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基于混沌粒子群自抗扰控制的轧染机张力控制

Tension control of rolling and dyeing machines based on chaotic particle swarmand auto disturbance rejection control
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摘要 以连续轧染机轧车部分织物张力控制为研究对象,针对其张力耦合等因素导致的张力控制难的问题,建立了轧车部分张力系统的非线性耦合数学模型,并推导出静态解耦模型。采用混沌粒子群优化算法与自抗扰控制技术结合的方法,设计了相邻轧车间的张力控制器,通过自抗扰算法主动估计和补偿张力系统动态耦合部分,实现了系统的静、动态解耦;并采用混沌粒子群算法在线自整定自抗扰控制器中的主要参数。通过仿真实验与常规PID控制器对比发现,混沌粒子群自抗扰控制器能使张力系统实现解耦控制及抑制内外部干扰引起的张力波动,保证轧车恒张力稳定运行,提高系统的稳定性和抗干扰性能。 The continuous pad dyeing machine is a typical multi-unit joint equipment.According to its pad dyeing process,the continuous pad dyeing machine is divided into unwinding,pad dyeing,drying and winding units.The continuous pad dyeing machine needs to be controlled by constant tension during operation to ensure uniform dyeing of the fabric.If the fabric is subjected to excessive tension,it will produce warp and weft contraction and even fracture,which will affect the quality of pad dyeing.If the fabric tension is too small,it will produce wrinkles or fabric deviation,which seriously affects the economic benefits of enterprises.The pad dyeing unit is the most critical unit of the continuous pad dyeing machine,and its tension control effect will directly affect the printing and dyeing quality of the fabric.Therefore,it is crucial to ensure the constant tension of the fabric during the operation of the continuous pad dyeing machine.In this paper,the tension control system of the pad dyeing unit of the continuous pad dyeing machine was taken as the research object.In view of the difficulty of tension control such as tension coupling,the nonlinear coupling mathematical model of the tension system of the rolling mill was established,the static decoupling model was obtained,and the control algorithm was designed and verified by simulation experiments.Firstly,according to the operation mechanism of pad dyeing unit and its structure diagram,the parameters such as moment of inertia inthe pad dyeing process were analyzed,and the dynamic model of the pad dyeing unit was established according to the law of mass conservation and Hooke's law.By observing the tension mathematical model of the pad dyeing unit,it is concluded that there are tension coupling and tension speed coupling between adjacent two rollers,and the system has nonlinear,time-varying,multi-interference and strong coupling characteristics.It is difficult to achieve the ideal control effect for the conventional PID controller of this kind of system.In this paper,the tension controller of adjacent rolling workshop was designed by using the combination of chaotic particle swarm optimization(CPSO)and active disturbance rejection control(ADRC).The dynamic coupling part of the tension system was estimated and compensated by the active disturbance rejection algorithm to realize the complete decoupling of the system,and the chaotic particle swarm optimization algorithm was used to adjust the main parameters of the active disturbance rejection controller online.The tension system of pad dyeing unit was simulated by MATLAB Simulink,and the control effect of chaotic particle swarm auto disturbance rejection controller and conventional PID controller was observed.The experimental results show that the chaotic particle swarm active disturbance rejection controller is insensitive to the change of internal parameters and has good anti-interference.The control accuracy and stability are better than those of the conventional PID controller,and it can effectively suppress the tension fluctuation caused by coupling and interference.It is of great significance to improve the overall operation performance of the continuous pad dyeing machine.
作者 李幸芳 赵世海 LI Xingfang;ZHAO Shihai(School of Mechanical Engineering,Tiangong University,Tianjin 300387,China;Tianjin Key Laboratory of Modern Mechanical and Electrical Equipment Technology,Tiangong University,Tianjin 300387,China)
出处 《现代纺织技术》 北大核心 2023年第6期207-215,共9页 Advanced Textile Technology
基金 天津市科技支撑重点计划项目(15ZCDGX00840)。
关键词 张力控制 解耦控制 抗干扰 自抗扰控制 混沌粒子群算法 tension control decoupling control anti-interference active disturbance rejection control chaos particle swarm optimization algorithm
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