Consensus control of singular multi-agent systems based on iterative learning approach

In this paper, the iterative learning control technique is applied for singular multi-agent systems to perform consensus tracking. Here, the communication among the followers is described by a directed graph, and only a portion of the followers can receive the leader's information. Based on the equivalent restrict decomposition form of singular agents, a unified distributed learning algorithm is proposed in both continuous-time domain and discrete-time domain. Furthermore, the convergence condition of the algorithm is presented and analyzed. It is shown that the algorithm can guarantee the states of the followers converge to the leader's trajectory on a finite time interval along the iteration axis. Finally, two simulation examples are given to illustrate the effectiveness of the theoretical results.