부산대학교 도서관

The consensus seeking problems for both discrete and continuous multi-agent networks are discussedfrom an iterative learning perspective. It is shown that the consensus seeking process can be viewed as an iterativelearning process for agents under directed networks to improve their performances from time to time in order toachieve consensus. If a desired consensus state is specified, then the multi-agent system can be guaranteed toreach consensus through reducing the tracking error between each agent’s state and the desired consensus statemonotonically to zero with respect to the increasing of time. If there is no desired consensus state, then the agentscan achieve consensus through reducing their states monotonically to the minimum quantity with increasing time. Simulations illustrate the observed results.