全状态约束切换系统的自适应神经网络控制  

Adaptive Neural Network Control for Switched Systems with Full State Constraints

作　　者：万敏[1] 杨山山 黄山山 邓启志 WAN Min;YANG Shanshan;HUANG Shanshan;DENG Qizhi(Southwest Petroleum University,Chengdu 610000,China;Petrochina Southwest Oil and Gas Field Company,Mianyang 621700,China)

机构地区：[1]西南石油大学,成都610000 [2]中国石油天然气股份有限公司西南油气田分公司,绵阳621700

出　　处：《空间控制技术与应用》2023年第1期40-52,共13页Aerospace Control and Application

基　　金：国家重点研发项目(2019YFC0312303-05)。

摘　　要：为了解决非线性约束切换系统的控制问题,针对一类具有非对称时变全状态约束、状态不完全可测以及未知外部干扰的切换严格反馈非线性系统进行研究,引入状态观测器、自适应神经网络和动态表面控制技术,设计了一种基于径向基函数(RBF)神经网络的自适应输出反馈控制方法.通过采用非对称时变障碍李亚普洛夫函数(barrier lyapunov function,BLF)使系统的全部状态满足非对称时变约束条件,而Lyapunov方法和平均驻留时间理论则保证了闭环系统所有信号是半全局一致最终有界.最后,在所提控制律的作用下,输出跟踪误差可以减小到任意小,2个仿真实验结果也验证了所提控制算法的有效性.In order to solve the control problem of switched nonlinear systems with nonlinear constraints,a class of switched strict feedback nonlinear systems with asymmetric time-varying full-state constraints,incomplete state measuability and unknown external disturbances are studied in this paper.State observer,adaptive neural network and dynamic surface control techniques are introduced.An adaptive output feedback control method based on RBF(radial basis function)neural network is designed.By adopting the asymmetric time-varying BLF(barrier lyapunov function),all states of the system meet the asymmetric time-varying constraints.The Lyapunov method and the average dwell time theory guarantee that all signals in a closed-loop system are semi-globally consistent and eventually bounded.Finally,under the action of the proposed control law,the output tracking error can be reduced to an arbitrarily small value,and two simulation results also verify the effectiveness of the proposed control algorithm.

关 键 词：动态面控制 全状态约束 非线性切换系统 神经网络状态观测器 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]