Research And Design Of Temperature Control System Based On ARM And Fuzzy PID

China has become one of the world's largest producers of beer.In the process of beer fermentation,the key to the success of beer brewing is whether the fermentation temperature can accurately track the fermentation process curve.Beer fermentation is a process of microbial metabolism.In order to improve the quality and taste of beer,the control of fermentation process must have excellent control performance.The temperature control in the process of beer fermentation has many problems of large time delay,nonlinear and time variability,which makes it difficult to meet the requirements of temperature control.Therefore,it is necessary to introduce intelligent control algorithm to control the stability and accuracy of the fermentation process.The design of hardware is based on STM32 as the core processor of temperature control in this dissertation.The improved particle swarm algorithm and the improved genetic algorithm to optimize the fuzzy PID control parameters are analyzed and compared by MATLAB.The improved particle swarm optimization algorithm to optimize the fuzzy PID control is chosen as the control algorithm of the temperature control system.Experiments are carried out on the designed temperature control system by physical loading,and the experimental results are analyzed.The main work of this dissertation includes the following aspects:(1)Design the hardware circuit to meet the performance requirements of temperature control system.With STM32 as the core processor of the control system,according to the characteristics of STM32 design power circuit,JTAG debug interface circuit,serial communication circuit,as well as the use of thermistor sensor temperature detection circuit,D/A conversion data circuit and alarm circuit to form a complete temperature control system and build hardware in kind.(2)The improved algorithm is used to optimize the fuzzy PID control parameters to control the temperature of the fermentation process.Firstly,the improved strategy is proposed for particle swarm optimization and genetic algorithm.In the particle swarm optimization algorithm,the formula of inertia weight and learning factor is improved respectively;in the genetic algorithm,the best individual did not change algebraic in the population is introduced into the crossover probability formula and mutation probability formula,and the simplex operator is introduced by genetic operation.Based on the adaptive fuzzy PID control algorithm,the Smith predictor compensation is used to cancel the pure hysteresis of the controlled object.Then,the improved particle swarm optimization algorithm and the genetic algorithm are used to optimize the control parameters of the adaptive fuzzy PID.Through MATLAB simulation software,they are compared and analyzed.The modified particle swarm optimization algorithm is selected to optimize the fuzzy PID control as the control algorithm of the temperature control system.(3)The software design of the temperature control system is completed by LabVIEW.Finally,the hardware and software design is used to simulate the temperature control system.The experimental results show that the system can control the temperature in the range of 55 ?0.3 ? within a short time.The stability and accuracy of the temperature control system are satisfied.