Adaptive tracking control of multi-agent systems with unknown nonlinear functions and stochastic noises

Abstract

Under the directed graph connection, this paper deals with the adaptive tracking control of a class of high order multi-agent systems with unknown nonlinear functions and stochastic noises. In view of the unknown nonlinear functions, the radial basis function neural network (RBF-NN) is used to approximate it, further output feedback controller was designed using the adaptive backstepping method. By selecting parameters, this paper proves the design of the adaptive controller can realize that the system output tracking errors can converge to a small enough neighborhood of the origin, and proves that the system states are all bounded and the adaptive parameters are also bounded. Matlab simulation further proves the effectiveness of the designed controller.