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