Skip to Main Content
A novel adaptive output recurrent cerebellar model articulation controller (AORCMAC) is utilized to control wheeled inverted pendulums (WIPs) that have a pendulum mounted on two coaxial wheels. This paper focuses mainly on adopting a self-dynamic balancing control strategy for such WIPs. Since the AORCMAC captures system dynamics, it is superior to conventional CMACs in terms of efficient learning and dynamic response. The AORCMAC parameters are adjusted online using the dynamic gradient descent method. The learning rates of the AORCMAC are determined using an analytical method based on a Lyapunov function, such that system convergence is achieved. The variable and optimal learning rates are derived to achieve rapid tracking-error convergence. A WIP standing control is utilized to experimentally verify the effectiveness of the proposed control system. Experimental results indicate that WIPs can stand upright stably with external disturbances via the proposed AORCMAC.