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基于模糊神经网络的激光热负荷温度控制系统研究
Alternative TitleResearch on laser thermal load temperature control system based on fuzzy neural network
李青宇
Thesis Advisor李少霞
2019-05-30
Degree Grantor中国科学院大学
Place of Conferral北京
Subtype硕士
Degree Discipline一般力学与力学基础
Keyword温度控制,模糊神经网络,pid整定,激光热负荷,matlab仿真
Abstract

  激光热负荷以激光为热源作用于材料或部件表面,实现对其服役环境中受热过程的模拟,是一种进行受热部件的热疲劳性能测试和寿命预测的重要途径。激光热负荷过程中温度波动直接影响试件的瞬态热应力响应,以及热疲劳裂纹演变过程,温度控制效果对最终结果的准确性有着重大影响。随着工业应用对温度控制系统的稳定性、高精度和智能化需求的不断提高,现有的常规温度控制方法难以满足需求,成为制约激光热负荷技术发展的瓶颈,本文基于模糊控制和神经网络设计并实现了一种新型温度控制器,并搭建了温度控制系统平台,编写了人机交互控制界面和控制软件,提升了温度控制系统的稳定性和温度加载的可控性,对于热负荷实验模拟复杂温度载荷环境,准确评价材料性能和寿命预测具有重要意义。

  本文通过理论研究、设计仿真和实验验证,实现了激光热负荷过程中的温度控制目标,所做工作及相应的结论如下:

      1.针对激光热负荷高周阶段温度开展控制策略研究,通过设计出几种不同类型的控制器,分别在MATLABSimulink仿真环境中进行仿真实验,在实验中设计相应的扰动和变化,对所设计的控制器系统进行动静态特性、抗干扰性和鲁棒性等控制性能测试。结果表明,模糊神经网络PID控制器可以实现参数的自整定,相较于常规PID控制器和模糊PID控制器拥有更好的控制性能。

      2.基于脉冲激光热负荷实验平台,开发了激光热负荷温度控制系统。完成了激光器外部控制电路、温度传感系统的设计与集成;编写了具有灵活友善人机操作界面的上位机控制软件,并实现了模糊神经网络PID控制算法。为实现脉冲激光热负荷过程中温度的精准闭环控制建立了软硬件基础。

      3.依据激光热负荷的工艺特点,分别进行了无控制器模式和加入模糊神经网络PID控制器模式下的激光热负荷实验,通过改变脉冲激光参数实现完整的变幅热循环过程;使用红外测温仪探测激光加热光斑中心点位置的温度,在高周阶段由所设计控制器对脉冲电流进行调整,实现了激光热负荷过程中温度的闭环控制。实验结果表明该控制器使得高周阶段温度稳定可控,温度振荡中值无超调、无稳态误差,达到预期的温度控制目标。

  本文设计并实现了一种基于模糊神经网络的参数自整定PID温度闭环控制系统,该系统具有良好的动静态特性、抗干扰性和鲁棒性,适用于包括激光热负荷在内的激光制造工艺过程中的稳定、快速的温度控制。该成果在航空航天材料、发动机燃烧室部件的激光热负荷性能测试或寿命评估等领域有着广泛应用前景。

Other Abstract

       Laser thermal load acts on the surface of the materials or components with the laser as a heat source to simulate the heating process in the service environment. It is an important way to test the thermal fatigue performance and life prediction of heated components. The temperature fluctuation in the process of the laser thermal load directly affects the transient thermal stress response and the thermal fatigue crack evolution process of the specimen. The effect of temperature control has a significant impact on the accuracy of the final results. With the increasing demand for the stability, high precision and intelligence of temperature control system in industrial applications, the existing conventional temperature control methods are difficult to meet the demand, which has become the bottleneck restricting the development of laser thermal load technology. Based on fuzzy control and neural network, a new type of temperature controller is designed and implemented in this paper, a temperature control system platform is built, and the human-computer interaction control interface and control software are compiled to improve the stability of the temperature control system and the controllability of temperature loading. It is of great significance for the thermal load experiment to simulate complex temperature loading environment and accurately evaluate material performance and life prediction.

    Through the theoretical research, design simulation and experimental verification, the temperature control target in the process of laser thermal load is achieved in this paper. The related work and corresponding conclusions are as follows:

    1. For the temperature of the laser thermal load in the high-cycle stage, some control strategies are studied and developed. By designing several different types of controllers, the simulation experiments are carried out in the simulation environment Simulink of MATLAB respectively. In the experiment, the corresponding disturbances and changes are designed, and the system of the designed controller is tested for the dynamic and static characteristics, anti-interference, robustness and other control performances. The results show that the fuzzy neural network PID controller can realize the self-tuning of parameters, and has better control performance than the conventional PID controller and fuzzy PID controller.

    2. Based on the experimental platform of pulse laser thermal load, a temperature control system of laser thermal load is developed. The design and integration of the external control circuit and temperature sensor system of the laser are completed. The control software of the host computer with the flexible and friendly man-machine interface is programmed, and the fuzzy neural network PID control algorithm is realized. In order to realize the precise closed-loop control of temperature in the process of pulse laser thermal load, the foundations of software and hardware are established.

    3. According to the technological characteristics of laser thermal load, the experiments of laser thermal load in the mode without the controller and the mode with the fuzzy neural network PID controller are carried out respectively. The whole process of the variable-amplitude thermal cycle is realized by changing the parameters of the pulsed laser. The temperature of the central point of the laser heating spot is detected by the infrared thermometer, and the pulse current is adjusted by the designed controller at the high cycle stage to realize the closed-loop temperature control in the process of laser thermal load. The experimental results show that the controller makes the temperature stable and controllable in the high cycle stage, and the median value of temperature oscillation has no overshoot and steady-state error, thus achieving the desired temperature control target.

    A closed-loop temperature control system based on the fuzzy neural network with self-setting PID parameters is designed realized in this paper. The system has good dynamic and static characteristics, anti-interference and robustness. It is suitable for stable and fast temperature control in the laser manufacturing process including laser thermal load. The results have broad application prospects in the fields of laser thermal load performance testing or life evaluation of aerospace materials and components of the engine combustion chamber.

Language中文
Document Type学位论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/79113
Collection先进制造工艺力学重点实验室
Recommended Citation
GB/T 7714
李青宇. 基于模糊神经网络的激光热负荷温度控制系统研究[D]. 北京. 中国科学院大学,2019.
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