混联双足机械腿的动力学建模与参数辨识

doi:10.3901/JME.260237

摘要/Abstract

摘要： 为提高基于混联机构的双足机械腿动力学参数辨识的速度与准确性，提出了一种基于改进的激励轨迹优化方法与常春藤算法(Ivy algorithm, IVYA)的整体参数辨识方法。将机械腿中并联关节简化处理为等效串联关节，基于Newton-Euler法建立单腿动力学线性化模型。与传统方法相比，首次将关节空间利用率约束引入激励轨迹优化，解决了混联机构关节空间紧凑导致的优化效率问题，并完成激励轨迹设计。采用5自由度混联机构的机械腿为实验对象进行动力学参数辨识实验，通过传感器采集关节角度和电流数据，滤波处理数据后使用IVY辨识算法进行参数估计，并将IVY算法辨识得到的预测结果与最小二乘法进行对比。实验结果表明，基于关节空间利用率约束的激励轨迹优化速度提高了17%且条件数误差极小；与最小二乘法相比，采用IVYA动力学辨识模型所得5个关节预测力矩与实际力矩的误差RMS值均更低，RMS值平均减小了13.42%，最大误差RMS值减小了2.68 N·m，证明了所提整体辨识方法的有效性，为基于模型的混联机器人精确控制提供了理论参考。

关键词: 参数辨识, 轨迹优化, 常春藤算法, 混联机构, 双足机器人

Abstract: To enhance the speed and accuracy of dynamic parameter identification for bipedal robotic legs based on hybrid mechanisms, an integrated parameter identification method is proposed, which combines an improved excitation trajectory optimization technique with the Ivy algorithm (IVYA). Initially, the parallel joints in the robotic leg are simplified into equivalent serial joints, and a linearized single-leg dynamic model is established using the Newton–Euler method. Compared with traditional methods, this research proposes the first integration of joint space utilization constraints into excitation trajectory optimization, addressing the optimization efficiency issues arising from the compact joint space of hybrid serial-parallel mechanisms, and successfully designs excitation trajectories. A dynamic parameter identification experiment is conducted on a 5-DOF robotic leg with a hybrid mechanism. Joint angle and current data are collected via sensors, and after filtering, the IVYA algorithm is employed for parameter estimation. The prediction results obtained using IVYA are compared with those derived from the least squares method. Experimental results indicate that the excitation trajectory optimized under joint space utilization constraints improves the optimization speed by 17% and exhibits negligible condition number error. Moreover, compared with the least squares method, the RMS errors between the actual torques and the predicted torques for the five joints obtained using the IVYA-based dynamic model are lower, with the average RMS error reduced by 13.42% and the maximum RMS error decreased by 2.68 N•m. These findings confirm the effectiveness of the proposed integrated identification method, providing a theoretical basis for precise model-based control of hybrid robots.

Key words: parameter identification, trajectory optimization, ivy algorithm, hybrid mechanism, bipedal robot

中图分类号:

TP242

孙鹏, 葛梦虎, 芮超, 曹利康, 陈波, 王剑斌, 李研彪. 混联双足机械腿的动力学建模与参数辨识[J]. 机械工程学报, 2026, 62(5): 192-203.

SUN Peng, GE Menghu, RUI Chao, CAO Likang, CHEN Bo, WANG Jianbin, LI Yanbiao. Dynamic Modeling and Parameter Identification of Hybrid Bipedal Robotic Legs[J]. Journal of Mechanical Engineering, 2026, 62(5): 192-203.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260237

http://www.cjmenet.com.cn/CN/Y2026/V62/I5/192

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