Engineering Research Center of Rolling Equipment and Complete Technology ofMinistry of Educations State Key Laboratory of Metastable Materials Science andTechnology, Yanshan University, Qinhuangdao 066004, Hebei, China
Welcome to the wonderful world of fuzzy logic, the new science you can use to powerfully get things done. Add the ability to utilize personal computer based fuzzy logic analysis and control to your technical and management skills and you can do things that humans and machines cannot otherwise do.
Following is the base on which fuzzy logic is built: As the complexity of a system increases, it becomes more difficult and eventually impossible to make a precise statement about its behavior, eventually arriving at a point of complexity where the fuzzy logic method born in humans is the only way to get at the problem. Fuzzy logic is used in system control and analysis design, because it shortens the timefor engineering development and sometimes, in the case of highly complex systems, is the only way to solve the problem. Although most of the time we think of \"control\" as having to do with controlling a physical system, there is no such limitation in the concept as initially presented by Dr. Zadeh. Fuzzy logic can apply also to economics, psychology, marketing, weather forecasting, biology, politics ...... to any large complex system.
The term \"fuzzy\" was first used by Dr. Lotfi Zadeh in the engineering journal, \"Proceedings of the IRE,\" a leading engineering journal, in 1962. Dr. Zadeh became, in 1963, the Chairman of the Electrical Engineering department of the University of California at Berkeley. That is about as high as you can go in the electrical
engineering field. Dr. Zadeh thoughts are not to be taken lightly. Fuzzy logic is not thewave of the future. It is now! There are already hundreds of millions of dollars of successful, fuzzy logic based commercial products, everything from self-focusing cameras to washing machines that adjust themselves according to how dirty the clothes are, automobile engine controls, anti-lock braking systems, color film developing systems, subway control systems and computer programs trading
successfully in the financial markets. Note that when you go searching for fuzzy-logicapplications in the United States, it is difficult to impossible to find a control system acknowledged as based on fuzzy logic. Just imagine the impact on sales if General Motors announced their anti-lock braking was accomplished with fuzzy logic! The general public is not ready for such an announcement.
Suppose you are driving down a typical, two way, 6 lane street in a large city, one mile between signal lights. The speed limit is posted at 45 Mph. It is usually optimum and safest to \"drive with the traffic,\" which will usually be going about 48 Mph. How do you define with specific, precise instructions \"driving with the traffic?\" It is difficult. But, it is the kind of thing humans do every day and do well. There will be some drivers weaving in and out and going more than 48 Mph and a few drivers driving exactly the posted 45 Mph. But, most drivers will be driving 48 Mph. They dothis by exercising \"fuzzy logic\" - receiving a large number of fuzzy inputs, somehow evaluating all the inputs in their human brains and summarizing, weighting and averaging all these inputs to yield an optimum output decision. Inputs being evaluatedmay include several images and considerations such as: How many cars are in front. How fast are they driving. Any \"old clunkers\" going real slow. Do the police ever set up radar surveillance on this stretch of road. How much leeway do the police allow over the 45 Mph limit. What do you see in the rear view mirror. Even with all this, and more, to think about, those who are driving with the traffic will all be going alongtogether at the same speed.
The same ability you have to drive down a modern city street was used by our ancestors to successfully organize and carry out chases to drive wooly mammoths intopits, to obtain food, clothing and bone tools.
Human beings have the ability to take in and evaluate all sorts of information from the physical world they are in contact with and to mentally analyze, average and summarize all this input data into an optimum course of action. All living things do this, but humans do it more and do it better and have become the dominant species of the planet.
If you think about it, much of the information you take in is not very precisely defined, such as the speed of a vehicle coming up from behind. We call this fuzzy input. However, some of your \"input\" is reasonably precise and non-fuzzy such as the speedometer reading. Your processing of all this information is not very precisely definable. We call this fuzzy processing. Fuzzy logic theorists would call it using fuzzy algorithms (algorithm is another word for procedure or program, as in a computer program). Fuzzy logic is the way the human brain works, and we can mimicthis in machines so they will perform somewhat like humans (not to be confused with Artificial Intelligence, where the goal is for machines to perform EXACTLY like humans). Fuzzy logic control and analysis systems may be electro-mechanical in nature, or concerned only with data, for example economic data, in all cases guided by \"If-Then rules\" stated in human language.
The fuzzy logic analysis and control method is, therefore:
1)Receiving of one, or a large number, of measurement or other assessment of conditions existing in some system we wish to analyze or control.
2)Processing all these inputs according to human based, fuzzy \"If-Then\" rules, which can be expressed in plain language words, in combination with traditional non-fuzzy processing.
3)Averaging and weighting the resulting outputs from all the individual rules intoone single output decision or signal which decides what to do or tells a controlled system what to do. The output signal eventually arrived at is a precise appearing defuzzified, \"crisp\" value.
Measured, non-fuzzy data is the primary input for the fuzzy logic method. Examples: temperature measured by a temperature transducer, motor speed, economicdata, financial markets data, etc. It would not be usual in an electro-mechanical control system or a financial or economic analysis system, but humans with their fuzzy perceptions could also provide input. There could be a human \"in-the-loop.\" In the fuzzy logic literature, you will see the term \"fuzzy set.\" A fuzzy set is a group of anything that cannot be precisely defined. Consider the fuzzy set of \"old houses.\"
How old is an old house? Where is the dividing line between new houses and old houses? Is a fifteen year old house an old house? How about 40 years? What about 39.9 years? The assessment is in the eyes of the beholder. Other examples of fuzzy sets are: tall women, short men, warm days, high pressure gas, small crowd, medium viscosity, hot shower water, etc. When humans are the basis for an analysis, we must have a way to assign some rational value to intuitive assessments of individual elements of a fuzzy set. We must translate from human fuzziness to numbers that can be used by a computer. We do this by assigning assessment of conditions a value fromzero to 1.0. For \"how hot the room is\" the human might rate it at .2 if the temperature were below freezing, and the human might rate the room at .9, or even 1.0, if it is a hot day in summer with the air conditioner off. You can see these perceptions are fuzzy, just intuitive assessments, not precisely measured facts. By making fuzzy evaluations, with zero at the bottom of the scale and 1.0 at the top, we have a basis foranalysis rules for the fuzzy logic method, and we can accomplish our analysis or control project. The results seem to turn out well for complex systems or systems where human experience is the only base from which to proceed, certainly better than doing nothing at all, which is where we would be if unwilling to proceed with fuzzy rules.[12]
Novices using personal computers and the fuzzy logic method can beat Ph.D. mathematicians using formulas and conventional programmable logic controllers. Fuzzy logic makes use of human common sense. This common sense is either applied from what seems reasonable, for a new system, or from experience, for a system that has previously had a human operator. Here is an example of converting human experience for use in a control system: I read of an attempt to automate a cement manufacturing operation. Cement manufacturing is a lot more difficult than you would think. Through the centuries it has evolved with human \"feel\" being absolutely necessary. Engineers were not able to automate with conventional control. Eventually,they translated the human \"feel\" into lots and lots of fuzzy logic \"If-Then\" rules basedon human experience. Reasonable success was thereby obtained in automating the
plant. Objects of fuzzy logic analysis and control may include: physical control, such as machine speed, or operating a cement plant; financial and economic decisions; psychological conditions; physiological conditions; safety conditions; security conditions; production improvement and much more.
Without personal computers, it would be difficult to use fuzzy logic to control machines and production plants, or do other analyses. Without the speed and versatility of the personal computer, we would never undertake the laborious and timeconsuming tasks of fuzzy logic based analyses and we could not handle the
complexity, speed requirement and endurance needed for machine control. You can dofar more with a simple fuzzy logic BASIC or C++ program in a personal computer running in conjunction with a low cost input/output controller than with a whole arrayof expensive, conventional, programmable logic controllers. Programmable logic controllers have their place! They are simple, reliable and keep American industry operating where the application is relatively simple and on-off in nature.
For a more complicated system control application, an optimum solution may be patching things together with a personal computer and fuzzy logic rules, especially if the project is being done by someone who is not a professional, control systems engineer.
A Milestone Passed for Intelligent Life On Earth。If intelligent life has appeared anywhere in the universe, \"they\" are probably using fuzzy logic. It is a universal principle and concept. Becoming aware of, defining and starting to use fuzzy logic is an important moment in the development of an intelligent civilization. On earth, we have just arrived at that important moment. You need to know and begin using fuzzy logic.
The discussion so far does not adequately prepare us for reading and
understanding most books and articles about fuzzy logic, because of the terminology used by sophisticated authors. Following are explanations of some terms which should help in this regard. This terminology was initially established by Dr. Zadeh when he originated the fuzzy logic concept.
Fuzzy - The degree of fuzziness of a system analysis rule can vary between beingvery precise, in which case we would not call it \"fuzzy\held by a human, which would be \"fuzzy.\" Being fuzzy or not fuzzy, therefore, has to do with the degree of precision of a system analysis rule. A system analysis rule need not be based on human fuzzy perception. For example, you could have a rule, \"If the boiler pressure rises to a danger point of 600 P as measured by a pressure transducer, then turn everything off. That rule is not \"fuzzy\".
Principle of Incompatibility (previously stated; repeated here) –
As the complexity of a system increases, it becomes more difficult and eventually impossible to make a precise statement about its behavior, eventually arriving at a point of complexity where the fuzzy logic method born in humans is the only way to get at the problem.
Fuzzy Sets - A fuzzy set is almost any condition for which we have words: short men, tall women, hot, cold, new buildings, accelerator setting, ripe bananas, high intelligence, speed, weight, spongy, etc., where the condition can be given a value between 0 and 1. Example: A woman is 6 feet, 3 inches tall. In my experience, I think she is one of the tallest women I have ever met, so I rate her height at .98. This line of reasoning can go on indefinitely rating a great number of things between 0 and 1.
In fuzzy logic method control systems, degree of membership is used in the following way. A measurement of speed, for example, might be found to have a degree of membership in \"too fast of\" .6 and a degree of membership in \"no change needed\" of .2. The system program would then calculate the center of mass between \"too fast\" and \"no change needed\" to determine feedback action to send to the input ofthe control system. This is discussed in more detail in subsequent chapters. Summarizing Information - Human processing of information is not based on two-valued, off-on, either-or logic. It is based on fuzzy perceptions, fuzzy truths, fuzzy inferences, etc., all resulting in an averaged, summarized, normalized output, which isgiven by the human a precise number or decision value which he or she verbalizes, writes down or acts on. It is the goal of fuzzy logic control systems to also do this.
The input may be large masses of data, but humans can handle it. The ability to manipulate fuzzy sets and the subsequent summarizing capability to arrive at an output we can act on is one of the greatest assets of the human brain. This
characteristic is the big difference between humans and digital computers. Emulating this human ability is the challenge facing those who would create computer based artificial intelligence. It is proving very, very difficult to program a computer to have human-like intelligence.
Fuzzy Variable - Words like red, blue, etc., are fuzzy and can have many shades and tints. They are just human opinions, not based on precise measurement in angstroms. These words are fuzzy variables.
If, for example, speed of a system is the attrribute being evaluated by fuzzy, \"fuzzy\" rules, then \"speed\" is a fuzzy variable.
Linguistic Variable - Linguistic means relating to language, in our case plain language words.
Speed is a fuzzy variable. Accelerator setting is a fuzzy variable. Examples of linguistic variables are: somewhat fast speed, very high speed, real slow speed, excessively high accelerator setting, accelerator setting about right, etc. A fuzzy variable becomes a linguistic variable when we modify it with descriptive words, suchas somewhat fast, very high, real slow, etc. The main function of linguistic variables isto provide a means of working with the complex systems mentioned above as being too complex to handle by conventional mathematics and engineering formulas. Linguistic variables appear in control systems with feedback loop control and can be related to each other with conditional, \"if-then\" statements. Example: If the speed is too fast, then back off on the high accelerator setting.
The World's First Fuzzy Logic Controller，In England in 1973 at the University of London, a professor and student were trying to stabilize the speed of a small steam engine the student had built. They had a lot going for them, sophisticated equipment like a PDP-8 minicomputer and conventional digital control equipment. But, they could not control the engine as well as they wanted. Engine speed would either
overshoot the target speed and arrive at the target speed after a series of oscillations, or the speed control would be too sluggish, taking too long for the speed to arrive at the desired setting,
The professor, E. Mamdani, had read of a control method proposed by Dr. Lotfi Zadeh, head of the electrical engineering department at the University of California at Berkeley, in the United States. Dr. Zadeh is the originator of the designation \"fuzzy\which everyone suspects was selected to throw a little \"pie in the face\" of his more orthodox engineering colleagues, some of whom strongly opposed the fuzzy logic concept under any name.
Professor Mamdani and the student, S. Assilian, decided to give fuzzy logic a try. They spent a weekend setting their steam engine up with the world's first ever fuzzylogic control system ....... and went directly into the history books by harnessing thepower of a force in use by humans for 3 million years, but never before defined andused for the control of machines. The controller worked right away, and worked betterthan anything they had done with any other method. The steam engine speed controlgraph using the fuzzy logic controller appeared 。
As you can see, the speed approached the desired value very quickly, did not overshoot and remained stable. It was an exciting and important moment in the history of scientific development. The Mamdani project made use of four inputs: boiler pressure error (how many temperature degrees away from the set point), rate of change of boiler pressure error, engine speed error and rate of change of engine speed error. There were two outputs: control of heat to the boiler and control of the throttle. They operated independently.
A fuzzy logic system does not have to include a continuous feedback control loopas in the above described Mamdani system in order to be a fuzzy-logic system, an impression you might receive from reading much of the fuzzy logic literature. There could be continuous feedback loop control, a combination of feedback loop control and on-off control or on-off control alone. A fuzzy logic control system could be as simple as: \"If the motor temperature feels like it is too hot, turn the motor off and
leave it off.\" Or, \"If the company's president and all the directors just sold every shareof stock they own, then WE sell!\"
A fuzzy logic system does not have to be directed toward electro-mechanical systems. The fuzzy logic system could be, for example, to provide buy-sell decisions to trade 30 million US dollars against the Japanese yen. Fuzzy logic controllers can control solenoids, stepper motors, linear positioners , etc., as well as, or concurrently with, continuous feedback control loops. Where there is continuous feedback control of a control loop, the response for varying degrees of error can be non-linear, tailoringthe response to meet unique or experience determined system requirements, even anomalies.
Controllers typically have several inputs and outputs. The handling of various tasks, such as monitoring and commanding a control loop and monitoring various inputs, with commands issued as appropriate, would all be sequenced in the computer program. The program would step from one task to the other, the program receiving inputs from and sending commands to the converter/controller. Inputs for a fuzzy logic controlled mechanical/physical system could be derived from any of thousands of real world, physical sensors/transducers. The Thomas Register has over 110 pages of these devices. Inputs for financial trading could come from personal assessments orfrom an ASCII data communication feed provided by a financial markets quote service.
Progress in Fuzzy Logic，From a slow beginning, fuzzy logic grew in applications and importance, until now it is a significant concept worldwide. Intelligent beings on the other side of our galaxy and throughout the universe have probably noted and defined the concept. Personal computer based fuzzy logic control is pretty amazing. It lets novices build control systems that work in places where eventhe best mathematicians and engineers, using conventional approaches to control, cannot define and solve the problem. A control system is an electronic or mechanical system that causes the output of the controlled system to automatically remain at some desired output (the \"set point\") set by the operator. The thermostat on your air
conditioner is a control system. Your car's cruise control is a control system. Control may be an on-off signal or a continuous feedback loop.
In the United States, fuzzy logic control is gaining popularity, but is not as widely used as in Japan, where it is a multi-million dollar industry. Japan sells fuzzy logic controlled cameras, washing machines and more. One Internet search engine returns over 16,000 pages when you search on “fuzzy-logic”. Personal computer based fuzzy logic control follows the pattern of human \"fuzzy\" activity. However, humans usually receive, process and act on more inputs than the typical computer based fuzzy logic controller. (This is not necessarily so; a computer based fuzzy logic control system in Japan trades in the financial markets and utilizes 800 inputs.)
Fuzzy Logic Control Input - Human and Computer，Computer based fuzzy logic machine control is like human fuzzy logic control, but there is a difference whenthe nature of the computer's input is considered. Humans evaluate input from their surroundings in a fuzzy manner, whereas machines/computers obtain precise appearing values, such as 112 degrees F, obtained with a transducer and an analog to digital converter. The computer input would be the computer measuring, let's say, 112 degrees F. The human input would be a fuzzy feeling of being too warm. The human says, \"The shower water is too hot.\" The computer as a result of analog input measurement says, \"The shower water is 112 degrees F and 'If-Then' statements in myprogram tell me the water is too warm.\" A human says, \"I see two tall people and one short one.\" The computer says, \"I measure two people, 6' 6\" and 6' 9\and one person 5' 1\" tall, and 'If-Then' statements in my program tell me there are twotall people and one short person.\"[13]
Even though transducer derived, measured inputs for computers appear to be more precise, from the point of input forward we still use them in a fuzzy logic method approach that follows our fuzzy, human approach to control. For a human, if the shower water gets too warm, the valve handle is turned to make the temperature go down a little. For a computer, an \"If-Then\" statement in the program would initiatethe lowering of temperature based on a human provided \"If-Then\" rule, with a
command output operating a valve. To create a personal computer based fuzzy logic control system, we:
1)Determine the inputs.
2)Describe the cause and effect action of the system with \"fuzzy rules\" stated in plain English words.
3)Write a computer program to act on the inputs and determine the output, considering each input separately.The rules become \"If-Then\" statements in the program.(As will be seen below, where feedback loop control is involved, use of graphical triangles can help visualize and compute this input-output action.)
4)In the program, use a weighted average to merge the various actions called for by the individual inputs into one crisp output acting on the controlled system.(In the event there is only one output, then merging is not necessary, only scaling the output as needed.)
Experienced, professional digital control engineers using conventional control might know how to proceed to fine tune a system. But, it can be difficult for us just plain folks. Fuzzy logic control makes it easier to visualize and set up a system and proceed through the cut and try process. It is only necessary to change a few plain English rules resulting in changing a few numbers in the program. In reading about fuzzy logic control applications in industry, one of the significant points that stands out is: fuzzy logic is used because it shortens the time for engineering development. Fuzzy logic enables engineers to configure systems quickly without extensive experimentation and to make use of information from expert human operators who have been performing the task manually. Perhaps your control need is something a lot more down to earth than flying helicopters or running subways. Maybe all you want to do is keep your small business sawmill running smoothly, with the wood changing and the blade sharpness changing. Or, perhaps you operate a natural gas compressor for some stripper wells that are always coming on and going off, and you need to havethe compressor automatically adjust in order to stay on line and keep the suction pressure low to get optimum production. Perhaps you dream of a race car that would
automatically adjust to changing conditions, the setup remaining optimum as effectively as the above mentioned helicopter adjusts to being without a rotor blade.
There are a million stories, and we cannot guess what yours is, but chances are, ifthere is something you want to control, and you are not an experienced, full time, professional control engineer financed by a multi-million dollar corporation, then fuzzy logic may be for you. If you are all those things, it still may be for you. Some of the greatest minds in the technical world try to explain to others why fuzzy logic works, for us just plain folks, the fact is fuzzy logic does work, seems to work better than many expensive and complicated systems and is understandable and affordable.
REFERENCES
[1] Heising. Pole-restraining control for Modular Multilevel Converters in electric-shipapplications. [J].2013,pp.11-13.
[2] Rasheed. Detection and study of various IR handheld remote control signals and using themfor home applications. [M].Education and e-Learning Innovations (ICEELI), 2012 InternationalConference on,2012,pp.10-15.
[3] Maria G. loannides, \"Design and Implementation of PLC-Based Monitoring Control System for Induction Motor,\" IEEE Transactions On Energy Conversion, VOL.19, NO.3,September 2004,pp. 469-476.
[4] Y. M. Jia, F. H. Su, J. Liu, \"An On-line Insulation Monitoring System Based On Fieldbus\" ,pp. 769-772.
[5] Chi Rong-hu. A new PI controller for freeway ramp metering based on fuzzy logic. [J].2013, pp. 46-76.
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