Distributed Adaptive Neural Network Stabilization Control for a Class of Uncertain Multi-agent Systems with Different Dimensions

A novel distributed feedback adaptive control firstly is developed for the stabilization tracking for a class of multi-agent robot manipulator system with unstructured model. In this study, the dynamical behavior of the leader system can be tracked perfectly by the states of each multi-agent robot manipulator system with 2 or 3 degrees of freedom (DOF) through the controller with the similar characters of its own structure, the similar elements such as matrices or vectors that are used to design a distributed feedback control with coupling weights adaptive laws and neural network weights online, in which these relative similar information of the follower agents and leader system can be calculated by the proposed algorithm according to the characters of each agent robot manipulator and the reference leader system. This method extends the existing works with identical multi-agent systems and leader system to nonidentical multi-agent systems with different dimensions contrast to other results in many literatures. Finally, the good stabilization performances are demonstrated to multi-agent robot manipulator system.