基于相位超前补偿的TCN神经网络柔性关节的复杂迟滞建模研究

TCN neural networks based on phase lead compensation for complex hysteresis modeling of joint

针对轻型工业机器人柔性关节的复杂迟滞特性建模问题,基于电机驱动电流与扭转角之间关系,描述关节的迟滞特性,提出了一个基于相位超前补偿的机器人柔性关节改进TCN(temporal convolutional network)迟滞模型。在通过卡尔曼滤波还原剔除噪声干扰后的关节迟滞特性数据基础上,主要研究设计一种改进TCN迟滞模型,在模型各分支引入不同因子的扩张卷积,解决了TCN神经网络迟滞模型在极值点存在较大误差问题;针对改进TCN迟滞模型依然存在由相位滞后引起的误差,设计相位超前环节与TCN神经网络模型串联,构造一个基于相位超前补偿的改进TCN神经网络迟滞模型,进一步提高了迟滞模型精度。与改进TCN迟滞模型及典型PI迟滞模型相比,实验结果验证了所提出的机器人关节迟滞模型,具有较高精度。

To address the modeling problems of complex hysteresis characteristics of flexible joints in lightweight industrial robots,an improved TCN(temporal convolutional network)hysteresis model for robot flexible joints based on phase lead compensation is proposed to describe the hysteresis characteristics of flexible joints between motor drive current and torsional angle.On the basis of restoring the joint hysteresis characteristic data after removing noise interference through Kalman filtering,research focuses on the following an improved TCN hysteresis model is designed,which incorporates dilated convolutions with different factors into each branch of the model to address the significant error issue at extremities points in the TCN neural network hysteresis model.In response to the residual errors caused by phase lag in the improved TCN hysteresis model,a phase lead component is designed and concatenated with the TCN neural network model,thereby constructing an improved TCN neural network hysteresis model with phase lead compensation and further enhancing the accuracy of the hysteresis model.Compared with the improved TCN hysteresis model and classical PI hysteresis model,the experimental results have verified the proposed robot joint hysteresis model with high accuracy.