扰动(地质)
移动机器人
控制理论(社会学)
计算机科学
自抗扰控制
鲁棒控制
控制(管理)
机器人
控制工程
人工智能
控制系统
工程类
生物
物理
古生物学
电气工程
非线性系统
量子力学
国家观察员
作者
Yao Huang,Lidong Zhang,Zhengwei Chu
标识
DOI:10.1108/ria-08-2024-0172
摘要
Purpose This paper aims to propose an active disturbance rejection control (ADRC)-based visual servoing strategy for regulating a wheeled mobile robot from varying initial poses to a desired pose at an exponential rate. It addresses challenges associated with non-holonomic constraints, uncertain depth information and unknown translational parameters in monocular vision systems. Design/methodology/approach The uncertain depth information in monocular vision and unknown camera-to-robot translational parameters are modeled as internal uncertainties of the visual servo system. An input-state scaling technique is used to decouple the system into two subsystems, controlled by angular and linear velocities, respectively. The angular velocity controller is designed to ensure strict exponential convergence, while the internal parametric and bounded uncertainties of the system are estimated and compensated for by an extended state observer and a switching linear velocity controller. Findings The separate design of the angular and linear velocity controllers effectively overcomes the non-holonomic constraints of the mobile robot, ensuring robust performance under diverse conditions. Furthermore, the ADRC-based strategy successfully handles uncertain depth information and unknown translational parameters. The convergence of the error system is rigorously proven using Lyapunov theory, and simulation results verify the effectiveness of the proposed scheme. Originality/value To the best of the authors’ knowledge, this study introduces, for the first time, a novel approach that combines ADRC with visual servoing for non-holonomic mobile robots. This approach enhances the adaptability and accuracy of the robot’s navigation in environments characterized by unknown system uncertainties. The proposed method demonstrates enhanced practical performance over conventional techniques by effectively managing the inherent uncertainties of the system.
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