Adaptive Data-Driven Control for Linear Time Varying Systems
In this paper, we propose an adaptive data-driven control approach for linear time varying systems, affected by bounded measurement noise. The plant to be controlled is assumed to be unknown, and no information in regard to its time varying behaviour is exploited. First, using set-membership identif...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/9/8/167 |
| _version_ | 1797523239309672448 |
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| author | Talal Abdalla |
| author_facet | Talal Abdalla |
| author_sort | Talal Abdalla |
| collection | DOAJ |
| description | In this paper, we propose an adaptive data-driven control approach for linear time varying systems, affected by bounded measurement noise. The plant to be controlled is assumed to be unknown, and no information in regard to its time varying behaviour is exploited. First, using set-membership identification techniques, we formulate the controller design problem through a model-matching scheme, i.e., designing a controller such that the closed-loop behaviour matches that of a given reference model. The problem is then reformulated as to derive a controller that corresponds to the minimum variation bounding its parameters. Finally, a convex relaxation approach is proposed to solve the formulated controller design problem by means of linear programming. The effectiveness of the proposed scheme is demonstrated by means of two simulation examples. |
| first_indexed | 2024-03-10T08:39:36Z |
| format | Article |
| id | doaj.art-fa8db96202d94883904a2fb06231435b |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T08:39:36Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-fa8db96202d94883904a2fb06231435b2023-11-22T08:24:39ZengMDPI AGMachines2075-17022021-08-019816710.3390/machines9080167Adaptive Data-Driven Control for Linear Time Varying SystemsTalal Abdalla0Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, ItalyIn this paper, we propose an adaptive data-driven control approach for linear time varying systems, affected by bounded measurement noise. The plant to be controlled is assumed to be unknown, and no information in regard to its time varying behaviour is exploited. First, using set-membership identification techniques, we formulate the controller design problem through a model-matching scheme, i.e., designing a controller such that the closed-loop behaviour matches that of a given reference model. The problem is then reformulated as to derive a controller that corresponds to the minimum variation bounding its parameters. Finally, a convex relaxation approach is proposed to solve the formulated controller design problem by means of linear programming. The effectiveness of the proposed scheme is demonstrated by means of two simulation examples.https://www.mdpi.com/2075-1702/9/8/167adaptive controlconvex relaxationlinear programmingLTV systemsmodel-matchingset-membership |
| spellingShingle | Talal Abdalla Adaptive Data-Driven Control for Linear Time Varying Systems Machines adaptive control convex relaxation linear programming LTV systems model-matching set-membership |
| title | Adaptive Data-Driven Control for Linear Time Varying Systems |
| title_full | Adaptive Data-Driven Control for Linear Time Varying Systems |
| title_fullStr | Adaptive Data-Driven Control for Linear Time Varying Systems |
| title_full_unstemmed | Adaptive Data-Driven Control for Linear Time Varying Systems |
| title_short | Adaptive Data-Driven Control for Linear Time Varying Systems |
| title_sort | adaptive data driven control for linear time varying systems |
| topic | adaptive control convex relaxation linear programming LTV systems model-matching set-membership |
| url | https://www.mdpi.com/2075-1702/9/8/167 |
| work_keys_str_mv | AT talalabdalla adaptivedatadrivencontrolforlineartimevaryingsystems |