Cooperative control of multi-agent systems deals with various challenges such as uncertain dynamics, external disturbances, and limitations on interactions with other agents or the leader. Here, a cooperative adaptive fuzzy controller is introduced for a class of higher-order affine nonlinear multi-agent systems. The agents can only interact with their neighbors, and only a portion of them have access to the leader states. The unknown dynamics of the agents are approximated by fuzzy systems. In addition, the artificial potential functions (APFs) are employed to model the interaction among the agents. The APFs could provide a more transparent and straightforward design in comparison with usual graph-based methods. By modifying the error function, the proposed controller extends our previous work by giving only a portion of the agents access to the leader states. Lyapunov stability theory is used to design appropriate update laws for the weights of the fuzzy system and verify the overall stability of the system. Simulations results indicate a lower steady state tracking error and lesser control energy consumption compared with a competing neural network-based approach.

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