Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller
This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carrie...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10328598/ |
| _version_ | 1797401035655872512 |
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| author | Hyuntae Kim Hamin Chang |
| author_facet | Hyuntae Kim Hamin Chang |
| author_sort | Hyuntae Kim |
| collection | DOAJ |
| description | This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the system’s state/input measurements. When its results are provided, we present conditions that guarantee a certain level of reference tracking performance, regardless of the identification method employed for the inverse model. Specifically, when Gaussian process regression (GPR) is used as the identification method, we propose sufficient conditions for the required data by applying some lemmas related to identification errors to the aforementioned conditions, ensuring that the Model Reference-GPR (MR-GPR) controller can guarantee a certain level of reference tracking performance. Finally, an example is provided to demonstrate the effectiveness of the MR-GPR controller. |
| first_indexed | 2024-03-09T02:04:03Z |
| format | Article |
| id | doaj.art-b01a3650d7424867b23237ef9fb27447 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-09T02:04:03Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b01a3650d7424867b23237ef9fb274472023-12-08T00:06:06ZengIEEEIEEE Access2169-35362023-01-011113437413438110.1109/ACCESS.2023.333642110328598Model Reference Gaussian Process Regression: Data-Driven State Feedback ControllerHyuntae Kim0https://orcid.org/0000-0002-7645-9016Hamin Chang1Department of Electrical and Computer Engineering, Automation and Systems Research Institute (ASRI), Seoul National University, Seoul, South KoreaDepartment of Electrical and Computer Engineering, Automation and Systems Research Institute (ASRI), Seoul National University, Seoul, South KoreaThis paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the system’s state/input measurements. When its results are provided, we present conditions that guarantee a certain level of reference tracking performance, regardless of the identification method employed for the inverse model. Specifically, when Gaussian process regression (GPR) is used as the identification method, we propose sufficient conditions for the required data by applying some lemmas related to identification errors to the aforementioned conditions, ensuring that the Model Reference-GPR (MR-GPR) controller can guarantee a certain level of reference tracking performance. Finally, an example is provided to demonstrate the effectiveness of the MR-GPR controller.https://ieeexplore.ieee.org/document/10328598/Data-driven controlnonlinear systemstability |
| spellingShingle | Hyuntae Kim Hamin Chang Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller IEEE Access Data-driven control nonlinear system stability |
| title | Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller |
| title_full | Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller |
| title_fullStr | Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller |
| title_full_unstemmed | Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller |
| title_short | Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller |
| title_sort | model reference gaussian process regression data driven state feedback controller |
| topic | Data-driven control nonlinear system stability |
| url | https://ieeexplore.ieee.org/document/10328598/ |
| work_keys_str_mv | AT hyuntaekim modelreferencegaussianprocessregressiondatadrivenstatefeedbackcontroller AT haminchang modelreferencegaussianprocessregressiondatadrivenstatefeedbackcontroller |