Bipartite Consensus Tracking Control for a Class of Multi-Agent Systems with Output Saturation

This paper proposes an iterative learning control (ILC) strategy for bipartite consensus tracking of linear multi-agent systems (MASs) with output saturation. This paper considers a class of MAS with fixed topology, where the desired trajectory information can be obtained by only some agents. Based on the output constraint conditions, we construct a consensus tracking error. Next, a P-type ILC strategy is designed, which is composed of the accumulation of output error information of agents, and the convergence condition for the algorithm is given. It is theoretically proved that the tracking error of the MAS can converge to zero as the iteration number approaches infinity. Finally, the effectiveness of the proposed iterative learning control algorithm is verified through simulation results.