Compression in Control

Large-scale dynamical systems arise naturally in many modern infrastructures, with power and social networks among the most prominent examples. These systems consist of thousands or even millions of interconnected components whose collective behavior evolves. Their scale, complexity, and heterogeneity create fundamental challenges for analysis, control, and real-time decision-making. Our work focuses on those large-scale systems, where high dimensionality often creates severe bottlenecks in sensing, actuation, computation, and communication. We investigate how to introduce randomized compression and sparsity into the structure of large-scale systems, such as through reduced sensing, selective actuation, or compressed updates, while still guaranteeing key control objectives, including stability, convergence, and robustness. 

The goal is to develop principled, theoretically grounded methods that make modern large-scale control systems more efficient, scalable, and resource aware. This involves identifying when and how randomness, sparsity, and compressed representations can be deliberately exploited to accelerate computation, reduce sensing and communication demands, and alleviate actuation constraints. Ultimately, our aim is to design a unified framework that leverages randomness and sparsity as tools for building reliable and scalable controllers for the next generation of large-scale interconnected systems.

• Sparse and Randomized Sensing/Actuation for Networked Systems

Designing frameworks that use sparsity, selective actuation, reduced sensing, and randomized compression to overcome sensing, computation, and communication bottlenecks.

• Resource-Aware Algorithms for Efficient and Robust System Operation

Creating principled algorithms that guarantee stability, convergence, and robustness while significantly reducing computational and communication costs in large-scale infrastructures.

• Z. Hadach, H. El Hammouti, E. Bergou and A. Saoud, “Just Few States are Enough: Randomized Sparse Feedback for Stability of Dynamical Systems,”  In : Proceedings of the AAAI Conference on Artificial Intelligence. 2026.