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Adaptive dynamic surface full state constraints control for stochastic Markov jump systems based on event-triggered strategy
Applied Mathematics and Computation ( IF 4 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.amc.2020.125563
Miao He , Taotao Rong , Junmin Li , Chao He

Abstract This paper aims to investigate the event-triggered adaptive dynamic surface full state constraints control for a class of stochastic nonlinear systems with Markov jumping parameters. Using the backstepping method, we propose two adaptive dynamic surface controllers with average dwell time and the event-triggered strategies simultaneously. The existed assumption of stochastic input-to-state stability (ISS) on the stochastic systems can be avoided by adding a correction terms into the controller to compensate for the measurement error. The method designed in this work can make all signals remain bounded in probability, all the states satisfy the constraints in probability and the tracking error signals eventually converge to the compact set in the sense of mean quartic value (SMQV) for closed-loop stochastic Markov jump nonlinear uncertain system. Furthermore, the designed relative threshold strategy which relies on the control signal reduces the frequency of events triggered. Finally, the validity of put forward method is shown in simulation results.

中文翻译:

基于事件触发策略的随机马尔可夫跳跃系统自适应动态曲面全状态约束控制

摘要 本文旨在研究一类具有马尔可夫跳跃参数的随机非线性系统的事件触发自适应动态表面全状态约束控制。使用反步法,我们提出了两个具有平均停留时间和事件触发策略的自适应动态表面控制器。通过在控制器中添加校正项来补偿测量误差,可以避免随机系统上存在的随机输入状态稳定性(ISS)假设。在这项工作中设计的方法可以使所有信号保持有界的概率,对于闭环随机马尔可夫跳跃非线性不确定系统,所有状态都满足概率约束,跟踪误差信号最终收敛到四次均值意义(SMQV)意义上的紧集。此外,设计的依赖于控制信号的相对阈值策略降低了触发事件的频率。最后,仿真结果表明了所提方法的有效性。
更新日期:2021-03-01
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