摘要
在显微视觉领域,压电驱动定位技术因其在微观尺度的高精度特性和灵活性备受关注。然而,由于定位过程中涉及图像处理、传输和控制等方面的时延,导致图像雅可比矩阵的估计会出现较大误差。因此,本文提出了一种改进扩展Kalman滤波算法用来预测图像雅可比矩阵,大幅度降低时间延迟因素。首先,将辨识得到的Bouc-Wen模型与扩展Kalman滤波算法的状态观测方程相结合,使得状态观测方程更全面地考虑压电平台的迟滞非线性特性,有效地提高了对压电平台速度和位置的预测;其次,结合Bouc-Wen模型的扩展Kalman滤波算法在面对非线性问题时,采用的是泰勒级数,这将导致扩展Kalman滤波算法对高度非线性的函数无法提供良好的近似,从而导致在估计雅可比矩阵的时候引入较大的近似误差,故本文将采用神经网络对高度非线性函数进行近似,进而对图像雅可比矩阵进行估计。最后,通过搭建一个显微视觉的压电驱动实验平台,进行位置跟踪实验,仿真实验表明,输入信号分别为正弦信号和三角波信号时,改进扩展Kalman滤波算法跟踪误差均值分别为0.199μm和0.132μm,而扩展Kalman滤波算法的跟踪误差均值分别为0.692μm和0.513μm,结果验证了改进算法的优越性和可行性。
In the field of microscopic vision,piezoelectric-driven positioning technology has attracted significant attention due to its high precision and flexibility at the microscale.However,the presence of delays in processes such as image processing,transmission,and control during positioning introduces significant estimation errors in the image Jacobian matrix.Therefore,this paper proposed an improved extended Kalman filter algorithm to predict the image Jacobian matrix and substantially reduce the impact of time delays.Firstly,the identified Bouc-Wen model was combined with the state observation equation of the extended Kalman filter algorithm.This comprehensive consideration of the hysteresis nonlinearity of the piezoelectric platform effectively enhanced the prediction of the platform′s velocity and position.Secondly,in dealing with nonlinear problems,the extended Kalman filter algorithm traditionally employed Taylor series,which may result in poor approximations for highly nonlinear functions,introducing significant errors when estimating the Jacobian matrix.To address this,the paper employed a neural network to approximate highly nonlinear functions and subsequently estimate the image Jacobian matrix.Finally,by constructing a piezoelectric-driven experimental platform for microscopic vision,position tracking experiments were conducted.Simulation experiments demonstrate that when the input signals are sinusoidal and triangular wave signals,the mean tracking errors of the improved Extended Kalman Filter algorithm are 0.199μm and 0.132μm,respectively,while the mean tracking errors of the Extended Kalman Filter algorithm are 0.692μm and 0.513μm,respectively.The results validate the superiority and feasibility of the improved algorithm.
作者
杨柳
何贺
程佳佳
李东洁
YANG Liu;HE He;CHENG Jiajia;LI Dongjie(School of Automation,Harbin University of Science and Technology,Harbin 150080,China;Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration,Harbin University of Science and Technology,Harbin 150040,China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2024年第12期1868-1878,共11页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.62203146)。
