文献类型：期刊文献

英文题名：Adaptive Neural Output Feedback Control for Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis and Unknown Control Directions

作者：Yu, Zhaoxu[1];Li, Shugang[2];Yu, Zhaosheng[3];Li, Fangfei[4]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Shanghai Univ, Dept Informat Management, Shanghai 200444, Peoples R China;[3]South China Univ Technol, Sch Elect Power, Guangzhou 510640, Guangdong, Peoples R China;[4]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China

年份：2018

卷号：29

期号：4

起止页码：1147

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20171003415880);WOS:【SCI-EXPANDED(收录号:WOS:000427859600031)】；

语种：英文

外文关键词：Adaptive control; backlash-like hysteresis; output feedback; stochastic nonlinear system; unknown control direction

摘要：This paper investigates the problem of output feedback adaptive stabilization for a class of nonstrict-feedback stochastic nonlinear systems with both unknown backlashlike hysteresis and unknown control directions. A new linear state transformation is applied to the original system, and then, control design for the new system becomes feasible. By combining the neural network's (NN's) parameterization, variable separation technique, and Nussbaum gain function method, an input-driven observer-based adaptive NN control scheme, which involves only one parameter to be updated, is developed for such systems. All closed-loop signals are bounded in probability and the error signals remain semiglobally bounded in the fourth moment (or mean square). Finally, the effectiveness and the applicability of the proposed control design are verified by two simulation examples.