Robust adaptive feedback linearization control for a class of MIMO uncertain nonlinear systems

Abstract

A robust adaptive feedback linearization control (RAFLC) is proposed for a class of uncertain nonlinear MIMO systems. A radial basis function neural network (RBFNN) is used as an approximator for the system unknown nonlinear functions. The control input comprises of an adaptive feedback linearization controller, a sliding mode controller (SMC), an adaptive neural network compensation controller and an adaptive state feedback controller. And the robust adaptive control is used for the weight learning and compensation to the modeling error and extern disturbances. The adaptation law of neural networks weights and the design method of robust controller are given out based on the Lyapunov stability analysis. Furthermore, the proposed controller can guarantee not only global stability but also a good control performance. Simulation results show that the system can perform good tracking and has strong robustness.