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Abstract

The paper discusses the challenges of enhancing the robustness of a control system for a complex dynamic plant by addressing nonlinearity in the warping process. Devices that ensure the stability of the control system against parameter non-stationarity on the warping machine are referred to as state controllers. The operating principle of these devices relies on providing artificial nonlinearity to the rear connection circuit of the control system's actuator. However, this nonlinearity is implemented using components that consider the parameters of low control quality. Therefore, it is necessary to continuously adjust the nonlinearity parameters to, on one hand, reduce the load on the actuator, and on the other hand, to ensure the required control accuracy. For this purpose, the synthesis of a correcting nonlinear element based on fuzzy logic for controlling the sensitivity of feedback sensors was examined to enhance the robustness of the control system by compensating for the nonlinearity of the process parameters. The Mamdani and Sugeno fuzzy inference models have been developed. Fuzzy logical inferences according to the Mamdani algorithm are carried out based on a fuzzy knowledge base. A fuzzy logical knowledge base according to the Sugeno algorithm (sometimes called the Takagi-Sugeno algorithm) was developed based on a fuzzy knowledge base. The effectiveness of the proposed approach was tested in the Simulink software environment of the engineering-specialized computing package MATLAB. Graphs of transient characteristics of the control system of the warping process under disturbance conditions were constructed, and overshoot was observed, amounting to 2%.

First Page

61

Last Page

70

References

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