A new weight selection algorithm using SPSA for model predictive control

Model Predictive Control (MPC) is one of the control methods for discrete time systems. The optimal input is calculated by using Linear Quadratic Regulator (LQR). The weight matrices in the evaluation function for LQR are determined by a designer with professional experience and a trial & er...

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Autores principales:	Sungmin CHO, Masatsugu OTSUKI, Takashi KUBOTA
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2019
Materias:	model predictive control weight selection simultaneous perturbation stochastic approximation overshoot settling time Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/f3839cfef97f43a7ae1db1a64b38d548
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spelling	oai:doaj.org-article:f3839cfef97f43a7ae1db1a64b38d5482021-11-29T05:47:05ZA new weight selection algorithm using SPSA for model predictive control2187-974510.1299/mej.19-00053https://doaj.org/article/f3839cfef97f43a7ae1db1a64b38d5482019-09-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/6/5/6_19-00053/_pdf/-char/enhttps://doaj.org/toc/2187-9745Model Predictive Control (MPC) is one of the control methods for discrete time systems. The optimal input is calculated by using Linear Quadratic Regulator (LQR). The weight matrices in the evaluation function for LQR are determined by a designer with professional experience and a trial & error approach. Therefore, even if the same system is targeted, the performance can differ depending on the designer. This paper proposes a new weight selection algorithm using Simultaneous Perturbation Stochastic Approximation (SPSA) for MPC. A new evaluation function is proposed for the selection algorithm. Numerical values of the overshoot and the settling time are directly applied as the user’s requirements in this evaluation function. The optimal weight matrices numerically satisfying those requirements can be selected by the proposed algorithm. Simulation study of a zero momentum spacecraft shows that the proposed method is effective for the weight selection with consideration of performance.Sungmin CHOMasatsugu OTSUKITakashi KUBOTAThe Japan Society of Mechanical Engineersarticlemodel predictive controlweight selectionsimultaneous perturbation stochastic approximationovershootsettling timeMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 6, Iss 5, Pp 19-00053-19-00053 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	model predictive control weight selection simultaneous perturbation stochastic approximation overshoot settling time Mechanical engineering and machinery TJ1-1570
spellingShingle	model predictive control weight selection simultaneous perturbation stochastic approximation overshoot settling time Mechanical engineering and machinery TJ1-1570 Sungmin CHO Masatsugu OTSUKI Takashi KUBOTA A new weight selection algorithm using SPSA for model predictive control
description	Model Predictive Control (MPC) is one of the control methods for discrete time systems. The optimal input is calculated by using Linear Quadratic Regulator (LQR). The weight matrices in the evaluation function for LQR are determined by a designer with professional experience and a trial & error approach. Therefore, even if the same system is targeted, the performance can differ depending on the designer. This paper proposes a new weight selection algorithm using Simultaneous Perturbation Stochastic Approximation (SPSA) for MPC. A new evaluation function is proposed for the selection algorithm. Numerical values of the overshoot and the settling time are directly applied as the user’s requirements in this evaluation function. The optimal weight matrices numerically satisfying those requirements can be selected by the proposed algorithm. Simulation study of a zero momentum spacecraft shows that the proposed method is effective for the weight selection with consideration of performance.
format	article
author	Sungmin CHO Masatsugu OTSUKI Takashi KUBOTA
author_facet	Sungmin CHO Masatsugu OTSUKI Takashi KUBOTA
author_sort	Sungmin CHO
title	A new weight selection algorithm using SPSA for model predictive control
title_short	A new weight selection algorithm using SPSA for model predictive control
title_full	A new weight selection algorithm using SPSA for model predictive control
title_fullStr	A new weight selection algorithm using SPSA for model predictive control
title_full_unstemmed	A new weight selection algorithm using SPSA for model predictive control
title_sort	new weight selection algorithm using spsa for model predictive control
publisher	The Japan Society of Mechanical Engineers
publishDate	2019
url	https://doaj.org/article/f3839cfef97f43a7ae1db1a64b38d548
work_keys_str_mv	AT sungmincho anewweightselectionalgorithmusingspsaformodelpredictivecontrol AT masatsuguotsuki anewweightselectionalgorithmusingspsaformodelpredictivecontrol AT takashikubota anewweightselectionalgorithmusingspsaformodelpredictivecontrol AT sungmincho newweightselectionalgorithmusingspsaformodelpredictivecontrol AT masatsuguotsuki newweightselectionalgorithmusingspsaformodelpredictivecontrol AT takashikubota newweightselectionalgorithmusingspsaformodelpredictivecontrol
_version_	1718407622261997568

A new weight selection algorithm using SPSA for model predictive control

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