Control Scheme Based on the Inverse System Method Online Learning BP Neural Network Adaptive Compensate
In this paper, an online BP neural network (BPNN) compensate control scheme based on inverse system method is presented for a class of singleinput-single-output nonlinear systems. Firstly, the error between the a-th derivative of the system output and the pseudo-control is analyzed and a BPNN is designed to compensate the error. Then, an adaptive algorithm of the BPNN, designed based on the Lyapunov stability theory, proves that tracking error of closed-loop system and weight estimation error of BPNN are uniform ultimate boundedness. Simulations for three nonlinear systems demonstrate the validity of the proposed control scheme.
neural network online learning inverse system nonlinear system compensate control
GAO Xiang-xiang JIANG Ru GAO Ming-ming
China North Vehicle Research Institute Beijing, China School of Control and Computer Engineering North China Electric Power University Beijing, China
国际会议
厦门
英文
874-878
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）