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Feedback stabilization of multi-DOF nonlinear stochastic Markovian jump systems. (English) Zbl 1430.93165

Summary: A feedback control strategy is designed to asymptotically stabilize a multi-degree-of-freedom (DOF) nonlinear stochastic systems undergoing Markovian jumps. First, a class of hybrid nonlinear stochastic systems with Markovian jumps is reduced to a one-dimensional averaged Itô stochastic differential equation for controlled total energy. Second, the optimal control law is deduced by applying the dynamical programming principle to the ergodic control problem of the averaged systems with an undetermined cost function. Third, the cost function is determined by the demand of stabilizing the averaged systems. A Lyapunov exponent is introduced to analyze approximately the asymptotic stability with probability one of the originally controlled systems. To illustrate the application of the present method, an example of stochastically excited two coupled nonlinear oscillators with Markovian jumps is worked out in detail.

MSC:

93D15 Stabilization of systems by feedback
93E15 Stochastic stability in control theory
93C10 Nonlinear systems in control theory
93D20 Asymptotic stability in control theory
60J76 Jump processes on general state spaces
Full Text: DOI

References:

[1] BabaaliM, PappasGJ. Observability of switched linear systems in continuous time. Paper presented at: International Workshop on Hybrid Systems: Computation and Control; 2005; Zurich, Switzerland.
[2] CostaOLV, FragosoMD, MarquesRP. Discrete‐Time Markov Jump Linear Systems. London, UK: Springer Science & Business Media; 2006.
[3] ShenL, SunJ, WuQ. Observability and detectability of discrete‐time stochastic systems with Markovian jump. Syst Control Lett. 2013;62(1):37‐42. · Zbl 1257.93020
[4] deSouzaCE. Robust stability and stabilization of uncertain discrete‐time Markovian jump linear systems. IEEE Trans Autom Control. 2006;51(5):836‐841. · Zbl 1366.93479
[5] LiY, SunH, ZongG, HouL. Composite anti‐disturbance resilient control for Markovian jump nonlinear systems with partly unknown transition probabilities and multiple disturbances. Int J Robust Nonlinear Control. 2017;27(14):2323‐2337. · Zbl 1373.93308
[6] HuanRH, ZhuWQ, MaF, YingZG. Asymptotic stability of a class of nonlinear stochastic systems undergoing Markovian jumps. Probabilistic Eng Mech. 2016;45(5):13‐21.
[7] ZhangL, HuangB, LamJ. H∞ model reduction of Markovian jump linear systems. Syst Control Lett. 2003;50(2):103‐118. · Zbl 1157.93519
[8] WeiY, QiuJ, KarimiHR, WangM. H∞ model reduction for continuous‐time Markovian jump systems with incomplete statistics of mode information. Int J Syst Sci. 2014;45(7):1496‐1507. · Zbl 1290.93032
[9] CostaOLV, GuerraS. Stationary filter for linear minimum mean square error estimator of discrete‐time Markovian jump systems. IEEE Trans Autom Control. 2002;47(8):1351‐1356. · Zbl 1364.93795
[10] MahmoudMS, ShiP, IsmailA. Robust Kalman filtering for discrete‐time Markovian jump systems with parameter uncertainty. J Comput Appl Math. 2004;169(1):53‐69. · Zbl 1067.93059
[11] WangZ, LamJ, LiuX. Exponential filtering for uncertain Markovian jump time‐delay systems with nonlinear disturbances. IEEE Trans Circuits Syst II Express Briefs. 2004;51(5):262‐268.
[12] BoukasEK. Stabilization of stochastic singular nonlinear hybrid systems. Nonlinear Anal Theory Methods Appl. 2006;64(2):217‐228. · Zbl 1090.93048
[13] AssawinchaichoteW, NguangSK, ShiP. Robust H∞ fuzzy filter design for uncertain nonlinear singularly perturbed systems with Markovian jumps: an LMI approach. Information Sciences. 2007;177(7):1699‐1714. · Zbl 1113.93078
[14] LakshmananS, RihanFA, RakkiyappanR, ParkJH. Stability analysis of the differential genetic regulatory networks model with time‐varying delays and Markovian jumping parameters. Nonlinear Anal Hybrid Syst. 2014;14:1‐15. · Zbl 1325.92062
[15] WenJ, NguangSK, ShiP, PengL. Finite‐time stabilization of Markovian jump delay systems-a switching control approach. Int J Robust Nonlinear Control. 2017;27(2):298‐318. · Zbl 1353.93115
[16] HuanRH, ZhuWQ, HuRC, YingZG. Asymptotic stability with probability one of random‐time‐delay‐controlled quasi‐integrable Hamiltonian systems. J Appl Mech. 2016;83(9):091009.
[17] SakthivelR, SathishkumarM, MathiyalaganK, Marshal AnthoniS. Robust reliable dissipative filtering for Markovian jump nonlinear systems with uncertainties. Int J Adapt Control Signal Process. 2017;31(1):39‐53. · Zbl 1358.93175
[18] HuRC, GuXD. Stationary response of nonlinear Markovian jump system under wide‐band random excitation. J Low Freq Noise Vib Active Control. 2018:1461348418811427.
[19] LiH, ShiP, YaoD, WuL. Observer‐based adaptive sliding mode control for nonlinear Markovian jump systems. Automatica. 2016;64:133‐142. · Zbl 1329.93126
[20] AliMS, YogambigaiJ, CaoJ. Synchronization of master‐slave Markovian switching complex dynamical networks with time‐varying delays in nonlinear function via sliding mode control. Acta Math Sci. 2017;37(2):368‐384. · Zbl 1389.93241
[21] WangG. Mode‐independent control of singular Markovian jump systems: a stochastic optimization viewpoint. Appl Math Comput. 2016;286:155‐170. · Zbl 1410.93116
[22] ChenW, XuS, ZhangB, QiZ. Stability and stabilisation of neutral stochastic delay Markovian jump systems. IET Control Theory Appl. 2016;10(15):1798‐1807.
[23] CostaM, HauzyC, LoeuilleN, MéléardS. Stochastic eco‐evolutionary model of a prey‐predator community. J Math Biol. 2016;72(3):573‐622. · Zbl 1335.60159
[24] DehghanpourK, AfsharniaS. Electrical demand side contribution to frequency control in power systems: a review on technical aspects. Renew Sustain Energy Rev. 2015;41:1267‐1276.
[25] KhasminskiiRZ. Stochastic Stability of Differential Equation. Berlin, Germany: Springer Science & Business Media; 2011.
[26] AfanasevVN, KolmanovskiiVB, NosovVR. Mathematical Theory of Control Systems Design. Dordrecht, The Netherlands: Springer Science & Business Media; 1996:341. Mathematics & Its Applications. · Zbl 0845.93001
[27] FlorchingerD. Feedback stabilization of affine in the control stochastic differential systems by the control Lyapunov function method. SIAM J Control Optim. 1997;35:500‐511. · Zbl 0874.93092
[28] KrsticM, DengH. Stabilization of Nonlinear Uncertain Systems. Berlin, Germany: Springer; 1998. · Zbl 0906.93001
[29] AriaratnamST, TamDSF, XieWC. Lyapunov exponents and stochastic stability of coupled linear systems under real noise excitation. Probabilistic Eng Mech. 1991;6(2):51‐56.
[30] OseledecVI. A multiplicative ergodic theorem: Lyapunov characteristic number for dynamical systems. Trans Mosc Math Soc. 1968;19:197‐231. · Zbl 0236.93034
[31] KhasminskiiRZ. Necessary and sufficient conditions for the asymptotic stability of linear stochastic systems. Theory Probab Appl. 1967;12:144‐147.
[32] AriaratnamST, XieWC. Lyapunov exponents and stochastic stability of coupled linear systems under real noise excitation. J Appl Mech. 1992;59(3):664‐673. · Zbl 0766.70017
[33] ZhuWQ, HuangZL. Lyapunov exponents and stochastic stability of quasi‐integrable‐Hamiltonian systems. J Appl Mech. 1999;66(1):211‐217.
[34] StratonovichRL. Topics in the Theory of Random Noise. New York, NY: Gordon and Breach; 1967. · Zbl 0183.22007
[35] KhasminskiiRZ. On the averaging principle for Itô stochastic differential equations. Kybernetica (Prague). 1968;4:260‐279. · Zbl 0231.60045
[36] YongJM, ZhouXY. Stochastic Control: Hamiltonian Systems and HJB Equations. New York, NY: Springer; 1999. · Zbl 0943.93002
[37] ZhuWQ. Feedback stabilization of quasi‐nonintegrable‐Hamiltonian systems by using Lyapunov exponent. Nonlinear Dynamics. 2004;36(2‐4):455‐470. · Zbl 1092.70021
[38] ZhuWQ, HuangZL, SuzukiY. Stochastic averaging and Lyapunov exponent of quasi partially integrable Hamiltonian systems. Int J Nonlinear Mech. 2002;37(3):419‐437. · Zbl 1346.70013
[39] SkorokhodAV. Asymptotic Methods of the Theory of Stochastic Differential Equations. Russia: American Mathematical Society; 2009.
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