【正文】 第4節(jié)介紹了一種先進的模糊控制，這是能夠進行自我調節(jié)的模糊控制規(guī)。第2節(jié)簡單介紹了一個正常的模糊控制及其結果。 TLU處于紙的目的是報告比較實驗結果。到目前為止，我們已經測試正常模糊控制，傳統(tǒng)的PID控制，在此演示模型和先進的模糊控制。許多所謂的傳統(tǒng)方法相比，模糊邏輯的方法，還是很新的BR1反應堆。演示模型結構圖1演示模型的工作原理。因此采用了另一種解決方案，那就是，我們設計并做了水位控制系統(tǒng)，簡稱為演示模式，這是適合于我們的測試和實驗。因此，TI，它是一個動態(tài)的系統(tǒng)有兩個入口和一個出口水流。只有當VI和V2閉合V3將被打開，因為它可以降低泵的壓力，從而延長其工作壽命。例如，在T2中當水位低于光電開關傳感器1的開閉閥V，將被打開（導通），在T2中當水位高于光電開關傳感器2的開 關閥VL將被關閉（關閉）。所有水龍頭在這個時候手動調整。 在這個演示模型，我們的目標是要控制在一個理想的水平，通過調整VL（閥用于大型控制塔T2）和VS（閥小型控制塔T3）的水位塔TI。因此采用了另一種解決方案，那就是，我們設計并做了水位控制系統(tǒng)，簡稱為演示模式，這是適合于我們的測試和實驗。在這種情況下，我們也外的反應器中進行的預先處理實驗的一部分，例如，的COM型坯的不同的方法和參數(shù)的初步選擇。在該項目的框架中，我們發(fā)現(xiàn)，雖然已經有許多模糊邏輯控制的應用程序，這是很難選擇最綏表格可用于檢測和比較我們的算法。即使核工程專家，做任何上線測試的官方許可是必要的。最主要的原因是，它是不可能做實驗，核工程，很容易在其他工業(yè)領域。今天，模糊邏輯控制技術是非常成熟的，在大多數(shù)工程領域，但在核工程，雖然已經做了一些研究（伯納德，1988年，哈和Lee，1994。關鍵詞：模糊控制，PID控制，模糊自適應控制。結果表明，模糊控制有更多方面的優(yōu)勢相對于PID控制的靈活性，魯棒性，并很容易地更新設備演示模型，但PID控制調控具有更高的分辨率，因為它積分項。在本文中，我們首先簡要報告建造演示模型，然后引入一個模糊控制的結果，比例 積分 微分（PID）控制和先進的模糊控制，其中先進的模糊控制模糊控制具有自適應功能，可以自我調節(jié)模糊控制規(guī)則。Controlling the power output of a nuclear reac tor with fuzzy logic, Information Scienkes, 110. 151 177. 模糊的比較研究控制，PID控制，先進的模糊控制用于模擬核反應堆運行李曉東和阮達比利時核研究中心（SCK CENBoeretang20082400摩爾，比利時（1999年3月15日） 基于模糊控制應用在比利時的第一座核反應堆（BRI）在比利時核研究中心（）的背景上，我們已經取得了真正的模糊邏輯控制的演示模型。97, Prague, Czech Republic, Vol. 3, pp. 126131. 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The adaptive function selects only some of the rules according to formula (1) for adjustment, not all activated rules like (Lin et al., 1997). This makes the transition of the rules more smooth, ., without or with less resonance. Selecting initial rules appropriately will benefit the control effect. For example, if the overshoot is strictly limited, we may initialize all rules with the conclusion part of NL, as was done in the previous experiment. Once some experience has been obtained, it can be transformed into the initial rules of the adaptive function, the advantage being that the rise time will be shorter (Li and Ruan, 1998). The rule, if D is ZE and DD is ZE then U is ZE, should be fixed, and this will help the system to bee stable. The adaptive function is very helpful in keeping the system stable in a steady state. It cannot guarantee no overshoot if the initial rules are randomly selected. The adaptive function cannot adjust membership functions and scale factors. 5 COMPARATIVE STUDY Each method has both advantages and disadvantages, the details of which are described in Table IV, where * is used to represent the degree of a property, and the more *, the higher the degree. For example, the realization of an adaptive fuzzy logic controller (FLC) is more difficult than a normal fuzzy controller, but a normal fuzzy controller is more difficult to realize than a PID controller. The PID control has the smallest static error and steady error. The dynamic regulation of TABLE IV Comparative study of FLC, PID and adaptive FLC the control rules in an adaptive controller can help in reducing the static error and steady error (Li et al., 1996a,b). As for robustness, it has been accepted that FLC is more robust than PID. Herein we also give one example, as shown as in Fig. 7. This experiment was carried out after tuning the Tap 1 (see Fig. 1) to make the outflow much smaller. We found that the reaction of FLC was better than that of PID, hough the FLC had a small static error. If we count the total number of * for each method, we will find that PID and FLC have the same score, 17. Adaptive FLC has a higher score of 20. This interesting result can be explained by the following facts. PID and FLC have their own strong points, and they pensate each other. Adaptive FLC adds an adaptive function to a normal FLC, therefore its score should be higher than that of FLC. A natural result is that bining FLC and PID should be be