Editing Fuzzy control system (section)

== History and applications ==
Fuzzy logic was proposed by [[Lotfi A. Zadeh]] of the [[University of California at Berkeley]] in a 1965 paper.<ref>{{cite q | Q25938993 |last1=Zadeh |first1=L.A. | author-link1 = Lotfi A. Zadeh | | journal = [[Information and Computation|Information and Control]] | doi-access = free }}</ref> He elaborated on his ideas in a 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 1976.<ref>{{cite journal |last1=Jensen |first1=P. MARTIN |title=Industrial applications of fuzzy logic control |journal=International Journal of Man-Machine Studies |date=23 May 1979 |volume=12 |issue=1 |pages=3–10 |doi=10.1016/S0020-7373(80)80050-2 |url=https://www.sciencedirect.com/science/article/pii/S0020737380800502 |access-date=21 December 2023}}</ref>

Fuzzy systems were initially implemented in [[Japan]].

* Interest in fuzzy systems was sparked by Seiji Yasunobu and Soji Miyamoto of [[Hitachi]], who in 1985 provided simulations that demonstrated the feasibility of fuzzy control systems for the [[Sendai Subway]]. Their ideas were adopted, and fuzzy systems were used to control accelerating, braking, and stopping when the [[Sendai Subway Namboku Line|Namboku Line]] opened in 1987.
* In 1987, 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 vehicle tries to keep a pole mounted on its top by a hinge upright by moving back and forth. Yamakawa subsequently made the demonstration more sophisticated by mounting a wine glass containing water and even a live mouse to the top of the pendulum: the system maintained stability in both cases. Yamakawa eventually went on to organize his own fuzzy-systems research lab to help exploit his patents in the field.
* Japanese engineers subsequently developed a wide range of fuzzy systems for both industrial and consumer applications. In 1988 Japan established the Laboratory for International Fuzzy Engineering (LIFE), a cooperative arrangement between 48 companies to pursue fuzzy research. The automotive company Volkswagen was the only foreign corporate member of LIFE, dispatching a researcher for a duration of three years.
* Japanese consumer goods often incorporate fuzzy systems. Matsushita vacuum cleaners use microcontrollers running fuzzy algorithms to interrogate dust sensors and adjust [[suction power]] accordingly. Hitachi washing machines 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 [[autofocus]]ing camera that uses a [[charge-coupled device]] (CCD) to measure the clarity of the image in six regions of its field 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 camera's fuzzy control system uses 12 inputs: 6 to obtain the current clarity data provided by the CCD 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.
*An industrial [[air conditioner]] designed by Mitsubishi uses 25 heating rules and 25 cooling rules. A temperature sensor provides input, with control outputs fed to an [[Variable-frequency drive|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, and uses fewer sensors.
* Other applications investigated or implemented include: character and [[handwriting recognition]]; optical fuzzy systems; robots, including one for making Japanese flower arrangements; [[voice control|voice-controlled]] robot helicopters (hovering is a "balancing act" rather similar to the inverted pendulum problem); rehabilitation robotics to provide patient-specific solutions (e.g. to control heart rate and blood pressure <ref name = "sarabadani">{{cite journal |doi=10.1109/TBME.2015.2391234 |title=Real-time closed-loop control of human heart rate and blood pressure|year=2015 |last1=Sarabadani Tafreshi |first1=Amirehsan |journal=IEEE Transactions on Biomedical Engineering|volume=62 |issue=5 |pages=1434–1442 |pmid=25594957 |last2=Klamroth-Marganska|first2=V. |last3=Nussbaumer |first3=S.|last4=Riener|first4=R.|s2cid=32000981}}</ref>); control of flow of powders in film manufacture; elevator systems; and so on.

Work on fuzzy systems is also proceeding in North America and Europe, although on a less extensive scale than in Japan.

* The [[US Environmental Protection Agency]] has investigated fuzzy control for [[Efficient energy use|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]], [[General Motors]], [[Allen-Bradley]], [[Chrysler]], [[Eaton Corporation|Eaton]], and [[Whirlpool Corporation|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 module" 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 system determines the optimum wash cycle for any load to obtain the best results with the least amount of energy, detergent, and water. It even adjusts for dried-on foods by tracking the last time the door was opened, and estimates the number of dishes by the number of times the door was opened.
* [[Xiera Technologies Inc.]] has developed the first auto-tuner for the fuzzy logic controller's knowledge base known as edeX. This technology was tested by Mohawk College and was able to solve non-linear 2x2 and 3x3 multi-input multi-output problems.<ref>{{Cite web|url=https://www.mohawkcollege.ca/ideaworks/energy-power-innovation-centre-epic/artificial-intelligence-controllers-for-industrial|title = Artificial Intelligence Controllers for Industrial Processes}}</ref>

Research and development is also continuing on fuzzy applications in software, as opposed to [[firmware]], design, including fuzzy [[expert system]]s and integration of fuzzy logic with [[artificial neural network|neural-network]] and so-called adaptive "[[genetic algorithm|genetic]]" software systems, with the ultimate goal of building "self-learning" fuzzy-control systems.<ref name="mamdani">{{cite journal|last1=Mamdani|first1=Ebrahim H|title=Application of fuzzy algorithms for control of simple dynamic plant|journal=Proceedings of the Institution of Electrical Engineers|year=1974|volume=121|issue=12|pages=1585–1588|doi=10.1049/piee.1974.0328}}</ref> These systems can be employed to control complex, nonlinear dynamic plants, for example, human body.<ref name = "sarabadani" /><ref name = "mamdani" /><ref>{{cite journal|last1=Bastian|first1=Andreas|title=Identifying fuzzy models utilizing genetic programming|journal=Fuzzy Sets and Systems|year=2000|volume=113|issue=3|pages=333–350|doi=10.1016/S0165-0114(98)00086-4|url=http://sci2s.ugr.es/keel/pdf/specific/articulo/science2_27.pdf |archive-url=https://web.archive.org/web/20070612104640/http://sci2s.ugr.es/keel/pdf/specific/articulo/science2_27.pdf |archive-date=2007-06-12 |url-status=live}}</ref>