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arXiv · 2505.19411

Split-as-a-Pro: behavioral control via operator splitting and alternating projections

Yu Tang·Carlo Cenedese·Alessio Rimoldi·Florian Dórfler·John Lygeros·Alberto Padoan
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Abstract

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.

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Yu Tang, Carlo Cenedese, Alessio Rimoldi, Florian Dórfler, John Lygeros, Alberto Padoan. 2025-05-26. Split-as-a-Pro: behavioral control via operator splitting and alternating projections. https://arxiv.org/abs/2505.19411

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