Adaptive controller design for uncertain nonlinear systems with input magnitude and rate limitations

An adaptive controller for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems with extern disturbance and control input limitations is presented in this paper. The controller is designed with a priori consideration of input limitation effects, hence it can generate control signals satisfying input limitations. This controller uses adaptive radial basis function (RBF) neural networks to approximate the unknown nonlinearities. To compensate the effects of input limitations, an auxiliary system is constructed and used in neural network parameter update laws. Furthermore, in order to deal with approximation errors for unknown nonlinearities and extern disturbances, a supervisory control is designed, which guarantees that the closed loop system achieves a desired level H
^∞
tracking performance. The closed loop system performance is analyzed by Lyapunov method. Steady state and transient tracking performance index are established and can be adjusted by design parameters. Computer simulations are presented to illustrate the efficiency and tracking performance of the proposed controller.