Adaptive neural control for a class of uncertain stochastic nonlinear systems with dead-zone  

作　　者：Zhaoxu Yu Hongbin Du 

机构地区：[1]Department of Automation,East China University of Science and Technology,Shanghai 200237,P.R.China

出　　处：《Journal of Systems Engineering and Electronics》2011年第3期500-506,共7页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China (60704013);the Special Foundation of East China University of Science and Technology for Youth Teacher (YH0157134)

摘　　要：The problem of adaptive stabilization is addressed for a class of uncertain stochastic nonlinear strict-feedback systems with both unknown dead-zone and unknown gain functions.By using the backstepping method and neural network（NN） parameterization,a novel adaptive neural control scheme which contains fewer learning parameters is developed to solve the stabilization problem of such systems.Meanwhile,stability analysis is presented to guarantee that all the error variables are semi-globally uniformly ultimately bounded with desired probability in a compact set.The effectiveness of the proposed design is illustrated by simulation results.The problem of adaptive stabilization is addressed for a class of uncertain stochastic nonlinear strict-feedback systems with both unknown dead-zone and unknown gain functions.By using the backstepping method and neural network（NN） parameterization,a novel adaptive neural control scheme which contains fewer learning parameters is developed to solve the stabilization problem of such systems.Meanwhile,stability analysis is presented to guarantee that all the error variables are semi-globally uniformly ultimately bounded with desired probability in a compact set.The effectiveness of the proposed design is illustrated by simulation results.

关 键 词：adaptive control neural network（NN） BACKSTEPPING stochastic nonlinear system. 

分 类 号：TP271[自动化与计算机技术—检测技术与自动化装置] TM343[自动化与计算机技术—控制科学与工程]