Distributed consensus of second-order multiagent systems with nonconvex input constraints

This paper addresses the input constrained consensus of second-order multiagent systems with nonconvex constraints. A new update law is proposed to make the position states of all agents converge to a common point and the velocities converge to zero, while the input of each agent stays in a certain constraint set. The closed-loop system is first converted to an equivalent system by taking a novel coordinate transformation. Then, it is proved that the input constrained consensus can be achieved if the graphs jointly have directed spanning trees by using the Metzler matrix theory. Finally, simulations are provided to demonstrate the effectiveness of the proposed algorithm.