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Updated: May 30, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
Published on: May 8, 2021
Predefined-time adaptive neural network decentralized control for large-scale interconnected systems with input
Xiaoli Li1, Guoju Zhang2, Yingshan Zhou2
1School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China.
Abstract:
This study endeavors to develop a predefined-time adaptive neural network decentralized controller for large-scale interconnected nonlinear systems with input hysteresis. Within the framework of the backstepping technique, the proposed control scheme guarantees that the tracking error converges to a small bounded set within a predefined settling time. The upper limit of this convergence time is determined by a single adjustable control parameter. Modified command filter not only tackles the inherent "complexity explosion" issue in traditional backstepping methods but also effectively avoids chattering phenomena possibly induced by sign function. An online approximator based on neural networks is utilized to address system uncertainties. Moreover, a novel predefined-time error compensation mechanism is constructed to compensate for the reduction in control accuracy caused by filtering errors. Two simulation case studies demonstrate the feasibility and effectiveness of the proposed control method.
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