Adaptive neural quantized control for full-state constrained Markov jumping nonlinear systems with incomplete transition probabilities and unknown control directions

被引:0
|
作者
Song X. [1 ]
Zhang J. [1 ]
Song S. [1 ]
机构
[1] School of Information Engineering, Henan University of Science and Technology, Luoyang
基金
中国国家自然科学基金;
关键词
Command filtered backstepping control; Full-state constraints; Hysteresis quantizer; Markov jumping nonlinear systems; Unknown control directions;
D O I
10.1016/j.neucom.2024.127821
中图分类号
学科分类号
摘要
This article devises an adaptive quantized control scheme for full-state constrained Markov jumping nonlinear systems with incomplete transition probabilities and unknown control directions. First, the barrier Lyapunov functions were utilized to achieve full-state constraints on the investigated system, while the Nussbaum function has been adopted to overcome the issue of unknown control directions. Then, by means of command filtered backstepping control technique, an adaptive quantized controller is developed, where an improved error compensation mechanism was established to eliminate the effect of filter error, and a hysteresis quantizer was employed to diminish the transmission rate. Furthermore, it is demonstrated that the designed controller assures that all signals of the closed-loop system are bounded in the mean square sense. Finally, two illustrative examples were provided to validate the effectiveness of the proposed control method. © 2024 Elsevier B.V.
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