Distributed adaptive dynamic programming for data-driven optimal control

Wentao Tang, Prodromos Daoutidis

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Adaptive dynamic programming (ADP), as an important optimal control technique, can be exploited in the setting of data-driven control based on an approximate regression-based solution of the Hamilton–Jacobi–Bellman (HJB) equations. Distributed optimization algorithms, which are extensively studied in statistics and machine learning, have not yet been applied to the solution of data-driven ADP problems. In this work, we identify the data-driven ADP problem as a consensus optimization problem for nonlinear affine systems, and apply the alternating direction method of multipliers (ADMM) and its accelerated variants for its solution. For the input-constrained optimal control problem, we define a combined optimal primal–dual function to develop a data-based version of the input-constrained HJB equation.

Original languageEnglish (US)
Pages (from-to)36-43
Number of pages8
JournalSystems and Control Letters
Volume120
DOIs
StatePublished - Oct 2018

Bibliographical note

Funding Information:
This work was supported by National Science Foundation, USA (NSF-CBET).

Publisher Copyright:
© 2018 Elsevier B.V.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Adaptive dynamic programming
  • Data-driven control
  • Distributed optimization
  • Process control

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