Hybrid Data-enabled Predictive Control: Incorporating model knowledge into the DeePC
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912577244626944 |
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| author | Watson, Jeremy D. |
| author_facet | Watson, Jeremy D. |
| contents | Predictive control can either be data-based (e.g. data-enabled predictive control, or DeePC) or model-based (model predictive control). In this paper we aim to bridge the gap between the two by investigating the case where only a partial model is available, i.e. incorporating model knowledge into DeePC. In our formulation, the partial knowledge takes the form of known state and output equations that are a subset of the complete model equations. We formulate an approach to take advantage of partial model knowledge which we call hybrid data-enabled predictive control (HDeePC). We prove feasible set equivalence and equivalent closed-loop behavior in the noiseless, LTI case. As we show, this has potential advantages over a purely data-based approach in terms of computational expense and robustness to noise in some cases. Furthermore, this allows applications to certain linear time-varying and nonlinear systems. Finally, a number of case studies, including the control of an energy storage system in a microgrid, a triple-mass system, and a larger power system, illustrate the potential of HDeePC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12467 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Data-enabled Predictive Control: Incorporating model knowledge into the DeePC Watson, Jeremy D. Optimization and Control Predictive control can either be data-based (e.g. data-enabled predictive control, or DeePC) or model-based (model predictive control). In this paper we aim to bridge the gap between the two by investigating the case where only a partial model is available, i.e. incorporating model knowledge into DeePC. In our formulation, the partial knowledge takes the form of known state and output equations that are a subset of the complete model equations. We formulate an approach to take advantage of partial model knowledge which we call hybrid data-enabled predictive control (HDeePC). We prove feasible set equivalence and equivalent closed-loop behavior in the noiseless, LTI case. As we show, this has potential advantages over a purely data-based approach in terms of computational expense and robustness to noise in some cases. Furthermore, this allows applications to certain linear time-varying and nonlinear systems. Finally, a number of case studies, including the control of an energy storage system in a microgrid, a triple-mass system, and a larger power system, illustrate the potential of HDeePC. |
| title | Hybrid Data-enabled Predictive Control: Incorporating model knowledge into the DeePC |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2502.12467 |