融合深度神经网络的气动执行系统建模与试验验证OA

To improve the accuracy of valve-controlled cylinder models under the strong nonlinearity of compressed air,we propose a data-driven modeling method for pneumatic systems.The method collects time-series data during system operation and uses cylinder displacement as the prediction target.It builds a feedforward neural network with six hidden layers,uses Smooth L1 Loss as the loss function,and combines the ADAM optimizer with an adaptive learning rate strategy.For comparison,we also develop a traditional physical model of the pneumatic system in Simulink and build an experimental testbed for the valve-controlled cylinder.The study validates displacement prediction under different load masses and supply pressures.The traditional method gives an root mean square error of 0.035 m and a coefficient of determination of 0.9532 for displacement.In contrast,the neural network model reduces the root mean square error to 0.003985 m,and its coefficient of determination exceeds 0.999169.These results show that the data-driven method predicts cylinder displacement much more accurately than the traditional physical model.This study demonstrates the clear advantage of data-driven modeling for high-precision pneumatic system modeling and shows strong potential for engineering applications.