【摘要】：中國已經(jīng)成為世界上啤酒產(chǎn)量最大的國家之一。在啤酒發(fā)酵過程中,發(fā)酵液的溫度能否準確跟蹤上發(fā)酵過程工藝曲線的要求是啤酒釀造能否成功的關(guān)鍵部分。啤酒發(fā)酵是一個利用微生物代謝生產(chǎn)的過程,其過程控制必須具備優(yōu)良的控制性能,才能提高啤酒的質(zhì)量和口感。啤酒發(fā)酵過程中的溫度控制具有大時滯、非線性和時變性等問題,這就使溫度控制的各種要求很難達到發(fā)酵過程所需要的精準度,所以需要引進智能控制算法來對發(fā)酵過程的穩(wěn)定性和準確性進行控制。本文在以STM32作為溫度控制核心處理器的基礎(chǔ)上進行硬件設(shè)計。通過MATLAB分別對改進的粒子群算法和遺傳算法在模糊PID控制參數(shù)的優(yōu)化方面進行了仿真分析和對比,選擇采用改進粒子群算法優(yōu)化模糊PID控制作為溫度控制系統(tǒng)的控制算法。通過實物搭載對設(shè)計的溫度控制系統(tǒng)進行實驗,并對實驗結(jié)果進行分析。論文的主要工作包括以下幾個方面:(1)設(shè)計符合溫度控制系統(tǒng)性能要求的硬件電路。以STM32作為控制系統(tǒng)的核心處理器,根據(jù)STM32的特性設(shè)計電源電路、JTAG調(diào)試接口電路、串口通信電路,以及設(shè)計采用熱敏電阻傳感器的溫度檢測電路,D/A轉(zhuǎn)換數(shù)據(jù)電路和報警電路,組成一個完整的溫度控制系統(tǒng),并且搭建硬件實物。(2)實現(xiàn)用改進算法優(yōu)化模糊PID控制參數(shù)來對發(fā)酵過程的溫度進行控制。首先針對粒子群算法和遺傳算法所具有的特性分別做出改進策略:其中在粒子群優(yōu)化算法中,分別對慣性權(quán)重參數(shù)和學習因子參數(shù)進行公式改進;在遺傳算法中,將種群最優(yōu)個體未變化代數(shù)引入到交叉概率和變異概率公式中,并結(jié)合遺傳操作引入擬單純形算子。在自適應(yīng)模糊PID控制算法基礎(chǔ)上加入Smith預(yù)估補償來抵消被控對象的純滯后現(xiàn)象,再分別采用改進的粒子群算法和遺傳算法來優(yōu)化自適應(yīng)模糊PID的控制參數(shù),通過MATLAB仿真軟件對控制算法進行對比分析,選擇改進粒子群算法優(yōu)化模糊PID控制作為溫度控制系統(tǒng)的控制算法。(3)溫度控制系統(tǒng)的軟件設(shè)計通過LabVIEW來完成,最后結(jié)合軟硬件設(shè)計來對溫度控制系統(tǒng)進行仿真實驗。實驗結(jié)果表明:系統(tǒng)能夠在短時間內(nèi)把溫度控制在55?0.3℃范圍以內(nèi)。滿足了本溫度控制系統(tǒng)所要求的穩(wěn)定性和準確性。
[Abstract]:China has become one of the world's biggest beer production countries. In the process of beer fermentation, the temperature of the fermentation liquor can accurately track the requirement of the process curve of the fermentation process, which is the key part of the success of the beer brewing. Beer fermentation is a process of using microbial metabolism, and its process control must have excellent control performance to improve the quality and taste of beer. The temperature control in the beer fermentation process has the problems of large time lag, non-linearity and time-time degeneration, which makes the various requirements of temperature control difficult to achieve the precision required by the fermentation process, so the intelligent control algorithm needs to be introduced to control the stability and the accuracy of the fermentation process. In this paper, the hardware design is carried out on the basis of STM32 as the core processor for temperature control. By means of MATLAB, the improved particle swarm optimization algorithm and the genetic algorithm are simulated and compared with the optimization of the fuzzy PID control parameters, and the improved particle swarm optimization algorithm is selected to optimize the fuzzy PID control as the control algorithm of the temperature control system. The temperature control system of the design is carried out by physical mounting, and the experimental results are analyzed. The main work of the paper includes the following aspects: (1) The hardware circuit is designed to meet the performance requirements of the temperature control system. using the STM32 as the core processor of the control system, a power supply circuit, a JTAG debug interface circuit, a serial port communication circuit and a temperature detection circuit, a D/ A conversion data circuit and an alarm circuit are designed in accordance with the characteristics of the STM32, A complete temperature control system is formed and a hardware object is constructed. And (2) implementing the improved algorithm to optimize the fuzzy PID control parameters to control the temperature of the fermentation process. Firstly, the improvement strategies are made for the characteristics of the particle swarm optimization algorithm and the genetic algorithm: in the particle swarm optimization algorithm, the inertia weight parameters and the learning factor parameters are respectively modified; in the genetic algorithm, In that formula of the crossover probability and the mutation probability, the optimal individual unchanging algebra of the population is introduce into the cross probability and the mutation probability formula, and the quasi-simplex operator is introduced in combination with the genetic operation. On the basis of the self-adaptive fuzzy PID control algorithm, the Smith predictive compensation is added to cancel the pure hysteresis of the controlled object, and the improved particle swarm optimization algorithm and the genetic algorithm are used to optimize the control parameters of the self-adaptive fuzzy PID, and the control algorithm is compared and analyzed by the MATLAB simulation software. The improved particle swarm 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 the LabVIEW, and finally the temperature control system is simulated and tested in combination with the hardware and software design. The experimental results show that the temperature of the system can be controlled within the range of 55-0.3 鈩，

本文編號：2494349