Credit assigned CMAC and its application to online learning robust controllers

In this paper, a novel,learning scheme is proposed to speed up the learning process in cerebellar model articulation controllers (CMAC). In the conventional CMAC learning scheme, the correcting amounts of errors are equally distributed into all addressed hypercubes, regardless the credibility of those hypercubes. The proposed learning approach is-to use the inverse of learned times of the addressed hypercubes as the credibility (confidence) of the learned values. With this idea, the learning speed can indeed become very fast. To further demonstrate the online learning capability of the proposed credit assigned CMAC learning scheme, this paper also presents a learning robust controller that can actually learn online. Based on the robust controllers presented in the literature; the proposed online learning robust controller uses the previous control input, the current output acceleration, and the current desired output as the state to define the nominal effective moment of the system from the. CMAC table. An initial trial mechanism for the early learning stage is also proposed in the paper.-With our proposed credit-assigned CMAC, the robust learning controller can accurately trace various trajectories online.