Online Learning CMAC Neural Network Control Scheme for Nonlinear Systems

Abstract

The cerebella model articulation controller (CMAC) neural network control scheme is a powerful tool for practical real-time nonlinear control applications. The conventional leaning controller based on CMAC can effectively reduce tracking error, but the CMAC control system can suddenly diverge after a long period of stable tracking, due to the influence of accumulative errors when tracking continuous variable signals such as sinusoidal wave. A new self-learning controller based on CMAC is proposed. It uses the dynamic errors of the system as input to the CMAC. This feature helps the controller to avoid the influence of the accumulative errors and the stability of the system is ensured. The simulation results show that the proposed controller is not only effective but also of good robustness. Moreover, it has a high learning rate, which is important to online learning.