Terminal Iterative Learning Scheme for Consensus Problem in Multi-Agent Systems with State Constraints

Abstract

In this paper, we investigate the consensus problem of multi-agent systems with state constraints. To achieve the consensus effectively, the terminal iterative learning approach is proposed. This learning strategy is designed without the tracking error. And the consensus state is obtained by the the information interaction between agents. Meanwhile, the constraint condition holds in terms of our learning strategy. It shows the consensus conditions ensure the achievement of the constraints. Finally, a numerical simulation is given to illustrate the effectiveness of the main results.