A continuous distributed control algorithm for time-varying networks of nonlinear agents with input saturation

In this article, we explore the possibility of designing a continuous control algorithm for time-varying networks of high-order nonlinear agents with input saturation. A class of neural network-based (NN) distributed control algorithms are proposed for the consensus of nonlinear agents with input saturation and nonidentical unknown control directions (UCDs) under uniformly quasi-strongly (Formula presented.) -connected graphs. An NN is used to cancel the unknown part of agent dynamics due to the well known NN function approximation property. Also, an adaptive smooth term is employed to compensate for the bounded disturbance, the approximation error. Moreover, an auxiliary system is designed to counteract the effect of input saturation. Furthermore, the control law is augmented with the Nussbaum-type function to obtain an estimation of the UCDs. It is proven rigorously that the consensus of nonlinear agents with input saturation and nonidentical UCDs can be guaranteed, and the boundedness of all signals of closed-loop systems is ensured. Finally, simulation examples are given to show the effectiveness of our theoretical results.