We solve output- and state-consensus problems for multi-agent high-order systems in feedback form. We consider systems interconnected over arbitrary (connected) undirected-topology networks as well as directed spanning-trees and directed cycles. We assume that the systems may be subject to multiple restrictions in the form of output or state constraints, such as limited-range measurements, physical limitations, etc. In addition, we suppose that the systems may be subject to external disturbances. Under these conditions, we present a control framework and a formal analysis that establishes robust stability in the input-to-state sense. The former relies on a modified backstepping method and the latter on multi-stability theory. Finally, we apply our approach to a case-study of interest in the aerospace industry: safety-aware rendezvous control of underactuated UAVs subject to connectivity and collision-avoidance constraints.

We propose a solution to the full consensus-based formation problem for torque-controlled nonholonomic mobile robots under time-varying communication delays and without velocity measurements. Under the assumptions of static and undirected communication topologies, and smooth and bounded delays, we propose a distributed smooth, time-varying control law which achieves the goal of acquiring a global formation pattern in a common consensus point for both position and orientation. The controller is a combination of a simple Proportional-Plus-Damping scheme and a simple globally exponentially convergent velocity observer. Consensus is reached independently of all initial conditions. Realistic simulations in the Gazebo-ROS environment illustrate the effectiveness of the proposed algorithm and the robustness with respect to measurement noise, non-smooth delays and uncertain dynamics.

We treat the problem of simultaneous collaborative multiple micro air vehicles’ localization and target tracking using time-varying range and (relative and absolute) velocity measurements. The proposed solution combines robustly local nonlinear observers that estimate the relative positions between agents and their neighbors, and cooperative filters that fuse each agent’s local estimates to globally localize them with respect to the target (and therefore to each other). These estimates are then introduced in a dynamic consensus-type control law that ensures the global collective target tracking while simultaneously estimating the target’s velocity, without needing any external reference which makes it applicable in GPS-denied environments. Finally, a simulation scenario is studied in order to show the efficiency of the proposed solution.