摘要
针对一类不确定高能随机非线性系统,开展自适应神经网络backstepping控制研究,并保证在任意切换信号下的预设跟踪性能.该高能系统假定系统动态和任意切换信号未知.首先,利用预设性能控制,保证跟踪控制性能;其次,RBF神经网络用来克服未知系统动态,仅用到单一自适应更新参数,从而克服过参数问题;最后,基于公共的Lyapunov稳定性理论提出自适应神经网络控制策略,并减少了学习参数.最终结果表明所设计的公共控制器能保证所有闭环信号半全局最终一致有界,并能在任意切换下保证预设的跟踪性能.仿真结果进一步表明所提出方法的有效性.
In this paper,the problem of adaptive neural backstepping control is investigated for uncertain high-power stochastic nonlinear systems with prescribed performance under arbitrary switchings.For the control of high-power nonlinear systems,it is assumed that unknown system dynamics and arbitrary switching signals are unknown.Firstly,by utilizing the prescribed performance control(PPC),the prescribed tracking control performance is ensured.Then,RBF neural networks are employed to deal with completely unknown system dynamics,and only one adaptive parameter is constructed to overcome the over-parameterization.Finally,based on the common Lyapunov stability method,the adaptive neural control method is proposed,which decreases the number of learning parameters.It is shown that the designed common controller can ensure that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB),and the prescribed tracking control performance is guaranteed under arbitrary switchings.The simulation results show the effectiveness of the proposed scheme.
作者
司文杰
董训德
SI Wen-jie;DONG Xun-de(School of Electrical and Control Engineering,Henan University of Urban Construction,Pingdingshan 467036,China;School of Automation Science and Engineering,South China University of Technology,Guangzhou 510640,China)
出处
《控制与决策》
EI
CSCD
北大核心
2020年第4期923-929,共7页
Control and Decision
基金
国家自然科学基金项目(61803145)
广东省自然科学基金项目(2018A030310367,2017A030310493).
作者简介
司文杰(1985-),男,副教授,博士,从事自适应神经网络控制等研究,E-mail:siwenjie2008@163.com;通讯作者:董训德(1985-),男,副教授,博士,从事确定学习及动态模式识别等研究,E-mail:audxd@scut.edu.cn.
