机理与数据驱动的软体手弯曲角度软测量模型

Soft sensor model of bending angle integrated mechanism and data for soft gripper

由于软体手材料强非线性,难以建立软体手弯曲角度精确的机理模型。针对以上难题,提出机理与数据驱动的软体手弯曲角度软测量模型。该模型由机理模型与信息素挥发及惯性权重的自适应块增量随机配置网络(ABSCN)补偿模型组成。采用最小二乘对机理模型进行参数辨识,针对高阶未建模动态,采用ABSCN预测补偿。通过对块增量随机配置网络(BSC)的增量块配置次数进行自适应优化,提高模型的紧凑性,减少模型的训练时间。最后通过混合模型的仿真实验与真实数据进行对比,结果表明所提方法在精度上有显著提升。

Due to the strong nonlinearity of material used in soft gripper,it is difficult to establish a precise mechanism model for measuring the bending angle of the soft gripper.To address this challenge,a mechanism and data-driven soft sensor model for the bending angle of the soft gripper is proposed.The model consists of a mechanism model and an adaptive block increment stochastic configuration networks(ABSCN)compensation model,which includes information scent evaporation and inertia weights.The mechanism model parameter is identified using least squares,and ABSCN is used to predict compensation for high order unmodeled dynamics.By adaptively optimizing the number of incremental block configurations in block incremental stochastic configuration networks(BSC),the compactness of the model is improved,and the training time is reduced.Finally,through simulation experiments and comparison with real data using a hybrid model,it is shown that the proposed method significantly improves the accuracy.