Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm

TL;DR

This paper introduces an adaptive neuro-fuzzy control method combined with sliding-mode theory to effectively control a spherical rolling robot, compensating for model uncertainties and disturbances.

Contribution

It proposes a novel control scheme integrating neuro-fuzzy networks with SMC-based online learning, ensuring stability and improved performance without explicit dynamic models.

Findings

Eliminates steady-state error in robot control

Enhances transient response performance

Proven stability via Lyapunov function

Abstract

As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations.