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1、模糊控制理论摘自 维基百科 2011年11月20日概述 模糊逻辑广泛适用于机械控制。这个词本身激发一个一定的怀疑,试探相当于“仓促的逻辑”或“虚假的逻辑”,但“模糊”不是指一个部分缺乏严格性的方法,而这样的事实,即逻辑涉及能处理的概念,不能被表达为“对”或“否”,而是因为“部分真实”。虽然遗传算法和神经网络可以执行一样模糊逻辑在很多情况下,模糊逻辑的优点是解决这个问题的方法,能够被铸造方面接线员能了解,以便他们的经验,可用于设计的控制器。这让它更容易完成机械化已成功由人执行。历史以及应用 模糊逻辑首先被提出是有Lotfi在加州大学伯克利分校在1965年的一篇论文。他阐述了他的观点在1973年的
2、一篇论文的概念,介绍了语言变量”,在这篇文章中相当于一个变量定义为一个模糊集合。其他研究打乱了,第二次工业应用中,水泥窑建在丹麦,即将到来的在线1975。 模糊系统在很大程度上在美国被忽略了,因为他们更多关注的是人工智能，一个被过分吹嘘的领域，尤其是在1980年中期年代,导致在诚信缺失的商业领域。 然而日本人对这个却没有偏见和忽略，模糊系统引发日立的Seiji Yasunobu和Soji Yasunobu Miyamoto的兴趣。,他于1985年的模拟,证明了模糊控制系统对仙台铁路的控制的优越性。他们的想法是被接受了,并将模糊系统用来控制加速、制动、和停车，当线于1987年开业。 1987年另
3、一项促进模糊系统的兴趣。在一个国际会议在东京的模糊研究那一年,Yamakawa论证使用模糊控制,通过一系列简单的专用模糊逻辑芯片,在一个“倒立摆“实验。这是一个经典的控制问题,在这一过程中,车辆努力保持杆安装在顶部用铰链正直来回移动。 这次展示给观察者家们留下了深刻的印象,以及后来的实验,他登上一Yamakawa酒杯包含水或甚至一只活老鼠的顶部的钟摆。该系统在两种情况下,保持稳定。Yamakawa最终继续组织自己的fuzzy-systems研究实验室帮助利用自己的专利在田地里的时候。 展示之后,日本工程师开发出了大范围的模糊系统用于工业领域和消费领域的应用。1988年,日本建立了国际模糊工程实
4、验室,建立合作安排48公司进行模糊控制的研究。 松下吸尘器使用微控制器运行模糊算法去控制传感器和调整吸尘力。日立洗衣机用模糊控制器Load-Weight,Fabric-Mix和尘土传感器及自动设定洗涤周期来最佳利用电能、水和洗涤剂。 佳能研制出的一种上相机使用电荷耦合器件(CCD)测量中的图像清晰的六个区域其视野和使用提供的信息来决定是否这个影像在焦点上(清晰)。它也可以追踪变化的速率在镜头运动的重点,以及它的速度以防止控制超调。相机的模糊控制系统采用12输入,6个输入了解解现行清晰所提供的数据和其他6个输入测量CCD镜头的变化率的运动。输出的位置是镜头。模糊控制系统应用13条规则,需要1.1
5、 千字节记忆信息。 另外一个例子是,三菱工业空调设计采用25加热规则和25冷却规则。温度传感器提供输入,输出一个控制逆变器,一个压缩机气阀,风扇电机。和以前的设计相比,新设计的模糊控制器增加五次加热冷却速度,降低能耗24%,增加温度稳定性的一个因素两个,使用较少的传感器。日本人对模糊逻辑的人情是反映在很广泛的应用范围上,他们一直在研究或实现:例如个性和笔迹识别光学模糊系统,机器人,声控机器人直升飞机。模糊系统的相关研究工作也在美国和欧洲进行着。美国环境保护署分析了模糊控制节能电动机,美国国家航空和宇宙航行局研究了模糊控制自动太空对接。仿真结果表明,模糊控制系统可大大降低燃料消耗。如波音公司、通
6、用汽车、艾伦-布拉德利、克莱斯勒、伊顿,和漩涡了模糊逻辑用于低功率冰箱、改善汽车变速箱。在1995年美泰克公司推出的一个“聪明” 基于模糊控制器洗碗机，“一站式感应模块”包括热敏电阻器,用来温度测量;电导率传感器,用来测量离子洗涤剂水平存在于洗;分散和浊度传感器用来检测透射光测量失禁的洗涤,以及一个磁致伸缩传感器来读取旋转速率。这个系统确定最优洗周期任何载荷,获得最佳的结果用最少的能源、洗涤剂、和水。 研究和开发还继续模糊应用软件,作为反对固件设计,包括模糊专家系统模糊逻辑与整合神经网络和所谓的自适应遗传软件系统,其最终目的是建立“自主学习”模糊控制系统。模糊集 输入变量在一个模糊控制系统是集
7、映射到一般由类似的隶属度函数,称为“模糊集”。转换的过程中,一个干脆利落的输入值模糊值称为“模糊化”。 一个控制系统也有各种不同的类型开关或“开关”,连同它的模拟输入输入,而这样的开关输入当然总有一个真实的价值等于要么1或0,但该方案能对付他们,简单的模糊函数,要么发生一个值或另一个。 赋予了“映射输入变量的隶属函数和进入真理价值,单片机然后做出决定为采取何种行动基于一套“规则”,每一组的形式。 在一个例子里,有两个输入变量是“刹车温度”和“速度”,定义为模糊集值。输出变量,“制动压力” ,也定义为一个模糊集,有价值观像“静” 、“稍微增大” “略微下降”,等等。这条规则本身很莫名其妙,因为它
8、看起来好像可以使用，会干扰到与模糊,但要记住,这个决定是基于一套规则。 所有的规则都调用申请,使用模糊隶属度函数和诚实得到输入值,确定结果的规则。这个结果将被映射成一个隶属函数和控制输出变量的真值。这些结果相结合,给出了具体的(“脆”)的答案,实际的制动压力,一个过程被称为解模糊化，结合了模糊操作规则 推理“描述”模糊专家系统”。 传统的控制系统是基于数学模型的控制系统,描述了使用一个或更多微分方程确定系统回应其输入。这类系统通常被作为“PID控制器”他们是产品的数十年的发展建设和理论分析,是非常有效的。 如果PID和其他传统的控制系统是如此的先进,何必还要模糊控制吗?它有一些优点。在许多情况
9、下,数学模型的控制过程可能不存在,或太“贵”的认识论的计算机处理能力和内存,与系统的基于经验规则可能更有效。 此外,模糊逻辑都适合低成本实现基于廉价的传感器、低分辨率模拟/数字转换器,或8位单片机芯片one-chip 4比特。这种系统可以很容易地通过增加新的规则升级来提高性能或添加新功能。在许多情况下,模糊控制可以用来改善现有的传统控制器系统通过增加了额外的情报电流控制方法。模糊控的细节 模糊控制器是很简单的理念上。它们是由一个输入阶段,一个处理阶段,一个输出阶段。地图传感器输入级或其他输入,比如开关等等,到合适的隶属函数和真理的价值。每一个适当的加工阶段调用规则和产生的结果对每个人来说,然后
10、结合结果的规则。最后,将结果输出阶段相结合的具体控制输出回他的价值。 最常见的形状是三角形的隶属度函数,尽管梯形和贝尔曲线也使用,但其形状通常比数量更重要曲线及其位置。从三人至七人通常是适当的覆盖曲线所需要的范围的一个输入值,或“宇宙的话语“在模糊术语。 作为讨论之前,加工阶段是基于规则的集合的形式逻辑IF - THEN报表,那里的部分叫做“之前”和后来的部分被称为“随之”。典型的模糊控制系统具有几十个规则。 这条规则的价值采用真理“温”的输入,真值的“冷”,产生的结果,在模糊集的“加热器“输出,“高”的价值。这个结果是用来与其他规则的结果,最终产生脆复合输出。很明显,越是真理价值的“冷”,真
11、值越高,“高”,但这并不一定就意味着输出本身会被设置为“高”,因为这是唯一准则在许多。在某些情况下,隶属函数可以修正“篱笆”相当于形容词。模糊限制语包括“关于“常见,“近”、“接近”、“大约”、“很”、“稍微”、“太”、“非常”、“有点”。这些操作可能有明确的定义,虽然可能有很大差别的定义不同的实现。“非常”,因为一个典型的例子,广场隶属函数;因为会员价值总是小于1,这减少了隶属函数。“非常”立方体价值观提供更大的缩小,而“有点“扩大功能以平方根的计算。 在实践中,模糊规则集,通常有几个来路综合利用模糊运算,如,或者,不,虽然再次定义每每变化,在一个受欢迎的定义,只是利用最小重量的雏形,而或采
12、用最大值。还有一个不经营者一个隶属函数减去从1到给“补充性”功能。 有几种方法可以定义一个规则的结果,而是一种最常见的和最简单的是“极大极小“推理法,给出了输出隶属函数的真值所产生的前提。 规则可以解决并联在硬件或软件。顺序结果所有的规则，其中的几个方法。在理论上有几十个,每个都有各种各样的优点和缺点。 “质心”的方法很受欢迎,在“的质心”的结果提供了清新的价值。另一个方法是“高度”方法,它以价值的主要因素。方法更利于统治质心与输出最大的区域,而高程法显然更利于规则和最大的输出值。 模糊控制系统的设计是基于经验方法,基本上一个系统的方法试误。大致过程如下: 1.文件系统的操作规范和输入与输出。
13、 2.文档模糊集的输入。 3.文件规则集。 4.确定解模糊化方法确定。 5.运行测试套件验证通过制度,调整细节的要求。 6.完整的文件,发布给生产。逻辑解释模糊控制 尽管有几个困难出现给一个严谨的逻辑解释If - Then规则。作为一个例子,解释一个规则,因为如果(温度是“冷”),那么(加热器是“高”)由第一阶表达式冷高和假设是一个输入这样冷是假的。然后公式冷高是适用于任何一个师,因此任何不正确的控制提供了一种给r。很明显,如果我们考虑系统的先例的规则类定义一个分区这样一个自相矛盾的现象不会出现。在任何情况下它有时是不考虑两个变量和在一条规则没有某种功能的依赖。严谨的逻辑正当化中给出的模糊控制
14、Hajek的书,被描绘成一个模糊控制理论的基本Hajek逻辑。在2005 Gerla模糊控制逻辑方法,提出了一种基于以下的想法。f模糊函数表示的系统与模糊控制相结合,即:给定输入,是模糊集合可能的输出。然后给出一个可能的输出的,我们把为真理程度的表示。更多的是任何系统的If - Then规则可转化为一个模糊的程序,在这种情况下模糊函数模糊谓词的解释很好在相关的最小模糊Herbrand模型。以这样一种方式成为一个章模糊控制的模糊逻辑编程。学习过程成为一个问题属于归纳逻辑理论。Fuzzy ControlFrom Wikipedia 20 November 2011Overview Fuzzy lo
15、gic is widely used in machine control. The term itself inspires a certain skepticism, sounding equivalent to half-baked logic or bogus logic, but the fuzzy part does not refer to a lack of rigour in the method, rather to the fact that the logic involved can deal with concepts that cannot be expresse
16、d as true or false but rather as partially true. Although genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases, fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience ca
17、n be used in the design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humans. History and applications Fuzzy logic was first proposed by Lotfi A. Zadeh of the University of California at Berkeley in a 1965 paper. He elaborated on his ideas in a
18、 1973 paper that introduced the concept of linguistic variables, which in this article equates to a variable defined as a fuzzy set. Other research followed, with the first industrial application, a cement kiln built in Denmark, coming on line in 1975. Fuzzy systems were largely ignored in the U.S.
19、because they were associated with artificial intelligence, a field that periodically oversells itself, especially in the mid-1980s, resulting in a lack of credibility within the commercial domain. The Japanese did not have this prejudice. Interest in fuzzy systems was sparked by Seiji Yasunobu and S
20、oji Miyamoto of Hitachi, who in 1985 provided simulations that demonstrated the superiority of fuzzy control systems for the Sendai railway. Their ideas were adopted, and fuzzy systems were used to control accelerating, braking, and stopping when the line opened in 1987. Another event in 1987 helped
21、 promote interest in fuzzy systems. During an international meeting of fuzzy researchers in Tokyo that year, Takeshi Yamakawa demonstrated the use of fuzzy control, through a set of simple dedicated fuzzy logic chips, in an inverted pendulum experiment. This is a classic control problem, in which a
22、vehicle tries to keep a pole mounted on its top by a hinge upright by moving back and forth. Observers were impressed with this demonstration, as well as later experiments by Yamakawa in which he mounted a wine glass containing water or even a live mouse to the top of the pendulum. The system mainta
23、ined stability in both cases. Yamakawa eventually went on to organize his own fuzzy-systems research lab to help exploit his patents in the field. Following such demonstrations, Japanese engineers developed a wide range of fuzzy systems for both industrial and consumer applications. In 1988 Japan es
24、tablished the Laboratory for International Fuzzy Engineering (LIFE), a cooperative arrangement between 48 companies to pursue fuzzy research. Matsushita vacuum cleaners use micro controllers running fuzzy algorithms to interrogate dust sensors and adjust suction power accordingly. Hitachi washing ma
25、chines use fuzzy controllers to load-weight, fabric-mix, and dirt sensors and automatically set the wash cycle for the best use of power, water, and detergent. Canon developed an autofocusing camera that uses a charge-coupled device (CCD) to measure the clarity of the image in six regions of its fie
26、ld of view and use the information provided to determine if the image is in focus. It also tracks the rate of change of lens movement during focusing, and controls its speed to prevent overshoot.The cameras fuzzy control system uses 12 inputs: 6 to obtain the current clarity data provided by the CCD
27、 and 6 to measure the rate of change of lens movement. The output is the position of the lens. The fuzzy control system uses 13 rules and requires 1.1 kilobytes of memory. As another example of a practical system, an industrial air conditioner designed by Mitsubishi uses 25 heating rules and 25 cool
28、ing rules. A temperature sensor provides input, with control outputs fed to an inverter, a compressor valve, and a fan motor. Compared to the previous design, the fuzzy controller heats and cools five times faster, reduces power consumption by 24%, increases temperature stability by a factor of two,
29、 and uses fewer sensors.The enthusiasm of the Japanese for fuzzy logic is reflected in the wide range of other applications they have investigated or implemented: character and handwriting recognition; optical fuzzy systems; robots, voice-controlled robot helicoptersWork on fuzzy systems is also pro
30、ceeding in the US and Europe. The US Environmental Protection Agency has investigated fuzzy control for energy-efficient motors, and NASA has studied fuzzy control for automated space docking: simulations show that a fuzzy control system can greatly reduce fuel consumption. Firms such as Boeing, Gen
31、eral Motors, Allen-Bradley, Chrysler, Eaton, and Whirlpool have worked on fuzzy logic for use in low-power refrigerators, improved automotive transmissions, and energy-efficient electric motors.In 1995 Maytag introduced an intelligent dishwasher based on a fuzzy controller and a one-stop sensing mod
32、ule that combines a thermistor, for temperature measurement; a conductivity sensor, to measure detergent level from the ions present in the wash; a turbidity sensor that measures scattered and transmitted light to measure the soiling of the wash; and a magnetostrictive sensor to read spin rate. The
33、system determines the optimum wash cycle for any load to obtain the best results with the least amount of energy, detergent, and water. Research and development is also continuing on fuzzy applications in software, as opposed to firmware, design, including fuzzy expert systems and integration of fuz
34、zy logic with neural-network and so-called adaptive genetic software systems, with the ultimate goal of building self-learning fuzzy control systems.Fuzzy sets The input variables in a fuzzy control system are in general mapped into by sets of membership functions similar to this, known as fuzzy set
35、s. 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 have a truth value equal to either 1 or 0, but the scheme can
36、 deal with them as simplified fuzzy functions that happen to be 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 of rules, each of the form. In one example, the t
37、wo input variables are brake temperature and speed that have values defined as fuzzy sets. The output variable, brake pressure, is also defined by a fuzzy 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
38、it could be used without bothering with fuzzy logic, but remember that the decision is based on a set of rules: All the rules that apply are invoked, using 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
39、 a membership function and truth value controlling the output variable. These results are combined to give a specific (crisp) answer, the actual brake pressure, a procedure known as defuzzification.This combination of fuzzy operations and rule-based inference describes a fuzzy expert system. Traditi
40、onal control systems are based on mathematical models in which the control system is described using one or more differential equations that define the system response to its inputs. Such systems are often implemented as PID controllers (proportional-integral-derivative controllers). They are the pr
41、oducts of decades of development and theoretical analysis, and are highly effective. If PID and other traditional control systems are so well-developed, why bother with fuzzy control? It has some advantages. In many cases, the mathematical model of the control process may not exist, or may be too ex
42、pensive 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 low-cost implementations based on cheap sensors, low-resolution analog-to-digital converters, and 4-bit or 8-bit one-chip microcontroller c
43、hips. 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 existing traditional controller systems by adding an extra layer of intelligence to the current control method.Fuzzy control in detail Fuzzy c
44、ontrollers 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, thumbwheels, and so on, to the appropriate membership functions and truth values. The processing stage invokes each appropr
45、iate 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 trapezoidal and bell curves are also used, but th
46、e shape is generally less important than the number of curves and their placement. From three to seven curves are generally appropriate to cover the required range of an input value, or the universe of discourse in fuzzy jargon. As discussed earlier, the processing stage is based on a collection of
47、logic rules in the form of IF-THEN statements, where the IF part is called the antecedent and the THEN part is called the consequent. This rule uses the truth value of the temperature input, which is some truth value of cold, to generate a result in the fuzzy set for the heater output, which is some
48、 value of high. This result is used with the results of other rules to finally generate the crisp composite output. Obviously, the greater the truth value of cold, the higher the truth value of high, though this does not necessarily mean that the output itself will be set to high since this is only one rule among many. In some cases, the membership functions can be modified by hedges that are equivalent to adjectives. Common hedges include
