Title :
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
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;
Conference_Titel :
Decision and Control, 1991., Proceedings of the 30th IEEE Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-0450-0
DOI :
10.1109/CDC.1991.261440
