V.N. Efanov1, D.F. Mufazzalov2

1,2 Ufa University of Science and Technology (Ufa, Russia)

1efanov@mail.ru, 2arsew@ya.ru

We consider the problem of studying complex control systems whose functioning takes place in a non-deterministic and unknown environment, in the presence of a large number of uncertain factors. Such systems are required to maintain the specified characteristics in unpredictable situations. Under these conditions, intelligent control algorithms have shown high efficiency due to their ability to self-learning in situations with a priori and a posteriori uncertainty. These include a variety of machine learning methods, including genetic algorithms. However, classical genetic algorithms are used to optimize functions of discrete variables, which requires their adaptation when solving the problem of controller parameter synthesis. To solve the above problem, we propose a modified genetic algorithm with real representation, which provides stabilization of complex control systems in a given range of uncertain characteristics of the control object. The following results were obtained in the course of the research. An interval model describing the behavior of the system in a given range of uncertain characteristics of the control object was developed. A system of inequalities for the parameters of the regulator, which provides exponential stability of the synthesized system, has been formed. A genetic algorithm for solving this system of inequalities is proposed. The practical significance of the proposed approach is confirmed by the example of synthesizing the control system of an aircraft.

Efanov V.N., Mufazzalov D.F. Genetic algorithm for stabilization of complex control systems. Information-measuring and Control Systems. 2024. V. 22. № 2. P. 31−43. DOI: https://doi.org/10.18127/j20700814-202402-04 (in Russian)

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