Self-tuning output recurrent cerebellar model articulation controller for a wheeled inverted pendulum control

Abstract

In this study, a model-free self-tuning output recurrent cerebellar model articulation controller (SORCMAC) is investigated to control a wheeled inverted pendulum (WIP). Since the proposed SORCMAC captures the system dynamics, it has superior capability compared to the conventional cerebellar model articulation controller in terms of an efficient learning mechanism and dynamic response. The dynamic gradient descent method is also adopted to adjust the SORCMAC parameters online. Moreover, an analytical method based on a Lyapunov function is proposed to determine the learning rates of the SORCMAC so that the convergence of the system can be guaranteed. Finally, the effectiveness of the proposed control system is verified by simulations of the WIP control. Simulation results show that the WIP can move forward and backward stably with uncertainty disturbance by using the proposed SORCMAC.