Robust adaptive backstepping controller design based on the CMAC neural network

A robust adaptive backstepping controller design method is proposed for a nonlinear system with uncertainty and unknown parameters based on the CMAC neural network. The CMAC neural network was used not only to approach the arbitrary model uncertainties but also to eliminate the bad effects of the uncertainties with robust terms in the controller and virtual controllers. Novel update and control laws are proposed to guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded in a Lyapunov sense. Simulation experimental showed the tracking control of the nonlinear system is achieved and this method is effectual.