Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach
In the Multiple Model Control (MMC) strategies, a bank of simple local models is used to describe the behavior of complex systems with vast operation space. In this approach, the system operation space is divided into several subspaces, and in each subspace, a simple local model is assigned to descr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024020176 |
| _version_ | 1797267715224764416 |
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| author | Mohammad Fathi Hossein Bolandi |
| author_facet | Mohammad Fathi Hossein Bolandi |
| author_sort | Mohammad Fathi |
| collection | DOAJ |
| description | In the Multiple Model Control (MMC) strategies, a bank of simple local models is used to describe the behavior of complex systems with vast operation space. In this approach, the system operation space is divided into several subspaces, and in each subspace, a simple local model is assigned to describe the system behavior. This study addresses the two main challenges in this field which involve determining the optimal number of required local models to form the model bank and identifying the optimal distribution of the local models across the system operation space. Providing appropriate answers to these questions directly affects the performance of the MMC system. In this paper, GA-based automatic clustering method is suggested to form an optimal model bank. In this regard, an appropriate mapping is established between the concepts of MMC and automatic clustering, and a novel unsupervised algorithm is designed to determine the optimal model bank. Unlike the existing methods in the literature, the proposed method can form the global optimal model bank without entrapment into local optima regardless of the initial conditions of the used search algorithm. In this paper, the formation of the optimal model bank using the proposed method is investigated by considering the spacecraft attitude dynamics as a complex, MIMO, non-linear case study and its satisfactory and promising performance is demonstrated. |
| first_indexed | 2024-03-08T00:00:25Z |
| format | Article |
| id | doaj.art-0d956fe0de98421eb699245eb2336b2f |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-25T01:20:59Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-0d956fe0de98421eb699245eb2336b2f2024-03-09T09:26:47ZengElsevierHeliyon2405-84402024-02-01104e25986Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approachMohammad Fathi0Hossein Bolandi1Electrical Engineering Department, Iran University of Science and Technology, Narmak, Tehran, IranCorresponding author.; Electrical Engineering Department, Iran University of Science and Technology, Narmak, Tehran, IranIn the Multiple Model Control (MMC) strategies, a bank of simple local models is used to describe the behavior of complex systems with vast operation space. In this approach, the system operation space is divided into several subspaces, and in each subspace, a simple local model is assigned to describe the system behavior. This study addresses the two main challenges in this field which involve determining the optimal number of required local models to form the model bank and identifying the optimal distribution of the local models across the system operation space. Providing appropriate answers to these questions directly affects the performance of the MMC system. In this paper, GA-based automatic clustering method is suggested to form an optimal model bank. In this regard, an appropriate mapping is established between the concepts of MMC and automatic clustering, and a novel unsupervised algorithm is designed to determine the optimal model bank. Unlike the existing methods in the literature, the proposed method can form the global optimal model bank without entrapment into local optima regardless of the initial conditions of the used search algorithm. In this paper, the formation of the optimal model bank using the proposed method is investigated by considering the spacecraft attitude dynamics as a complex, MIMO, non-linear case study and its satisfactory and promising performance is demonstrated.http://www.sciencedirect.com/science/article/pii/S2405844024020176Multiple model controlAutomatic clusteringGenetic algorithmOptimal model bank |
| spellingShingle | Mohammad Fathi Hossein Bolandi Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach Heliyon Multiple model control Automatic clustering Genetic algorithm Optimal model bank |
| title | Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach |
| title_full | Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach |
| title_fullStr | Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach |
| title_full_unstemmed | Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach |
| title_short | Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach |
| title_sort | unsupervised optimal model bank for multiple model control systems genetic based automatic clustering approach |
| topic | Multiple model control Automatic clustering Genetic algorithm Optimal model bank |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024020176 |
| work_keys_str_mv | AT mohammadfathi unsupervisedoptimalmodelbankformultiplemodelcontrolsystemsgeneticbasedautomaticclusteringapproach AT hosseinbolandi unsupervisedoptimalmodelbankformultiplemodelcontrolsystemsgeneticbasedautomaticclusteringapproach |