Direct adaptive recurrent interval type 2 fuzzy neural networks control using for a ball robot with a four-motor inverse-mouse ball drive

Abstract

This paper presents a direct adaptive RIT2FNN-based control for motion control of a ball robot with a four-motor inverse mouse-ball driving mechanism actuated by four independent brushless motors simultaneously. A dynamic model of the robot with viscous and Coulomb frictions is derived using Lagrangian mechanics. With the model, a direct adaptive RIT2FNN-based control with the backstepping slidingmode methodology is proposed to accomplish robust self-balancing and trajectory tracking of the robot in the presence of mass variations, viscous and Coulomb frictions with unknown parameters and uncertainties. The proposed motion controller is proven asymptotically stable using Lyapunov stability theory. Computer simulations are conducted for illustration of the effectiveness of the proposed control method.