The Design of Multi-Lead-Compensators for Stabilization and Pole Placement in Double-Integrator Networks

We study decentralized controller design for stabilization and pole-placement, in a network of autonomous agents with double-integrator internal dynamics and arbitrary observation topology. We show that a simple multi-lead-compensator architecture, in particular one in which each agent uses a derivative-approximation compensator with three memory elements, can achieve both stabilization and effective pole placement while subdividing complexity/actuation among the agents. Through a scaling argument, we also demonstrate that the multi-lead-compensator can stabilize the double-integrator network under actuator saturation constraints.