Sliding-Mode Control Augmented with Broad Learning System for Self-Balancing Inverse-Atlas Ball-Riding Robots with Uncertainties

Abstract

The paper presents a novel sliding-mode control method augmented with broad learning system (BLS), or abbreviated as BLS-SMC, for trajectory tracking and station keeping of an uncertain Inverse-Atlas ball-riding robot (IASBRR) driven by three omnidirectional wheels. After brief description of the dynamic model of the robot with frictions and gravity, a BLS-SMC controller is proposed to accomplish robust self-balancing and trajectory tracking of the IASBRR in the presence of unknown frictions, mass variations and model uncertainties. The proposed BLS-SMC controller is proven asymptotically stable using Lyapunov stability theory and Barbalta's lemma. Three comparative simulations and two experiments are conducted to show the effectiveness and merits of the proposed control method. The comparative results also indicate that the proposed controller is superior more efficient by comparing to an existing method.