Iterative learning approach for consensus tracking of partial difference multi-agent systems with control delay under switching topology

In this paper, the consensus tracking problem for the linear and nonlinear partial difference multi-agent systems with switching communication topology and control delay is investigated. Based on relative local measurements of neighboring followers, while considering spatio-temporal discretization and initial state deviation, a discrete distributed consensus protocol with initial value learning is designed for each agent via D-type iterative learning approach. Through rigorous mathematical theoretical analysis, the necessary and sufficient conditions are obtained. Under the switching of the communication topology, these conditions ensure that the consensus tracking control of the MASs can be solved. After applying the designed protocol, in the sense of the L2 norm and along the positive direction of the iteration axis, the consensus tracking error between any two agents can converge to zero. Finally, some simulation examples are used to demonstrate the validity of the protocol and theoretical results. •A novel partial difference MASs dynamic model is established to describe the discrete spatio-temporal evolution behavior.•A new distributed iterative learning approach with initial value learning for partial difference MASs is given.•The proposed control approach can solve the problem of switching topology and control delay simultaneously.