Optimal adaptive compensation control for a class of MIMO nonlinear systems with actuator failures

Abstract

An optimal adaptive compensation control scheme is proposed for a class of multi-input multi-output (MIMO) affine nonlinear systems with actuator failures. Considering stuck actuators and partial effectiveness failures, an adaptive dynamic programming method is adopted by using neural network to approximate the cost function. It adjust the weights of the neural network by using an online adaptive algorithm. An adaptive parameter adjustment law is designed to estimate the actuator failure coefficients. The proposed optimal adaptive compensation law can guarantee that the closed-loop system with actuator failures is stable and that the given reference signals are effectively tracked. Simulation results demonstrate the effectiveness of the proposed method.