://dx.doi.org/10.4271/2014-01-0145.

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12

TSUKAMOTO, Taro. "Stochastic Robust Control Design Using Density-Ratio Estimation." AEROSPACE TECHNOLOGY JAPAN, THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 13 (2014): 51–54. http://dx.doi.org/10.2322/astj.13.51.

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Xu, Yunjun. "Nonlinear Robust Stochastic Control for Unmanned Aerial Vehicles." Journal of Guidance, Control, and Dynamics 32, no. 4 (July 2009): 1308–19. http://dx.doi.org/10.2514/1.40753.

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14

Li, Jian-ning, Ya-Jun Pan, Trent Hilliard, and Hongye Su. "Robust Stochastic Control for Networked Bilateral Teleoperation Systems." IFAC Proceedings Volumes 46, no. 20 (2013): 112–17. http://dx.doi.org/10.3182/20130902-3-cn-3020.00028.

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15

Abedi, F., and W. J. Leong. "Dynamic robust stabilization of stochastic differential control systems." IMA Journal of Mathematical Control and Information 30, no. 4 (January 28, 2013): 559–69. http://dx.doi.org/10.1093/imamci/dns040.

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16

Han-Fu, Chen, and Lei Guo. "Robust Identification and Adaptive Control for Stochastic Systems." IFAC Proceedings Volumes 21, no. 9 (August 1988): 815–19. http://dx.doi.org/10.1016/s1474-6670(17)54829-6.

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17

Aurnhammer, A., and K. Marti. "Robust Control of Robots by Stochastic Optimisation Techniques." PAMM 1, no. 1 (March 2002): 454. http://dx.doi.org/10.1002/1617-7061(200203)1:1<454::aid-pamm454>3.0.co;2-3.

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18

Boukas, E. K., P. Shi, and A. Andijani. "Robust inventory-production control problem with stochastic demand." Optimal Control Applications and Methods 20, no. 1 (January 1999): 1–20. http://dx.doi.org/10.1002/(sici)1099-1514(199901/02)20:1<1::aid-oca642>3.0.co;2-l.

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19

Allan, Andrew L., and Samuel N. Cohen. "Pathwise stochastic control with applications to robust filtering." Annals of Applied Probability 30, no. 5 (October 2020): 2274–310. http://dx.doi.org/10.1214/19-aap1558.

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TSUKAMOTO, Taro. "Stochastic Robust Control Design Using Density Ratio Estimation." TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES, AEROSPACE TECHNOLOGY JAPAN 13 (2015): 11–15. http://dx.doi.org/10.2322/tastj.13.11.

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21

Gao, Huijun, Junli Wu, and Peng Shi. "Robust sampled-data H∞ control with stochastic sampling." Automatica 45, no. 7 (July 2009): 1729–36. http://dx.doi.org/10.1016/j.automatica.2009.03.004.

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22

Bayer, Florian A., Matthias Lorenzen, Matthias A. Müller, and Frank Allgöwer. "Robust economic Model Predictive Control using stochastic information." Automatica 74 (December 2016): 151–61. http://dx.doi.org/10.1016/j.automatica.2016.08.008.

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23

Rustem, Berç. "Stochastic and robust control of nonlinear economic systems." European Journal of Operational Research 73, no. 2 (March 1994): 304–18. http://dx.doi.org/10.1016/0377-2217(94)90267-4.

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24

Marti, Kurt, and Andreas Aurnhammer. "Robust Feedback Control of Robots using Stochastic Optimization." PAMM 3, no. 1 (December 2003): 497–98. http://dx.doi.org/10.1002/pamm.200310518.

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25

Wang, Cheng. "Robust Control and Stability Analysis for Stochastic Systems with Markov Jump." Applied Mechanics and Materials 631-632 (September 2014): 684–87. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.684.

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Stochastic systems with Markov jump is a new type of stochastic system in recent years, which is a new field integrated by information, control and Markov process. This paper introduces the research history and the newest research trends of stochastic systems with Markov jump, and presents many widespread theoretical and application problems. Moreover, some new research topics and directions related to stochastic systems with Markov jump are proposed.

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26

Wang, Cheng. "Robust stochastic stabilization andH∞control of uncertain stochastic interval time-delay systems." IMA Journal of Mathematical Control and Information 33, no. 2 (November 24, 2014): 401–25. http://dx.doi.org/10.1093/imamci/dnu047.

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27

Gang, Ting-Ting, Jun Yang, Qing Gao, Yu Zhao, and Jianbin Qiu. "A Fuzzy Approach to Robust Control of Stochastic Nonaffine Nonlinear Systems." Mathematical Problems in Engineering 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/439805.

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This paper investigates the stabilization problem for a class of discrete-time stochastic non-affine nonlinear systems based on T-S fuzzy models. Based on the function approximation capability of a class of stochastic T-S fuzzy models, it is shown that the stabilization problem of a stochastic non-affine nonlinear system can be solved as a robust stabilization problem of the stochastic T-S fuzzy system with the approximation errors as the uncertainty term. By using a class of piecewise dynamic feedback fuzzy controllers and piecewise quadratic Lyapunov functions, robust semiglobal stabilization condition of the stochastic non-affine nonlinear systems is formulated in terms of linear matrix inequalities. A simulation example illustrating the effectiveness of the proposed approach is provided in the end.

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28

Zhang, Yingqi, Wei Cheng, Xiaowu Mu, and Caixia Liu. "Stochasticℋ∞Finite-Time Control of Discrete-Time Systems with Packet Loss." Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/897481.

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This paper investigates the stochastic finite-time stabilization andℋ∞control problem for one family of linear discrete-time systems over networks with packet loss, parametric uncertainties, and time-varying norm-bounded disturbance. Firstly, the dynamic model description studied is given, which, if the packet dropout is assumed to be a discrete-time homogenous Markov process, the class of discrete-time linear systems with packet loss can be regarded as Markovian jump systems. Based on Lyapunov function approach, sufficient conditions are established for the resulting closed-loop discrete-time system with Markovian jumps to be stochasticℋ∞finite-time boundedness and then state feedback controllers are designed to guarantee stochasticℋ∞finite-time stabilization of the class of stochastic systems. The stochasticℋ∞finite-time boundedness criteria can be tackled in the form of linear matrix inequalities with a fixed parameter. As an auxiliary result, we also give sufficient conditions on the robust stochastic stabilization of the class of linear systems with packet loss. Finally, simulation examples are presented to illustrate the validity of the developed scheme.

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29

Yang, Hong, Yu Zhang, and Le Zhang. "Novel Robust Control of Stochastic Nonlinear Switched Fuzzy Systems." Mathematical Problems in Engineering 2021 (September 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/9743351.

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This paper addresses the problem of designing novel switching control for a class of stochastic nonlinear switched fuzzy systems with time delay. Firstly, a stochastic nonlinear switched fuzzy system can precisely describe continuous and discrete dynamics as well as their interactions in the complex real-world systems. Next, novel control algorithm and switching law design of the state-dependent form are developed such that the stability is guaranteed. Since convex combination techniques are used to derive the delay independent criteria, some subsystems are allowed to be unstable. Finally, various comparisons of the elaborated examples are conducted to demonstrate the effectiveness of the proposed control design approach. All results illustrate good control performances as desired.

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30

Mayne, David Q. "Competing methods for robust and stochastic MPC." IFAC-PapersOnLine 51, no. 20 (2018): 169–74. http://dx.doi.org/10.1016/j.ifacol.2018.11.010.

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31

Mark, Christoph, and Steven Liu. "Stochastic MPC with Distributionally Robust Chance Constraints." IFAC-PapersOnLine 53, no. 2 (2020): 7136–41. http://dx.doi.org/10.1016/j.ifacol.2020.12.521.

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32

Poznyak, Alex S. "Robust stochastic maximum principle: Complete proof and discussions." Mathematical Problems in Engineering 8, no. 4-5 (2002): 389–411. http://dx.doi.org/10.1080/10241230306722.

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This paper develops a version of Robust Stochastic Maximum Principle (RSMP) applied to the Minimax Mayer Problem formulated for stochastic differential equations with the control-dependent diffusion term. The parametric families of first and second order adjoint stochastic processes are introduced to construct the corresponding Hamiltonian formalism. The Hamiltonian function used for the construction of the robust optimal control is shown to be equal to the Lebesque integral over a parametric set of the standard stochastic Hamiltonians corresponding to a fixed value of the uncertain parameter. The paper deals with a cost function given at finite horizon and containing the mathematical expectation of a terminal term. A terminal condition, covered by a vector function, is also considered. The optimal control strategies, adapted for available information, for the wide class of uncertain systems given by an stochastic differential equation with unknown parameters from a given compact set, are constructed. This problem belongs to the class of minimax stochastic optimization problems. The proof is based on the recent results obtained for Minimax Mayer Problem with a finite uncertainty set [14,43-45] as well as on the variation results of [53] derived for Stochastic Maximum Principle for nonlinear stochastic systems under complete information. The corresponding discussion of the obtain results concludes this study.

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33

Ku, Cheung-Chieh, and Guan-Wei Chen. "H∞Gain-Scheduled Control for LPV Stochastic Systems." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/854957.

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A robust control problem for discrete-time uncertain stochastic systems is discussed via gain-scheduled control scheme subject toH∞attenuation performance. Applying Linear Parameter Varying (LPV) modeling approach and stochastic difference equation, the uncertain stochastic systems can be described by combining time-varying weighting function and linear systems with multiplicative noise terms. Due to the consideration of stochastic behavior, the stability in the sense of mean square is applied for the system. Furthermore, two kinds of Lyapunov functions are employed to derive their corresponding sufficient conditions to solve the stabilization problems of this paper. In order to use convex optimization algorithm, the derived conditions are converted into Linear Matrix Inequality (LMI) form. Via solving those conditions, the gain-scheduled controller can be established such that the robust asymptotical stability andH∞performance of the disturbed uncertain stochastic system can be achieved in the sense of mean square. Finally, two numerical examples are applied to demonstrate the effectiveness and applicability of the proposed design method.

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34

Dai, Xisheng, Feiqi Deng, and Jianxiang Zhang. "RobustH∞Control for Linear Stochastic Partial Differential Systems with Time Delay." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/489408.

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This paper investigates the problems of robust stochastic mean square exponential stabilization and robustH∞for stochastic partial differential time delay systems. Sufficient conditions for the existence of state feedback controllers are proposed, which ensure mean square exponential stability of the resulting closed-loop system and reduce the effect of the disturbance input on the controlled output to a prescribed level ofH∞performance. A linear matrix inequality approach is employed to design the desired state feedback controllers. An illustrative example is provided to show the usefulness of the proposed technique.

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35

Li, Minghao, Wuneng Zhou, Huijiao Wang, Yun Chen, Renquan Lu, and Hongqian Lu. "Delay-Dependent Robust H∞ Control for Uncertain Stochastic Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 6004–9. http://dx.doi.org/10.3182/20080706-5-kr-1001.01013.

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36

Xu, Lijuan, Tianping Zhang, and Yang Yi. "Robust dissipative control for time-delay stochastic jump systems." Journal of Systems Engineering and Electronics 22, no. 2 (April 2011): 314–21. http://dx.doi.org/10.3969/j.issn.1004-4132.2011.02.019.

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37

Li, Rong, Qingxian Wu, and Mou Chen. "Robust Adaptive Control for Unmanned Helicopter with Stochastic Disturbance." Procedia Computer Science 105 (2017): 209–14. http://dx.doi.org/10.1016/j.procs.2017.01.212.

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38

Poznyak, A. S., T. E. Duncan, B. Pasik-Duncan, and V. G. Boltyansky. "Robust optimal control for minimax stochastic linear quadratic problem." International Journal of Control 75, no. 14 (January 2002): 1054–65. http://dx.doi.org/10.1080/00207170210156242.

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39

Ji Feng Zhang and Le Yi Wang. "Performance lower bounds in stochastic robust and adaptive control." IEEE Transactions on Automatic Control 46, no. 7 (July 2001): 1137–41. http://dx.doi.org/10.1109/9.935071.

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40

Marti, K. "Stochastic Optimization Methods in Robust Adaptive Control of Robots." IFAC Proceedings Volumes 36, no. 11 (June 2003): 183–86. http://dx.doi.org/10.1016/s1474-6670(17)35660-4.

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41

Mohammadkhani, Mohammadali, Farhad Bayat, and Ali Akbar Jalali. "Robust Output Feedback Model Predictive Control: A Stochastic Approach." Asian Journal of Control 19, no. 6 (July 19, 2017): 2085–96. http://dx.doi.org/10.1002/asjc.1575.

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42

Zasadzinski, M., S. Halabi, H. Rafaralahy, H. Souley Ali, and M. Darouach. "STOCHASTIC ROBUST REDUCED ORDER H-INFINITY OBSERVER-BASED CONTROL." IFAC Proceedings Volumes 38, no. 1 (2005): 435–40. http://dx.doi.org/10.3182/20050703-6-cz-1902.01017.

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43

Fetisov, V. N. "Some Problems of Robust Control of a Stochastic Object." Automation and Remote Control 65, no. 4 (April 2004): 594–602. http://dx.doi.org/10.1023/b:aurc.0000023536.06877.12.

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44

TSUKAMOTO, Taro. "Probability Inference Approach for Stochastic Robust Flight Control Design." TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES, AEROSPACE TECHNOLOGY JAPAN 17, no. 2 (2019): 197–202. http://dx.doi.org/10.2322/tastj.17.197.

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45

Wang, Jiang, Hanqiao Gao, and Huiyan Li. "Adaptive robust control of nonholonomic systems with stochastic disturbances." Science in China Series F: Information Sciences 49, no. 2 (April 2006): 189–207. http://dx.doi.org/10.1007/s11432-006-0189-5.

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46

Lei, Guo. "Robust Stochastic Adaptive Control for Non-minimum Phase Systems." IFAC Proceedings Volumes 21, no. 10 (August 1988): 201–5. http://dx.doi.org/10.1016/b978-0-08-036620-3.50039-9.

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47

He, Shu-Ping, and Fei Liu. "Robust finite-time H ∞ control of stochastic jump systems." International Journal of Control, Automation and Systems 8, no. 6 (December 2010): 1336–41. http://dx.doi.org/10.1007/s12555-010-0620-y.

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48

Zhang, Huanshui, David Zhang, Lihua Xie, and Jun Lin. "Robust filtering under stochastic parametric uncertainties." Automatica 40, no. 9 (September 2004): 1583–89. http://dx.doi.org/10.1016/j.automatica.2004.04.002.

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49

Qiu, Jiqing, Haikuo He, and Peng Shi. "Robust Stochastic Stabilization and H ∞ Control for Neutral Stochastic Systems with Distributed Delays." Circuits, Systems, and Signal Processing 30, no. 2 (November 30, 2010): 287–301. http://dx.doi.org/10.1007/s00034-010-9222-4.

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50

Xu, Shengyuan, Peng Shi, Yuming Chu, and Yun Zou. "Robust stochastic stabilization and H∞ control of uncertain neutral stochastic time-delay systems." Journal of Mathematical Analysis and Applications 314, no. 1 (February 2006): 1–16. http://dx.doi.org/10.1016/j.jmaa.2005.03.088.

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Journal articles on the topic 'Stochastic robust control'

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