Adaptive dynamic programming for finite-horizon optimal control of linear time-varying discrete-time systems

Bo Pang, Tao Bian, Zhong Ping Jiang

Research output: Contribution to journalArticle

Abstract

This paper studies data-driven learning-based methods for the finite-horizon optimal control of linear time-varying discrete-time systems. First, a novel finite-horizon Policy Iteration (PI) method for linear time-varying discrete-time systems is presented. Its connections with existing infinite-horizon PI methods are discussed. Then, both data-driven off-policy PI and Value Iteration (VI) algorithms are derived to find approximate optimal controllers when the system dynamics is completely unknown. Under mild conditions, the proposed data-driven off-policy algorithms converge to the optimal solution. Finally, the effectiveness and feasibility of the developed methods are validated by a practical example of spacecraft attitude control.

Original languageEnglish (US)
Pages (from-to)73-84
Number of pages12
JournalControl Theory and Technology
Volume17
Issue number1
DOIs
StatePublished - Feb 1 2019

Keywords

  • Optimal control
  • adaptive dynamic programming
  • policy iteration (PI)
  • time-varying system
  • value iteration (VI)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Aerospace Engineering
  • Control and Optimization

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