MCS-51單片機智能溫度控制系統(tǒng)設計【含CAD圖紙和說明書】

喜歡這個資料需要的話就充值下載吧。資源目錄里展示的全都有預覽可以查看的噢，下載就有,請放心下載，原稿可自行編輯修改=【QQ：11970985 可咨詢交流】=喜歡就充值下載吧。資源目錄里展示的全都有，下載后全都有,請放心下載，原稿可自行編輯修改=【QQ：197216396 可咨詢交流】= 信息與電子工程系畢業(yè)設計（中英文資料） 畢業(yè)設計(論文)中英文資料 信 電 系 工業(yè)電氣自動化 專業(yè) 04 級 1 班課題名稱：MCS-51單片機智能溫度控制系統(tǒng)設計畢業(yè)設計(論文)起止時間：2006年 2月10日6月8日(共17周)學生姓名： 學號： 10 指導教師： 報告日期： 年3月 1日 摘要：本文根據模糊控制和PID控制的特點及其原理，把模糊控制和PID控制結合起來，形成模糊PID控制，有效的克服了它們的缺點而發(fā)揮了它們的優(yōu)勢。本文詳細闡述了該系統(tǒng)中模糊PID控制器的實現(xiàn)方法、系統(tǒng)的各種控制、故障檢測以及狀態(tài)顯示。 模糊PID控制器實際上跟傳統(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象的模型結構，而模糊控制器因為它的非線性特性，所以控制性能優(yōu)于傳統(tǒng)PID控制器。對于時變系統(tǒng)，如果能夠很好地采用模糊控制器進行調節(jié)，其控制結果的穩(wěn)定性和活力性都會有改善。但是，如果調節(jié)效果不好，執(zhí)行器會因為周期振蕩影響使用壽命，特別是調節(jié)器是閥門的場合，就必須考慮這個問題。為了解決這個問題，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固定的初始域，這樣相當程度上簡化了模糊控制的設定問題以及實現(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，通過減少隸屬函數的數量以及改善解模糊化的方法縮短控制信號計算時間，有效的改善了控制的實時性。模糊控制器的一個主要缺陷就是調整的參數太多。特別是參數設定的時候，因為沒有相關的書參考，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定有很大關聯(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化了。而且我們要控制的參數大多有其實際的物理意義，所以模糊控制器完全可以利用PID算法的控制規(guī)律進行近似的調整。也就是說最簡單的模糊PID控制器就是同時采用幾種基本模糊控制算法（P+I+D或者PI+D），控制過程中它會根據控制要求，做出適當的選擇，保證在處理跟蹤以抗階躍干擾問題上，其控制性能接近于任何一種PID控制。假設模糊集的初始域是對稱的，兩個調節(jié)器的參數采用Ziegler-Nichols方法。為了改善上述設計的模糊控制器，我們有必要考模糊控制器的參數問題，有兩種方法可以采納，一種采用手動的方法改變，另一種就是采用一些相關的優(yōu)化算法。其中遺傳算法就是一種?？刂破鞑捎玫膮挡煌?，其收斂的優(yōu)化值也會不一樣。這些參數包括模糊集的分布，模糊集的個數，映射規(guī)則，基本模糊控制器的參數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)化過程很耗時，更有甚者，有些優(yōu)化方法要已知系統(tǒng)的精確模型，但是實際過程中難以得到系統(tǒng)的精確模型，所以在大多數情況下，這些優(yōu)化算法不能直接應用在實際過程。也就是說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那些傳遞函數未知的或者結構難以辨識的系統(tǒng)進行控制，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，把模糊控制和傳統(tǒng)的PID控制算法結合起來，更能體現(xiàn)這種算法的優(yōu)點，因為它大大簡化實際過程的調整。 參數集的啟發(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)化法處理參數集，特別要注意這里的調節(jié)器出現(xiàn)了兩個比例環(huán)節(jié)，所以它的控制可能不同于傳統(tǒng)的PID算法。但是我們調整的參數它們本身具有實際的物理意義，值得一提的是前面所提到的控制可以通過改變采樣時間而不改變定義域的范圍實現(xiàn)調整。 關鍵詞：單片機；熱處理溫度控制；模糊 PIDAbstract: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. INTRODUCTIONOne 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 regulatorthe 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.3