Adaptive Prescribed Performance Control for Nonlinear Multiagent Systems with Disturbance

This paper is dedicated to studying the prescribed performance control (PPC) problem for nonlinear multiagent systems with saturation and unknown disturbance. A filter that integrates second-order sliding mode techniques is designed to reduce the computation burden. According to the approximation characteristic of neural networks (NNs), the disturbance observer is designed to compensate the unknown disturbance. With PPC technology, the tracking error converges to a certain range. Different from the existing results, this paper examines the saturation issue that exists in the first step of the virtual controller. NN s are utilized to approximate the unknown saturation function. Furthermore, by exploiting Lyapunov stability theory, all closed-loop system signals are ensured to be semiglobally uniformly ultimately bounded. Finally, the rationality of the presented strategy is confirmed by the numerical simulation.