New Kinematic Control Scheme for Redundant Robotic Manipulators Perturbed by Harmonic Noise

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2025-03-19 DOI:10.1109/TIE.2025.3548995
Xiyuan Zhang;Zhonghao Zhang;Dongsheng Guo;Weidong Zhang;Weibing Li;Shuai Li
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Abstract

Kinematic control is one of the fundamental issues in the study of redundant robotic manipulators, and various schemes that utilize the pseudoinverse of the Jacobian have been crafted in this field. However, these schemes may be ineffective due to the lack of suppressing additive noise, especially the harmonic noise widely encountered in practice. This article proposes a new kinematic control scheme for redundant robotic manipulators affected by harmonic noise, aiming to overcome the identified limitations. Such a scheme, which incorporates the adaptive learning mechanism based on harmonic frequency, can simulate the harmonic noise, suppress its disturbance, and eventually realize the effective control purpose. It is then theoretically proven that the Cartesian error generated by the proposed scheme exhibits the convergence property, thus guaranteeing the control performance on redundant robotic manipulators even in the presence of harmonic noise. Simulation and experiment results under PA10 and Panda robotic manipulators further verify the efficacy and superiority of the proposed kinematic control scheme.
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谐波干扰下冗余机器人运动控制新方案
运动学控制是冗余机械臂研究的基本问题之一,利用雅可比矩阵的伪逆已经形成了各种方案。然而,由于缺乏对加性噪声的抑制,特别是在实际应用中广泛遇到的谐波噪声,这些方案往往效果不佳。针对受谐波噪声影响的冗余机器人,提出了一种新的运动控制方案。该方案结合了基于谐波频率的自适应学习机制,能够模拟谐波噪声,抑制谐波噪声的干扰,最终实现有效的控制目的。从理论上证明了该方案产生的笛卡尔误差具有收敛性,从而保证了冗余度机器人在谐波噪声存在下的控制性能。在PA10和Panda机器人上的仿真和实验结果进一步验证了所提运动控制方案的有效性和优越性。
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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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