Game Theoretical Reinforcement Learning for Robust H∞ Tracking Control of Discrete-Time Linear Systems with Unknown Dynamics

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

Robust H ∞ control has been widely studied to improve control performance for industrial process control systems against disturbances. However, most of existing robust H ∞ control are model-based, and their deployment in some industrial facilities may greatly increase the installation and maintenance costs due to requiring system identification. Towards this end, a model-free robust H ∞ tracking control scheme is developed based on game theoretical reinforcement learning (RL) for discrete-time linear systems with unknown dynamics. The normal robust H ∞ tracking control problem is first modeled as a two-player zero-sum game with the controller and disturbance as the two players. A model-based solution by solving game discrete-time differential Riccati equation (GDARE) is introduced to show the solvability of the robust H ∞ tracking control problem, and then a novel off-policy RL algorithm is developed to replace the GDARE method for model-free robust H ∞ tracking control of the discrete-time linear systems with unknown dynamics. Stability of the learning algorithm is analyzed. Finally, a simulation study upon a de-oiling hydrocyclone system is conducted to demonstrate the effectiveness of the proposed algorithm.
Original languageEnglish
Title of host publicationProceedings of 2021 4th International Conference on Intelligent Autonomous Systems (ICoIAS)
Number of pages6
PublisherIEEE
Publication dateMay 2021
Pages290-295
ISBN (Print)978-1-6654-4196-4
ISBN (Electronic)978-1-6654-4195-7
DOIs
Publication statusPublished - May 2021
Event2021 4th International Conference on Intelligent Autonomous Systems - Wuhan, China
Duration: 14 May 202116 May 2021
http://www.icias.org/

Conference

Conference2021 4th International Conference on Intelligent Autonomous Systems
Country/TerritoryChina
CityWuhan
Period14/05/202116/05/2021
Internet address

Keywords

  • H control
  • de-oiling hydrocyclone system
  • game theory
  • reinforcement learning
  • zero-sum game

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