Motion Control of Manipulator Robots via Two PD-Based Nonlinear Controllers: Design and Comparative Analysis

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-01-17 DOI:10.3103/S0146411624701086
Hassène Gritli, Sahar Jenhani, Jyotindra Narayan
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Abstract

The motion control or trajectory tracking is a fundamental and benchmark problem while controlling the manipulator robots to move along a predefined trajectory. Extensive research in the literature has addressed the motion control problem for robotic manipulators. To address this challenge effectively, it becomes imperative to design suitable controllers and make adequate choices regarding the appropriate feedback gain values. In this study, we consider and investigate two PD-based nonlinear control laws for controlling the motion of manipulator robots, namely the PD-plus-compensation controller and the augmented one. We establish conditions for determining the feedback gains of these two controllers, ensuring tracking error stability. Moreover, conditions guaranteeing the uniqueness of the zero error of the closed-loop robotic system are developed. Finally, we consider a two-degree-of-freedom manipulator robot to reveal the effectiveness of the two proposed controllers in tracking a desired trajectory and hence evaluating their performance in addressing the motion control problem.

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基于两种pd非线性控制器的机械臂运动控制:设计与比较分析
运动控制或轨迹跟踪是控制机械臂机器人沿预定轨迹运动的基础和基准问题。大量的文献研究已经解决了机器人操纵器的运动控制问题。为了有效地解决这一挑战,必须设计合适的控制器,并针对适当的反馈增益值做出适当的选择。在本研究中,我们考虑并研究了两种基于pd的非线性控制律,即pd +补偿控制器和增强控制律。建立了确定这两个控制器反馈增益的条件,保证了跟踪误差的稳定性。此外,给出了保证闭环机器人系统零误差唯一性的条件。最后,我们考虑了一个二自由度的机械臂机器人,以揭示所提出的两种控制器在跟踪期望轨迹方面的有效性,从而评估它们在解决运动控制问题方面的性能。
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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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