Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model
In this paper, we present a data-driven tuning method for model-free control based on an ultra-local model (MFC-ULM), which is also called intelligent proportional-integral-derivative control. In industries, the control design must be easy, and it is important that the control law can be applied to...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9815258/ |
| _version_ | 1811294227414384640 |
|---|---|
| author | Shuichi Yahagi Itsuro Kajiwara |
| author_facet | Shuichi Yahagi Itsuro Kajiwara |
| author_sort | Shuichi Yahagi |
| collection | DOAJ |
| description | In this paper, we present a data-driven tuning method for model-free control based on an ultra-local model (MFC-ULM), which is also called intelligent proportional-integral-derivative control. In industries, the control design must be easy, and it is important that the control law can be applied to nonlinear systems. The MFC-ULM has most of these features. However, in practice, trial-and-error tuning of MFC-ULM design parameters is necessary. To address this problem, we adopt a data-driven tuning approach. In the proposed method, the MFC-ULM design parameters can be tuned from single-experiment data without requiring system identification, and optimal parameters for the MFC-ULM are obtained using the least-squares method. Additionally, we adopt <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-norm regularization to avoid overlearning. The effectiveness of this method was examined using simulations of two nonlinear systems. The results revealed that the MFC-ULM design parameters can be obtained directly without knowing the characteristics of the controlled object. |
| first_indexed | 2024-04-13T05:14:19Z |
| format | Article |
| id | doaj.art-00f8ae853478412bac32e134a505edb4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T05:14:19Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-00f8ae853478412bac32e134a505edb42022-12-22T03:00:56ZengIEEEIEEE Access2169-35362022-01-0110727737278410.1109/ACCESS.2022.31887139815258Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local ModelShuichi Yahagi0https://orcid.org/0000-0002-8992-815XItsuro Kajiwara1https://orcid.org/0000-0003-2651-72896th Research Department, ISUZU Advanced Engineering Center Ltd., Fujisawa-shi, Kanagawa, JapanDivision of Mechanical and Aerospace Engineering, Hokkaido University, Sapporo, Hokkaido, JapanIn this paper, we present a data-driven tuning method for model-free control based on an ultra-local model (MFC-ULM), which is also called intelligent proportional-integral-derivative control. In industries, the control design must be easy, and it is important that the control law can be applied to nonlinear systems. The MFC-ULM has most of these features. However, in practice, trial-and-error tuning of MFC-ULM design parameters is necessary. To address this problem, we adopt a data-driven tuning approach. In the proposed method, the MFC-ULM design parameters can be tuned from single-experiment data without requiring system identification, and optimal parameters for the MFC-ULM are obtained using the least-squares method. Additionally, we adopt <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-norm regularization to avoid overlearning. The effectiveness of this method was examined using simulations of two nonlinear systems. The results revealed that the MFC-ULM design parameters can be obtained directly without knowing the characteristics of the controlled object.https://ieeexplore.ieee.org/document/9815258/Data-driven controlmodel-free controlparameter tuningPID control |
| spellingShingle | Shuichi Yahagi Itsuro Kajiwara Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model IEEE Access Data-driven control model-free control parameter tuning PID control |
| title | Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model |
| title_full | Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model |
| title_fullStr | Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model |
| title_full_unstemmed | Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model |
| title_short | Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model |
| title_sort | non iterative data driven tuning of model free control based on an ultra local model |
| topic | Data-driven control model-free control parameter tuning PID control |
| url | https://ieeexplore.ieee.org/document/9815258/ |
| work_keys_str_mv | AT shuichiyahagi noniterativedatadriventuningofmodelfreecontrolbasedonanultralocalmodel AT itsurokajiwara noniterativedatadriventuningofmodelfreecontrolbasedonanultralocalmodel |