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Iterative learning consensus tracking control for distributed parameter models multi-agent with disturbance

In this paper, we study the consensus tracking for a class of distributed parameter models multi-agent systems. Taking into account the presence of disturbance in the system, using knowledge between neighboring agents, the virtual leader technology is introduced. A distributed P-type iterative learning control protocol is proposed. When the ILC law is applied to the system, the consensus tracking error between any two agents under the sense of L2 norm can converge to zero after a sufficient number of iterations in a finite time interval. Finally, simulation examples demonstrate the effectiveness of the method.