Coordinated Fuzzy Adaptive Iterative Learning Control for Nonlinear Multi-agent Systems

In this work, we design a new fuzzy adaptive iterative control (AILC) scheme for a class of unknown nonlinear leader-following multi-agent systems (MAS), where the influence of unknown input disturbance is considered as well. We adopt the distributed initial learning protocol and use the fuzzy logic system to estimate the unknown function of each agent. Through rigorous analysis, we prove that under the new scheme, all agents track the leader node completely on finite time interval. Finally, one illustrative example is given to show the effectiveness of the algorithm.