Effect of Geometric Error on Friction Behavior of Cylinder Seals

The tribological characteristics of the cylinder directly affect the operation accuracy of the pneumatic servo system. However, the geometric error has a significant effect on its tribological behavior and the related research is insufficient. Thus, the dynamic friction process of rubber seals has been investigated considering the influence of geometric errors. Firstly, based on the self-made friction test platform, the friction force of the rubber seals was studied and the influence law of geometric error on the contact area of the rubber seal ring was revealed. Secondly, the numerical model of the friction and contact of the rubber seals for the cylinder segment was developed by using the finite element simulation method and the influence laws of machining errors, such as roundness and straightness on the friction characteristics, were revealed. Finally, synergy effects of roundness and straightness in the friction behavior of rubber seals considering geometric errors was investigated, which lays a foundation for the accurate prediction of cylinder dynamic mechanical properties.

Neural Network Identification and Direct Adaptive Fuzzy Neural Network (DAFNN) Controller for Unknown Nonlinear Non-affine Pneumatic Servo System

In this paper, a direct adaptive fuzzy neural network (DAFNN) controller for trajectory tracking control of the non-linear non-affine pneumatic servo system is presented. First, using a neural network identifier, the non-linear dynamics of a real pneumatic servo system is simulated. By comparing the output of the neural network and the output of the experimental setup, it is observed that the non-linear pneumatic actuator system is well-identified using neural networks. By incorporating the Lyapunov stability theorem, the adaptive laws for the parameters of the controller are obtained, parameter boundedness and stability of the closed-loop system are guaranteed. Finally, practical results are successfully implemented for trajectory tracking control of the pneumatic servo system, in which the effect of the simultaneous updating of the antecedent and consequent’s parameters of the fuzzy neural network controller has been investigated. The tracking error ±1.3mm and ±1 mm for proposed updating method compared to ±2.5mm and ±3.5mm, for a case that the weigh parameters are merely adjusted, are obtained. The results indicate the proposed adjustment method improves the performance of the controller in the presence of unknown nonlinearities and dynamics uncertainty.