Trajectory tracking of a self-balancing two-wheeled robot using backstepping sliding-mode control and fuzzy basis function networks

Abstract

This paper presents an adaptive backstepping sliding-mode motion controller using fuzzy basis function networks (FBFN) method for trajectory tracking of a self-balancing two-wheeled robot (SBTWR) with parameter variations. A decoupling method is proposed to decouple the robot's dynamic model such that the tracking controller can be synthesized using backstepping and sliding-mode control in both kinematic and dynamic levels. The FBFN is employed to on-line learn the uncertain parts of the tracking controller, thus achieving adaptive capability. Simulations results indicate that the proposed adaptive tracking controller is capable of providing satisfactory trajectory tracking performance.