基于PID增益的HVAC系统的新兴模糊控制设计毕业论文外文翻译.docx

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1、华南理工大学毕业设计（论文）外文资料A Novel Fuzzy Controller DesignBased-on PID Gains for HVAC Systems*Lv Hongli Duan Peiyong Jia Lei School of Information and Electrical Engineering School of Control Science and Engineering Shandong Jianzhu University Shandong University Jinan, Shandong Province, China, 250101 Jinan

2、, Shandong Province, China, 250061 hllv jialeiAbstract - The Heating, Ventilating, and Air-Conditioning systems (HVAC systems) are typical nonlinear time-variable multivariate systems with disturbances and uncertainties. A new fuzzy control strategy based on the PID parameters tuning to control HVAC

3、 systems is proposed in this paper. It takes full advantage of mature technologies of PID controller to improve the design of fuzzy controller. The mathematical analytical expression of parameters between fuzzy controllers and gains coefficients of PID controllers is got based on the structure analy

4、sis of fuzzy controllers. and the fuzzy controller is designed through gains tuning of PID controller based the analytical relations. Then this fuzzy controller was applied into temperature control in HVAC systems. The simulation test results showed it is effective and compared with the conventional

5、 PID control, the proposed fuzzy control algorithm has less overshoot, shorter setting time and better robustness etc.Index Terms - Fuzzy control, Structure analysis, Robustness, HVAC systemsI. INTRODUCTIONModern process control problems in the process industries are dominated by nonlinear time-vary

6、ing behavior, disturbances and uncertainties1. However more than 90% plants are controlled by the well-established PID controllers in industrial automation and process until today2. Morover, conventional PID controllers have gone through a technological evolution because many sophisticated algorithm

7、s have been used to improve its work under difficult conditions. Especially fuzzy logic inferences and neuron network based on self-tuning schemes of PID controllers have also been proposed to enhance the control performance. Qiang Bi et al gave an advanced auto-tuning PID controller then applied it

8、 into HVAC systems successfully3. Hanxiong Li proposed an improved robust fuzzy-PID controller with optimal fuzzy reasoning 4. But they can not change the linear essence of PID controllers.Fuzzy logic control technique based-on the concept of the fuzzy algorithm by Zadeh in 1973 has been successfull

9、y applied in many engineering areas since the pioneer work of Mamdani in 19745, 6. Fuzzy controllers possess advantages of strong robustness, better global control effects etc and no need mathematical model. But fuzzy controller design is still more a matter of art than technology and can not play t

10、he main role in industrial processes.In order to absorb the advantages of existing two combination of fuzzy and PID controllers, a novel idea to design PID controller-based fuzzy controller is attempted to be proposed in this paper. At first we get the mathematical relation of parameters of fuzzy co

11、ntroller and linear gains of PID controllers based on the structure analysis of fuzzy controller. And then the simulation results show the effective performance of this fuzzy controller and experiment tests results indict that the proposed fuzzy control approach is effective for the temperature cont

12、rol of air handling unit in HVAC systems.II. DESIGN OF FUZZY CONTROLLER OF BASED ON PID PARAMETERSIt is assumed that the process under control can be modeled as the following first order plus dead time (FOPDT) dynamics in figure 1:It is easy to design the PID controller for it, so that the gain coef

13、ficients can be achieved respectively. P I D Kp ,Ki ,Kd2.1 design of nominal fuzzy controller In order to design the PID parameters based-on fuzzy controller, at first the simplest structure of two-input single output nominal fuzzy controller is given. At any given time instance n with a sampling ti

14、me Ts, the two input variables of fuzzy controller, error state variable and error change are defined as e(n)=y(n)-r(n) and e(n)=e(n)-e(n-1). And its output variable u(n) is the control signal of process.Generally the membership functions of two input Variables e,e used triangular shapes and the mem

15、bership functions of output variables u used singleton fuzzy sets. The membership functions of input variables e,e are defined in figure 2. The fuzzy inference rule-bases in nominal controller use the following four rules:2.2 Design of normalized factorsThe fuzzy controller in figure 3 is used to co

16、ntrol the above FOPDT process. The nominal fuzzy controller is used in each fuzzy controller. But the input variables of fuzzy controller e(n),e(n ) are normalized by the normalized Gei and Gei before fuzzification，while i=1,2. The output variables of fuzzy controller u(n),u(n) arenormalized by the

17、normalized factors Gu and Gu after defuzzification. That is, e* Gei e(n),e* Gei e(n),u(n) Gu u*,u(n) Gu u * (2) where i =1,2 and e, e, u *,u * -1,1.The membership functions of both input variables e*,e*and output variables u *,u * are each defined the same as before. Both of two fuzzy inference rule

18、-bases use the nominal fuzzy rules. In fuzzy controller 2, the output variable u * is instead of u * in fuzzy rules. In fuzzy inference logic algorithms, if AND operation uses “product”, OR operation uses “max”, IMPLICATION operation use “min”. The defuzzification uses centre of gravity method. When

19、 arbitrary input variables e(n),e(n) act on the fuzzy control system,passing through normalizing, fuzzification, fuzzy reasoning and defuzzification, the outputs of fuzzy controllers are as follows:In order to simplify calculation, we chooseThen the whole output of fuzzy controller isIn order to cor

20、respond towards conventional PID controllers, we chooseThen the fuzzy controller can be transformed intoIt is a PID controller in style, but it is a fuzzy control at a certain sampling time in nature. Then we can take good use of these equations to design the fuzzy controller on base of parameters t

21、uning of PID controllers. First of all, since the gains tuning of PID controller service for fuzzy controller, we regard the error normalized factor Ge as a free variable for a while, and (7) can be changed into following equal styles:2.3 Tuning of normalized factorsIn fact, the prominent characteri

22、stics of proposed PID based-on fuzzy controller is no need of model identification except to design normalized factors of fuzzy controller through PID gain parameters.The first step is to design nominal fuzzy controller according to the general structure of fuzzy controller. The fuzzy data base and

23、rules, the fuzzification and defuzzification algorithm are chosen and the fuzzy inference operators are decided accordingly. The second step is to tune the design parameters of fuzzy controller, Gei, Gei , Gu and Gu , based on the relationship between the normalized factors of fuzzy controllers and

24、gains of PID controller shown in equation (9).We suppose the initial error normalized factor, which is marked by Ge0 , is fixed. Then they can directly be used to tune normalized factors in fuzzy controller off-line firstly. In order to make fuzzy controller better fit for the dynamical process furt

25、hermore convenient to be applied into industrial plant, according to the designing formulate (9), the concrete design is given as follows:When K P is increasing in PID controller, the output response will speed and the steady state error will reduce. Accordingly in (9), the system response arises fr

26、om increase of K P turns into very comparatively relaxative and more recipient, in stead of simple change of proportional gain. will increase, Gu will increase and G do not change. Similarly, When K I is increasing in PID controller, Ge will decrease in fuzzy controller Gu will increase, and Gu will

27、 increase; When K D is increasing, Ge will decrease, Gu will increase, and Gu do not change.III. SIMULATION EXAMPLE TEST3.1 Design of fuzzy controller for first-degree plus dead time processThe simulation study is conducted to verify the proposed fuzzy controller design algorithm based on the parame

28、ters tuning of the PID controller. Considering the following this kind of FOPDT processAt first, we get the gain parameters of its PID controller, that isSecondly, the fuzzy controller of process (10) is designed according to the algorithm given in Section 2 and its PID controller. Then according to

29、 the experiments and experience, The tuning parameters of fuzzy controller, the normalized factors were got . Thesimulation result in figure 4 showed that it is similarly available to apply the proposed fuzzy controller and PIDcontroller. But the fuzzy controller has extraordinary stronger disturban

30、ce rejection capability than PID controller.3.2 Experimental result in HVAC systemsThe schematic diagram of AHU in HVAC system is shown as in Figure 5, which consists of cooling coil, air dampers, fans, chilled water pumps and valves. There are two physical loops in the cooling coil unit (CCU): chil

31、led water loop and air loop. The dry-bulb temperature, web-bulb temperature and airflow rate of the on-coil air are Tai , Taiwb and m a, respectively. Likewise, the off-coil dry-bulb and wet-bulb air temperatures descend to Tao and Taowb , through heat transfer with chilled water in the cooling coil

32、 pipes. The off-coil temperature Tao and chilled water flow rate m chw are the process output to be controlled and manipulated variables, respectively. The on-coil temperature of the chilled water is assumed as a constant, and the airflow rate m a varies in corresponding to cooling load demand of co

33、nditioned space, these two variables are considered as disturbances to the process. Thus the output Tao can be described aswhere f is a nonlinear time varying function between the system output and the state variables. In steady-state, as the transient response of the dynamics for air and chilled wa

34、ter loop are very difficult to model accurately, it can be approximated in a small region, respectively, by a first order plus dead time (FOPDT) models 10 and given byWhere Kchw , Tchw , Lchw , Ka , Ta and La a the process gain, time constant and time delay for chilled water loop and air loop respec

35、tively. Before the real process testing, a simulation study is conducted to verify the proposed fuzzy controller design. Consider a heat exchange model described by (12) corresponding to its operating range as shown in Table 1.Then the PID controller based on low level model is designed, the paramet

36、ers Kp, Ki, Kd are determined as 1.1,1.22 and 0.09 respectively and fuzzy controllers are designed on base of the designed PID controller. Figure 6 shows the performance of the fuzzy controller and PID controllers under a squarewave set point change. It is clear that a better performance is achieved

37、 t through proposed fuzzy controller, whereas it is difficult to apply one set of PID controller parameters to obtain good performance for the whole operation range.A pilot centralized HVAC systems is showed as in figure 7. The system has three chillers, three zones with three AHUS three cooling tow

38、ers and flexible partitions up to twelve rooms. All motors (fans, pumps and compressors) are equipped with VSDs. The cooling coils for system are two rows with the dimension of 25*25*8 cm2 . The experiment is conducted under the following conditions: The chilled water supply temperature is fixed the

39、 cooling load variation is achieved through the air and water flow rates. The experiments result in Figure 7 showed that it is available to apply the proposed fuzzy control strategy to control off-coil dry-bulb temperature Tao of AHU systems. Compared with PID controller, the novel fuzzy controller

40、design technology has better robustness.IV. CONCLUSIONIn this paper, a new approach to design fuzzy controller was firstly proposed. It take full advantages of mature technologies of PID controller to improve the performance of fuzzy controller and it will be regarded the new relationship and combin

41、ation of fuzzy and PID controller. The gains tuning of PID controller were used to design normalized factors of fuzzy controller so that the fuzzy controller can be more easily understood and can be applied in practical industrial process on large scale. The simulation and experiment results in HVAC

42、 systems gave the comparisonbetween both of them, and shown that this novel design of fuzzy controller appeared good performance.REFERENCES1 R. J. P Defiguerredo and G. Chen. Nonlinear feedback control systems: an operator theory approach, New York, Academic, 1993.2 Astrom, K. J. and T. Hagglund. PI

43、D controllers: theory, design, and tuning (2nd ed.), Research Triangle Park, NC: Instrument Society of America, 1995.3 Qiang Bi, Wen-Jian Cai, Qing-Guo Wang, et al. Advanced controller auto-tuning and its application in HVAC systems. Control Eng. Practice, Vol.8, pp.633-644, 2000.4 Hanxiong Li. An i

44、mproved robust fuzzy-PID controller with optimal fuzzy reasoning. IEEE trans on SMC, vol. 35, no. 6, pp.1283-1294, 2005.5 L. A. Zadeh. Outline of a new approach to the analysis of complex systems and decision processes. IEEE trans. SMC, vol. 3, no. 1, pp.28-44, 1973.6 E. H. Mamdani. Application of f

45、uzzy algorithms for simple dynamic plant. Proc. Inst. Elect. Eng., vol. 121, no. 12, pp.1585-1588, 1974.7 R. bandyopadhyay, U. K. Chakraborty, D. Patranabis. Autotuning a PID controller: a fuzzy-genetic approach. Journal of systems architecture, vol. 47, pp.663-673, 2001.8 Leonid Reznik, Omar Ghanay

46、em, Anna Bourmistrov. PID plus fuzzy controller structures as a design base for industrial applications. Engineering application of artificial intelligence, vol. 13, pp.419-430, 2000.9 James Carvajal, Guanrong Chen, Haluk Ogmen. Fuzzy PID controller: design, performance evaluation, and stability ana

47、lysis. Information science, vol. 123, pp.249-270, 2000.10He Ming, Wenjian Cai. Multiple Fuzzy Model-based Temperature predictive control for HVAC systems. Information science, vol.169, pp.155-174, 2005.中文译文基于PID增益的HVAC系统的新兴模糊控制设计摘要加热、通风和空气调节系统（HVAC系统）是典型的非线性时变系统，同时还有很多不定的干扰信号。本文介绍了一种基于PID增益的HVAC的新型模

48、糊控制方法。这中方法充分利用成熟的PID控制技术来改进模糊控制的设计。模糊控制和PID控制器的参数的数学分析表示是通过模糊控制的结构分析来获得的，通过关系分析使得模糊控制器和PID控制器协调一致。然后，模糊控制器被应用于HVAC控制系统。模拟测试结果表明这种控制是非常有效果，和传统的PID控制相比，这里推荐的模糊控制算法更少超调，可以减少设置时间并且有更好的鲁棒性等等。关键词：模糊控制，结构分析，鲁棒性，HVAC系统1介绍现代的工业过程中的过程控制问题主要是非线性，时变的操作、干扰和不确定的因素。然而直到今天，有超过90%的工厂的工业自动化和工业过程都通过良好稳定的PID控制器进行控制。此外，依靠复杂精确的算法，传统的PID控制器在不同的条件下不断改进，已经向着工艺演变。特别是推出了用于平衡控制性能的自整定功能的模糊逻辑和神经元网络的PID控制器。Qiang Bi和CAi已经推出了一款先进的自整定PID控制器，并且成功的应用于HVAC系统。李汉熊推荐了一款具有完美模糊推理的强大的模糊PID控制器。但是，这些控制器都不能改变其线性的本质。模糊逻辑控制技术是Zadeh于1973年提出的并且自从1974年被Mamdani首次应用后成功应用于很多工程领域的模糊算法。模糊控制器的优点是由很强的鲁棒性