Seeking fixed-time practical consensus tracking of networked nonlinear agent systems with saturation via improved extended state observer

Abstract

This paper addresses the adaptive fixed-time practical consensus tracking control problem of networked systems subject to unknown dynamics, external disturbances, and input saturations. At first, an Improved Extended State Observer (IESO) is developed to estimate state and external disturbances of the leader model accurately. Subsequently, neural networks are utilized to approximate the lumped uncertainties, which include the unknown dynamics and external disturbances of Euler-Lagrange Systems (ELSs), in real-time. Adaptive update laws are formulated to ensure the boundedness of the neural network estimation error. Additionally, an Auxiliary Dynamic System (ADS) is introduced to mitigate the effects of input saturation. A novel adaptive fixed-time controller is proposed and coupled with the ADS, ensuring that the tracking error converges to a predefined residual set. Through the fine-tuning of parameters within the observer and controller, the convergence time of the system can be precisely controlled. The fixed-time convergence of the proposed control scheme is rigorously demonstrated using Lyapunov stability theory. The efficacy of the proposed control strategy is substantiated through simulation examples.