The design of multi-lead-compensators for stabilization and pole placement in double-integrator networks under saturation

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. Through a scaling argument, we also demonstrate that the multi-lead-compensator can stabilize the double-integrator network under actuator saturation constraints. The multilead-compensator design is practical for modern dynamical network applications, in that it subdivides actuation effort and complexity among the agents and in many cases is robust to agent failure.