Distributed adaptive coordination control for uncertain nonlinear multi-agent systems with dead-zone input

In this paper, a distributed design scheme is developed for consensus tracking control of multi-agent system with nonlinear input under a weighted directed graph topology. Each agent is modeled by a strict-feedback nonlinear system with unknown nonlinear dynamics and unknown external disturbances. The time-varying leader node only gives commands to a small portion of the followers. By using backstepping technique and neural networks method, adaptive distributed controllers for each follower node are constructed, which only require relative state information between themselves and their neighbors. The proposed controllers and adaptive laws guarantee that the tracking errors between all followers and the leader convergence to a small neighborhood of the origin. Moreover, by employing the maximum norm of the unknown neural network weight vectors as the estimated parameter, the algorithm proposed in this paper contains only N(N represents the number of the followers) adaptive parameters that need to be updated online. The number of online learning parameters is independent of the number of the neural networks׳ nodes, which reduces the computation burden significantly. Finally, a numerical example demonstrates the effectiveness of the proposed approach.