Open AccessOn the use of neural regulators
Author(s) -
Aleksandr Voevoda,
Dmitry Romannikov
Publication year - 2021
Publication title -
sbornik naučnyh trudov ngtu
Language(s) - English
Resource type - Journals
ISSN - 2307-6879
DOI - 10.17212/2307-6879-2021-1-53-63
Subject(s) - artificial neural network , computer science , state vector , controller (irrigation) , set (abstract data type) , reinforcement learning , state (computer science) , artificial intelligence , statement (logic) , control theory (sociology) , time delay neural network , control (management) , algorithm , physics , classical mechanics , law , political science , agronomy , biology , programming language
The application of neural networks for the synthesis of control systems is considered. Examples of synthesis of control systems using methods of reinforcement learning, in which the state vector is involved, are given. And the synthesis of a neural controller for objects with an inaccessible state vector is discussed: 1) a variant using a neural network with recurrent feedbacks; 2) a variant using the input error vector, where each error (except for the first one) enters the input of the neural network passing through the delay element. The disadvantages of the first method include the fact that for such a structure of a neural network it is not possible to apply existing learning methods with confirmation and for training it is required to use a data set obtained, for example, from a previously calculated linear controller. The structure of the neural network used in the second option allows the application of reinforcement learning methods, but the article provides a statement and its proof that for the synthesis of a control system for objects with three or more integrators, a neural network without recurrent connections cannot be used. The application of the above structures is given on examples of the synthesis of control systems for objects 1/s2 and 1/s3 presented in a discrete form.