基于反馈线性化预测器的高自由度机械臂时延控制

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS
M. Bagheri, P. Naseradinmousavi, M. Krstić
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引用次数: 7

摘要

我们为高自由度机械臂制定了一个基于预测器的控制器,以补偿在拾取和放置任务中的时不变输入延迟。机器人机械手由于其运动可靠、速度快、精度高,但具有较大的时滞,在远程操作系统中得到了广泛的应用。在这类时滞系统上采用通用的控制算法不仅会导致控制性能差,而且在工程应用中会产生灾难性的不稳定性。因此,在设计鲁棒控制律时,需要对时滞进行补偿。作为一个案例研究,我们关注的是受三种不同输入延迟影响的7-DOF Baxter机械手。首先,利用拉格朗日方法推导了Baxter机械臂的无延迟动力学方程。然后,我们制定了一个基于预测器的控制器,在存在输入延迟的情况下,以跟踪期望的轨迹。最后,研究了在缺乏鲁棒预测器的情况下输入延迟的影响,然后对基于预测器的控制器的性能进行了实验评估,以揭示所制定算法的鲁棒性。仿真和实验结果表明,基于预测器的控制器能有效补偿输入时滞,实现闭环稳定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time Delay Control of a High-DOF Robot Manipulator Through Feedback Linearization Based Predictor
We formulate a predictor-based controller for a high-DOF manipulator to compensate a time-invariant input delay during a pick-and-place task. Robot manipulators are widely used in tele-manipulation systems on the account of their reliable, fast, and precise motions while they are subject to large delays. Using common control algorithms on such delay systems can cause not only poor control performance, but also catastrophic instability in engineering applications. Therefore, delays need to be compensated in designing robust control laws. As a case study, we focus on a 7-DOF Baxter manipulator subject to three different input delays. First, delay-free dynamic equations of the Baxter manipulator are derived using the Lagrangian method. Then, we formulate a predictor-based controller, in the presence of input delay, in order to track desired trajectories. Finally, the effects of input delays in the absence of a robust predictor are investigated, and then the performance of the predictor-based controller is experimentally evaluated to reveal robustness of the algorithm formulated. Simulation and experimental results demonstrate that the predictor-based controller effectively compensates input delays and achieves closed-loop stability.
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来源期刊
Mechatronic Systems and Control
Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.40
自引率
66.70%
发文量
27
期刊介绍: This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.
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