融合正逆程特征的GRU神经网络关节迟滞特性建模研究  

Forward and reverse trajectory features fused GRU neural network for hysteresis characteristics modeling of joint

作　　者：党选举[1,2] 张一晨 DANG Xuanju;ZHANG Yichen(School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,CHN;Key Laboratory of Guangxi College Intelligent Comprehensive Automation,Guilin 541004,CHN)

机构地区：[1]桂林电子科技大学电子工程与自动化学院,广西桂林541004 [2]广西智能综合自动化高校重点实验室,广西桂林541004

出　　处：《制造技术与机床》2023年第11期107-115,共9页Manufacturing Technology & Machine Tool

基　　金：国家自然科学基金项目资助(62263004,61863008)。

摘　　要：针对未配置负载转矩传感器的低成本轻型工业机器人柔性关节复杂迟滞特性的高精度建模问题,文章以电机驱动电流间接反映负载转矩的变化,采用驱动电流-扭转角之间的关系描述柔性关节迟滞特性,提出了一种融合正、逆程特征的GRU神经网络迟滞模型。将关节迟滞特性中正、逆程特有的特征融入GRU神经网络迟滞模型中,利用基于卡尔曼滤波的电流增量,提取正程和逆程的特征,描述电流-扭转角迟滞特性中正、逆程所表现出的多值特性。将历史预测结果作为新信息输入模型,构造具有记忆能力和非线性映射能力的动态GRU神经网络迟滞模型。实验结果验证了所提出的柔性关节迟滞模型具有良好的预测能力和较高的模型精度。To address the problem of high-precision modeling of the complex hysteresis characteristics exhibited by the flexible joints of low-cost and lightweight industrial robots without load torque sensors,the motor drive current is used to indirectly reflect the change of load torque,and the relationship between drive current and torsion angle is employed to describe the hysteresis characteristics of flexible joints,the forward and reverse trajectory features-based GRU neural network hysteresis model is proposed.The features specific to the forward and reverse trajectory features in the joint hysteresis characteristics are fused into the GRU neural network.The Kalman filter-based current increments are used to extract the features of the forward and reverse trajectory to describe the multi-valued characteristics exhibited by the forward and reverse trajectory features in the current-torsion angle hysteresis characteristics.The historical prediction results are taken as the new information input model to construct a dynamic GRU neural network hysteresis model with memory capability and nonlinear mapping ability.The experimental results verify that the proposed flexible joint hysteresis model has good prediction ability and high model accuracy.

关 键 词：特征融合 正逆程特征 迟滞建模 数据驱动 

分 类 号：TP242.2[自动化与计算机技术—检测技术与自动化装置]