Prospects for control methods in engineering systems

Abstract

The article discusses the prerequisites and natural consequences of the control methods development in engineering systems: (1) a simple deviation and perturbation controller, (2) a fuzzy logic controller with a fuzzifier and a 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 first description of the principles of regulation in 1910 and the level of information technologies design, in particular the neural network method of machine learning and the enormous computing potential of computer devices, today can be integrated into a fundamentally new method of discrete neural network regulation.
 The review carried out in the article is aimed at identifying and demonstrating the significance of experimental and operational data, which must be properly structured and marked up at the stage of their collection and archiving. It is this approach that will allow us to quickly implement neural network controllers in engineering systems, since the most important stage for their creation is the process of learning and optimizing the architecture of neural networks.
 The principle of operation, advantages and disadvantages, the optimal stages of a neural network controller improvement based on two neural networks for the formation of a control strategy, taking into account the most probable state of the system at the next point in time, are given.