Cover Image, Volume 5, Number 2, June 2025

IF 3.4 Q1 ENGINEERING, MECHANICAL
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引用次数: 0

Abstract

Front Cover Caption: Control of a lambda-robot based on machine learning surrogates for inverse kinematics and kinetics: Tracking control of multibody systems with closed-loop mechanisms presents significant computational challenges due to the complexity of inverse kinematics and dynamics. This study introduces an innovative approach that replaces traditional model-based methods with artificial intelligence by training surrogate models on simulation data. Using the λ-robot, a parallel mechanism, as a case study, the workspace is analyzed to ensure comprehensive data coverage for training. The trained surrogates provide control inputs that enable the use of a linear quadratic regulator (LQR) for trajectory tracking. An additional feedback loop addresses model uncertainties. Simulation results validate the effectiveness of this AI-enhanced, data-driven control framework.

Abstract Image

封面图片,第五卷,第2期,2025年6月
封面说明:基于逆运动学和动力学的机器学习代理的lambda机器人控制:由于逆运动学和动力学的复杂性,具有闭环机构的多体系统的跟踪控制提出了重大的计算挑战。本研究引入了一种创新的方法,通过在仿真数据上训练代理模型,用人工智能取代传统的基于模型的方法。以并联机构λ-机器人为例,对工作空间进行了分析,以保证训练数据的全面覆盖。经过训练的代理人提供控制输入,使使用线性二次调节器(LQR)进行轨迹跟踪。一个附加的反馈回路解决了模型的不确定性。仿真结果验证了这种人工智能增强的数据驱动控制框架的有效性。
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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