The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying.It is impractical to identify a model that can restore the model of real papermaking process.Determining...The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying.It is impractical to identify a model that can restore the model of real papermaking process.Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process.In this study,a strange nonchaotic particle swarm optimization(SNPSO)algorithm is proposed to identify the models of real papermaking processes,and this identification ability is significantly enhanced compared with particle swarm optimization(PSO).First,random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions.Furthermore,the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO.The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization.To illustrate the effectiveness of SNPSO,step response data are used to identify the models of real industrial processes.Compared with classical PSO,PSO with timevarying acceleration coefficients(PSO-TVAC)and modified particle swarm optimization(MPSO),the simulation results demonstrate that SNPSO has stronger identification ability,faster convergence speed,and better robustness.展开更多
Basis weight is an important indicator for evaluating paper quality and a major factor directly affecting the economic benefits of enterprises.Focusing on the large time-delay,time-varying,and nonlinear characteristic...Basis weight is an important indicator for evaluating paper quality and a major factor directly affecting the economic benefits of enterprises.Focusing on the large time-delay,time-varying,and nonlinear characteristics of a basis weight control system,a two-degree-of-freedom(TDF)internal model control(IMC)method based on a particle swarm optimization(PSO)algorithm was proposed.The method took the integral of time multiplied by the absolute error(ITAE)as the objective function,and the PSO algorithm was used to optimize the time constant of the tuning IMC filter.The simulation results for the control system under the proposed TDF-IMC method based on the PSO algorithm demonstrate good set-point tracking performance,strong anti-interference capabilities,and good robustness properties.The application results revealed that the basis weight fluctuation range of the paper was±2 g/m2,which significantly improved both the control quality and the product quality.展开更多
Traditionally, basis weight control valve is driven by a constant frequency pulse signal. Therefore, it is difficult for the valve to match the control precision of basis weight. Dynamic simulation research using Matl...Traditionally, basis weight control valve is driven by a constant frequency pulse signal. Therefore, it is difficult for the valve to match the control precision of basis weight. Dynamic simulation research using Matlab/Simulink indicates that there is much more overshoot and fluctuating during the valve-positioning process. In order to improve the valve-positioning precision, the control method of trapezoidal velocity curve was studied. The simulation result showed that the positioning steady-state error was less than 0.0056%, whereas the peak error was less than 0.016% by using trapezoidal velocity curve at 10 positioning steps. A valve-positioning precision experimental device for the stepper motor of basis weight control valve was developed. The experiment results showed that the error ratio of 1/10000 positioning steps was 4% by using trapezoidal velocity curve. Furthermore, the error ratio of 10/10000 positioning steps was 0.5%. It proved that the valve-positioning precision of trapezoidal velocity curve was much higher than that of the constant frequency pulse signal control strategy. The new control method of trapezoidal velocity curve can satisfy the precision requirement of 10000 steps.展开更多
基金support received from the National Natural Science Foundation of China(Grant No.62073206)Technical Innovation Guidance Project of Shaanxi Province(Grant No.2020CGHJ-007).
文摘The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying.It is impractical to identify a model that can restore the model of real papermaking process.Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process.In this study,a strange nonchaotic particle swarm optimization(SNPSO)algorithm is proposed to identify the models of real papermaking processes,and this identification ability is significantly enhanced compared with particle swarm optimization(PSO).First,random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions.Furthermore,the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO.The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization.To illustrate the effectiveness of SNPSO,step response data are used to identify the models of real industrial processes.Compared with classical PSO,PSO with timevarying acceleration coefficients(PSO-TVAC)and modified particle swarm optimization(MPSO),the simulation results demonstrate that SNPSO has stronger identification ability,faster convergence speed,and better robustness.
基金supported by the Shaanxi Key Innovation Team Project of Science and Technology (2014KCT-15)the Shaanxi Science & Technology Co-ordination & InnovationProject (2016KTCQ01-35)
文摘Basis weight is an important indicator for evaluating paper quality and a major factor directly affecting the economic benefits of enterprises.Focusing on the large time-delay,time-varying,and nonlinear characteristics of a basis weight control system,a two-degree-of-freedom(TDF)internal model control(IMC)method based on a particle swarm optimization(PSO)algorithm was proposed.The method took the integral of time multiplied by the absolute error(ITAE)as the objective function,and the PSO algorithm was used to optimize the time constant of the tuning IMC filter.The simulation results for the control system under the proposed TDF-IMC method based on the PSO algorithm demonstrate good set-point tracking performance,strong anti-interference capabilities,and good robustness properties.The application results revealed that the basis weight fluctuation range of the paper was±2 g/m2,which significantly improved both the control quality and the product quality.
基金supported by the International S&T Cooperation Program of China(GrantNo.2010DFB43660)National Natural Science Foundation of China(Grant No.51375286)Scientific Research Program Funded by Shaanxi Provincial Education Department(Program No.16JF005)
文摘Traditionally, basis weight control valve is driven by a constant frequency pulse signal. Therefore, it is difficult for the valve to match the control precision of basis weight. Dynamic simulation research using Matlab/Simulink indicates that there is much more overshoot and fluctuating during the valve-positioning process. In order to improve the valve-positioning precision, the control method of trapezoidal velocity curve was studied. The simulation result showed that the positioning steady-state error was less than 0.0056%, whereas the peak error was less than 0.016% by using trapezoidal velocity curve at 10 positioning steps. A valve-positioning precision experimental device for the stepper motor of basis weight control valve was developed. The experiment results showed that the error ratio of 1/10000 positioning steps was 4% by using trapezoidal velocity curve. Furthermore, the error ratio of 10/10000 positioning steps was 0.5%. It proved that the valve-positioning precision of trapezoidal velocity curve was much higher than that of the constant frequency pulse signal control strategy. The new control method of trapezoidal velocity curve can satisfy the precision requirement of 10000 steps.
