Split-as-a-Pro: behavioral control via operator splitting and alternating projections
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916758844080128 |
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| author | Tang, Yu Cenedese, Carlo Rimoldi, Alessio Dórfler, Florian Lygeros, John Padoan, Alberto |
| author_facet | Tang, Yu Cenedese, Carlo Rimoldi, Alessio Dórfler, Florian Lygeros, John Padoan, Alberto |
| contents | The paper introduces Split-as-a-Pro, a control framework that integrates behavioral systems theory, operator splitting methods, and alternating projection algorithms. The framework reduces dynamic optimization problems - arising in both control and estimation - to efficient projection computations. Split-as-a-Pro builds on a non-parametric formulation that exploits system structure to separate dynamic constraints imposed by individual subsystems from external ones, such as interconnection constraints and input/output constraints. This enables the use of arbitrary system representations, as long as the associated projection is efficiently computable, thereby enhancing scalability and compatibility with gray-box modeling. We demonstrate the effectiveness of Split-as-a-Pro by developing a distributed algorithm for solving finite-horizon linear quadratic control problems and illustrate its use in predictive control. Our numerical case studies show that algorithms obtained using Split-as-a-Pro significantly outperform their centralized counterparts in runtime and scalability across various standard graph topologies, while seamlessly leveraging both model-based and data-driven system representations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19411 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Split-as-a-Pro: behavioral control via operator splitting and alternating projections Tang, Yu Cenedese, Carlo Rimoldi, Alessio Dórfler, Florian Lygeros, John Padoan, Alberto Optimization and Control Systems and Control The paper introduces Split-as-a-Pro, a control framework that integrates behavioral systems theory, operator splitting methods, and alternating projection algorithms. The framework reduces dynamic optimization problems - arising in both control and estimation - to efficient projection computations. Split-as-a-Pro builds on a non-parametric formulation that exploits system structure to separate dynamic constraints imposed by individual subsystems from external ones, such as interconnection constraints and input/output constraints. This enables the use of arbitrary system representations, as long as the associated projection is efficiently computable, thereby enhancing scalability and compatibility with gray-box modeling. We demonstrate the effectiveness of Split-as-a-Pro by developing a distributed algorithm for solving finite-horizon linear quadratic control problems and illustrate its use in predictive control. Our numerical case studies show that algorithms obtained using Split-as-a-Pro significantly outperform their centralized counterparts in runtime and scalability across various standard graph topologies, while seamlessly leveraging both model-based and data-driven system representations. |
| title | Split-as-a-Pro: behavioral control via operator splitting and alternating projections |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2505.19411 |