Research On Temperature Control System Of Vacuum Annealing Furnace Based On Particle Swarm Fuzzy PID

As an important equipment in the heat treatment process of aviation workpieces,vacuum annealing furnace is widely used in the heat treatment process of new materials such as rare metal workpieces because of its simple process,low energy consumption,low pollution and high efficiency.However,the issues can not better meet the control index,e.g.,complex vacuum annealing furnace control system,long lag time of furnace temperature control,temperature rise process inertia and non-linear characteristics,production sites often use ordinary proportionalintegral-derivative(PID)control.Therefore,it is vital to study the advanced control strategy and improve the temperature control index of vacuum annealing furnace.This topic takes a rare metal workpiece vacuum annealing furnace in an aviation workpiece heat treatment workshop in Shenyang as the research object,then conducts an in-depth and specific study on the phenomenon that the current control cannot meet the requirements of the rare metal workpiece heat treatment process,e.g.,simple site and the furnace temperature control index.The main work is as follows:Firstly,the demand for the vacuum annealing furnace temperature control system in this workshop was analyzed to complete the overall system design.According to the equipment structure and process flow of the vacuum annealing furnace,the temperature control system requirements were analyzed in conjunction with the actual problem,then an improved control scheme was proposed on the basis of the existing hardware and software platform on site.A set of temperature control system by using MATLAB could complete the core algorithm with Programmable Logic Controller(PLC)acquisition control and upper WINCC monitoring and recording.Secondly,the hardware and software of the temperature control system were designed.The hardware design took PLC as the core to complete the acquisition of vacuum annealing furnace temperature and vacuum degree and other states,thus realizing the automatic control of the heater and various pumps,valves and other actuators;it adopted modular design thinking to complete the control system software design,mainly including system initialization,real-time data acquisition and analysis processing,output control,alarm and other subroutines;then designed the upper computer so that the user could view the operating status,execute control commands,view the system real-time/remote,and monitor the system,The upper computer was designed to facilitate users to view the operation status,execute control commands,view real-time/historical data and curves and alarm status.Thirdly,the design of the improved Particle Swarm Optimization Fuzzy PID(PSO Fuzzy PID)temperature controller was completed.Based on the equipment characteristics and operating characteristics of the annealing furnace,the corresponding furnace temperature mathematical model was established,also the fuzzy PID controller was selected and designed to achieve temperature adaptive control;for the problem of empirical priority of fuzzy control,a control method was proposed based on the improved particle swarm algorithm to iteratively solve the key parameters in the variable domain algorithm of fuzzy PID,while the improved particle swarm fuzzy PID controller was designed and the control model was constructed the control algorithm.Finally,step,disturbance and temperature following simulation experiments were designed to simulate and analyze the temperature control effect of traditional PID,fuzzy PID and improved particle swarm fuzzy PID on temperature control system.The results showed that the maximum overshoot of conventional PID,fuzzy PID and improved swarm fuzzy PID was 13.84%,11.2%and 4% in the step simulation;the conventional PID,fuzzy PID and improved swarm fuzzy PID recovered the steady state at 1035 s,919s and 890 s in the anti-disturbance simulation;the conventional PID,fuzzy PID and improved swarm fuzzy PID recovered the steady state at 1035 s,919s and 890 s in the temperature following simulation;the traditional PID,fuzzy PID and improved particle swarm fuzzy PID started to follow the set target temperature steadily at 233 s,200s and 190 s in the temperature following simulation.To summarize the simulation results,it can be seen that the maximum overshoot of the improved particle swarm fuzzy PID is significantly reduced,the recovery time of steady state is significantly shortened with faster speed,and the overshoot of the system control process is less than 5%,which has strong resistance to dryness and temperature,thus better adapting to the changes of the system control output and meeting the control requirements of system temperature accuracy,stability and speed.