To solve the problems of slow convergence speed, poor robustness, and complex calculation of image Jacobian matrix in image-based visual servo system, a hybrid regression model based on multiple adaptive regression spline and online sequential extreme learning machine is proposed to predict the product of pseudo inverse of image Jacobian matrix and image feature error and online sequential extreme learning machine is proposed to predict the product of pseudo inverse of image Jacobian matrix and image feature error. In MOS-ELM, MARS is used to evaluate the importance of input features and select specific features as the input features of online sequential extreme learning machine, so as to obtain better generalization performance and increase the stability of regression model. Finally, the method is applied to the speed predictive control of the manipulator end effector controlled by image-based visual servo and the prediction of machine learning data sets. Experimental results show that the algorithm has high prediction accuracy on machine learning data sets and good control performance in image-based visual servo.

针对基于图像的视觉伺服系统中存在收敛速度慢、鲁棒性差、图像雅可比矩阵计算复杂等问题，提出了一种基于多元自适应回归样条和在线序列极限学习机的混合回归模型来预测图像的乘积。提出图像雅可比矩阵与图像特征误差的伪逆和在线序列极限学习机来预测图像雅可比矩阵的伪逆与图像特征误差的乘积。在 MOS-ELM 中，MARS 用于评估输入特征的重要性，选择特定的特征作为在线序列极限学习机的输入特征，从而获得更好的泛化性能，增加回归模型的稳定性。最后，该方法应用于基于图像的视觉伺服控制的机械手末端执行器的速度预测控制和机器学习数据集的预测。实验结果表明，该算法在机器学习数据集上具有较高的预测精度，在基于图像的视觉伺服中具有良好的控制性能。