Instant MPC for Linear Systems and Dissipativity-Based Stability Analysis

Keisuke Yoshida, Masaki Inoue, Takeshi Hatanaka

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


This letter is devoted to the concept of instant model predictive control (iMPC) for linear systems. An optimization problem is formulated to express the finite-time constrained optimal regulation control, like conventional Model predictive control (MPC). Then, iMPC determines the control action based on the optimization process rather than the optimizer, unlike MPC. The iMPC concept is realized by a continuous-time dynamic algorithm of solving the optimization; the primal-dual gradient algorithm is directly implemented as a dynamic controller. On the basis of the dissipativity evaluation of the algorithm, the stability of the control system is analyzed. Finally, a numerical experiment is performed in order to demonstrate that iMPC emulates MPC and to show its less computational burden.

Original languageEnglish
Article number8718794
Pages (from-to)811-816
Number of pages6
JournalIEEE Control Systems Letters
Issue number4
Publication statusPublished - 2019 Oct


  • Model predictive control
  • dissipativity
  • optimization embedded control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization


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