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

Zican Li, Tao Wu, Jing Na, Jun Zhao, Guanbin Gao, Guido Herrmann

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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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.
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
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages467-472
Number of pages6
ISBN (Electronic)9781538654163
ISBN (Print)9781538654170
DOIs
Publication statusPublished - Dec 2018

Keywords

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

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