Data-driven inverse dynamics modeling using neural-networks and regression-based techniques

IF 2.6 2区 工程技术 Q2 MECHANICS
Maciej Pikuliński, Paweł Malczyk, Ronald Aarts
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Abstract

This research proposes a novel approach for the residual modeling of inverse dynamics employed to control a real robotic device. Specifically, we use techniques based on linear regression for residual modeling while a nominal model is discovered by physics-informed neural networks such as the Lagrangian Neural Network and the Feedforward Neural Network. We introduce an efficient online learning mechanism for the residual models that utilizes rank-one updates based on the Sherman–Morrison formula. This enables faster adaptation and updates to effects not captured by the neural networks. While the time complexity of updating the model is comparable to other successful learning methods, the method excels in prediction complexity, which depends solely on the model dimension. We propose two online learning strategies: a weighted approach that gradually diminishes the influence of past measurements on the model, and a windowed approach that sharply excludes the oldest data from impacting the model. We explore the relationship between these strategies, offering recommendations for parameter selection and practical application. Special attention is given to optimizing the computation time of the weighted approach when recomputation techniques are implemented, which results in comparable or even lower execution times of the weighted controller than the windowed one. Additionally, we assess other methods, such as the Woodbury identity, QR decomposition, and Cholesky decomposition, which can be implicitly used to update the model. We empirically validate our approach using real data from a 2-degrees-of-freedom flexible manipulator, demonstrating consistent improvements in feedforward controller performance.

Abstract Image

利用神经网络和回归技术进行数据驱动的逆动力学建模
本研究提出了一种用于控制真实机器人设备的反动力学残差建模新方法。具体来说,我们使用基于线性回归的技术进行残差建模,同时通过拉格朗日神经网络和前馈神经网络等物理信息神经网络发现名义模型。我们为残差模型引入了一种高效的在线学习机制,利用基于谢尔曼-莫里森公式的秩一更新。这样就能更快地适应和更新神经网络未捕捉到的效应。虽然更新模型的时间复杂度与其他成功的学习方法相当,但该方法在预测复杂度方面表现出色,而预测复杂度仅取决于模型维度。我们提出了两种在线学习策略:一种是加权法,逐渐减少过去的测量数据对模型的影响;另一种是窗口法,大幅排除最旧数据对模型的影响。我们探讨了这些策略之间的关系,为参数选择和实际应用提供了建议。我们特别关注了在实施重新计算技术时如何优化加权方法的计算时间,这使得加权控制器的执行时间与窗口控制器相当,甚至更短。此外,我们还评估了其他方法,如伍德伯里特性、QR分解和乔莱斯基分解,这些方法可以隐式地用于更新模型。我们使用一个 2 自由度柔性机械手的真实数据对我们的方法进行了经验验证,证明了前馈控制器性能的持续改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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