Adaptive Neural Network Finite-Time Event Triggered Intelligent Control for Stochastic Nonlinear Systems With Time-Varying Constraints

Jia Liu;Jiapeng Liu;Qing-Guo Wang;Jinpeng Yu
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引用次数: 0

Abstract

Finite-time command-filter event-trigger control based on adaptive neural network is presented in this article for a class of output-feedback stochastic nonlinear system (SNS) with output time-varying constraints and unmeasured states. The adaptive neural network combined with backstepping is utilized to approximate the unknown nonlinear functions of the system. The finite-time command-filter is employed to reduce the difficulty of complex calculation caused by backstepping technique. An adaptive observer is developed to estimate unmeasured states, and a controller is designed to be triggered only when the event-triggered condition is met. The time-varying barrier Lyapunov function is utilized to ensure the output time-varying constraint. The control method proposed in this article not only guarantees the finite-time stability of the system but also meets the output constraint. The effectiveness of the method is demonstrated on the ship maneuvering system with three degrees of freedom.
时变约束随机非线性系统的自适应神经网络有限时间事件触发智能控制
针对一类具有输出时变约束和状态不可测的输出反馈随机非线性系统,提出了基于自适应神经网络的有限时间命令滤波事件触发控制方法。利用自适应神经网络结合反推法对系统的未知非线性函数进行逼近。采用有限时间命令滤波器,降低了反演技术带来的复杂计算难度。设计了自适应观测器来估计非测量状态,设计了控制器,使其仅在满足事件触发条件时才触发。利用时变势垒Lyapunov函数来保证输出时变约束。本文提出的控制方法既能保证系统的有限时间稳定性,又能满足输出约束。在三自由度船舶操纵系统中验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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