TY - JOUR
T1 - Instant MPC for Linear Systems and Dissipativity-Based Stability Analysis
AU - Yoshida, Keisuke
AU - Inoue, Masaki
AU - Hatanaka, Takeshi
N1 - Funding Information:
Manuscript received March 1, 2019; revised April 29, 2019; accepted May 10, 2019. Date of publication May 21, 2019; date of current version June 6, 2019. This work was supported in part by the Grant-in-Aid for Young Scientists (B) through Japan Society for the Promotion of Science under Grant 17K14704, and in part by Mirai-Program through Japan Science and Technology Agency under Grant 18077648. Recommended by Senior Editor M. Guay. (Keisuke Yoshida and Masaki Inoue contributed equally to this work.) (Corresponding author: Masaki Inoue.) K. Yoshida and M. Inoue are with the Department of Applied Physics and Physico-Informatics, Keio University, Kanagawa 223-8521, Japan (e-mail: minoue@appi.keio.ac.jp).
Publisher Copyright:
© 2017 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
KW - Model predictive control
KW - dissipativity
KW - optimization embedded control
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U2 - 10.1109/LCSYS.2019.2918095
DO - 10.1109/LCSYS.2019.2918095
M3 - Article
AN - SCOPUS:85067092141
SN - 2475-1456
VL - 3
SP - 811
EP - 816
JO - IEEE Control Systems Letters
JF - IEEE Control Systems Letters
IS - 4
M1 - 8718794
ER -