Distributed average tracking for double-integrator multi-agent systems with reduced requirement on velocity measurements

This paper addresses distributed average tracking for a group of physical double-integrator agents under an undirected graph with reduced requirement on velocity measurements. The idea is that multiple agents track the average of multiple time-varying input signals, each of which is available to only one agent, under local interaction with neighbors. We consider two cases. First, a distributed algorithm and filter are proposed, where each agent needs its own and neighbors' filter outputs obtained through communication besides its local relative positions and its input signal, input velocity and input acceleration. Here, the requirement for either absolute or relative velocity measurements is removed. The algorithm is robust to initialization errors and can deal with a wide class of input signals with bounded deviations in input signals, input velocities, and input accelerations. Second, a distributed algorithm and filter are proposed to remove the requirement for communication. Here, each agent needs to measure the relative positions between itself and its neighbors and its own velocity. However, the requirement for relative velocity measurements between the agent and its neighbors is removed. The algorithm is robust to initialization errors and can deal with the case, where the input signals, input velocities and input accelerations are all bounded.