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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. [ABSTRACT FROM AUTHOR]

This paper addresses the leader–follower flocking problem with a moving leader for networked Lagrange systems with parametric uncertainties under a proximity graph. Here a group of followers move cohesively with the moving leader to maintain connectivity and avoid collisions for all time and also eventually achieve velocity matching. In the proximity graph, the neighbor relationship is defined according to the relative distance between each pair of agents. Each follower is able to obtain information from only the neighbors in its proximity, involving only local interaction. We consider two cases: (i) the leader moves with a constant velocity, and (ii) the leader moves with a varying velocity. In the first case, a distributed continuous adaptive control algorithm accounting for unknown parameters is proposed in combination with a distributed continuous estimator for each follower. In the second case, a distributed discontinuous adaptive control algorithm and estimator are proposed. Then the algorithm is extended to be fully distributed with the introduction of gain adaptation laws. In all proposed algorithms, only one-hop neighbors’ information (e.g., the relative position and velocity measurements between the neighbors and the absolute position and velocity measurements) is required, and flocking is achieved as long as the connectivity and collision avoidance are ensured at the initial time and the control gains are designed properly. Numerical simulations are presented to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]