考虑控制电信号中间量的机床运动控制误差数据驱动建模方法

Data-driven Machine Tools Motion Control Error Modeling Method Using Control Signal as Intermediate Value

现有数据驱动的机床运动控制误差建模方法通常使用端到端的模型,即通过机器学习算法直接构建参考轨迹信息(速度、加速度等)与伺服误差之间的模型,以降低建模复杂度。然而,该方法忽视了控制电信号对运动控制系统非线性扰动的反映,而导致建立的模型精度受限。为解决此问题,提出了一种使用控制电信号作为中间量的数据驱动运动控制误差建模方法。该方法采集参考轨迹信息(速度、加速度、急动度等)、控制电信号、跟踪误差以及构造的换向特征,构建并训练基于参考轨迹信息的控制电信号预测网络,以及基于电信号和参考轨迹信息的运动控制误差预测网络,利用控制电信号这一中间量有效反应系统所受非线性扰动的特点,实现了高精度的运动控制误差数据驱动建模。在实际验证测试时,将参考轨迹信息输入电信号预测网络,而后将得到的预测控制电信号和参考轨迹信息输入跟踪误差预测网络,即可实现运动控制误差的预测。通过实验对所提出的建模方法进行了验证,所提出方法相对于传统的端到端建模方法,运动控制误差的预测精度在X轴和Y轴分别提升16.33%和20.42%,误差补偿后运动控制轮廓精度相较于未补偿提升85.59%,验证了所提出方法的可行性。

Existing data-driven feed drive system modeling methods use end-to-end model, which employs machine learning algorithms to construct the model between the reference trajectory information(displacement, velocity, acceleration etc.) and the servo error directly, reducing the complexity of modeling. However, this kind of modeling method ignores the non-linear disturbance in motion control system reflected in the control signal, limiting the accuracy of constructed model. To solve the problem, a data-driven motion control error modeling method using control signal as intermediate value is proposed. Proposed method samples the reference trajectory information(velocity, acceleration, jerk etc.), control signal, tracking error, and constructs a reverse feature.Then, the control signal prediction network based on reference trajectory information and tracking error prediction network using control signal and reference trajectory information are constructed and trained, setting up the proposed model. Utilizing the feature that control signal can effectively reflect the nonlinear disturb, the accuracy of data-driven motion control error model is improved. In the validation and testing, the reference trajectory information is input into control signal prediction network to get the predicted control signal. And, the predicted control signal and reference trajectory information are input into tracking error prediction network, attaining the utilization of control signal as intermediate value in the data-driven modeling of feed drive system. Proposed modeling method is validated through experiments. Compared with traditional end-to-end modeling method without control signal, the proposed method shows better prediction accuracy. The prediction accuracy improves 16.33% and 20.42% respectively in X axis and Y axis, and the motion control contour accuracy improves 85.59% after error compensation, proving the effectiveness and feasibility of proposed method.