A Discrete-Time System Adaptive Control Using Multiple Models and RBF Neural Networks
A new control scheme using multiple models and RBF neural networks is developed in this paper. The proposed scheme consists of multiple feedback linearization controllers, which are based on the known nominal dynamics model and a compensating controller, which is based on RBF neural networks. The compensating controller is applied to improve the transient performance. The neural network is trained online based on Lyapunov theory and learning convergence is thus guaranteed. Simulation results are presented to demonstrate the validity of the proposed method.
KeywordsAdaptive Control Multiple Model Lyapunov Theory IEEE Control System Magazine NARMAX Model
Unable to display preview. Download preview PDF.
- 5.Gang, F.: Position Control of A PM Stepper Motor Using Neural Networks. In: Proceedings of the 39th Conference on Decision and Control, Sydney, Australia, pp. 1766–1769 (2000)Google Scholar