Paper:
Language: English:

Reinforcement Learning for Online Industrial Process Control

James J. Govindhasamy^*, Seán F. McLoone^**, George W. Irwin^, John J. French^, and Richard. P. Doyle^*

Intelligent System and Control Research Group, Queen's University Belfast, Belfast BT9 5AH, Northern Ireland, United Kingdom
Department of Electronic Engineering, National University of Ireland Maynooth Maynooth, Co. Kildare, Ireland
Seagate Technology Media Ltd., 99 Dowland Road, Aghanloo Industrial Estate Limavady BT49 OHR, Northern Ireland, United Kingdom

Received: October 30, 2004

Accepted: November 1, 2004

Keywords: reinforcement learning, action dependent adaptive critic, neural networks, nonlinear process control, intelligent control

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.9, No.1 pp. 23-30, 2005

Abstract

Reinforcement learning, in the form of Adaptive Critic Designs (ACDs), have the ability to analyse or evaluate a situation and respond to it accordingly. They offer an excellent alternative for adaptively controlling and optimising the highly nonlinear processes found in industry. Here, an enhanced implementation of the action dependent adaptive critic design (ADAC) of Si and Wang [9] is investigated for modelling and control of an industrial grinding process used in the manufacture of hard disk drive platters. This study, one of the first reported industrial applications of this emerging technology, shows that the proposed ADAC control scheme can achieve a 33% reduction in platter rejects compared to an existing proprietary controller.

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