Iterative learning based consensus control for distributed parameter type multi-agent differential inclusion systems

This article considers the learning consensus problem of distributed parameter type multi-agent differential inclusion systems including parabolic type and hyperbolic type. By imposing a Lipschitz condition on a set-valued mapping and utilizing distributed P$$ P $$-type iterative learning consensus control protocols, an iterative learning process is established. Further, sufficient conditions for the convergence of the consensus error between any two agents in the sense of the certain norm are obtained with the help of Steiner-type selectors. Finally, numerical simulations verify the validity of the proposed method.