Adaptive sliding-mode control with Gaussian network
| Authors: | Ma Lei, Universität Würzburg, Germany
Schilling Klaus, Universität Würzburg, Germany
Schmid Christian, Ruhr-Universität Bochum, Germany |
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| Topic: | 1.2 Adaptive and Learning Systems |
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| Session: | Learning and Intelligent Control |
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| Keywords: | Adaptive control, Sliding-mode control, Radial base functionnetworks, Gaussian functions, Neural Networks, Nonlinear systems |
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Abstract
This paper is concerned with the adaptive sliding-mode control ofa class of nonlinear systems with model uncertainties. A directadaptive sliding-mode control scheme is presented. A network ofGaussian radial basis functions with variable weights was used tocompensate the model uncertainties. A new growing scheme of thisnetwork is proposed. It starts with a loose structure in order toreduce the computational effort. More nodes are added to thenetwork progressively in order to improve the transient behaviour.The adaptive law developed using the Lyapunov synthesis approachguarantee the stability of the overall control scheme, even in thepresence of modelling error. The performance of the control schemeis illustrated by simulation studies with convincing results.
