A Consideration on Approximation Methods of Model Matching Error for Data-Driven Controller Tuning
This paper proposes two kinds of data-driven controller tuning. The proposed methods are derived from the approximated model matching errors expressed by the filtered ideal model matching error. The main contribution of the paper is to find out specific filters that characterize the proposed data-dr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-11-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.9746/jcmsi.13.291 |
| Summary: | This paper proposes two kinds of data-driven controller tuning. The proposed methods are derived from the approximated model matching errors expressed by the filtered ideal model matching error. The main contribution of the paper is to find out specific filters that characterize the proposed data-driven methods. Similar filters are also presented in the existing virtual reference feedback tuning and fictitious reference iterative tuning as well. The comparison among the filters for the approximations clarifies the relation among them as well as the novelty of the proposed approach. The paper shows two numerical examples: one is a flexible transmission system and the other is a plant with an unstable zero. The numerical examples show the superiority of the proposed method to existing methods. |
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| ISSN: | 1884-9970 |