Fuzzy Adaptive Leader-Following Consensus Control for Nonlinear Multi-Agent Systems with Unknown Control Directions

Two new fuzzy adaptive leader-following consensus control algorithms are proposed for nonlinear multi-agent systems with unknown identical and nonidentical control directions. In this paper, agents are modeled as first-order nonlinear systems with uncertain dynamics and unknown control directions. Two types of multi-agent systems are considered: i.e. all the agents have the same control direction and different agents have different control directions. The objective of this paper is to design distributed controllers for follower agents, such that all the follower agents can keep consensus with the leader even if only a fraction of followers can receive the information from the leader. The fuzzy neural network (FNN) is constructed to approximate the unknown dynamics by its powerful learning performance and exemption from prior knowledge of the systems. Finally, the numerical examples are provided to illustrate the effectiveness of the designed controller.