Closed‐loop consensus control of partial difference multi‐agent systems via variable gain iterative learning strategy

Abstract

In the research on consensus control of multi-agent systems (MASs), it is the key to ensure that the state quantities (speed, displacement, etc.) of all agents tend to be consensus. Under this premise, how to accelerate the convergence rate of the consensus error is also an important issue. Aiming at the accelerated consensus problem of MASs composed of partial difference equations, based on the network topology and the output form of each follower, a distributed closed-loop accelerated iterative learning control (ILC) protocol with variable gain was proposed, which was designed to improve the speed of consensus error. With the help of basic mathematical tools such as discrete Gronwall inequality and contraction mapping method, the sufficient conditions for the convergence of the consensus error are derived and analyzed. Finally, numerical simulations show that the proposed acceleration control protocol is more effective than the traditional open-loop and closed-loop ILC protocols.