摘要
针对现有基于PID的生物离心机光电编码器定位精度误差大的问题,通过采用人工鱼群优化BP神经网络初始权值与阈值选择,提出一种生物离心机自动定位控制误差补偿方法。仿真结果表明,所提方法可有效补偿生物离心机光电编码器的误差,提高离心机自动定位精度,误差补偿后的最大误差为0.039°,最小误差为-0.013 5°,平均误差为4.86″,标准差为0.32,相较于误差补偿前,整体精度提高了3.34倍,具有一定的有效性。
Aiming at the problem of large positioning accuracy error of the existing photoelectric encoder of biological centrifuge based on PID,an improved BP neural network error compensation method for automatic positioning control of biological centrifuge is proposed by using artificial fish swarm algorithm to optimize the initial weight and threshold of BP neural network,and using the improved BP neural network to compensate the error.The simulation results show that the proposed method can effectively compensate the error of the photoelectric encoder of the biological centrifuge and improve the automatic positioning accuracy of the centrifuge.After error compensation,the maximum error is 0.039°,the minimum error is-0.0135°,the average error is 4.86″,and the standard deviation is 0.32.Compared with before error compensation,the overall accuracy is improved by 3.34 times,which is effective.
作者
窦敏娜
王晓霞
DOU Minna;WANG Xiaoxia(Xianyang Vocational Technical College,Xianyang Shanxi 712000,China)
出处
《自动化与仪器仪表》
2024年第1期85-88,共4页
Automation & Instrumentation
基金
2021年全国轻工职业教育教学指导委员会课题《高职生物化学课程思政教学改革研究与实践》(QGHZW2021134)
陕西省教育科学“十四五”规划2021年度课题《高职生物化学课程混合式教学的研究与实践》(SGH21Y0593)。
关键词
生物离心机
自动定位
BP神经网络
人工鱼群算法
误差补偿
biological centrifuge
automatic positioning
BP neural network
artificial fish swarm algorithm
error compensation
