Abstract. In the article, the problem of optimization of the control law for rotating the blades of a wind generator with the purpose of increasing its output power is considered. The dynamics of a wind generator can be described by a nonlinear mathematical model, in which the input effect is the variable wind speed. This model is used to develop a fuzzy regulator whose parameters are tuned using a genetic algorithm. The computational experiments carried out show the practical importance of the obtained control law to ensure the effective use of the wind generator.

УДК 681.5 М.В. БУРАКОВ, М.С. БРУНОВ СТРУКТУРНАЯ ИДЕНТИФИКАЦИЯ НЕЧЕТКОЙ МОДЕЛИ Бураков М.В., Брунов М.С. Структурная идентификация нечеткой модели. Аннотация. Цель данной работы заключается в рассмотрении математического инструментария для построения моделей нелинейных систем по входным и выходным данным. Фазовая плоскость системы разбивается на подобласти, с каждой из которых связана линейная модель. Каждая линейная модель представлена в форме пространства состояний. Для идентификации выбранных параметров линейных систем используется метод наименьших квадратов. Для получения общего выхода нелинейной системы используется нечеткое представление. Предлагаемая методология проверена на цифровых примерах. Ключевые слова: идентификация, нелинейная система, T-S нечеткая модель.Burakov M.V., Brunov M.S. Structural identification of fuzzy model. Abstract. The purpose of this paper is to present a mathematical tool to build a fuzzy model of a nonlinear system using its input-output data. The phase plane of system is divided into subregions and a linear model is assigned for each of these regions. This linear model is represented either in state-space form. To determine the pre-selected parameters of the linear system model under study, least-square identification method is used. Then these linear models are arranged in a fuzzy manner to characterize the overall system behavior. The proposed methodology is verified through simulation on a numeric example.

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The problem of constructing an adaptive PID controller based on the Hopfield neural network for a linear dynamic plant of the second order is considered. A description of the plant in the form of a discrete transfer function is used, the coefficients of which are determined with the help of a neural network that minimizes the discrepancy between the outputs of the plant and the model. The neural network processes the current and delayed input and output signals of the plant, forming an output for estimating the coefficients of the model. Another neural network determines the PID regulator coefficients at which the dynamics of the system approach the dynamics of the reference process. The calculation of weights and displacements of neurons in Hopfield networks used for identification and control is based on the construction of Lyapunov functions. The proposed methodology can be used to organize adaptive control of a wide class of linear dynamic systems with variable parameters. The results of the simulation in the article show the effectiveness of the proposed method.