Robust Sliding Mode Control for Uncertain Semi-Markov Jump Systems with Random Sensor Time Delay

被引：0
|
作者：
Zhang L.-C. [1 ]
Sun Y.-H. [1 ]
Wang J.-X. [1 ]
Zhang Y.-H. [1 ]
Hou D.-C. [1 ]
Wang S. [1 ]
机构：
[1] College of Energy and Electrical Engineering, Hohai University, Jiangsu, Nanjing
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2023年 / 40卷 / 07期
基金：
中国国家自然科学基金; 中央高校基本科研业务费专项资金资助;
关键词：
mode-dependent Luenberger observer; random sensor time delay; robust control; Semi-Markov jump systems; sliding mode control;
D O I：
10.7641/CTA.2022.20459
中图分类号：
学科分类号：
摘要：
In practical systems, random changes of system parameters and structures, unknown external disturbance, sensor time delay and other phenomena occur from time to time, which seriously affect the stable operation of the system. In order to solve this problem, this paper proposes a robust sliding mode control method for a class of uncertain semi-Markov jump systems with stochastic sensor time delay, in which the sensor time delay is described by Bernoulli stochastic distribution. Considering that the system state information cannot be measured, the mode-dependent Luenberger observer is designed to estimate the operating state of the semi-Markov jump system. Then, an integral sliding mode surface is constructed and two stochastic stability analysis methods for semi-Markov jump systems are proposed based on stochastic Lyapunov theory. Furthermore, the observer-based sliding mode control method is proposed to make the system states reach the sliding mode surface in finite time and the sliding mode dynamic is stochastically stable with H∞ performance index γ. Finally, the effectiveness and correctness of the proposed sliding mode control method are verified by a numerical simulation example based on the separately excited DC motor model. © 2023 South China University of Technology. All rights reserved.
引用
收藏
页码：1 / 9
页数：8
相关论文
共 31 条
  • [1] BARNES A K, BALDA J C, ESCOBAR-MEJIA A., A semi-Markov model for control of energy storage in utility grids and microgrids with PV generation, IEEE Transactions on Sustainable Energy, 6, 2, pp. 546-556, (2015)
  • [2] LI F, SHI P, WU L, Et al., Quantized control design for cognitive radio networks modeled as nonlinear semi-Markovian jump systems, IEEE Transactions on Industrial Electronics, 62, 4, pp. 2330-2340, (2014)
  • [3] BARBU V S, LIMNIOS N., Semi-Markov chains and hidden semi-Markov models toward applications: their use in reliability and DNA analysis, (2009)
  • [4] REN C, SONG J, HE S., Extended-state-observer-based finite-time asynchronous control of a class of stochastic positive hidden Markov jump systems, Control Theory & Applications, 38, 11, pp. 1891-1900, (2021)
  • [5] WAN H, LUAN X, LIU F., Derandomization based finite-frequency control for Markov jump system, Control Theory & Applications, 35, pp. 1002-1008, (2018)
  • [6] HUANG J, SHI Y., Stochastic stability and robust stabilization of semi-Markov jump linear systems, International Journal of Robust and Nonlinear Control, 23, 18, pp. 2028-2043, (2013)
  • [7] ZHANG L, SUN Y, LI H, Et al., Event-triggered fault detection for nonlinear semi-Markov jump systems based on double asynchronous filtering approach, Automatica, 138, (2022)
  • [8] ZHANG L, SUN Y, PAN Y, Et al., Reduced-Order fault detection filter design for fuzzy semi-Markov jump systems with partly unknown transition rates, IEEE Transactions on Systems, Man, and Cybernetics: Systems, (2022)
  • [9] CAI B, ZHANG L, SHI Y., Observed-mode-dependent state estimation of hidden semi-Markov jump linear systems, IEEE Transactions on Automatic Control, 65, 1, pp. 442-449, (2019)
  • [10] NING Z, ZHANG L, COLANERI P., Semi-Markov jump linear systems with incomplete sojourn and transition information: Analysis and synthesis, IEEE Transactions on Automatic Control, 65, 1, pp. 159-174, (2019)
1234