Nonobserver-Based Fixed-Time Output-Feedback Control for Uncertain Bridge Cranes System

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2024-12-24 DOI:10.1109/TIE.2024.3515256
Xiaoshuang Zhou;Yana Yang;Heng Zhang;Junpeng Li;Changchun Hua
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Abstract

Bridge cranes system (BCS) displays a high degree of nonlinearity and is underactuated, making the control problem significantly more challenging. Most of the existing control algorithms for the BCS can only achieve asymptotic stabilization, which results in the control speed and precision being challenging to meet the demand of current production and application. This article is concerned with the fixed-time stabilization control for a class of BCS subjected to unavailable velocity signals in the measurement and unknown disturbances. By skillfully combining the Lyapunov method and the system homogeneous theory, a novel output feedback fixed-time controller is designed to ensure the trolley reaches the target position in a preset time while restraining the load swing quickly. The salient characteristic of this control method is that any observer is not designed such that the control complexity is reduced. At last, we demonstrate the plausibility of the proposed control approach through simulations and practical experiments.
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不确定桥式起重机系统的非观测器定时输出反馈控制
桥式起重机系统具有高度的非线性和欠驱动特性,使其控制问题更具挑战性。现有的BCS控制算法大多只能实现渐近镇定,控制速度和精度难以满足当前生产和应用的要求。本文研究了一类测量中速度信号不可用和干扰未知的BCS的定时镇定控制问题。巧妙地将李雅普诺夫方法与系统齐次理论相结合,设计了一种新颖的输出反馈定时控制器,保证小车在预设时间内到达目标位置,同时快速抑制负载的摆动。这种控制方法的显著特点是没有设计任何观测器,从而降低了控制的复杂度。最后,通过仿真和实际实验验证了所提控制方法的可行性。
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来源期刊
IEEE Transactions on Industrial Electronics
IEEE Transactions on Industrial Electronics 工程技术-工程:电子与电气
CiteScore
16.80
自引率
9.10%
发文量
1396
审稿时长
6.3 months
期刊介绍: Journal Name: IEEE Transactions on Industrial Electronics Publication Frequency: Monthly Scope: The scope of IEEE Transactions on Industrial Electronics encompasses the following areas: Applications of electronics, controls, and communications in industrial and manufacturing systems and processes. Power electronics and drive control techniques. System control and signal processing. Fault detection and diagnosis. Power systems. Instrumentation, measurement, and testing. Modeling and simulation. Motion control. Robotics. Sensors and actuators. Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems. Factory automation. Communication and computer networks.
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