Prospects for control methods in engineering systems

Abstract

This article highlights the prerequisites and natural effects of control method development in engineering systems: (1) a simple deviation and perturbation controller, (2) a fuzzy logic controller with a fuzzifier and rule base, (3) a neural network controller for dynamically adjusting the coefficients of the corresponding links, (4) a discrete neural network controller with a neural approximator and controller. The experience gained by researchers and engineers since the initial description of regulatory principles in 1910, including the level of information technology design, particularly the neural network approach to machine learning and the enormous computing potential of computer devices, now enable the integration of a fundamentally novel method of discrete neural network regulation.

The article’s review aims to identify and demonstrate the importance of experimental and operational data, which must be organized and annotated at the time of collection and archiving. This approach will allow us to rapidly implement neural network controllers in engineering systems, as the most critical phase in their development is involves learning and optimization of neural network architecture.

The article presents the principle of operation, benefits, and drawbacks, and the optimal stages for enhancing a neural network controller based on two neural networks, which form a control strategy while considering the most probable state of the system at the next point in time.

Keywords

regulator / regulation methods / neural networks / regulation efficiency / regulation strategy

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RIGHTS & PERMISSIONS

Mamedov V.M., Arkharov I.A.