Process Control

Adaptive Sliding-Mode Control of Nonlinear Systems Using Neural Network Approach

C. Schmid
Ruhr-Universität Bochum

Abstract

This paper is concerned with the adaptive sliding-mode control of nonlinear dynamic systems with model uncertainties. The proposed control method combines the advantages of sliding-mode control and backstepping methodology, such that the requirement of the restrictive matching condition is removed, which seriously clams the application of sliding-mode control. In the control scheme, networks of Gaussian radial basis functions with variable weights are used to compensate the model uncertainties. The adaptive law developed using the Lyapunov synthesis approach guarantees the stability of the control scheme. The performance is illustrated by experimental studies with a flexible-joint manipulator.

Full paper

073.pdf

Session

Robust and Adaptive Control (Lecture)

Reference

Schmid, C.: Adaptive Sliding-Mode Control of Nonlinear Systems Using Neural Network Approach. Editors: Fikar, M., Kvasnica, M., In Proceedings of the 17th International Conference on Process Control ’09, Štrbské Pleso, Slovakia, 346–352, 2009

BibTeX
@inProceedings{pc09-073,
author = {Schmid, C.},
title = {Adaptive Sliding-Mode Control of Nonlinear Systems Using Neural Network Approach},
booktitle = {Proceedings of the 17th International Conference on Process Control '09},
year = {2009},
pages = {346-352},
editor = {Fikar, M. and Kvasnica, M.},
address = {Štrbské Pleso, Slovakia},
publisher = {Slovak University of Technology in Bratislava},
url = {http://www.kirp.chtf.stuba.sk/pc09/data/papers/073.pdf}}
© 2009 Institute of Information Engineering, Automation and Mathematics, FCFT STU in Bratislava. All rights reserved.