Bioinspired control design using cerebellar model articulation controller network for omnidirectional mobile robots

Abstract

As a learning mechanism that emulates the structure of the cerebellum, cerebellar model articulation controllers have been widely adopted in the control of robotic systems because of the fast learning ability and simple computational structure. In this article, a cerebellar model articulation controller–based neural network controller is developed for an omnidirectional mobile robot. With the powerful learning ability of cerebellar model articulation controller, a cerebellar model articulation controller neural network is constructed to learn the complex dynamics of the omnidirectional mobile robot such that the robot is controlled without a priori knowledge of the robot dynamics. In addition, to overcome the limitation of the neural network controller, a global control technique with a group of smooth switching functions is designed such that the global ultimately uniformly boundedness of cerebellar model articulation controller is achieved instead of conventional semi-global ultimately uniformly boundedness. Moreover, smooth decreasing boundary functions are synthesized into the controller to guarantee the transient control performance. Based on an omnidirectional mobile robot, numerical experiments have been conducted to demonstrate the effectiveness of the proposed cerebellar model articulation controller controller.