摘要
针对协作机器人柔性关节传动过程中固有复杂迟滞特性影响执行精度的问题,提出基于中间变量的非线性自回归移动平均(NARMAX)结构的改进加权最小二乘支持向量机(WLSSVM)迟滞模型。引入PI算子产生中间变量,将迟滞的多值映射转换为单值映射,引入输出力矩和扭转角历史值,使模型具有动态特性;LSSVM求解问题时易受模型输出误差大的数据点影响,在目标优化函数中增加具有自适应调整因子的模型输出误差构成的正则项,达到进一步提高模型精度和抗干扰能力目的。以FRANKA协作机器人关节为对象进行建模与验证,实验结果表明,相比LSSVM迟滞模型和NARMAX迟滞模型,改进WLSSVM迟滞模型具有较高的模型精度。
Aiming at the problem that the inherent complex hysteresis characteristics in the transmission process for the flexible joints of collaborative robots seriously affect the execution accuracy,an improved weighted least squares support vector machine(WLSSVM)hysteresis model based on a nonlinear autoregressive moving average with intermediate variables(NARMAX)structure is proposed.The PI operator is introduced as the intermediate variable to convert the multi-mapping of hysteretic into singlemapping,and the introduction of historical values of output torque and torsion angle makes the model have dynamic characteristics.The LSSVM is susceptible to the influence of data points with large model output errors when solving the problem,hence a regular term composed of model output errors with an adaptive adjustment factor is added to the objective optimization function to further improve the model accuracy and anti-interference capability.The proposed hysteresis model has been verified on the FRANKA collaborative robot platform.Compared with the LSSVM hysteresis model and the NARMAX hysteresis model,the improved WLSSVM hysteresis model has higher model accuracy.
作者
党选举
马樑海
DANG Xuanju;MA Lianghai(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)
出处
《制造技术与机床》
北大核心
2022年第12期33-39,共7页
Manufacturing Technology & Machine Tool
基金
国家自然科学基金项目(61863008)。
关键词
协作机器人
柔性关节
复杂迟滞特性
中间变量
自适应调整因子
collaborative robot
flexible joint
complex hysteresis characteristics
inter-mediate variable
adaptive adjustment factor
