Self-Triggered Model Predictive Control Using Optimization with Prediction Horizon One

Self-Triggered Model Predictive Control Using Optimization with Prediction Horizon One

Self-triggered control is a control method that the control input and the sampling period are computed simultaneously in sampled-data control systems and is extensively studied in the field of control theory of networked systems and cyber-physical systems. In this paper, a new approach for self-trig...

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Main Authors: Koichi Kobayashi, Kunihiko Hiraishi
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2013/916040
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spelling doaj-983ec33e2f874763b21b81d7439788ec2020-11-24T23:00:31ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/916040916040Self-Triggered Model Predictive Control Using Optimization with Prediction Horizon OneKoichi Kobayashi0Kunihiko Hiraishi1School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1292, JapanSchool of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1292, JapanSelf-triggered control is a control method that the control input and the sampling period are computed simultaneously in sampled-data control systems and is extensively studied in the field of control theory of networked systems and cyber-physical systems. In this paper, a new approach for self-triggered control is proposed from the viewpoint of model predictive control (MPC). First, the difficulty of self-triggered MPC is explained. To overcome this difficulty, two problems, that is, (i) the one-step input-constrained problem and (ii) the N-step input-constrained problem are newly formulated. By repeatedly solving either problem in each sampling period, the control input and the sampling period can be obtained, that is, self-triggered MPC can be realized. Next, an iterative solution method for the latter problem and an approximate solution method for the former problem are proposed. Finally, the effectiveness of the proposed approach is shown by numerical examples.http://dx.doi.org/10.1155/2013/916040
collection DOAJ
language English
format Article
sources DOAJ
author Koichi Kobayashi
Kunihiko Hiraishi
spellingShingle Koichi Kobayashi
Kunihiko Hiraishi
Self-Triggered Model Predictive Control Using Optimization with Prediction Horizon One
Mathematical Problems in Engineering
author_facet Koichi Kobayashi
Kunihiko Hiraishi
author_sort Koichi Kobayashi
title Self-Triggered Model Predictive Control Using Optimization with Prediction Horizon One
title_short Self-Triggered Model Predictive Control Using Optimization with Prediction Horizon One
title_full Self-Triggered Model Predictive Control Using Optimization with Prediction Horizon One
title_fullStr Self-Triggered Model Predictive Control Using Optimization with Prediction Horizon One
title_full_unstemmed Self-Triggered Model Predictive Control Using Optimization with Prediction Horizon One
title_sort self-triggered model predictive control using optimization with prediction horizon one
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2013-01-01
description Self-triggered control is a control method that the control input and the sampling period are computed simultaneously in sampled-data control systems and is extensively studied in the field of control theory of networked systems and cyber-physical systems. In this paper, a new approach for self-triggered control is proposed from the viewpoint of model predictive control (MPC). First, the difficulty of self-triggered MPC is explained. To overcome this difficulty, two problems, that is, (i) the one-step input-constrained problem and (ii) the N-step input-constrained problem are newly formulated. By repeatedly solving either problem in each sampling period, the control input and the sampling period can be obtained, that is, self-triggered MPC can be realized. Next, an iterative solution method for the latter problem and an approximate solution method for the former problem are proposed. Finally, the effectiveness of the proposed approach is shown by numerical examples.
url http://dx.doi.org/10.1155/2013/916040
work_keys_str_mv AT koichikobayashi selftriggeredmodelpredictivecontrolusingoptimizationwithpredictionhorizonone
AT kunihikohiraishi selftriggeredmodelpredictivecontrolusingoptimizationwithpredictionhorizonone
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