基于刚度预测-神经网络的调姿平台误差补偿

Stiffness prediction-neural network based error compensation for attitude adjustment platform

变形误差对高精度调姿装备末端定位精度的影响不可忽视。为提高2RRPU/2RPU/U两轴并联调姿平台精度,提出了基于刚度模型预测误差趋势与神经网络算法提升预测精度的误差补偿模型。首先,在调姿平台全雅克比矩阵和弹性变形矩阵的基础上建立理论刚度模型,与Ansys数据刚度模型预测对比验证受载变形趋势预测的有效性。然后,搭建基于Simulink-Adams-Ansys-OPC的模拟仿真环境,随机载荷下采集平台全姿态仿真数据,基于刚度模型和速度雅克比矩阵对姿态误差和驱动误差趋势进行预测,基于速度雅可比实现末端误差到驱动补偿的映射,进一步利用神经网络算法提升误差预测的精度。仿真实验结果表明采用误差补偿模型后调姿平台获得位姿精度提高9%,验证了“刚度预测-神经网络”模型对平台姿态精度提升的有效性。

The impact of deformation error on the end positioning accuracy of high-precision attitude adjustment equipment cannot be ignored. To improve the accuracy of a 2RRPU/2RPU/U two-axis parallel attitude platform,an error compensation model is proposed based on a stiffness model to predict the error trend and a neural network algorithm to improve the prediction accuracy. The theoretical stiffness model is first established based on the full Jacobi and elastic deformation matrices of the attitude-adjusting platform.The validity of the prediction of the loaded deformation trend is verified by comparing it with the prediction by the Ansys data stiffness model. Then,a Simulink-Adams-Ansys-OPC-based simulation environment is built,and the platform full attitude simulation data is collected under random load. Next,the attitude and drive error trends are predicted based on the stiffness model and velocity Jacobi matrix,and the mapping from end error to drive compensation is realized based on the velocity Jacobi. The accuracy of the error prediction is further improved by using a neural network algorithm. The simulation results show that the attitude accuracy of the platform is improved by 9% after adopting the error compensation model,which verifies the effectiveness of the“stiffness prediction-neural network”model in the improvement of the platform attitude accuracy.