【作者】 张晨；

【导师】 凌宏江；

【作者基本信息】 华中科技大学 ， 材料加工工程， 2012， 硕士

【摘要】 型砂发气性测试仪是铸造工艺中的重要检测设备，发气仪温度控制的准确及稳定性将直接影响到发气量测试的准确性，进而影响到铸件的质量。电阻炉炉温控制系统具有非线性、时变性、滞后性、不对称性，常规PID控制器很难实现对温度的精确控制，且参数整定困难，不具备自适应的能力。为了提高系统的自适应能力和抗干扰能力，本文提出了基于BP神经网络的PID控制算法用于实现对发气仪的精确温控。智能PID控制包含专家式智能自整定PID控制、模糊PID控制以及神经网络PID控制，它们具有自学习、自适应及在线自整定参数的特点。但专家PID控制的专家知识库不容易建立，模糊PID控制的模糊规则不易确定并且控制精度不高。综合比较几种智能控制算法后，本文决定采用神经网络PID控制算法。人工神经网络以神经元为节点，采用网络拓扑结构构成活性网络，可以用来描述几乎任意的非线性系统。传统的PID调节器具有算法简单、调整方便等特点，将人工神经网络与传统的PID控制相结合，构成智能型的PID控制器，能够自动整定控制器参数、适应被控过程参数的变化，是解决传统PID控制器不易在线实时整定参数、难于对一些复杂过程和参数慢时变系统进行有效控制的不足。本文首先介绍了常见的型砂发气性测试仪的国内外现状和发展趋势。之后，详细介绍了PID控制原理并比较了各种PID算法以及智能控制算法的优缺点。接着详细说明了基于BP神经网络的PID控制在发气性测试仪温控系统中的软件实现。最后，本文提供了温度的实时控制曲线。结果表明，采用BP神经网络PID控制的温度控制曲线超调量只有1℃，温度曲线无振荡，无静差。并且在半小时内温度就已经稳定下来了，满足精度要求，可以进行发气性实验，达到了预期的控制效果。更多还原

【Abstract】 Gas evolution tester is an important equipment of foundry technology, and thetemperature dynamic-characteristic has directly influenced the quality of production.Electric heater is an object featuring in nonlinear, time variability, large time lag andasymmetry, general PID controller can not obtain high control precision, parameter isn’teasily adjusted and badly adaptive ability. In order to improve the system’s adaptive abilityand anti-jamming capability, this paper propose BP neural network PID control algorithmto realize precise temperature control of gas evolution tester.Intelligent PID control includes expert-type smart self-tuning PID control, fuzzy PIDcontrol and neural network PID control，which have characteristics of self-learning, selfadaptive and self-tuning parameters online. However, the expert knowledge base ofexpert-type PID control is not easy to be established, the fuzzy rules of fuzzy PID controlis not easy to be determined and the control accuracy is not high. With comprehensivecomparison of several intelligent control algorithm, the neural network PID controlalgorithm was chosen in this thesis.Artificial Neural Network is a simple units(neurons) for the node, using a networktopology consisting of active network and can be used to describe virtually any nonlinearsystem. General PID controller has a simple algorithm, easy to adjust, etc. Artificial neuralnetwork with a combination of traditional PID control to constitute intelligent PIDcontroller, it can auto-tuning controller parameters, adapt to changes in controlled processparameters. It can easy to solve the online real-time tuning parameters, the process isdifficult for some complicated and slow time-varying system parameters for the lack ofeffective control.Firstly, this paper describes the common Gas evolution tester and development trendof domestic and foreign. Secondly, this paper introduced the PID control theory andcompare the various algorithms and intelligent control algorithm advantages anddisadvantages. Thirdly, this paper describes the artificial neural network PID controltheory and software design.Finally, this paper provides real-time temperature control curves. The results show that BP neural network PID control temperature control curve of the overshoot is only1℃, the temperature curve without oscillation, no static error. And in half an hour thetemperature had stabilized to meet the accuracy requirements, gas experiment can be made.Neural PID control achieve the desired control effect.更多还原