Data-Driven Predictive Control Using Closed-Loop Data: An Instrumental Variable Approach
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866911779873882112 |
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| author | Wang, Yibo Qiu, Yiwen Sader, Malika Huang, Dexian Shang, Chao |
| author_facet | Wang, Yibo Qiu, Yiwen Sader, Malika Huang, Dexian Shang, Chao |
| contents | Current data-driven predictive control (DDPC) methods heavily rely on data collected in open-loop operation with elaborate design of inputs. However, due to safety or economic concerns, systems may have to be under feedback control, where only closed-loop data are available. In this context, it remains challenging to implement DDPC using closed-loop data. In this paper, we propose a new DDPC method using closed-loop data by means of instrumental variables (IVs). By drawing from closed-loop subspace identification, the use of two forms of IVs is suggested to address the closed-loop issues caused by feedback control and the correlation between inputs and noise. Furthermore, a new DDPC formulation with a novel IV-inspired regularizer is proposed, where a balance between control cost minimization and weighted least-squares data fitting can be made for improvement of control performance. Numerical examples and application to a simulated industrial furnace showcase the improved performance of the proposed DDPC based on closed-loop data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_05916 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data-Driven Predictive Control Using Closed-Loop Data: An Instrumental Variable Approach Wang, Yibo Qiu, Yiwen Sader, Malika Huang, Dexian Shang, Chao Optimization and Control Current data-driven predictive control (DDPC) methods heavily rely on data collected in open-loop operation with elaborate design of inputs. However, due to safety or economic concerns, systems may have to be under feedback control, where only closed-loop data are available. In this context, it remains challenging to implement DDPC using closed-loop data. In this paper, we propose a new DDPC method using closed-loop data by means of instrumental variables (IVs). By drawing from closed-loop subspace identification, the use of two forms of IVs is suggested to address the closed-loop issues caused by feedback control and the correlation between inputs and noise. Furthermore, a new DDPC formulation with a novel IV-inspired regularizer is proposed, where a balance between control cost minimization and weighted least-squares data fitting can be made for improvement of control performance. Numerical examples and application to a simulated industrial furnace showcase the improved performance of the proposed DDPC based on closed-loop data. |
| title | Data-Driven Predictive Control Using Closed-Loop Data: An Instrumental Variable Approach |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2309.05916 |