| The compressive strength of textile materials is one of the important mechanical and physical properties and a key criterion for evaluating textile performance.It is essential to conduct rapid and accurate testing of the compressive strength of textile materials.The transmission and isobaricity of pneumatic technology enable the pneumatic system to achieve multiple isobaric outputs.In this study,a pneumatic pressure-controlled textile compressive performance testing system was designed to test the compressive performance of various textile materials.However,the strong nonlinearity,coupling,and time-varying characteristics of the pneumatic pressure control system pose significant difficulties in modeling and controller design.This thesis focuses on the pressure tracking control and coupling problem of the textile compressive performance testing machine and establishes a mathematical model of the pneumatic pressure control system.An adaptive integral compensation neural network fuzzy control algorithm is proposed,and the simulation results and experimental data demonstrate that the proposed method significantly enhances the pressure tracking control performance of the system and effectively solves the coupling problem.The specific research contents are as follows:(1)Analyze the principle and requirements of the pneumatic system,design a pneumatic pressure closed-loop control system,and establish the mathematical model of pneumatic components such as high-speed switching valves.(2)Design a distributed structure-based textile compressive performance testing system experimental platform.The upper computer in the testing system structure is mainly responsible for resource allocation,instruction transmission,data acquisition and processing,parameter calculation and curve display,and report output.The lower computer is responsible for pneumatic pressure closed-loop control and data acquisition.To prevent unexpected power outages and other situations,the system also has power-off protection and data recovery functions to ensure the stability of the experiment.(3)Considering the nonlinearity and small signal dead zone time of high-speed switching valves,an adaptive integral compensation fuzzy control algorithm is proposed.By using the approximation capability of fuzzy control,the rapid switching characteristic of high-speed switching valves is fully utilized to overcome its nonlinearities and other drawbacks,and combined with an adaptive integral controller,the precision of pneumatic pressure control is improved,and the system has stronger robustness.By comparing the performance of the proposed algorithm with hardware switch quantity control,PID control,and fuzzy control algorithms on the experimental platform,the effectiveness of the proposed algorithm is verified.(4)To solve the coupling oscillation problem between branches in the multi-loop pneumatic pressure control system,an adaptive integral compensation neural network fuzzy control algorithm combined with artificial neural networks is proposed.A multiinput pneumatic control system model is built in AMESim software,and the proposed control algorithm is used in MATLAB to perform joint simulation and analysis of the system,significantly improving the 16-way multi-input pneumatic pressure tracking control performance and solving the coupling problem in the 64-way parallel control system. |
