Online learning in adaptive neurocontrol schemes with a sliding mode algorithm

Author

Topalov, Andon Venelinov ; Kaynak, Okyay

Author_Institution

Dept. of Control Syst., Tech. Univ. of Sofia, Plovdiv, Bulgaria

Volume

31

Issue

3

fYear

2001

fDate

6/1/2001 12:00:00 AM

Firstpage

445

Lastpage

450

Abstract

The novel features of an adaptive PID-like neurocontrol scheme for nonlinear plants are presented. The controller tuning is based on an estimate of the command-error on its output by using a neural predictive model. A robust online learning algorithm, based on the direct use of sliding mode control (SMC) theory is applied. The proposed approach allows handling of the plant-model mismatches, uncertainties and parameters changes. The results show that both the plant model and the controller inherit some of the advantages of SMC, such as high speed of learning and robustness

Keywords

neural nets; neurocontrollers; robust control; stability; three-term control; tuning; variable structure systems; PID-like neurocontrol scheme; adaptive neurocontrol schemes; controller tuning; learning; neural predictive model; nonlinear plants; online learning; plant-model mismatches; robustness; sliding mode algorithm; Artificial neural networks; Control engineering; Control systems; Noise measurement; Predictive models; Robust control; Robust stability; Sliding mode control; Uncertainty; Variable structure systems;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/3477.931542

Filename

931542