Limited-complexity controller tuning: A set membership data-driven approach
Data-driven tuning is an alternative to model-based controller design where controllers are directly identified from data, avoiding a plant identification step. In this paper, an approach to tune limited-complexity controllers from data for linear systems is proposed. The controller is parametrized...
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| Vydáno v: | European journal of control Ročník 58; s. 82 - 89 |
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| Jazyk: | angličtina |
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01.03.2021
Elsevier Limited |
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| ISSN: | 0947-3580, 1435-5671 |
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| Abstract | Data-driven tuning is an alternative to model-based controller design where controllers are directly identified from data, avoiding a plant identification step. In this paper, an approach to tune limited-complexity controllers from data for linear systems is proposed. The controller is parametrized as a linear combination of a large set of basis functions and the proposed algorithm allows to select a sparse subset of bases, guaranteeing a bounded approximation error. A feasibility condition allows to adjust the trade-off between accuracy and sparsity. The controller design is performed by solving a set of linear programming problems, allowing to handle large data-sets. The proposed strategy is evaluated by means of a Monte-Carlo simulation experiment on a flexible transmission benchmark model. Results show that the proposed solution offers similar results than previous approaches for large data-sets, requiring less adjustable parameters. However, for reduced data-sets, the presented algorithm shows better performance than the compared approaches. |
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| AbstractList | Data-driven tuning is an alternative to model-based controller design where controllers are directly identified from data, avoiding a plant identification step. In this paper, an approach to tune limited-complexity controllers from data for linear systems is proposed. The controller is parametrized as a linear combination of a large set of basis functions and the proposed algorithm allows to select a sparse subset of bases, guaranteeing a bounded approximation error. A feasibility condition allows to adjust the trade-off between accuracy and sparsity. The controller design is performed by solving a set of linear programming problems, allowing to handle large data-sets. The proposed strategy is evaluated by means of a Monte-Carlo simulation experiment on a flexible transmission benchmark model. Results show that the proposed solution offers similar results than previous approaches for large data-sets, requiring less adjustable parameters. However, for reduced data-sets, the presented algorithm shows better performance than the compared approaches. |
| Author | Valderrama, Freddy Ruiz, Fredy |
| Author_xml | – sequence: 1 givenname: Freddy surname: Valderrama fullname: Valderrama, Freddy email: f.valderrama@javeriana.edu.co organization: Escuela De Ciencias Básicas Tecnología e Ingeniería, UNAD, Bogotá, Colombia – sequence: 2 givenname: Fredy surname: Ruiz fullname: Ruiz, Fredy email: fredy.ruiz@polimi.it organization: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy |
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| SubjectTerms | Algorithms Basis functions Complexity Control systems design Controllers Datasets Experiments Identification for control Linear programming Linear systems Methods Model/Controller reduction Optimization Tuning Uncertain systems |
| Title | Limited-complexity controller tuning: A set membership data-driven approach |
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