Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909659079639040 |
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| author | Boldocký, Ján Javan, Shahriar Dadras Gulan, Martin Mönnigmann, Martin Drgoňa, Ján |
| author_facet | Boldocký, Ján Javan, Shahriar Dadras Gulan, Martin Mönnigmann, Martin Drgoňa, Ján |
| contents | We propose a novel approach to solving input- and state-constrained parametric mixed-integer optimal control problems using Differentiable Predictive Control (DPC). Our approach follows the differentiable programming paradigm by learning an explicit neural policy that maps control parameters to integer- and continuous-valued decision variables. This policy is optimized via stochastic gradient descent by differentiating the quadratic model predictive control objective through the closed-loop finite-horizon response of the system dynamics. To handle integrality constraints, we incorporate three differentiable rounding strategies. The approach is evaluated on a conceptual thermal energy system, comparing its performance with the optimal solution for different lengths of the prediction horizon. The simulation results indicate that our self-supervised learning approach can achieve near-optimal control performance while significantly reducing inference time by avoiding online optimization, thus implying its potential for embedded deployment even on edge devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19646 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control Boldocký, Ján Javan, Shahriar Dadras Gulan, Martin Mönnigmann, Martin Drgoňa, Ján Systems and Control We propose a novel approach to solving input- and state-constrained parametric mixed-integer optimal control problems using Differentiable Predictive Control (DPC). Our approach follows the differentiable programming paradigm by learning an explicit neural policy that maps control parameters to integer- and continuous-valued decision variables. This policy is optimized via stochastic gradient descent by differentiating the quadratic model predictive control objective through the closed-loop finite-horizon response of the system dynamics. To handle integrality constraints, we incorporate three differentiable rounding strategies. The approach is evaluated on a conceptual thermal energy system, comparing its performance with the optimal solution for different lengths of the prediction horizon. The simulation results indicate that our self-supervised learning approach can achieve near-optimal control performance while significantly reducing inference time by avoiding online optimization, thus implying its potential for embedded deployment even on edge devices. |
| title | Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2506.19646 |