Fuzzy Logic Tech

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Fuzzy Logic was developed at University of California Berkeley in 1965 as an generalization of the bi-value, true/false mathematical logic. [1]. Mathematically, Fuzzy Logic is a transform like the better-known Laplace and Fourier transforms of the Calculus. Real world problems are transformed into the Fuzzy Domain, solved there and the solution re-transformed to the real world for implementation [2].

Because it was incorrectly coupled with the artificial intelligence hoopla and subsequent crash in the 1980s, Fuzzy Logic has been unfairly branded as without substance. [3] Additionally the phrase “fuzzy logic”, in the US culture, represents unclear thinking, mistruths, and such.

Since 1970 the theory has been developed by many researchers and application engineers. More than 37,000 scholarly papers have been published addressing fuzzy logic, according to INSPEC, the leading English-language database comprising records of scientific and technical articles published worldwide and MathSciNet, the database of the American Mathematical Society [4]. There are 5,417 US issued patents for Fuzzy Logic systems. Medtronic, Inc. has patented Fuzzy Logic for the closed loop control of blood glucose using insulin [5]. Industry leading system control development tools manufacturers such as Mathworks and Rockwell Automation support fuzzy logic techniques.

The first implementation in control systems was accomplished by Mamdani in 1974, who demonstrated the viability of FL for a small model steam engine [6]. The mathematical foundation of FL control was provided by Wang who showed that fuzzy systems are universal approximators [7].

As of 1995, at General Electric fuzzy logic control technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result GE has experienced an increased number of Fuzzy Logic applications including turboshaft aircraft engine control, steam turbine startup, steam turbine cycling optimization, and resonant converter power supply control [8].

Compared with conventional MPC and PID control approaches, FL control utilizes more information from experts and relies less on mathematical modeling about a physical system. It is also preferable, especially when low cost and easy operations are involved. The advantages of FL control over other applicable techniques have been given by many researchers [9,10,11,12].

Fuzzy logic provides a powerful vehicle that allows engineers to incorporate human reasoning in the control algorithm. Unlike PID and MPC controllers, fuzzy logic design need not be based on the explicit mathematical model of the process. The controller designed using fuzzy logic implements human reasoning that has been programmed into fuzzy logic language algorithms. However, similar to PID and MPC controllers, FL controllers may incorporate real-time methods for adapting the controller to the changing conditions of the system being controlled. [14,15,16,17,18].
References

[1] L.A. Zadeh, “Fuzzy Sets,” Inform. Contr., vol. 8, pp. 338-353, 1965
[2] Ian S. Shaw (1998), Fuzzy Control of Industrial Systems: Theory and Applications, Kluwer Academic Publishers; ISBN: 0792382498
[3] World Technology Evaluation Center, http://www.wtec.org/loyola/kb/c5_s4.htm
[4] “Statistics on the impact of fuzzy logic” http://www.cs.berkeley.edu/~zadeh/stimfl.html
[5] US Patent 6,572,542, Medtronic, Inc. (Minneapolis, MN), “System and method for monitoring and controlling the glycemic state of a patient.”
[6] Mamdani EH. Application of fuzzy algorithms for control of simple dynamic plant. Proc IEE 1974;121(12):1585-8.
[7] Wang LX. Adaptive fuzzy systems and control: design and stability analysis. Englewood Cliffs, NJ: Prentice-Hall; 1994.
[8] Industrial applications of fuzzy logic at General Electric Bonissone, P.P.; Badami, V.; Chiang, K.H.; Khedkar, P.S.; Marcelle, K.W.; Schutten, M.J. Proceedings of the IEEE Volume 83, Issue 3, Mar 1995 Page(s):450 – 465
[9] Verbruggen HB, Bruijn PM. Fuzzy control and conventional control: What is (and can be) the real contribution of fuzzy systems? Fuzzy Sets Syst 1997;90:151-60.
[10] Akkizidis IS, Roberts GN, Ridao P, Batlle J. Designing a fuzzy-like PD controller for an underwater robot. Contr Eng Pract 2000;11:471-80. [14] Ghiaus C. Fuzzy model and control of a fan-coil. Energy Build 2000;22:545-51.
[11] Antonio CC, Pacifico MP. Fuzzy control of heat recovery systems from solid bed cooling. Appl Therm Eng 2000;20:49-67.
[12] Wai RJ, Lin CH, Hsu CF. Adaptive fuzzy sliding-mode control for electrical servo drive. Fuzzy Sets Syst 2004;143(2):295-310.
[13] Nomura H, Hayashi I, Wakami N. A self-tuning method of fuzzy logic control by descent method. Central Research Laboratory, Matushita Electric Company Co, LTD, Osaka Japan. 1990.
[14] Wang LX. Adaptive fuzzy systems and control: design and stability analysis. Englewood Cliffs, NJ: Prentice-Hall;1994.
[15] Lin FC, Yang SM. Adaptive fuzzy-logic velocity observer for servo motor drives. Mechatronics 2003;13:229–41.
[16] Wai RJ, Lin CH, Hsu CF. Adaptive fuzzy sliding-mode control for electrical servo drive. Fuzzy Sets Syst 2004;143(2):295–310.
[17] Jee S, Koren Y. Adaptive fuzzy logic controller for feed drives of a CNC machine tool. Mechatronics
2004;14(3):299–326.
[18] Woo ZW, Chung HY, Lin JJ. A PID type fuzzy controller with self-tuning scaling factors. Fuzy Sets Syst 2000;115:321–6.