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Data-Driven Based Optimal Output-Feedback Control of Continuous-Time Systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Zican Li
  • Tao Wu
  • Jing Na
  • Jun Zhao
  • Guanbin Gao
  • Guido Herrmann
Original languageEnglish
Title of host publication2018 International Conference on Modelling, Identification and Control (ICMIC 2018)
Subtitle of host publicationProceedings of a meeting held 2-4 July 2018, Guiyang, China
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Pages467-472
Number of pages6
ISBN (Electronic)9781538654163
ISBN (Print)9781538654170
DOIs
DateAccepted/In press - 10 May 2018
DateE-pub ahead of print - 12 Nov 2018
DatePublished (current) - Dec 2018

Abstract

In this paper, we propose a novel method to solve the optimal output-feedback control problem of continuous-time (CT) linear systems based on a data-driven based reinforcement learning (RL). An output-feedback Riccati equation is first derived by further tailoring its counterpart of state-feedback optimal control. Then, based on this modified Riccati equation, we further derive an output Lyapunov function, where only the system output rather than the unknown state is involved. This allows to obtain the optimal output-feedback gain based on the measured output only. Then, an online data-driven based policy iteration is suggested to obtain the feedback gain K and matrix P. Finally, a simulation example is given to prove the effectiveness of the proposed algorithm.

    Research areas

  • Optimal control, Output-feedback control, Data-driven, Policy iteration, Riccati equation

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at https://ieeexplore.ieee.org/document/8529962. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 503 KB, PDF document

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