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信息與電子工程系畢業(yè)設(shè)計(jì)（中英文資料）
畢業(yè)設(shè)計(jì)(論文)中英文資料
信 電 系 工業(yè)電氣自動(dòng)化 專業(yè) 04 級(jí) 1 班
課題名稱：MCS-51單片機(jī)智能溫度控制系統(tǒng)設(shè)計(jì)
畢業(yè)設(shè)計(jì)(論文)起止時(shí)間：
2006 年 2月10日～6月8日(共17周)
學(xué)生姓名： 學(xué)號(hào)： 10
指導(dǎo)教師：
報(bào)告日期： 年3月 1日
摘要：
本文根據(jù)模糊控制和PID控制的特點(diǎn)及其原理，把模糊控制和PID控制結(jié)合起來(lái)，形成模糊PID控制，有效的克服了它們的缺點(diǎn)而發(fā)揮了它們的優(yōu)勢(shì)。本文詳細(xì)闡述了該系統(tǒng)中模糊PID控制器的實(shí)現(xiàn)方法、系統(tǒng)的各種控制、故障檢測(cè)以及狀態(tài)顯示。
模糊PID控制器實(shí)際上跟傳統(tǒng)的PID控制器有很大聯(lián)系。區(qū)別在于傳統(tǒng)的控制器的控制前提必須是熟悉控制對(duì)象的模型結(jié)構(gòu)，而模糊控制器因?yàn)樗姆蔷€性特性，所以控制性能優(yōu)于傳統(tǒng)PID控制器。對(duì)于時(shí)變系統(tǒng)，如果能夠很好地采用模糊控制器進(jìn)行調(diào)節(jié)，其控制結(jié)果的穩(wěn)定性和活力性都會(huì)有改善。但是，如果調(diào)節(jié)效果不好，執(zhí)行器會(huì)因?yàn)橹芷谡袷幱绊懯褂脡勖貏e是調(diào)節(jié)器是閥門的場(chǎng)合，就必須考慮這個(gè)問(wèn)題。為了解決這個(gè)問(wèn)題，出現(xiàn)了很多模糊控制的分析方法。本文提出的方法采用一個(gè)固定的初始域，這樣相當(dāng)程度上簡(jiǎn)化了模糊控制的設(shè)定問(wèn)題以及實(shí)現(xiàn)。文中分析了振蕩的原因并分析如何抑制這種振蕩的各種方法，最后，還給出一種方案，通過(guò)減少隸屬函數(shù)的數(shù)量以及改善解模糊化的方法縮短控制信號(hào)計(jì)算時(shí)間，有效的改善了控制的實(shí)時(shí)性。
模糊控制器的一個(gè)主要缺陷就是調(diào)整的參數(shù)太多。特別是參數(shù)設(shè)定的時(shí)候，因?yàn)闆](méi)有相關(guān)的書參考，所以它的給定非常困難。眾所周知，優(yōu)化方法的收斂性跟它的初始化設(shè)定有很大關(guān)聯(lián)，如果模糊控制器的初始域是固定的，那么它的控制就明顯的簡(jiǎn)化了。而且我們要控制的參數(shù)大多有其實(shí)際的物理意義，所以模糊控制器完全可以利用PID算法的控制規(guī)律進(jìn)行近似的調(diào)整。也就是說(shuō)最簡(jiǎn)單的模糊PID控制器就是同時(shí)采用幾種基本模糊控制算法（P+I+D或者PI+D），控制過(guò)程中它會(huì)根據(jù)控制要求，做出適當(dāng)?shù)倪x擇，保證在處理跟蹤以抗階躍干擾問(wèn)題上，其控制性能接近于任何一種PID控制。假設(shè)模糊集的初始域是對(duì)稱的，兩個(gè)調(diào)節(jié)器的參數(shù)采用Ziegler-Nichols方法。
為了改善上述設(shè)計(jì)的模糊控制器，我們有必要考模糊控制器的參數(shù)問(wèn)題，有兩種方法可以采納，一種采用手動(dòng)的方法改變，另一種就是采用一些相關(guān)的優(yōu)化算法。其中遺傳算法就是一種?？刂破鞑捎玫膮?shù)不同，其收斂的優(yōu)化值也會(huì)不一樣。這些參數(shù)包括模糊集的分布，模糊集的個(gè)數(shù)，映射規(guī)則，基本模糊控制器的參數(shù)和不同的算法組合等。要注意的是在優(yōu)化前必須選定模糊推理及解模糊的方法。很明顯，優(yōu)化過(guò)程很耗時(shí)，更有甚者，有些優(yōu)化方法要已知系統(tǒng)的精確模型，但是實(shí)際過(guò)程中難以得到系統(tǒng)的精確模型，所以在大多數(shù)情況下，這些優(yōu)化算法不能直接應(yīng)用在實(shí)際過(guò)程。也就是說(shuō)模型不精確直接影響優(yōu)化成敗。模糊控制的主要思想就是針對(duì)那些傳遞函數(shù)未知的或者結(jié)構(gòu)難以辨識(shí)的系統(tǒng)進(jìn)行控制，這也是模糊控制的性能為什么優(yōu)于傳統(tǒng)方法的原因。同時(shí)，把模糊控制和傳統(tǒng)的PID控制算法結(jié)合起來(lái)，更能體現(xiàn)這種算法的優(yōu)點(diǎn)，因?yàn)樗蟠蠛?jiǎn)化實(shí)際過(guò)程的調(diào)整。
參數(shù)集的啟發(fā)式優(yōu)化法也適用于模糊PI控制器，它采用固定的定義域，其參數(shù)的選取和傳統(tǒng)的PI控制器都一樣。我們采用的控制方法是結(jié)合模糊PI算法和PD算法并利用啟發(fā)式優(yōu)化法處理參數(shù)集，特別要注意這里的調(diào)節(jié)器出現(xiàn)了兩個(gè)比例環(huán)節(jié)，所以它的控制可能不同于傳統(tǒng)的PID算法。但是我們調(diào)整的參數(shù)它們本身具有實(shí)際的物理意義，值得一提的是前面所提到的控制可以通過(guò)改變采樣時(shí)間而不改變定義域的范圍實(shí)現(xiàn)調(diào)整。
關(guān)鍵詞：?jiǎn)纹瑱C(jī)；熱處理溫度控制；模糊 PID
Abstract:
This paper adopts fuzzy PID control algorithm which combines fuzzy control and PID control according individual characteristic and theory effectively gets over their disadvantage, at the same time, preserving their merits. The methods of the fuzzy-PID controller, system-controlling, failure-detecting，states-displaying are described in details.
A fuzzy PID controllers are physically related to classical PID controller. The settings of classical controllers are based on deep common physical background. Fuzzy controller can embody better behavior comparing with classical linear PID controller because of its non linear characteristics. Well tuned fuzzy controller can be also more stable and more robust for the time varying systems. On the other hand, when the fuzzy controller is tuned badly it can exhibit limit cycle which can decrease lifetime of the actuator. This phenomenon is critical especially when the actuator is valve. Knowing about these problems, more analytical methods of tuning fuzzy controllers can be found. The method with unified universe considerably simplifies the setting and realization of fuzzy controllers. This paper tries to analyze causes of oscillations and it outlines the possibilities how to reduce them. The paper also shows solution how to reduce time needed for computation of control signal by decreasing the number of membership functions and by changing defuzzification method.
1. INTRODUCTION
One of the main drawbacks of fuzzy controllers is big amount of parameters to be tuned. It is especially difficult to make initial approximate adjustment because there is no cookery book how to do it. Also it is very well known that good convergence of optimum method is strongly dependent on initial settings. The adjustment of fuzzy controllers is considerably simplified when fuzzy controller with a unified universe is used. The parameters to be tune then have their physical meaning and fuzzy controller can be approximately adjusted using known rules for classical controllers. Probably the easiest way how to implement fuzzy PID controller is to create it as a parallel combination of basic fuzzy controllers (P+I+D [4] or PI+D [5]). Suitable choice of inference method can ensure behavior which is close to one of classical PID controller for both the tracking problem and the step disturbance rejection. The fuzzy sets are assumed to have initially symmetrical layout and the parameters of both regulators are tuned using for example by Ziegler- Nichols method.
To improve behavior of such designed fuzzy controller it is necessary either to manually change the quantities of fuzzy controller or to use some optimum methods which do this operation. One which can be implied are genetic algorithms. Different quantities can be changed to reach the optimum values. These quantities are fuzzy set layout, number of fuzzy sets, rule base mapping, the parameters of basic fuzzy controllers and their various combinations. Note that all the optimum must be always performed according to the chosen inference and defuzzification method. It is apparent that process of optimum can take a lot of time. Moreover this method is contingent on existence of accurate mathematical model of the process because in vast majority of the cases it is not possible to perform any kind of optimum directly on real process. The model usually does not correspond to real system which limits the success of optimum methods. The prime idea of fuzzy control was to apply it at the place where there is no deep knowledge of transfer function of controlled system and where this knowledge can be hardly identified. These are often the cases where the fuzzy control leads to better performance comparing with classical approach. Also for this instance it seems to be advantageous to have physical connection between fuzzy controller and its classical counterpart because it can significantly simplify the adjustment of regulator for real process.
The heuristic optimum of parameters settings is also suitable for fuzzy PI controller with unified universe where the parameters are the same as the ones of classical PI controller. The parallel combination of fuzzy PI and PD controllers can be used for heuristic optimum of parameters settings but it should be noted that because of the presence of double proportional part in this regulator
the adjusted parameters will differ from the ones of classical PID controller. But important thing is that the adjustment of this parameters is still in the same physical meaning. Note that for all previously mentioned controllers it is also possible to employ time transformation (sample time modification) without having to change the scope of universes.
Keyword：SCM；Temperature control；Fuzzy PID.
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