使用半递归多体公式进行机器人机械手的系统识别和力估算

IF 2.6 2区 工程技术 Q2 MECHANICS
Lauri Pyrhönen, Aki Mikkola, Frank Naets
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引用次数: 0

摘要

多体动力学中的力估算在很大程度上依赖于对系统模型的高精度了解。然而,在机器人或移动机械等复杂的机电一体化系统中,模型参数值可能只能根据 CAD 数据等设计信息进行粗略估算。因此,模型参数的误差会直接影响力估算的准确性,因为估算器会通过改变外力估算值来补偿错误的惯性力、摩擦力和作用力。本研究旨在介绍开环多体结构的系统识别和状态/力估算工作流程。系统识别采用机器人技术中的线性回归识别方法,并将其调整到多体框架中。特别是半递归多体公式,作为系统识别和力估计的公式进行了研究。多体状态/力估算器使用扩展卡尔曼滤波器构建。本文的具体目的是展示如何利用这些已知的建模、识别和估算工具,解决目前在多体系统虚拟传感中缺乏完整工具链的问题。研究方法通过史陶比尔 TX40 机械手的人工数据和实验数据进行了测试。在实验分析中,使用了公开的基准数据集。人工数据是通过运行反动力学分析创建的,惯性和摩擦参数取自文献。结果表明,多体惯性和摩擦参数可以被准确识别,识别出的模型可以用来产生合理的外力估计值。所提出的多体系统识别方法本身为调整产品开发中使用的多体模型带来了新的机遇。此外,有效利用系统识别和状态估算有助于建立更精确的估算器。当系统模型被准确识别后,状态估计器观察未知输入(如外力)的能力就会显著增强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

System identification and force estimation of robotic manipulator using semirecursive multibody formulation

System identification and force estimation of robotic manipulator using semirecursive multibody formulation

Force estimation in multibody dynamics relies heavily on knowing the system model with a high level of accuracy. However, in complex mechatronic systems, such as robots or mobile machinery, the values of model parameters may be only roughly estimated based on design information, such as CAD data. The errors in model parameters consequently have a direct effect on force estimation accuracy because the estimator compensates the erroneous inertia, friction, and applied forces by changing the value of estimated external force. The objective of this study is to present the workflow of system identification and state/force estimation of an open-loop multibody structure. The system identification utilizes a linear regression identification method used in robotics adapted to the multibody framework. The semirecursive multibody formulation, in particular, is studied as a formulation for both system identification and force estimation. The multibody state/force estimator is constructed using extended Kalman filter. The specific aim of this paper is to demonstrate the utilization of these per se known modeling, identification, and estimation tools to address their current lack of integration as a complete toolchain in virtual sensing of multibody systems. The methodology of the study is tested with both artificial and experimental data of Stäubli TX40 robotic manipulator. In the experimental analysis, an openly available benchmark data set was used. Artificial data were created by running an inverse dynamics analysis with inertia and friction parameters taken from literature. The results show that the multibody inertia and friction parameters can be accurately identified and the identified model can be used to produce decent estimates of external forces. The proposed multibody system identification method itself opens new opportunities in tuning the multibody models used in product development. Moreover, effective use of system identification together with state estimation helps to build more accurate estimators. When the system model is accurately identified, the capability of state estimator to observe unknown inputs, such as external forces, is significantly enhanced.

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来源期刊
CiteScore
6.00
自引率
17.60%
发文量
46
审稿时长
12 months
期刊介绍: The journal Multibody System Dynamics treats theoretical and computational methods in rigid and flexible multibody systems, their application, and the experimental procedures used to validate the theoretical foundations. The research reported addresses computational and experimental aspects and their application to classical and emerging fields in science and technology. Both development and application aspects of multibody dynamics are relevant, in particular in the fields of control, optimization, real-time simulation, parallel computation, workspace and path planning, reliability, and durability. The journal also publishes articles covering application fields such as vehicle dynamics, aerospace technology, robotics and mechatronics, machine dynamics, crashworthiness, biomechanics, artificial intelligence, and system identification if they involve or contribute to the field of Multibody System Dynamics.
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