Title :
Adaptive Neural Network H∞ tracking control for a class of uncertain nonlinear systems
Author :
Hui, Hu ; Liu Guo-Rong ; Guo Peng
Author_Institution :
Dept of Electr. & Inf. Eng., Hunan Inst. of Eng., Xiangtan, China
Abstract :
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.
Keywords :
H¿ control; Lyapunov methods; asymptotic stability; control nonlinearities; control system synthesis; neural nets; nonlinear control systems; observers; tracking; uncertain systems; H∞ controller design; Lyapunov theory; adaptive neural network H∞ tracking control; approximation errors; asymptotic convergence; nonlinear disturbance; parameters update laws; state observer; tracking error; uncertain nonaffine nonlinear systems; Adaptive control; Adaptive systems; Approximation error; Control systems; Neural networks; Nonlinear control systems; Nonlinear systems; Observers; Programmable control; State estimation; neural network; non-affine nonlinear; observer;
Conference_Titel :
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4754-1
Electronic_ISBN :
978-1-4244-4738-1
DOI :
10.1109/ICICISYS.2009.5358278
