Reinforcement Learning for Online Control and Optimisation

Govindhasamy, J.J. and McLoone, Sean F. and Irwin, G.W. and French, J.J. and Doyle, R.P. (2005) Reinforcement Learning for Online Control and Optimisation. IEE Control Engineering Book Series, 70 (9). pp. 293-326.

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An intelligent controller has the ability to analyse an unknown situation and to respond to it accordingly. Approximate dynamic programming, or reinforcement learning as it is more commonly known, in the form of Adaptive Critic Designs (ACDS), falls into this category (56). ACDs offer an interesting alternative for adaptive control and optimisation of highly nonlinear industrial processes. In this chapter, the action dependent adaptive critic (ADAC) (47) is used and a suitable second-order training algorithm is presented to ensure fast convergence and stability. The performance of the training algorithm is first compared in simulation for the control of an inverted pendulum. The ADAC scheme is then applied to the control of an aluminium subtrate disk grinding process where the learning is based on actual industrial historical data. Results here indicate that the ADAC controller can control the unloading thickness variation of the process to achieve a 33% reduction in rejects.

Item Type: Article
Additional Information: This paper is a postprint of a paper submitted to and accepted for publicatin in (journal/conference) and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library
Keywords: Adaptive Critic Designs, action dependend adaptive critic, online control
Academic Unit: Faculty of Science and Engineering > Electronic Engineering
Item ID: 687
Depositing User: Sean McLoone
Date Deposited: 24 Aug 2007
Journal or Publication Title: IEE Control Engineering Book Series
Publisher: Institution of Engineering and Technology
Refereed: Yes

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