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Reinforcement learning for condition-based control of gas turbine engines

Sanusi, I., Mills, A., Trodden, P., Kadirkamanathan, V. and DODD, Tony (2019) Reinforcement learning for condition-based control of gas turbine engines. In: 2019 18th European Control Conference (ECC), 25-28 Jun 2019. IEEE, Naples, Italy, pp. 3928-3933. ISBN 9783907144008

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Abstract or description

A condition-based control framework is proposed for gas turbine engines using reinforcement learning and adaptive dynamic programming (RL-ADP). The system behaviour, specifically the fuel efficiency function and constraints, exhibit unknown degradation patterns which vary from engine to engine. Due to these variations, accurate system models to describe the true system states over the life of the engines are difficult to obtain. Consequently, model-based control techniques are unable to fully compensate for the effects of the variations on the system performance. The proposed RL-ADP control framework is based on Q-learning and uses measurements of desired performance quantities as reward signals to learn and adapt the system efficiency maps. This is achieved without knowledge of the system variation or degradation dynamics, thus providing a through life adaptation strategy that delivers improved system performance. In order to overcome the long standing difficulties associated with the application of adaptive techniques in a safety critical setting, a dual-control loop structure is proposed in the implementation of the RL-ADP scheme. The overall control framework maintains guarantees on the main thrust control loop whilst extracting improved performance as the engine degrades by tuning sets of variable geometry components in the RL-ADP control loop. Simulation results on representative engine data sets demonstrate the effectiveness of this approach as compared to an industry standard gain scheduling.

Item Type: Book Chapter, Section or Conference Proceeding
Additional Information: © 2019 EUCA. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Faculty: School of Creative Arts and Engineering > Engineering
Depositing User: Library STORE team
Date Deposited: 15 Jul 2020 14:34
Last Modified: 15 Jul 2020 14:34
URI: http://eprints.staffs.ac.uk/id/eprint/6228

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