Robust adaptive neural network consensus tracking control of multi-robot systems

This paper studies the problem of distributed consensus tracking control for multi-robot systems with different friction coefficients and external disturbances based on the multi-agent theory. For the case where the communication topology is a weighted directed graph and leader node is the neighbor of a small portion of the follower nodes. Each follower nodes are modeled as two second-order nonlinear dynamical system with coupling parts. A distributed robust adaptive control law based on the neural networks (NN) is designed for each follower node, where the controller requires only relative state information from its adjacent neighbors. With such control scheme, the states of all follower nodes ultimately synchronize to the leader node with bounded residual error. Simulation results are provided to validate the effectiveness of the algorithm.