Title: Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm
Authors: Kayacan, Erkan ×
Kayacan, Erdal
Ramon, Herman
Saeys, Wouter #
Issue Date: 1-Feb-2013
Publisher: Institute of Electrical and Electronics Engineers
Series Title: IEEE Transactions on Systems, Man and Cybernetics B, Cybernetics vol:43 issue:1 pages:170-179
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.
ISSN: 1083-4419
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Division of Mechatronics, Biostatistics and Sensors (MeBioS)
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
J2.pdfOA article Published 891KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science