Adaptive multi-model predictive control with optimal model bank formation: Consideration of local models uncertainty and stability
The Multiple Model Control (MMC) structure comprises three main components: the model bank, controller bank, and supervisor algorithm. Precise design of these components is crucial for achieving high control performance within the MMC framework, albeit this effort is not without its challenges. Thes...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024162846 |
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| author | Mohammad Fathi Hossein Bolandi Bahman Ghorbani Vaghei Saeid Ebadolahi |
| author_facet | Mohammad Fathi Hossein Bolandi Bahman Ghorbani Vaghei Saeid Ebadolahi |
| author_sort | Mohammad Fathi |
| collection | DOAJ |
| description | The Multiple Model Control (MMC) structure comprises three main components: the model bank, controller bank, and supervisor algorithm. Precise design of these components is crucial for achieving high control performance within the MMC framework, albeit this effort is not without its challenges. These challenges involve optimizing the model and controller banks ensuring system stability when dealing with uncertainties in the local models and enabling smooth switching between model-controller pairs. This paper addresses these challenges by presenting a comprehensive approach. Firstly, the optimal model bank is designed using an automatic clustering approach. Then, the design of the adaptive multi-model predictive controller bank and a supervisor algorithm capable of performing soft switching are discussed. The proposed control system exhibits the capability to ensure closed-loop system stability within each individual subspace, as well as during the transitioning between distinct subspaces. This stability is preserved even in the face of inherent uncertainties associated with the local models comprising the model bank. To evaluate and validate the performance of the proposed control system, it is applied to a satellite attitude control system. The results confirm the effectiveness and performance of the control system. The proposed control system holds promise for controlling highly nonlinear, complex, or switched systems, ensuring closed-loop system stability, and achieving high control performance. |
| format | Article |
| id | doaj-art-b5f63a0a442949608cd90dd5ffcdb1b6 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-b5f63a0a442949608cd90dd5ffcdb1b62024-11-30T07:12:15ZengElsevierHeliyon2405-84402024-11-011022e40253Adaptive multi-model predictive control with optimal model bank formation: Consideration of local models uncertainty and stabilityMohammad Fathi0Hossein Bolandi1Bahman Ghorbani Vaghei2Saeid Ebadolahi3Electrical Engineering Department, Iran University of Science and Technology, Narmak, Tehran, IranElectrical Engineering Department, Iran University of Science and Technology, Narmak, Tehran, Iran; Corresponding author.School of Railway Engineering, Iran University of Science and Technology, Narmak, Tehran, IranElectrical Engineering Department, Iran University of Science and Technology, Narmak, Tehran, IranThe Multiple Model Control (MMC) structure comprises three main components: the model bank, controller bank, and supervisor algorithm. Precise design of these components is crucial for achieving high control performance within the MMC framework, albeit this effort is not without its challenges. These challenges involve optimizing the model and controller banks ensuring system stability when dealing with uncertainties in the local models and enabling smooth switching between model-controller pairs. This paper addresses these challenges by presenting a comprehensive approach. Firstly, the optimal model bank is designed using an automatic clustering approach. Then, the design of the adaptive multi-model predictive controller bank and a supervisor algorithm capable of performing soft switching are discussed. The proposed control system exhibits the capability to ensure closed-loop system stability within each individual subspace, as well as during the transitioning between distinct subspaces. This stability is preserved even in the face of inherent uncertainties associated with the local models comprising the model bank. To evaluate and validate the performance of the proposed control system, it is applied to a satellite attitude control system. The results confirm the effectiveness and performance of the control system. The proposed control system holds promise for controlling highly nonlinear, complex, or switched systems, ensuring closed-loop system stability, and achieving high control performance.http://www.sciencedirect.com/science/article/pii/S2405844024162846Multi-model predictive control systemAutomatic clusteringGenetic algorithmOptimal model bankSoft switchingStability |
| spellingShingle | Mohammad Fathi Hossein Bolandi Bahman Ghorbani Vaghei Saeid Ebadolahi Adaptive multi-model predictive control with optimal model bank formation: Consideration of local models uncertainty and stability Heliyon Multi-model predictive control system Automatic clustering Genetic algorithm Optimal model bank Soft switching Stability |
| title | Adaptive multi-model predictive control with optimal model bank formation: Consideration of local models uncertainty and stability |
| title_full | Adaptive multi-model predictive control with optimal model bank formation: Consideration of local models uncertainty and stability |
| title_fullStr | Adaptive multi-model predictive control with optimal model bank formation: Consideration of local models uncertainty and stability |
| title_full_unstemmed | Adaptive multi-model predictive control with optimal model bank formation: Consideration of local models uncertainty and stability |
| title_short | Adaptive multi-model predictive control with optimal model bank formation: Consideration of local models uncertainty and stability |
| title_sort | adaptive multi model predictive control with optimal model bank formation consideration of local models uncertainty and stability |
| topic | Multi-model predictive control system Automatic clustering Genetic algorithm Optimal model bank Soft switching Stability |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024162846 |
| work_keys_str_mv | AT mohammadfathi adaptivemultimodelpredictivecontrolwithoptimalmodelbankformationconsiderationoflocalmodelsuncertaintyandstability AT hosseinbolandi adaptivemultimodelpredictivecontrolwithoptimalmodelbankformationconsiderationoflocalmodelsuncertaintyandstability AT bahmanghorbanivaghei adaptivemultimodelpredictivecontrolwithoptimalmodelbankformationconsiderationoflocalmodelsuncertaintyandstability AT saeidebadolahi adaptivemultimodelpredictivecontrolwithoptimalmodelbankformationconsiderationoflocalmodelsuncertaintyandstability |