Over the last two decades, significant scholarly work in systems and control has focused on networked multi-agent systems, where the central challenge lies in designing controllers under communication constraints such as transmission delays, sampled communication, and information exchange topologies. Traditional approaches often prioritize distributed architectures over sampling and transmission constraints. This has led to reliance on conservative robustness arguments in sampled-data distributed control.
In our work, we advocate a different approach—directly designing distributed sampled-data controllers. By exploiting the inherent structure of the control objective and the interplay between temporal (sampling) and spatial (distributed) communication constraints, our method simplifies the design process even in highly constrained scenarios. Focusing on the fundamental “agreement problem” with asynchronous sampling, we will demonstrate that treating these constraints simultaneously leads to tractable and simple solutions that do not require a priori knowledge of the spatial graph or the sampling sequence.
