In the conventional CMAC-based adaptive controller design, a switching compensator is designed to guarantee system stability
in the Lyapunov stability sense but the undesirable chattering phenomenon occurs. This paper proposes a CMAC-based smooth
adaptive neural control (CSANC) system that is composed of a neural controller and a saturation compensator. The neural controller
uses a CMAC neural network to online mimic an ideal controller and the saturation compensator is designed to dispel the approximation
error between the ideal controller and neural controller without any chattering phenomena. The parameter adaptive algorithms
of the CSANC system are derived in the sense of Lyapunov stability, so the system stability can be guaranteed. Finally, the
proposed CSANC system is applied to a Chua’s chaotic circuit and a DC motor driver. Simulation and experimental results show
the CSANC system can achieve a favorable tracking performance. It should be emphasized that the development of the proposed
CSANC system doesn’t need the knowledge of the system dynamics.
