Split-as-a-Pro: behavioral control via operator splitting and alternating projections
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.
Cite
@article{arxiv.2505.19411,
title = {Split-as-a-Pro: behavioral control via operator splitting and alternating projections},
author = {Yu Tang and Carlo Cenedese and Alessio Rimoldi and Florian Dórfler and John Lygeros and Alberto Padoan},
journal= {arXiv preprint arXiv:2505.19411},
year = {2025}
}