智能变频空调模糊神经网络控制系统的设计与实现
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摘要
传统空调器通过温度传感器感受室内温度变化来控制压缩机的启动和停止来实现对温度的控制。它对温度的调节是一种断续的变化过程,它的缺点是不能根据环境温度变化及时调整空调器的工作状态。人们为了解决传统空调在室温波动时压缩机工作状态频繁切换的问题,将模糊控制算法引入到空调系统的控制中来,使得系统的自调整性有了很大提高。该系统能够根据室内温度变化,通过对压缩机进行连续、动态、实时地变频调速来调节室内温度。
尽管常规模糊变频空调与传统空调相比,技术上有了很大进步,但是由于隶属函数和控制规则的获取经常依赖专家经验,而专家经验的正确与否经直接影响空调的控制效果。另外,当隶属函数和控制规则确定之后,一旦环境温度发生突变,空调器工作状态仍然按照既定的隶属函数和控制规则进行变化,中间没有过渡区,造成的温度突变会使人感到不适。
根据神经网络自学习能力强的特点,本文尝试采用将模糊控制与神经网络相结合的方法对压缩机进行控制,解决模常规模糊控制存在的不足。论文内容如下:
首先分析了传统空调控制方法和常规模糊变频空调控制的优点与不足之处,对国内外的研究现状及发展趋势进行了综述,提出了智能变频空调模糊神经网络控制系统设计与实现的研究思路;通过对已有智能变频空调设计方案的比较,结合国内实际情况,给出了一种新的控制方案,并对其硬件控制系统及算法进行了初步设计。在此基础上,采用模糊控制和神经网络控制方法相结合的实现手段,给出了智能变频空调系统的模糊神经网络控制算法。根据变频空调控制过程的非线性与时变性特点,以温度偏差和温度偏差变化率作为输入变量、变频压缩机工作频率变化作为输出控制量,设计了变频空调模糊神经网络控制器和预测器。通过对隶属函数和控制规则的调整,提高了变频空调系统的智能化程度。仿真结果表明了所提方法的有效性,实现了变频空调的智能控制。根据智能变频空调对硬件控制的要求,分别设计了室内机和室外机控制系统。依据硬件控制电路设计了相应的软件。通过实验研究,优化了相关的控制参数,同时也验证了本文方法的可行性。最后对全文内容进行了总结,并提出本设计存在的不足及需要研究改进之处。
Temperature control system of older air-conditioner depends on its sensors to monitor indoor temperature's changes and to control the compressor to run at a certain speed. This kind of air-conditioning system is a discontinuous process to adjust temperature and its shortcoming is that it can not adjust compressor's speed according to the changes of indoor temperature inevitably. In order to overcome PID's shortcoming ,people have put a new fuzzy control algorithms into practice so that the system's regulative quality has been improved greatly. The control system, named fuzzy variable frequency air-conditioning system, has the intelligence character to adjust temperature automatically and continuously according to temperature's changes indoors. All this will avoid frequent switches of the compressor which always appears on the traditional air-conditioning system.
Although fuzzy variable frequency air-conditioning system has made great improvement in technic compared with the traditional control method, it has a main problem, that is, its control rules are set by expert's experience beforehand, mostly under standard environment. When indoor environment changes suddenly, the fuzzy variable frequency air-conditioning system is controlled sharply at set membership functions and control rules. It will have a great effect on comfortability of human.
Because the neural network has the high ability of automatically learning,the auther tries to combine the fuzzy control and neural networks to control the compressor. The contents of the thesis are as following:
The project designs the fuzzy neural networks controller (FNNC) and neural network predicter (NNP), which employes the error of temperature and the changing rate of temperature as input, the working frequency of compressor as output. The control system improves the variable frequency air-conditioning system's intelligence by adjusting subordinate function and control rules. The air-conditioner's working state can adjust automatically according to the dweller's request and the changes of temperature.
Firstly, the auther introduces the advantage and shortcoming of traditional air-conditioning control system and fuzzy variable frequency air-conditioning system. Then the auther introduces the current research situation both home and abroad, and raises the new project "Design and Realization of Intelligent Variabale Frequency Air-Conditioner Control System with Fuzzy Neural Networks". Based on it, the schema of the control system is determined and design the hardware control system briefly.After studying the method of fuzzy control and neural network, the auther summarizes the combination method of of the two theories and designs the fuzzy neural networks controller for the air-conditioner. The fuzzy neural network controller and neural network predicter are designed according to the character of the nonlinear and time-varying of the control procedure and it is simulated. The result proves its intelligence. The control system and software of the indoor machine and the outdoor machine are designed respectively to meeting the demand of the air-conditioning system to the hardware. The experiment studying makes a research on the experiment and optimizes some parameters, which proves the availability of the theory. At last, the auther summarizes the contents of the dissertation and bring forth the questions that needed to solve.
尽管常规模糊变频空调与传统空调相比,技术上有了很大进步,但是由于隶属函数和控制规则的获取经常依赖专家经验,而专家经验的正确与否经直接影响空调的控制效果。另外,当隶属函数和控制规则确定之后,一旦环境温度发生突变,空调器工作状态仍然按照既定的隶属函数和控制规则进行变化,中间没有过渡区,造成的温度突变会使人感到不适。
根据神经网络自学习能力强的特点,本文尝试采用将模糊控制与神经网络相结合的方法对压缩机进行控制,解决模常规模糊控制存在的不足。论文内容如下:
首先分析了传统空调控制方法和常规模糊变频空调控制的优点与不足之处,对国内外的研究现状及发展趋势进行了综述,提出了智能变频空调模糊神经网络控制系统设计与实现的研究思路;通过对已有智能变频空调设计方案的比较,结合国内实际情况,给出了一种新的控制方案,并对其硬件控制系统及算法进行了初步设计。在此基础上,采用模糊控制和神经网络控制方法相结合的实现手段,给出了智能变频空调系统的模糊神经网络控制算法。根据变频空调控制过程的非线性与时变性特点,以温度偏差和温度偏差变化率作为输入变量、变频压缩机工作频率变化作为输出控制量,设计了变频空调模糊神经网络控制器和预测器。通过对隶属函数和控制规则的调整,提高了变频空调系统的智能化程度。仿真结果表明了所提方法的有效性,实现了变频空调的智能控制。根据智能变频空调对硬件控制的要求,分别设计了室内机和室外机控制系统。依据硬件控制电路设计了相应的软件。通过实验研究,优化了相关的控制参数,同时也验证了本文方法的可行性。最后对全文内容进行了总结,并提出本设计存在的不足及需要研究改进之处。
Temperature control system of older air-conditioner depends on its sensors to monitor indoor temperature's changes and to control the compressor to run at a certain speed. This kind of air-conditioning system is a discontinuous process to adjust temperature and its shortcoming is that it can not adjust compressor's speed according to the changes of indoor temperature inevitably. In order to overcome PID's shortcoming ,people have put a new fuzzy control algorithms into practice so that the system's regulative quality has been improved greatly. The control system, named fuzzy variable frequency air-conditioning system, has the intelligence character to adjust temperature automatically and continuously according to temperature's changes indoors. All this will avoid frequent switches of the compressor which always appears on the traditional air-conditioning system.
Although fuzzy variable frequency air-conditioning system has made great improvement in technic compared with the traditional control method, it has a main problem, that is, its control rules are set by expert's experience beforehand, mostly under standard environment. When indoor environment changes suddenly, the fuzzy variable frequency air-conditioning system is controlled sharply at set membership functions and control rules. It will have a great effect on comfortability of human.
Because the neural network has the high ability of automatically learning,the auther tries to combine the fuzzy control and neural networks to control the compressor. The contents of the thesis are as following:
The project designs the fuzzy neural networks controller (FNNC) and neural network predicter (NNP), which employes the error of temperature and the changing rate of temperature as input, the working frequency of compressor as output. The control system improves the variable frequency air-conditioning system's intelligence by adjusting subordinate function and control rules. The air-conditioner's working state can adjust automatically according to the dweller's request and the changes of temperature.
Firstly, the auther introduces the advantage and shortcoming of traditional air-conditioning control system and fuzzy variable frequency air-conditioning system. Then the auther introduces the current research situation both home and abroad, and raises the new project "Design and Realization of Intelligent Variabale Frequency Air-Conditioner Control System with Fuzzy Neural Networks". Based on it, the schema of the control system is determined and design the hardware control system briefly.After studying the method of fuzzy control and neural network, the auther summarizes the combination method of of the two theories and designs the fuzzy neural networks controller for the air-conditioner. The fuzzy neural network controller and neural network predicter are designed according to the character of the nonlinear and time-varying of the control procedure and it is simulated. The result proves its intelligence. The control system and software of the indoor machine and the outdoor machine are designed respectively to meeting the demand of the air-conditioning system to the hardware. The experiment studying makes a research on the experiment and optimizes some parameters, which proves the availability of the theory. At last, the auther summarizes the contents of the dissertation and bring forth the questions that needed to solve.
引文
1.黄晨,范存养.我国房间空调器生产和发展过程的剖析、前景和对策[J].制冷技术,2003(2):18-20.
2.高明.浅谈变频空调[J].电子世界,2004(6):20-23.
3.Huang H,Li Q H,Yuan D X,et al.An experimental study on variable air volume operation of ducted air-conditioning with digital scroll compressor and conventional scroll compressor[J].Applied Thermal Engineering,2008,28(7):761-766.
4.Lin J L,Yeh T J.Modeling,identification and control of air-conditioning systems [J],International Journal of Refrigeration,2007,30(2):209-220.
5.Sakuo Sugawara,Takane Suzuki.Application of fuzzy control to air conditioning environment[J].Journal of Thermal Biology,1993,18(5-6):465-472.
6.王书青,杨桦.模糊PID复合控制在变频空调中应用研究[J].制冷学报,2006(8):19-22.
7.郑宗和,梁江.变频空调系统应用神经元PID控制的仿真研究[J].暖通空调,2006(12):93-96.
8.贾少青,陈平,李爱华.基于神经网络的变频空调控制系统[J].计算机测量与控制,2006(14):56-60.
9.刘纬华,丁义行.基于智能功率模块的开关磁阻电动机功率电路研究[J].船电技术,2005(4):7-10.
10.丁强.变频空调压缩机频率控制算法的设计与研究[J].制冷与空调,2006,(5):38-41.
11.Li S Z,Zhang W G,Zhang R G,et al.Cascade fuzzy control for gas driven heat pump[J].Energy Conversion and Management,2005,46(11-12):1757-1766.
12.王晓亮,陈光明.神经网络理论在制冷与空调中的应用[J].西南工学院学报,1998(3):28-30.
13.Xu X G,Deng S M,Chan M Y.A new control algorithm for direct expansion air-conditioning systems for improved indoor humidity control and energy efficiency[J],Energy Conversion and Management,2008,49(4):578-586.
14.刘晓华.非线性系统模糊控制研究[D].大连理工大学,2002.
15.Bimal K B.Electrical machines for variable-speed drives[J].Power Electronics and Motor Drives,2006:325-390.
16.Yang K-H,Hwang R-L.An improved assessment model of variable frequencydriven direct expansion air_conditioning system in commercial buildings for Taiwan green building rating system[J].Building and Environment,2007,42(10):3582-3588.
17.蒋庆全.人工神经网络新发展[J].兵工自动化,1993(1).
18.李士勇.模糊控制[M].哈尔滨:哈尔滨工业大学出版社,1998.
19.Tian C Q,Li X T.Transient behavior evaluation of an automotive air conditioning system with a variable displacement compressor[J].Applied Thermal Engineering,2005,25(13):1922-1948.
20.文新辉,牛明洁.神经网络与预测方法研究[J].预测,1992(4):22-25.
21.周鲜成.模糊控制技术在变频空调器中的应用[J].电子技术应用,2001(2):17-22.
22.Soteris A.Kalogirou.Artificial intelligence for the modeling and control of combustion processes:A review[J].Progress in Energy and Combustion Science,2003,29(6):515-566.
23.Hosoz M,Ertunc H M.Artificial neural network analysis of an automobile air conditioning system[J].Energy Conversion and Management,2006,47(11-12):1574-1587.
24.蒋宗礼.人工神经网络导论[M].北京:高等教育出版社,2001.
25.孙增新.智能控制理论与技术[M].北京:清华大学出版社,1997.
26.Qureshi T Q,Tassou S A.Variable-speed capacitycontrol in refrigeration systems [J].Applied Thermal Engineering,1996,16(2):103-113.
27.刘伯春.神经网络在控制系统中的应用[J].电气自动化,2000(4):18-21.
28.Yu X H,Chen G A,Chneg S X.Dynamic learning rate optimization of the backprogation algorithm[J].IEEE Transaction on Neural Networks,1995,6(3):669-677.
29.Kreider J F,Wang X A.Artificial neural networks demonstration for automated generation of energy use predictiors for commercial buildings[J].The Use of Artificial Intelligence in Building Systems.ASHRAE,1995:193-198
30.Curtiss P S,Brandemuehl M J.Energy management in central HVAC plant using neural networks[J].The Use of Artificial Intelligence in Building Systems.ASHRAE,1995:199-216.
31.康赐容.用人工神经网络实现模糊控制[J].电脑与信息技术,2001(3):11-12.
32.Ahmad M I.Embedded fuzzy applications[J].Fuzzy Logic for Embedded Systems Applications,2004:69-98.
33.胡建平.模糊控制技术在暖通空调领域的应用综述[J].制冷空调,2005,(26):105-108.
34.Li X M,Jean C V.A Neural network prototype for fault detection and diagnosis [J].The Use of Artificial Intelligence in Building Systems.ASHRAE traps.1997,103(1):634-644.
35.Bourhan T,Molhim M,AL-Rousan M.Dynamic model of an HVAC system for control analysis[J].Energy 2005,30:1729-1745.
36.Chen W,Zhou X X,Deng S M.Development of control method and dynamic model for multi-evaporator air conditioners(MEAC)[J].Energy Conversion and Management,2005,46(3):451-465.
37.Xie T,Ghiaasiaan S M,Karrila S.Artificial neural network approach for flow regime classification in gas-liquid-fiber flows based on frequency domain analysis of pressure signals[J].Chemical Engineering Science,2004,59(11):2241-2251.
38.高俊岭,李咏梅.IPM高智能功率模块在变频器中的应用[J].煤矿机械,2004,(11):10-13.
39.梁延东,刘剑.神经网络在中央空调控制系统中的应用[J].自动化与仪器仪表,2002(4):25-27.
40.邵贝贝.微控制器抗干扰能力与电磁兼容性[J].电子技术,2003(11):40-43.
41.李名兆.单片机系统电磁兼容技术的研究[J].电测与仪表,1999(7):20-22.
42.童杏文.综论变频空调器[J].中国建设信息供热制冷,2004(10):24-26.
43.Kolokotsa D,Stavrakakis G S,Kalaitzakis K,et al.Genetic algorithms optimized fuzzy controller for the indoor environmental management in buildings implemented using PLC and local operating networks[J].Engineering Applications of Artificial Intelligence,2002,15(5):417-428.
44.Leether Y,William A S.Nonlinear parameter estimation via the genertic algorithm[J].IEEE Transactions on Signal Processing,1994,42(4):927-935.
2.高明.浅谈变频空调[J].电子世界,2004(6):20-23.
3.Huang H,Li Q H,Yuan D X,et al.An experimental study on variable air volume operation of ducted air-conditioning with digital scroll compressor and conventional scroll compressor[J].Applied Thermal Engineering,2008,28(7):761-766.
4.Lin J L,Yeh T J.Modeling,identification and control of air-conditioning systems [J],International Journal of Refrigeration,2007,30(2):209-220.
5.Sakuo Sugawara,Takane Suzuki.Application of fuzzy control to air conditioning environment[J].Journal of Thermal Biology,1993,18(5-6):465-472.
6.王书青,杨桦.模糊PID复合控制在变频空调中应用研究[J].制冷学报,2006(8):19-22.
7.郑宗和,梁江.变频空调系统应用神经元PID控制的仿真研究[J].暖通空调,2006(12):93-96.
8.贾少青,陈平,李爱华.基于神经网络的变频空调控制系统[J].计算机测量与控制,2006(14):56-60.
9.刘纬华,丁义行.基于智能功率模块的开关磁阻电动机功率电路研究[J].船电技术,2005(4):7-10.
10.丁强.变频空调压缩机频率控制算法的设计与研究[J].制冷与空调,2006,(5):38-41.
11.Li S Z,Zhang W G,Zhang R G,et al.Cascade fuzzy control for gas driven heat pump[J].Energy Conversion and Management,2005,46(11-12):1757-1766.
12.王晓亮,陈光明.神经网络理论在制冷与空调中的应用[J].西南工学院学报,1998(3):28-30.
13.Xu X G,Deng S M,Chan M Y.A new control algorithm for direct expansion air-conditioning systems for improved indoor humidity control and energy efficiency[J],Energy Conversion and Management,2008,49(4):578-586.
14.刘晓华.非线性系统模糊控制研究[D].大连理工大学,2002.
15.Bimal K B.Electrical machines for variable-speed drives[J].Power Electronics and Motor Drives,2006:325-390.
16.Yang K-H,Hwang R-L.An improved assessment model of variable frequencydriven direct expansion air_conditioning system in commercial buildings for Taiwan green building rating system[J].Building and Environment,2007,42(10):3582-3588.
17.蒋庆全.人工神经网络新发展[J].兵工自动化,1993(1).
18.李士勇.模糊控制[M].哈尔滨:哈尔滨工业大学出版社,1998.
19.Tian C Q,Li X T.Transient behavior evaluation of an automotive air conditioning system with a variable displacement compressor[J].Applied Thermal Engineering,2005,25(13):1922-1948.
20.文新辉,牛明洁.神经网络与预测方法研究[J].预测,1992(4):22-25.
21.周鲜成.模糊控制技术在变频空调器中的应用[J].电子技术应用,2001(2):17-22.
22.Soteris A.Kalogirou.Artificial intelligence for the modeling and control of combustion processes:A review[J].Progress in Energy and Combustion Science,2003,29(6):515-566.
23.Hosoz M,Ertunc H M.Artificial neural network analysis of an automobile air conditioning system[J].Energy Conversion and Management,2006,47(11-12):1574-1587.
24.蒋宗礼.人工神经网络导论[M].北京:高等教育出版社,2001.
25.孙增新.智能控制理论与技术[M].北京:清华大学出版社,1997.
26.Qureshi T Q,Tassou S A.Variable-speed capacitycontrol in refrigeration systems [J].Applied Thermal Engineering,1996,16(2):103-113.
27.刘伯春.神经网络在控制系统中的应用[J].电气自动化,2000(4):18-21.
28.Yu X H,Chen G A,Chneg S X.Dynamic learning rate optimization of the backprogation algorithm[J].IEEE Transaction on Neural Networks,1995,6(3):669-677.
29.Kreider J F,Wang X A.Artificial neural networks demonstration for automated generation of energy use predictiors for commercial buildings[J].The Use of Artificial Intelligence in Building Systems.ASHRAE,1995:193-198
30.Curtiss P S,Brandemuehl M J.Energy management in central HVAC plant using neural networks[J].The Use of Artificial Intelligence in Building Systems.ASHRAE,1995:199-216.
31.康赐容.用人工神经网络实现模糊控制[J].电脑与信息技术,2001(3):11-12.
32.Ahmad M I.Embedded fuzzy applications[J].Fuzzy Logic for Embedded Systems Applications,2004:69-98.
33.胡建平.模糊控制技术在暖通空调领域的应用综述[J].制冷空调,2005,(26):105-108.
34.Li X M,Jean C V.A Neural network prototype for fault detection and diagnosis [J].The Use of Artificial Intelligence in Building Systems.ASHRAE traps.1997,103(1):634-644.
35.Bourhan T,Molhim M,AL-Rousan M.Dynamic model of an HVAC system for control analysis[J].Energy 2005,30:1729-1745.
36.Chen W,Zhou X X,Deng S M.Development of control method and dynamic model for multi-evaporator air conditioners(MEAC)[J].Energy Conversion and Management,2005,46(3):451-465.
37.Xie T,Ghiaasiaan S M,Karrila S.Artificial neural network approach for flow regime classification in gas-liquid-fiber flows based on frequency domain analysis of pressure signals[J].Chemical Engineering Science,2004,59(11):2241-2251.
38.高俊岭,李咏梅.IPM高智能功率模块在变频器中的应用[J].煤矿机械,2004,(11):10-13.
39.梁延东,刘剑.神经网络在中央空调控制系统中的应用[J].自动化与仪器仪表,2002(4):25-27.
40.邵贝贝.微控制器抗干扰能力与电磁兼容性[J].电子技术,2003(11):40-43.
41.李名兆.单片机系统电磁兼容技术的研究[J].电测与仪表,1999(7):20-22.
42.童杏文.综论变频空调器[J].中国建设信息供热制冷,2004(10):24-26.
43.Kolokotsa D,Stavrakakis G S,Kalaitzakis K,et al.Genetic algorithms optimized fuzzy controller for the indoor environmental management in buildings implemented using PLC and local operating networks[J].Engineering Applications of Artificial Intelligence,2002,15(5):417-428.
44.Leether Y,William A S.Nonlinear parameter estimation via the genertic algorithm[J].IEEE Transactions on Signal Processing,1994,42(4):927-935.