Neural network optimization of areas of existence of the sliding mode on the basis of qualitative analysis of phase space projections

The paper proposes a method for neural network optimization of the regions of existence of a sliding mode in the projections of the phase space of a control object for the purpose of subsequent synthesis of control systems with sliding modes. Expanding the regions of existence of a sliding mode provides greater freedom in choosing sliding surfaces, including nonlinear ones, and allows us to expect an improvement in the quality of control. The purpose of the study is to determine the applicability of modern machine learning methods, in particular neural networks and genetic algorithms, in problems of optimizing the regions of existence of a sliding mode using the example of a 4th order nonlinear system.