Adaptive recurrent cerebellar model articulation controller for linear ultrasonic motor with optimal learning rates

In this study, an adaptive recurrent cerebellar model articulation controller (ARCMAC) is investigated for the motion control of linear ultrasonic motor (LUSM). The proposed ARCMAC has superior capability to the conventional cerebellar model articulation controller in efficient learning mechanism and dynamic response. The dynamic gradient descent method is adopted to online adjust the ARCMAC parameters. Moreover, the analytical method based on a Lyapunov function is proposed to determine the learning-rates of ARCMAC so that the stability of the system can be guaranteed. Furthermore, the variable optimal learning-rates are derived to achieve the fastest convergence of tracking error. Finally, the effectiveness of the proposed control system is verified by the experiments of LUSM motion control. Experimental results show that high-precision tracking response can be achieved by using the proposed ARCMAC.