Iterative learning consensus control for one‐sided Lipschitz multi‐agent systems

Abstract

AbstractBy applying iterative learning control approach, the consensus is studied for multi‐agent systems (MASs) with one‐sided Lipschitz (OSL) nonlinearity. Firstly, the P‐type and D‐type learning schemes with initial state learning are introduced for such MASs. Then, utilizing the OSL and the quadratically inner‐bounded constraints, the convergence conditions of the consensus algorithms are presented and analyzed under a directed communication graph. We show that both algorithms, on a fixed finite‐time interval, can achieve perfect consensus tracking. Finally, the correctness of the obtained results is illustrated with simulation examples.