基于ARM和模糊PID的温控系统的研究和设计
发布时间：2019-06-06 11:55
【摘要】：中国已经成为世界上啤酒产量最大的国家之一。在啤酒发酵过程中,发酵液的温度能否准确跟踪上发酵过程工艺曲线的要求是啤酒酿造能否成功的关键部分。啤酒发酵是一个利用微生物代谢生产的过程,其过程控制必须具备优良的控制性能,才能提高啤酒的质量和口感。啤酒发酵过程中的温度控制具有大时滞、非线性和时变性等问题,这就使温度控制的各种要求很难达到发酵过程所需要的精准度,所以需要引进智能控制算法来对发酵过程的稳定性和准确性进行控制。本文在以STM32作为温度控制核心处理器的基础上进行硬件设计。通过MATLAB分别对改进的粒子群算法和遗传算法在模糊PID控制参数的优化方面进行了仿真分析和对比,选择采用改进粒子群算法优化模糊PID控制作为温度控制系统的控制算法。通过实物搭载对设计的温度控制系统进行实验,并对实验结果进行分析。论文的主要工作包括以下几个方面:(1)设计符合温度控制系统性能要求的硬件电路。以STM32作为控制系统的核心处理器,根据STM32的特性设计电源电路、JTAG调试接口电路、串口通信电路,以及设计采用热敏电阻传感器的温度检测电路,D/A转换数据电路和报警电路,组成一个完整的温度控制系统,并且搭建硬件实物。(2)实现用改进算法优化模糊PID控制参数来对发酵过程的温度进行控制。首先针对粒子群算法和遗传算法所具有的特性分别做出改进策略:其中在粒子群优化算法中,分别对惯性权重参数和学习因子参数进行公式改进;在遗传算法中,将种群最优个体未变化代数引入到交叉概率和变异概率公式中,并结合遗传操作引入拟单纯形算子。在自适应模糊PID控制算法基础上加入Smith预估补偿来抵消被控对象的纯滞后现象,再分别采用改进的粒子群算法和遗传算法来优化自适应模糊PID的控制参数,通过MATLAB仿真软件对控制算法进行对比分析,选择改进粒子群算法优化模糊PID控制作为温度控制系统的控制算法。(3)温度控制系统的软件设计通过LabVIEW来完成,最后结合软硬件设计来对温度控制系统进行仿真实验。实验结果表明:系统能够在短时间内把温度控制在55?0.3℃范围以内。满足了本温度控制系统所要求的稳定性和准确性。
[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，
本文编号：2494349
[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，
本文编号：2494349
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