Leader–Follower Consensus Control of Multiple Quadcopters Under Communication Delays

Abstract

This paper develops a novel decentralized leader–follower consensus algorithm for multiple-quadcopter systems under uniform constant and asynchronous time-varying communication delays. The consensus problem is formulated as the stability analysis and static controller design problem of a delayed system by defining the consensus error dynamics. Lyapunov-based methods along with the linear matrix inequality (LMI) techniques are utilized to derive the sufficient conditions for the control gain design that ensure asymptotic consensusability in the constant delay case, and consensus with bounded errors in the time-varying delay case. Also the computational complexity of solving control gains can be significantly reduced by decomposing the sufficient conditions into a set of equivalent low-dimensional conditions under undirected communication topologies. Simulation results show that larger systems are generally more susceptible to communication delays, and systems are more robust to delays when more followers are directly connected to the leader.

Consensus in multi-agent systems: a review

“…For this purpose, they considered six virtual follower agents and one leader agent. Axial alignment for four mobile robots (with a control algorithm based on prescribed consensus law) was explored in Huang (2017).…”

Broadcast control of multi-robot systems with norm-limited update vector

“…For example, robot x 1 is required to go to target position p 1 , robot x 2 is required to go to target position p 2 , and so on. Readers may refer to Cao et al, 29 Wasiela et al, 30 and Huang et al, 31 for other examples of consensus task. Note that we used the same parameter values as in Table 1 for this task except aðkÞ, cðkÞ, and e. For the gains aðkÞ and cðkÞ, we set the parameters a 0 ¼ 2:2, a v ¼ 450, a p ¼ 0:95, c 0 ¼ 0:26, and c p ¼ 0:16 while the value of e is set to be e :¼ 1.…”

Cooperative UAVs Gas Monitoring using Distributed Consensus

“…One simple and effective approach is to use a leaderfollower formation: the leader is in charge of moving inside the environment using a controlled reactive behaviour or just following a predefined trajectory (that is the solution here considered), while the followers are adapt to the leader's motion to preserve the formation. Using [22] and assuming that the i-th agent is the leader, it is possible to use where u i (t) is reported in (5), ξ d (t) is the desired trajectory, ξ d (t) the desired trajectory dynamic and k i > 0 is a tuning parameter. Similarly, for the k-th follower, with k = i,…”