Neuro-fuzzy multi-model control using Sugeno inference and Kohonen tuning in parameter space

Author

Tomescu, Bogdan ; Vanlandingham, H.F.

Author_Institution

Bradley Dept. of Electr. Eng., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA

Volume

2

fYear

1997

fDate

12-15 Oct 1997

Firstpage

1028

Abstract

A multi-model adaptive control scheme is introduced. It makes use of Kohonen neural structures and Sugeno fuzzy inference in adapting and switching the control action of the multi-model bank respectively. The structure can be easily put in a parametrized gain scheduling framework, practical to engineering situations. A simple example of a tracking filter (radar) has been simulated with good results

Keywords

adaptive control; control system synthesis; fuzzy control; fuzzy logic; inference mechanisms; neurocontrollers; self-organising feature maps; transfer functions; tuning; Kohonen neural structures; Kohonen tuning; Sugeno inference; control action; multi-model adaptive control scheme; neuro-fuzzy multi-model control; parameter space; parametrized gain scheduling framework; tracking filter; Adaptive control; Control systems; Electric variables control; Power electronics; Power system modeling; Radar tracking; State feedback; State-space methods; Tracking loops; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1062-922X

Print_ISBN

0-7803-4053-1

Type

conf

DOI

10.1109/ICSMC.1997.638083

Filename

638083