Authors V. V. Ignatyev, D. A. Beloglazov, V. M. Kureychik, O. B. Spiridonov, A. S. Ignatyeva
Month, Year 03, 2018 @en
Index UDC 004.896
Abstract The paper proposes a method to improve the quality of control of technical objects, the problem of automation of which was solved with the use of the classical control theory and such controllers as PI, PD, PID. The basic idea of the method consists in the gradual replacement of these controllers by their hybrid analogs realized with the use of the fuzzy logic apparatus (fuzzy – PI, fuzzy - PID, etc.). At the first stage, statistical information is collected about the inputs and outputs of the controller used (system error value , error integral θdt, magnitude of the generated control action U). The stage is considered complete when a set of data describing the operation of the controller for all modes of its operation will be created for the automated object. The next step involves the use of the obtained statistical data for synthesizing the base of the control rules of the hybrid controller(s). The solution of this problem is possible in a manual mode, but the process is very labor-consuming, it is associated with a significant risk of errors, manifested in the creation of duplicate or, more critically contradictory, control rules. The identification of these errors may require the developer of great effort and does not guarantee a successful outcome. In this connection, the problem of developing and using specialized software becomes urgent, which was done in this work. As a result, the duration of the developer"s time costs decreased by 98%, and the risk of errors was reduced to zero. For ease of use, the result of applying the proposed software is presented in the form of a *.fis file, which can be opened and analyzed in Fuzzy Logic Toolbox. In the future, the software application under consideration can be transferred from the MATLAB modeling environment to any other programming language.

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Keywords Automation; control; hybrid controller; software application; MATLAB; classical model; fuzzy model; fuzzy rule base.
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