Decentralized Model Predictive Control via Dual Decomposition

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  • Wakasa Yuji
    Graduate School of Science and Engineering, Yamaguchi University
  • Arakawa Mizue
    Graduate School of Science and Engineering, Yamaguchi University
  • Tanaka Kanya
    Graduate School of Science and Engineering, Yamaguchi University
  • Akashi Takuya
    Graduate School of Science and Engineering, Yamaguchi University

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Other Title
  • 双対分解による分散型モデル予測制御
  • ソウツイ ブンカイ ニ ヨル ブンサンガタ モデル ヨソク セイギョ

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Abstract

This paper proposes a decentralized model predictive control method based on a dual decomposition technique. A model predictive control problem for a system with multiple subsystems is formulated as a convex optimization problem. In particular, we deal with the case where the control outputs of the subsystems have coupling constraints represented by linear equalities. A dual decomposition technique is applied to this problem in order to derive the dual problem with decoupled equality constraints. A projected subgradient method is used to solve the dual problem, which leads to a decentralized algorithm. In the algorithm, a small-scale problem is solved at each subsystem, and information exchange is performed in each group consisting of some subsystems. Also, it is shown that the computational complexity in the decentralized algorithm is reduced if the dynamics of the subsystems are all the same.<br>

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