Adaptive classification and control-rule optimisation via a learning algorithm for controlling a dynamic system

Author

Huang, Runhe ; Fogarty, Terence C.

Author_Institution

Transputer Centre, Bristol Polytech., UK

fYear

1991

fDate

11-13 Dec 1991

Firstpage

867

Abstract

The authors present a control-rule optimizing algorithm. They describe a learning algorithm, for controlling a dynamic system, in which an incremental version of the genetic algorithm is used to learn classification of the state-space of process control while a batch version of the genetic algorithm is used to optimize a set of control actions. The dynamic system chosen was a motor-driven cart on which a pole was mounted. The learning algorithm for controlling a cart-pole balancing system has been implemented by using a real-time parallel computation architecture

Keywords

control system synthesis; genetic algorithms; learning (artificial intelligence); neural nets; optimal control; state-space methods; adaptive classification; cart-pole balancing system; control-rule optimisation; dynamic system control; genetic algorithm; learning algorithm; real-time parallel computation architecture; state-space; Adaptive control; Automatic control; Automation; Computer architecture; Concurrent computing; Control systems; Fuzzy logic; Genetic algorithms; Partitioning algorithms; Process control; Programmable control; Real time systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1991., Proceedings of the 30th IEEE Conference on

Conference_Location

Brighton

Print_ISBN

0-7803-0450-0

Type

conf

DOI

10.1109/CDC.1991.261440

Filename

261440