Abstract: The conventional learning controller based on CMAC neural network can effectively reduce tracking error, but can also destabilize a control system which is otherwise stable due to the influence of accumulative errors when tracking continuous variable signals such as sinusoidal wave. Hence, a new self-learning controller based on CMAC neural network is proposed in this paper. It uses the dynamic errors of the system as input parameters to the CMAC neural network. This feature helps the controller to avoid the influence of the accumulative errors and then the stability of the system is ensured. The simulation results demonstrate 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.