Event-Triggered-Based Adaptive Practical Fixed-Time Consensus Tracking Control for Nonlinear Multiagent Systems With Unmeasurable States

This article addresses the issue of the event-triggered fixed-time consensus tracking control (ET-FT-CTC) for a class of strict-feedback nonlinear multiagent systems (NMASs) with unmeasurable states. The main challenges in these systems include partial unmeasurable states, completely unknown nonlinearities, and the occasional unavailability of the reference signal and its first-and second-order derivatives. To address these challenges, a nonlinear observer is designed to estimate the unmeasurable states, and a set of radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinearities. Additionally, a novel update law is introduced to manage the unavailability of the reference signal. The dynamic surface control (DSC) approach is utilized to reduce the computational burden associated with the backstepping method, while a set of novel virtual controllers is constructed to avoid singularity problems. The proposed adaptive ET controller ensures that the CTC objective is achieved within a fixed time, without the occurrence of Zeno behavior. Two illustrative examples are provided to demonstrate the effectiveness of the proposed method.