Research on adaptive sliding mode control methods to improve the efficiency of brushless motors in industrial applications

Abstract

The article is devoted to the development and analysis of an adaptive sliding control mode of a synchronous valve motor (SVD) based on the IMRAD structure (Introduction, Methods, Results, and Discussion). The study examines the importance of creating effective and reliable control systems for SVD used in industry, transport and other applications, with an emphasis on the need to adapt and increase the stability of control under changing loads and external disturbances. This aspect is relevant for the development of modern electrical installations and achievements in the field of electromechanics. The article discusses in detail the application of the sliding control mode (SRU) for the SVD; describes the basic principles of SRU, ensuring high stability and performance of the system. Real-time adaptation algorithms aimed at adjusting the parameters of the SRU to account for changes in the dynamics of the system and external factors are presented. Mathematical models were used to formulate and analyze adaptive algorithms, which makes it possible to significantly refine the data and increase the accuracy of predictions. Graphs and tables illustrating the dynamic behavior of the SVD under various operating modes and perturbations are presented, as well as the results of modeling and experimental studies conducted to confirm the effectiveness of the proposed adaptive SRU. A comparison with traditional management methods has been carried out, which allows us to see the advantages of an adaptive approach for speed, stability and efficiency. The article summarizes the results obtained, emphasizes the achievement of a significant improvement in the characteristics of the SVD control through the use of an adaptive sliding mode, and provides recommendations for the practical application and inclusion of the developed methods in existing industrial processes. In conclusion, the prospects for further research and possible ways to optimize the proposed methodology are outlined.