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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Bibliographic Details
Published in:European journal of control Vol. 58; pp. 82 - 89
Main Authors: Valderrama, Freddy, Ruiz, Fredy
Format: Journal Article
Language:English
Published: Philadelphia Elsevier Ltd 01.03.2021
Elsevier Limited
Subjects:
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
Linear systems
Uncertain systems
Identification for control
Model/Controller reduction
ISSN:0947-3580, 1435-5671
Online Access:Get full text
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Summary: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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ISSN:0947-3580
1435-5671
DOI:10.1016/j.ejcon.2020.07.002