Adaptive recurrent cerebellar model articulation controller for unknown dynamic systems with optimal learning-rates

In this study, an adaptive recurrent cerebellar model articulation controller (ARCMAC) is designed for feedback control system with unknown dynamics. The proposed ARCMAC has superior capability to the conventional cerebellar model articulation controller (CMAC) in efficient learning mechanism, guaranteed system stability and dynamic response. Temporal relations are embedded in ARCMAC by adding feedback connections in the association memory space so that the ARCMAC captures the dynamic response. The dynamic gradient descent method is adopted to adjust ARCMAC parameters on-line. Moreover, the variable optimal learning-rates are derived to achieve most rapid convergence of tracking error. Finally, the effectiveness of the proposed control system is verified by experimental results of linear piezoelectric ceramic motor (LPCM) position control system. Experimental results show that accurate tracking response and superior dynamic performance can be obtained because of the powerful on-line learning capability of the proposed ARCMAC.