Hardware design of CMAC neural network for control applications

CMAC neural network is one useful learning technique based on the cerebellum's motor behavior. CMAC uses a table look-up method to resolve the complex non-linear system instead of a numerical calculation method. The result is in better and faster controller for nonlinear dynamical system. In this paper, we propose a CMAC neural network for controlling a non-linear system. The simulation results show that the proposed CMAC controllers for a simple non-linear function and a DC motor speed control reduce tracking errors and improve the stability of its learning controllers. The validity of the proposed CMAC controller is also proved by the real-time tension control. Besides, hardware design of CMAC for control application has been implemented to confirm the proposed approach and architecture.