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

被引：119

作者：

Kayacan, Erkan ^{[1
]}

Kayacan, Erdal ^{[1
]}

Ramon, Herman ^{[1
]}

Saeys, Wouter ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Dept Biosyst, Div Mechatron Biostat & Sensors, B-3001 Louvain, Belgium

来源：

IEEE TRANSACTIONS ON CYBERNETICS | 2013年 / 43卷 / 01期

关键词：

Adaptive neuro-fuzzy control; sliding-mode learning algorithm; spherical rolling robot; MOBILE ROBOTS; MOTION; DESIGN; SYSTEM;

D O I：

10.1109/TSMCB.2012.2202900

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：170 / 179

页数：10

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