This paper proposes a data-driven statistical learning method to identify the force-pressure hysteresis of a pneumatic end-effector based on Gaussian process regression (GPR). Given the actual complex characteristics of the hysteresis phenomena, GPR is used to establish the relationship between the input and output variables of the hysteresis as a first-order nonlinear differential equation ignoring the high-order dynamics without specifying any hyperparameters or considering the special features of hysteresis loops. The inverse hysteresis model can be derived directly. The parameters of the GPR are determined by choosing low-frequency triangle-wave pressure excitations as the training set, and then the prediction performance of the present model is tested under different types, amplitudes, and period conditions of pressure signals as the verification sets. Compared with two types of modified Prandtl-Ishlinskii model, the proposed model achieves better accuracy in both training and verification sets. Based on the inverse hysteresis feedforward compensator, comparative experiments of the force-tracking control are conducted in the form of the open-loop and closed-loop controllers, of which the results indicate the effectiveness and superiority of the proposed model.

本文提出了一种基于数据驱动的统计学习方法，该方法基于高斯过程回归（GPR）来识别气动末端执行器的力-压力滞后。给定磁滞现象的实际复杂特性，GPR用于建立磁滞输入和输出变量之间的关系，作为一阶非线性微分方程，忽略了高阶动力学，而无需指定任何超参数或不考虑磁滞现象的特殊特征。磁滞回线。逆磁滞模型可以直接导出。通过选择低频三角波压力激励作为训练集来确定GPR的参数，然后在不同类型，幅度，压力信号的周期条件作为验证集。与两种改进的Prandtl-Ishlinskii模型相比，该模型在训练集和验证集上均具有更高的准确性。基于逆磁滞前馈补偿器，以开环和闭环控制器的形式进行了力跟踪控制的对比实验，结果表明了该模型的有效性和优越性。