Applications using Fuzzy Logic:

While fuzzy logic evolved in the United States, practical applications are mushrooming in Japan. Camcorders, TVs, Cameras employing new electronic circuits that promise enhanced images, washing machines, elevators, and anti lock brake systems employing fuzzy logic have been introduced.

The first person to demonstrate the practical possibilities of fuzzy algorithm was Abe Mamdani, an electrical engineer at Queen Mary College, London. In the early 1970s, he applied his algorithms to control the pressure and speed of a steam engine.

In 1980, a Danish company F. L. Smidth, used fuzzy logic to control the operation of a cement kiln. This was the world's first industrial application of the fuzzy theory.

The mid 1980s saw the first industrial applications in Japan. Fuji Electric Company used fuzzy theory to control a water purification plant in Akita City, and Hitachi developed a fuzzy predictive system to operate the automated underground trains in Sendai City. In 1990, the fuzzy wave reached the consumer product. Cameras, washing machines, microwave ovens and dozens of other consumer goods featuring fuzzy logic began to appear.

Japanese engineer, for example now use fuzzy logic to improve the efficiency of automated transmission in cars, to control the injection of chemicals in plants for purifying water, and to simulate the shutdown of a nuclear reactor with a computer. Japanese industry now offers more than 50 consumer goods featuring fuzzy logic. These appliances are extremely easy to operate and the performance is better than of conventional models.

In the medical field, expert systems using 'fuzzy inference' help doctors to diagnose diabetes and prostatic cancer, while anaesthetists rely on fuzzy logic to control the blood pressure of patients undergoing surgery. NASA engineers developed natural language rules to run the controller and are testing in it a multi vehicle simulation by substituting the fuzzy controller for the simulator's normal human inputs. The rule base was extracted from the experience of human operators, and the efficiency of the controller was tuned based on flight profiles recorded from actual missions and simulations.

NASA is also exploring other application of fuzzy control in space. Among the projects being considered is the use of inexpensive cameras for constant tracking of objects around a space station. Fuzzy control can contribute to collision-avoidance systems, robot arm control and traffic management.

It is estimated that by the end of the century, the electronic components in automobiles will double. Among the areas of interest to fuzzy based system design are emission control system that would continuously monitor the exhaust gages and make adjustments to the carburettor and ignition system to keep levels of hydrocarbons and other toxic elements within specified limits. Interior climate control could adjust to the number of passengers, just as some fuzzy controlled air conditioners do today. Fuzzy controlled digital signal processors are being investigated for use in interior noise cancellation systems.

Today's digital anti lock braking systems work by cycling the brakes rapidly on and off. A fuzzy controller would provide smooth anti lock braking by adjusting hydraulic pressure in response to slight variations in wheel rotation.

One fuzzy item already under active development is a multiple mode automatic transmission. By sensing rotation torque, engine speed and throttle position, a fuzzy controller could determine the proper gear shift points. By selecting a different set of rules and membership functions, the driver could set the transmission for smooth shifting, optimal economy shifting or sport shifting.

Matsushita, better known for its Panasonic and Technics brand names, has several household appliances which employ fuzzy logic. For example there is the 'Aisaigo Day Fuzzy' washing machine. 'Aisaigo' in Japanese language literally means 'beloved wife'. The washing machine features no less than 600 washing cycles. The fuzzy controller relies on a battery of sensors to determine which one to choose. These sensors check the size and weight of the wash load, how dirty the clothes are and what type of detergent is being used. The machine even takes into account what type of fabrics are being washed. All the user, or Aisaigo, needs to do is press one button and the system looks after itself just as a housewife would do.

Matsushita's Canister 7 vacuum cleaner senses what type of floor surface it is cleaning, whether carpets or smooth surface. Coupled with a dust quantity sensor, suction power of the system is varied accordingly, thereby saving energy.
Matsushita is already improving its fuzzy logic technology by replacing ordinary microcomputer with a special processor tied to a simple neural network. These improved devices can handle more sensors and make complex decisions. Buyers are already faced with the choice of first and second generation fuzzy logic appliances from the company.

Mitsubishi's fuzzy logic air conditioners differ from conventional models by doing away with a simple thermostat. Internal and external air temperature and humidity are constantly evaluated and applied to a set of 50 fuzzy logic rules. Cooling power is varied progressively instead of being simply switched on or off by a thermostat, with a power saving of up to 25 per cent.

Sony uses fuzzy logic to recognise handwriting and calligraphy in its pen based protable personal computers. This is an ideal application for the Japanese duetot the nature of their writing.

The most common fuzzy logic application is in camera autofocus system. Sanyo, Fisher, Richoj and Olympus video camcorders use such systems to make focusing more reliable. An example is that, once set, the subject will remain in focus even if it moves about on the screen. Conventional systems would produce a shaky picture.

One of the first fuzzy camcorders, the Canon handheld H800 which was introduced in 1990, adjusts the autofocus based on 13 fuzzy rules. Sensors measure the clarity of images in six areas. The rules take up about a kilobyte of memory and convert the sensor data to new lens settings.

One of the most complex fuzzy systems is a model helicopter, designed by Michio Sugeno of the Tokyo Institute of Technology. Foure elements of the craft the elevator, aileron, throlle and rudder respond to 13 fuzzy voice commands, such as 'up', 'land' and 'hover'. The fuzzy controler can make the craft hoer in place, a difficult task even for a human pilot.