1、模糊控制软启动器设计相关翻译资料 1 INTRODUCTION TO FUZZY LOGIC & FUZZY CONTROL Fuzzy logic has become a common buzzword in machine control. However, the term itself inspires a certain skepticism, sounding equivalent to half-baked logic or bogus logic.Some other nomenclature might have been preferable, but its too l
2、ate now, and fuzzy logic is actually very straightforward.Fuzzy logic is a way of interfacing inherently analog processes, that move through a continuous range of values, to a digital computer, that likes to see things as well-defined discrete numeric values. For example, consider an antilock brakin
3、g system, directed by a microcontroller chip. The microcontroller has to make decisions based on brake temperature, speed, and other variables in the system. The variable temperature in this system can be divided into a range of states, such as: cold, cool, moderate, warm, hot, very hot. Defining th
4、e bounds of these states is a bit tricky. An arbitrary threshold might be set to divide warm from hot, but this would result in a discontinuous change when the input value passed over that threshold. The way around this is to make the states fuzzy, that is, allow them to change gradually from one st
5、ate to the next. You could define the input temperature states using membership functions such as the following: With this scheme, the input variables state no longer jumps abruptly from one state to the next. Instead, as the temperature changes, it loses value in one membership function while gaini
6、ng value in the next. At any one time, the truth value of the brake temperature will almost always be in some degree part of two membership functions: 0.6 nominal and 0.4 warm, or 0.7 nominal and 0.3 cool, and so on. The input variables in a fuzzy control system are in general mapped into by sets of
7、 membership functions similar to this, known as fuzzy sets. The process of converting a crisp input value to a fuzzy value is called fuzzification. A control system may also have various types of switch, or ON-OFF, inputs along with its analog inputs, and such switch inputs of course will always hav
8、e a truth value equal to either 1 or 0, but the scheme can deal with them as simplified fuzzy functions that are either one value or another. Given mappings of input variables into membership functions and truth values, the microcontroller then makes decisions for what action to take based on a set
9、of rules, each of the form: IF brake temperature IS warm AND speed IS not very fast THEN brake pressure IS slightly decreased. In this example, the two input variables are brake temperature and speed that have values defined as fuzzy sets. The output variable, brake pressure, is also defined by a fu
10、zzy set that can have values like static, slightly increased, slightly decreased, and so on. This rule by itself is very puzzling since it looks like it could be used without bothering with fuzzy logic, but remember the decision is based on a set of rules: All the rules that apply are invoked, using
11、 the membership functions and truth values obtained from the inputs, to determine the result of the rule. This result in turn will be mapped into a membership function and truth value controlling the output variable. These results are combined to give a specific (crisp) answer, the actual brake pres
12、sure, a procedure known as defuzzification. This combination of fuzzy operations and rule-based inference describes a fuzzy expert system. Traditional control systems are based on mathematical models in which the the control system is described using one or more differential equations that define th
13、e system response to its inputs. Such systems are often implemented as proportional-integral-derivative (PID) controllers. They are the products of decades of development and theoretical analysis, and are highly effective. If PID and other traditional control systems are so well-developed, why bothe
14、r with fuzzy control? It has some advantages. In many cases, the mathematical model of the control process may not exist, or may be too expensive in terms of computer processing power and memory, and a system based on empirical rules may be more effective. Furthermore, fuzzy logic is well suited to
15、low-cost implementations based on cheap sensors, low-resolution analog-to-digital converters, and 4-bit or 8-bit one-chip microcontroller chips. Such systems can be easily upgraded by adding new rules to improve performance or add new features. In many cases, fuzzy control can be used to improve exi
16、sting traditional controller systems by adding an extra layer of intelligence to the current control method. 1 对模糊逻辑和模糊控制的介绍 “模糊控制”技术已经成为机电控制的核心内容，在机器控制中应用广泛。然而，词的本身听上去好象和“半逻辑”或“伪逻辑”相等 ，使人产生怀疑态度。一些其他的命名法虽然可以令人满意，但是，现在太晚了，并且，模糊逻辑实际上非常简单。模糊逻辑的定义我们可以看成是把不连续的数据通过计算机模拟界面转化成连续的数据的一种方法。 例如，认为刹车系统的抱闸微控制器系统的
17、接头是根据微控制器的抱闸温度，速度和其他的变量进行决定的。这个系统里的“温度”能类似的被分为一组“状态”的变量：“凉”、“适度的”、“ 寒冷”、“假热”、“热” 、“非常热”。定义这些状态的界限是相对的，当输入度值通过规定临界值的时候顺序从“ 热”到“温暖的”分配的每一套的任意的临界值，但是这中结果将是不连续的变化。这种电路状态换句话说是“模糊”的概念，使他们由 1 状态逐渐地变成到下一个你能够定义的类似的以下“数据全体”起作用的输入的温度状态。 用这种方法，输入变量的状态不在从 1 状态突然急速的变到下一个变量，反而，如同温度变化的那样，得到下一个有用的价值，它失去了 1 状态在任何 1 时
18、间的应用价值，抱闸温度的“真实评价”将在一些 2 的状态： 0.6 的状 态部分和 0.4 加热的状态或 0.7 中总的状态，并且， 0.3 的变凉，与其他是相同的。 模糊的监督体制里的输入变量是有模式的，一般来说，“模糊”的编入与其他有相似的功能。使最开始的输入值改变为模糊的值的过程被叫做“模糊规则” 。另外，监督体制是可以有输入的，总是将自然有的用其模拟输入和这样的开断等于 1 和 0 的真实值的输入，但是希望能和他们象作为任何一个 1 值的简化的模糊功能那样分成的开断或“是 -否”的各种形式或其他的。得到输入的变量的“映像”成为状态模式和真实值，然后微控制器把决定做成需求的行动放在“规则
19、”的一套形式的每个中： 抱闸温度不高，并且，如果速度不太高时闸压力轻微的减少形成牢固不太高。就这个例子来说，2 输入的变量是把值定义为模糊的模式的“温度减少”和“速度刹住”。另外，变量输出的“压力刹住”能让人把值控制在适合的“ 静电噪声”和“轻微地增加”及“轻微地减少”等等的模糊的定义 。这源于本身的规则非常逻辑，它好像是能不为模糊逻辑的事心绪不宁而使用，但是决定应根据这套规则使用： 适用的全部规则都适用从输入被得到，为决定规则结果的状态模式和真实值被可求。 结果将按顺序被编入在控制输出的变量的状态和真实值表中 。 这些 结果给了特定回答的实际的闸压力，这个过程被结合 。 这中模糊的操作和规则
20、是根据“推理”相结合描述的“模糊系统专家” 。传统性的控制系统基于假设的数学式样，系统给予定义回答系统的描述的使用的一个或更微分的样式的控制其输入方法。这样的系统象“ PID”操纵器那样经常被实现 。他们所基发的理论上的分析对 10 年的产品非常有效 。如果 PID 和其他的传统性的控制系统如此好好地发展，为什么还为模糊控制系统而发愁？它有许多优点，在很多的例子中，不可以存在或者也许不太有关计算机处理能力和记忆控制过程的数学模型，并且，根据实践经 验的规则的系统也许更有效。此外，模糊逻辑好好地适应是根据便宜的传感器的低成本而实现的，低分辨率的模 数转换器和 4 位或者 8 位的一个接头的微控制
21、器接头的应用。这样的系统能通过为了改善应用，或者加新特点加新规则简单地升级。在很多的例子，模糊控制能被用来利用目前的传统性的操纵器系统进行把智能的额外的层加到现在的控制方法上。 2 FUZZY CONTROL IN DETAIL Fuzzy controllers are very simple conceptually. They consist of an input stage, a processing stage, and an output stage. The input stage maps sensor or other inputs, such as switches, t
22、humbwheels, and so on, to the appropriate membership functions and truth values. The processing stage invokes each appropriate rule and generates a result for each, then combines the results of the rules. Finally, the output stage converts the combined result back into a specific control output value. The most common shape of membership functions is triangular, although trapezoids and bell curves are also used, but the shape
