精密检测平台定位误差自适应补偿技术

发布时间：2019-03-08 09:18

【摘要】：为了实现光学元件精密检测平台定位误差的自适应补偿,以保证在不同的检测环境中平台能够自行保持高精度,提出了基于检测环境监测和支持向量回归机的定位误差自适应补偿方法。首先,以多组检测环境中温度、湿度和气压的具体测量值作为训练数据,利用支持向量回归机建立定位误差最大值的预测模型,进行最大值预测。然后,将最大值同温度、湿度、气压等环境因素和位置信息一起作为训练数据,迭代使用支持向量回归机,建立任意位置定位误差预测模型。最后,将预测到的定位误差值传入检测平台控制器中进行补偿。应用雷尼绍激光干涉仪,温度、湿度和气压传感器等仪器设备,在光学元件精密检测平台上进行了具体实验。实验结果表明该技术切实可行,预测数据与实测数据差值绝对值的平均值为0.88μm,Pearson相关系数的平方为0.99,自适应补偿后平均定位误差由43μm降为1.4μm。
[Abstract]:In order to realize the adaptive compensation of the positioning error of the precision detection platform for optical elements, the platform can maintain high precision in different detection environments. An adaptive compensation method for positioning error based on detection environment monitoring and support vector regression is proposed. Firstly, with the specific measurement values of temperature, humidity and air pressure as the training data, the prediction model of the maximum positioning error is established by using the support vector regression machine, and the maximum value prediction is carried out. Then, the maximum value is used as the training data together with the environmental factors and position information such as temperature, humidity, air pressure and so on, and the support vector regression is iteratively used to establish the prediction model of the positioning error at any position. Finally, the predicted positioning error is transmitted to the controller of the detection platform to compensate. By using Renishaw laser interferometer, temperature, humidity and air pressure sensors, specific experiments have been carried out on a precise testing platform for optical elements. The experimental results show that this technique is feasible. The average absolute value of the difference between the predicted data and the measured data is 0.88 渭 m, the square of the correlation coefficient of Pearson is 0.999, and the average positioning error after adaptive compensation is reduced from 43 渭 m to 1.4 渭 m.
【作者单位】：厦门大学机电工程系;中国工程物理研究院激光聚变研究中心;
【基金】：国家自然科学基金项目(51570343) 福建省自然科学基金项目(2012J05098)
【分类号】：TH74;TP274

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