基于神经网络的不确定随机非线性时滞系统自适应有界镇定  

Neural-network-based bounded adaptive stabilization for uncertain stochastic nonlinear systems with time-delay

作　　者：余昭旭[1] 杜红彬[1] 

机构地区：[1]华东理工大学自动化系,上海200237

出　　处：《控制理论与应用》2010年第7期855-860,共6页Control Theory & Applications

基　　金：国家自然科学基金青年基金资助项目(60704013);上海市重点学科建设项目(B504)

摘　　要：针对一类不确定严格反馈随机非线性时滞系统的自适应有界镇定问题,利用神经网络参数化和Backstepping方法,提出一种新的且含较少学习参数的神经网络自适应控制策略,以保证系统半全局随机有界.稳定性分析证明闭环系统的所有误差信号概率意义下有界.仿真结果表明所提出控制器设计方法的有效性.The problem of bounded adaptive stabilization is investigated for a class of uncertain stochastic nonlinear strict-feedback systems with unknown time-delay. Based on the technique of neural-network（NN） parameterization and the Backstepping method, we develop a novel adaptive neural control scheme which contains fewer learning parameters to solve the stabilization problem of such systems. In addition, the stability analysis is given to show that all the error variables in the closed-loop system are bounded in probability. The effectiveness of the proposed design is verified by simulation results.

关 键 词：自适应控制 神经网络 BACKSTEPPING 随机系统 时滞 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]