This paper addresses the problem of time-coordination of a team of cooperating multirotor unmanned aerial vehicles that exchange information over a supporting time-varying network. A distributed control law is developed to ensure that the vehicles meet the desired temporal assignments of the mission, while flying along predefined collision-free paths, even in the presence of faulty communication networks, temporary link losses, and switching topologies. In this paper, the coordination task is solved by reaching consensus on a suitably defined coordination state. Conditions are derived under which the coordination errors converge to a neighborhood of zero. Simulation and flight test results are presented to validate the theoretical findings.Note to Practitioners-This paper presents an approach which enables a fleet of multirotor UAVs to follow a set of desired trajectories and coordinate along them, thus satisfying specific spatial and temporal assignments. The proposed solution can be employed in applications in which multiple vehicles are tasked to execute cooperative, collision-free maneuvers, and accomplish a common goal in a safely manner. An example is sequential monitoring, in which the UAVs have to visit and monitor a set of points of interest, while maintaining a desired temporal separation between each other. In this paper, we also simulate a scenario in which the vehicles, positioned in a square room, are required to exchange position with each other. It is shown that the proposed control algorithm not only ensures that the UAVs arrive at the final destinations at the same time, but also guarantees safety, i.e., the vehicles avoid collision with each other at all times.

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This paper presents an L 1 adaptive controller for a class of nonlinear uncertain systems, nonaffine in control. As compared to previous L 1 architectures for nonlinear nonaffine-in-control systems, the solution discussed in this paper admits a broader class of nonlinear systems by relaxing some of the (restrictive) assumptions previously made. We also consider a more general filtering structure and prove the results using partial knowledge of the control input actuator model. With the presented control architecture we are able to improve the performance and robustness of the closed-loop adaptive system, compared to prior results of L 1 adaptive control for nonaffine-in-control systems. 0018-9286 (c)