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
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}} |