【摘要】 基于现代控制理论的经验建模法,在针对数控机床不同生产工况时,难以建立一种共性的热误差解决方案。探索了在数字孪生框架下以无模型驱动方式实现数控机床热误差自适应预测的研究。首先,建立了机床“热传感-映射-融合与优化-驱动”数字孪生框架,实现热特征信息在数字孪生体中的存储与融合。然后,基于MISO系统的假设条件和动态线性化几何释义,提出了一种不受被控系统任何结构数据影响的热误差无模型自适应控制(MFAC)方法。进一步,基于动态发现概率和自适应步长的DACS-MFAC算法按照一定的周期更新系统参数,实现数字孪生系统下热误差预测值的动态优化。实验结果表明,DACS-MFAC方法具有适应强、精度高、收敛性好等优点。更多还原

【Abstract】 The empirical modeling method based on modern control theory is complex to establish a standard thermal error solution for different production conditions of CNC machine tools. Explore the research on adaptive prediction of thermal errors in CNC machine tools using model-free driving under the digital twin framework. Firstly, a digital twin framework based on the “thermal sensing-mapping fusion-optimization-drive” structure of machine tools is established to achieve the storage and fusion of thermal feature information in the digital twin. Then, based on the assumptions of the MISO system and the dynamic linearization geometric interpretation, a thermal error model free adaptive control(MFAC) method is proposed that is not affected by any structural data of the controlled system. Furthermore, based on the dynamic discovery probability and adaptive step size of the DACS-MFAC algorithm, the system parameters are updated according to a certain period to achieve dynamic optimization of thermal error prediction values in the digital twin system. Experimental result shows that the DACS-MFAC method has advantages such as strong adaptability, high accuracy, and good convergence.更多还原