Reinforcement learning based closed‐loop reference model adaptive flight control system design

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dc.contributor.authorYuksek, Burak
dc.contributor.authorInalhan, Gokhan
dc.date.accessioned2020-10-28T14:06:06Z
dc.date.available2020-10-28T14:06:06Z
dc.date.freetoread2021-10-08
dc.date.issued2020-10-07
dc.description.abstractIn this study, we present a reinforcement learning (RL)‐based flight control system design method to improve the transient response performance of a closed‐loop reference model (CRM) adaptive control system. The methodology, known as RL‐CRM, relies on the generation of a dynamic adaption strategy by implementing RL on the variable factor in the feedback path gain matrix of the reference model. An actor‐critic RL agent is designed using the performance‐driven reward functions and tracking error observations from the environment. In the training phase, a deep deterministic policy gradient algorithm is utilized to learn the time‐varying adaptation strategy of the design parameter in the reference model feedback gain matrix. The proposed control structure provides the possibility to learn numerous adaptation strategies across a wide range of flight and vehicle conditions instead of being driven by high‐fidelity simulators or flight testing and real flight operations. The performance of the proposed system was evaluated on an identified and verified mathematical model of an agile quadrotor platform. Monte‐Carlo simulations and worst case analysis were also performed over a benchmark helicopter example model. In comparison to the classical model reference adaptive control and CRM‐adaptive control system designs, the proposed RL‐CRM adaptive flight control system design improves the transient response performance on all associated metrics and provides the capability to operate over a wide range of parametric uncertainties.en_UK
dc.identifier.citationYuksek B, Gokhan I. (2021) Reinforcement learning based closed‐loop reference model adaptive flight control system design. International Journal of Adaptive Control and Signal Processing, Volume 35, Issue 3, March 2021, pp. 420-440en_UK
dc.identifier.cris28610325
dc.identifier.issn0890-6327
dc.identifier.urihttps://doi.org/10.1002/acs.3181
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15924
dc.language.isoenen_UK
dc.publisherWileyen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectVariable closed-loop reference model adaptive controlen_UK
dc.subjectReinforcement learningen_UK
dc.subjectAdaptive flight control systemen_UK
dc.subjectResilient controlen_UK
dc.titleReinforcement learning based closed‐loop reference model adaptive flight control system designen_UK
dc.typeArticleen_UK

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