Adaptive Fixed-Time Containment Control for Distributed Parameter Multi-Agent Systems under Input Quantization

This paper investigates the adaptive fixed-time containment control problem for distributed parameter multi-agent systems (DP-MASs) under input quantization. To address the deployment issue of large-scale multi-agent systems, a partial differential equation (PDE) is employed to establish the macroscopic dynamic model, by which the coordination task is reformulated as a dynamic target tracking problem. In practical scenarios, a hysteretic quantizer is integrated into the control channel to effectively alleviate the communication burden. Moreover, to compensate for the effects of inherent system uncertainties and quantization errors, this paper presents a control scheme combining radial basis function (RBF) neural network approximation and adaptive quantization compensation. Based on Lyapunov stability analysis, it is proved that the tracking error converges to a small neighborhood of the origin within a fixed time, and a series of numerical simulations are conducted to validate the effectiveness and feasibility of the proposed control strategy.