基于ANFIS-PID控制的局部通风机风量自动调节系统仿真研究
发布时间：2018-08-12 20:38
【摘要】：局部通风机是矿山井下掘进工作面中的主要通风机械设备,能够稀释和排放工作面中的有毒有害气体,保证给井下作业人员一个安全、可靠、良好的工作条件。目前,我国大多数金属矿山井下局部通风机调速能力较差,智能化程度较低,并且多数是不调速的,风机一通电便开始长期无休止地恒速运转,其转速不会随有毒有害气体浓度的变化而变化,常发生“一风吹”通风现象,不能针对井下实际情况来调整工作区域所需风量,这不仅浪费了大量电能,而且空气质量经常不合格,直接影响到矿井的安全生产。针对矿山通风存在的问题,本文提出了将PID控制,模糊控制和神经网络相结合的控制策略,设计了一种基于ANFIS-PID控制的局部通风机风量自动调节系统。该方法能够实时调节工作区域所需风量,及时排出有毒有害气体,同时还能节约电能。本文所设计的控制器是以有毒有害气体的浓度偏差E和偏差变化率EC为输入变量,PID控制器的输出变量来模拟控制现场情况。在ANFIS中采用的是混合学习算法,即在向前运算中,保持所有条件参数不变,采用最小二乘法来改变结论参数;在改进后的参数不变的情况下,采用误差逆向传播法来改变条件参数,这样就可以达到改变隶属度函数形状的目的,最后使整个样本集的均方差达到系统所规定的精度要求。在MATLAB中利用Simulink搭建系统模型,对本文提出的ANFIS-PID控制算法进行仿真试验,并与PID控制算法、自适应模糊PID控制算法进行仿真比较。仿真结果表明:ANFIS-PID控制器比其它两种控制器的动态响应曲线好、响应时间短、无超调量、稳态精度高和鲁棒性强。研究表明:本文提出的ANFIS-PID控制策略,不需要被控对象的精确数学模型,在具有时滞大、时变性、非线性的风量调节系统中工作稳定、适应性好、抗干扰能力强、鲁棒性强,而且系统设计方法简单,能方便用于实际工业控制中。
[Abstract]:Local ventilator is the main ventilation mechanical equipment in the underground driving face, which can dilute and discharge the poisonous and harmful gas in the working face, and ensure a safe, reliable and good working condition for the underground workers. At present, local fans in most metal mines in our country have poor speed regulation ability and lower degree of intelligence, and most of them do not adjust speed. As soon as the fan is electrified, the fan starts to run endlessly at a constant speed for a long time. The rotational speed does not change with the change of the concentration of toxic and harmful gases. The phenomenon of "one wind blowing" often occurs. It is impossible to adjust the required air volume in the working area according to the actual situation in the underground, which not only wastes a lot of electric energy. And air quality is often not qualified, directly affect the safety of mine production. Aiming at the existing problems of mine ventilation, this paper puts forward a control strategy combining PID control, fuzzy control and neural network, and designs an automatic control system of local fan air flow based on ANFIS-PID control. This method can adjust the air volume needed in the working area in real time, discharge toxic and harmful gases in time, and save electric energy at the same time. The controller designed in this paper uses the concentration deviation E of toxic and harmful gases and the rate of variation of deviation EC as the output variables of the pid controller to simulate the control field. In ANFIS, a hybrid learning algorithm is used, that is, in forward operation, all conditional parameters remain unchanged, and the least square method is used to change the conclusion parameters. The error reverse propagation method is used to change the conditional parameters so that the shape of the membership function can be changed and the RMS of the whole sample set can meet the precision requirements of the system. The ANFIS-PID control algorithm proposed in this paper is simulated and compared with the PID control algorithm and the adaptive fuzzy PID control algorithm by using Simulink to build the system model in MATLAB. The simulation results show that the ratio ANFIS-PID controller has better dynamic response curve, shorter response time, no overshoot, higher steady-state accuracy and better robustness than the other two controllers. The results show that the proposed ANFIS-PID control strategy does not need the precise mathematical model of the controlled object, and it is stable, adaptable, robust and robust in the air volume control system with large time-delay, time-varying and nonlinear. Moreover, the system design method is simple and can be used in practical industrial control conveniently.
【学位授予单位】：南华大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TD441
本文编号：2180296
[Abstract]:Local ventilator is the main ventilation mechanical equipment in the underground driving face, which can dilute and discharge the poisonous and harmful gas in the working face, and ensure a safe, reliable and good working condition for the underground workers. At present, local fans in most metal mines in our country have poor speed regulation ability and lower degree of intelligence, and most of them do not adjust speed. As soon as the fan is electrified, the fan starts to run endlessly at a constant speed for a long time. The rotational speed does not change with the change of the concentration of toxic and harmful gases. The phenomenon of "one wind blowing" often occurs. It is impossible to adjust the required air volume in the working area according to the actual situation in the underground, which not only wastes a lot of electric energy. And air quality is often not qualified, directly affect the safety of mine production. Aiming at the existing problems of mine ventilation, this paper puts forward a control strategy combining PID control, fuzzy control and neural network, and designs an automatic control system of local fan air flow based on ANFIS-PID control. This method can adjust the air volume needed in the working area in real time, discharge toxic and harmful gases in time, and save electric energy at the same time. The controller designed in this paper uses the concentration deviation E of toxic and harmful gases and the rate of variation of deviation EC as the output variables of the pid controller to simulate the control field. In ANFIS, a hybrid learning algorithm is used, that is, in forward operation, all conditional parameters remain unchanged, and the least square method is used to change the conclusion parameters. The error reverse propagation method is used to change the conditional parameters so that the shape of the membership function can be changed and the RMS of the whole sample set can meet the precision requirements of the system. The ANFIS-PID control algorithm proposed in this paper is simulated and compared with the PID control algorithm and the adaptive fuzzy PID control algorithm by using Simulink to build the system model in MATLAB. The simulation results show that the ratio ANFIS-PID controller has better dynamic response curve, shorter response time, no overshoot, higher steady-state accuracy and better robustness than the other two controllers. The results show that the proposed ANFIS-PID control strategy does not need the precise mathematical model of the controlled object, and it is stable, adaptable, robust and robust in the air volume control system with large time-delay, time-varying and nonlinear. Moreover, the system design method is simple and can be used in practical industrial control conveniently.
【学位授予单位】：南华大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TD441
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1 韩艳杰;基于ANFIS-PID控制的局部通风机风量自动调节系统仿真研究[D];南华大学;2015年
，本文编号：2180296
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