Output-feedback adaptive stochastic nonlinear stabilization using neural networks  

作　　者：Weisheng Chen 

机构地区：[1]Department of Applied Mathematics, Xidian University, Xi'an 710071, E R. China

出　　处：《Journal of Systems Engineering and Electronics》2010年第1期81-87,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 (60804021)

摘　　要：For the first time, an adaptive backstepping neural network control approach is extended to a class of stochastic non- linear output-feedback systems. Different from the existing results, the nonlinear terms are assumed to be completely unknown and only a neural network is employed to compensate for all unknown nonlinear functions so that the controller design is more simplified. Based on stochastic LaSalle theorem, the resulted closed-loop system is proved to be globally asymptotically stable in probability. The simulation results further verify the effectiveness of the control scheme.For the first time, an adaptive backstepping neural network control approach is extended to a class of stochastic non- linear output-feedback systems. Different from the existing results, the nonlinear terms are assumed to be completely unknown and only a neural network is employed to compensate for all unknown nonlinear functions so that the controller design is more simplified. Based on stochastic LaSalle theorem, the resulted closed-loop system is proved to be globally asymptotically stable in probability. The simulation results further verify the effectiveness of the control scheme.

关 键 词：neural network OUTPUT-FEEDBACK nonlinear stochastic systems backstepping. 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程] TP183[自动化与计算机技术—控制科学与工程]