Sequential Learning for Adaptive Critic Design: An Industrial Control Application.
This paper investigates the feasibility of applying
reinforcement learning (RL) concepts to industrial process
optimisation. A model-free action-dependent adaptive critic
design (ADAC), coupled with sequential learning neural
network training, is proposed as an online RL strategy
suitable for both modelling and controller optimisation. The
proposed strategy is evaluated on data from an industrial
grinding process used in the manufacture of disk drives.
Comparison with a proprietary control system shows that
the proposed RL technique is able to achieve comparable
performance without any manual intervention.
Conference or Workshop Item
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||Reinforcement learning, action-dependent adaptive critic
||Faculty of Science and Engineering > Electronic Engineering
||24 Aug 2007
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