Cooperative adaptive fuzzy tracking control for a class of nonlinear multi-agent systems

Here, we present a distributed adaptive control scheme for cooperative tracking of a class of nonlinear multiagent systems with partially unknown dynamics. The agents should reach an agreement on a time-varying reference trajectory as their virtual leader. The approach combines the artificial potential functions method with robust control terms to reach an appropriate controller for each agent. The unknown dynamics of each agent is approximated by an adaptive fuzzy system, with adaptive laws derived from Lyapunov stability analysis. Theoretical analysis reveals that Hœ performance criterion is satisfied and the effects of uncertainties are kept below a desired attenuation level. The proposed method is applied to the state tracking problem of five inverted pendulums. Results indicate the effectiveness of the proposed approach in handling noise and bounded uncertainties as well as reaching consensus with high precision.