Applications of fuzzy control to industrial processes are mainly of multivariable structure. Using the traditional Zadeh principle would require a multidimensional relation to be developed representing a fuzzy model of the system. Such a multidimensional relation would result in memory overload to most industrial computers. Moreover, it would result in a highly complex compositional rule of inference to achieve the final output(s) of the system. This paper proposes a new simplified technique that avoids such complexity as well as memory overload for multivariable structure. Section 2 describes the proposed simplified multivariable technique to avoid memory overload. Section 3 demonstrates these techniques in the form of a robotic welding example where the objective is to control the speed of a robotic arm following an irregular path of weld. The speed value is dependent on the cavity size and determined by the cavity width and cavity depth as inputs. Section 4 describes an experimental application of the technique applied to an industrial process in the manufacture of force transducers termed as the ‘cornering process’. This application is composed of a two-inputs-two-outputs system.
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Gupta, M. M., Kiszka, J. B. and Trojan, G. M. (1986) Multivariable structure of fuzzy control systems. IEEE Transactions on Systems Man and Cybernetics, 1,16(5).
Hirota, K., Arai, Y. and Pedrycz, W. (1985) Robot control based on membership and vagueness, in Approximate Reasoning in Expert Systems, Gupta et al. (eds).
Kouatli, I. M. (1990) An Improved Method for the design of fuzzy controllers, PhD Thesis, University of Birmingham, UK.
Kouatli, I. M. (1993) Design of a multivariable technique based upon human behaviour, in IEE Colloquium on Two Decades of Fuzzy Control—Part 2, 19 May, The Institute of Electrical Engineers, Savoy, London, UK.
Kouatli, I. M. and Jones, B. (1990) A guide to the design of fuzzy control system for manufacturing processes. Journal of Intelligent Manufacturing, 1, 231–244.
Lakov, D. (1985) Adaptive robot under fuzzy control. Fuzzy Sets and Systems, 17, 1–8.
Pedrycz, W. (1989) Fuzzy Control and Fuzzy Systems, Research Studies Press, Taunton, Somerset, UK.
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Kouatli, I.M. A simplified fuzzy multivariable structure in a manufacturing environment. J Intell Manuf 5, 365–387 (1994). https://doi.org/10.1007/BF00123657
- Fuzzy control
- multivariable control
- process control
- industrial computers applications
- human factors