Adaptive Optimal Control of Linear Periodic Systems: An Off-Policy Value Iteration Approach

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Abstract

This article studies the infinite-horizon adaptive optimal control of continuous-time linear periodic (CTLP) systems. A novel value iteration (VI) based off-policy adaptive dynamic programming (ADP) algorithm is proposed for a general class of CTLP systems, so that approximate optimal solutions can be obtained directly from the collected data, without the exact knowledge of system dynamics. Under mild conditions, the proofs on uniform convergence of the proposed algorithm to the optimal solutions are given for both the model-based and model-free cases. The VI-based ADP algorithm is able to find suboptimal controllers without assuming the knowledge of an initial stabilizing controller. Application to the optimal control of a triple inverted pendulum subjected to a periodically varying load demonstrates the feasibility and effectiveness of the proposed method.

Original languageEnglish (US)
Article number9069169
Pages (from-to)888-894
Number of pages7
JournalIEEE Transactions on Automatic Control
Volume66
Issue number2
DOIs
StatePublished - Feb 2021

Keywords

  • Adaptive dynamic programming
  • linear periodic systems
  • optimal control
  • value iteration

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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