Adaptive cooperative tracking control for a class of uncertain nonlinear multiagent systems
Jing Wang · 2017
In this paper, we propose a new adaptive cooperative tracking control algorithm for a class of uncertain nonlinear multiagent systems with unknown control coefficients. It is assumed that there exist parametric uncertainties and unknown dynamics with the informed agent as well, and only a limited number of agents have the access to the state value of the informed agent. The proposed adaptive cooperative tracking control solves the problem of making all agents asymptotically track the desired trajectory specified by an informed agent based on the use of neural network parameterization of unknown dynamics of agents. The unknown bounds of neural network approximation errors are also estimated online. Using Lyapunov stability theorem, it is rigorously proved that asymptotically cooperative tracking can be achieved under the assumption that the sensing/communication topology among agents is undirected and connected. A simulation example is given to illustrate the effectiveness of the proposed control design.