Adaptive Neural Network H∞ Tracking Control for a Class of Uncertain Nonlinear Systems
An adaptive neural network H∞ tracking control architecture with state observer is proposed for a class of non-affine nonlinear systems with external disturbance and unavailable states. The controller consists of an equivalent controller and H∞ controller. H∞ controller is designed to attenuate the effect of external disturbance and approximation errors of the neural network, and a state observer is used to estimate the system output derivatives which are unavailable for measurement. The overall control scheme and the parameters update laws based on Lyapunov theory can guarantee asymptotic convergence of the tracking error to zero and attenuate the effect of the disturbance to a prescribed level. Simulation results illustrate the effectiveness of the scheme.
neural network non-affine nonlinear observer
HuHui LIU Guo-rong GUO Peng
Dept of Electrical and Information Engineering Hunan Institute of Engineering Hunan Xiangtan,China Dept of Computer Science Hunan Institute of Engineering Hunan Xiangtan,China
国际会议
上海
英文
1061-1065
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）