Neural network control of a class of nonlinear systems with actuator saturation

In this paper, neural net (NN)-based actuator saturation compensation scheme for the nonlinear systems in Brunovsky canonical form is presented. The scheme that leads to stability, command following and disturbance rejection is rigorously proved, and verified using a nonlinear system of "pendulum type". The on-line weights tuning law, the overall closed-loop performance, and the boundness of the NN weights are derived and guaranteed based on Lyapunov approach. The actuator saturation is assumed to be unknown, and the compensator is inserted into a feedforward path. The simulation results indicate that the proposed scheme can effectively compensate for the saturation nonlinearity in the presence of system uncertainty.