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The mathematical description of the algorithm of the fuzzy neuro controller

Keywords:

Т. S. Legotkina – Ph.D. (Eng.), Associate Professor, Department of Automation and Telemechanics, Perm National Research Polytechnic University. E-mail: luda@at.pstu.ru
Yu. N. Khizhnyakov – Dr.Sci. (Eng.), Professor, Department of Automation and Telemechanics, Perm National Research Polytechnic University. E-mail: luda@at.pstu.ac.ru


Lack of knowledge about the object of regulation related to the structure by the absence of the transfer function of the coefficients and their nonlinearity eliminates the use of the classical laws of regulation. One of the most effective approaches to solving this problem is the fuzzy logic and fuzzy sets theory or neural networks. However, comparing the fuzzy control with neural control, it should be noted the transparency of the large number of fuzzy control algorithms realization. The most effective of these is the Takagi–Sugeno algorithm, reduce the problem of formation of the receiving solution matrix, which greatly expands the range of application of fuzzy control. The paper proposes an alternative method of forming the matrix, allowing control of low inertia regulated entities, such as the study transients of AC machines. The successful use of a non-strict management due to the clear design of the algorithm and the mathematical description of its elements: fuzzification, fuzzy inference unit and defuzzification. Fuzzy controller with the latest extensions to the fuzzy output and defazzifikatore allows static error and is not adaptive. The paper shows not only a way to make the fuzzy controller adaptive properties of both the statics and dynamics using a natural method of neuron with a serial learning. Astatic properties of the adaptive fuzzy controller can be achieved if the regulated entity without self-leveling, or in objects with automatic compensation regulator control has astatic properties (angle of slide, etc.). Successful projects tirovanie adaptive fuzzy controller is a versatile contribute sobom regulation by non-deterministic objects in this concluding etsya relevance of this article.
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