Open AccessA method for the synthesis of neural regulators for linear objects
Author(s) -
Dmitry Romannikov
Publication year - 2020
Publication title -
naučnyj vestnik novosibirskogo gosudarstvennogo tehničeskogo universiteta
Language(s) - English
Resource type - Journals
eISSN - 2658-3275
pISSN - 1814-1196
DOI - 10.17212/1814-1196-2020-4-111-120
Subject(s) - artificial neural network , control theory (sociology) , controller (irrigation) , computer science , state vector , object (grammar) , artificial intelligence , state (computer science) , regulator , control engineering , control (management) , algorithm , engineering , physics , classical mechanics , agronomy , biology , biochemistry , chemistry , gene
The article proposes a method for the synthesis of a neural controller for closed-loop systems with linear objects. The scientific novelty of the proposed method lies in the fact that the neural controller, to the input of which the object state vector is fed, must be trained to stabilize in one of the possible desired values, and to ensure regulation in other desired values. For objects with an inaccessible state vector, it is possible to use the estimation vector of the object state vector. It is proposed to proportionally decrease/increase the signal of the state vector and increase/decrease the control signal formed by the neural regulator. Also, other advantages of the proposed method include: 1) the absence of the need for training on several desired values, which greatly simplifies and accelerates the training of the neural network, and also eliminates control errors in the range of values for which the neural controller was not trained; 2) the possibility of learning from an initially unstable state of a closed-loop system. The proposed method for the synthesis of a neural controller for a closed-loop system with a linear object was tested on the example of the synthesis of a controller for an object 1/s 3, which is unstable. A neural network is used as a regulator, which is proposed to be trained using one of the reinforcement learning methods (in the article, the Deterministic Policy Gradient method allowed us to obtain the best results). The resulting graphs of transient processes allow us to conclude about its successful application. The article ends with conclusions and considerations about further lines of research, which include the quality of the transient process and the possibility of adjusting it by changing the reward function, which will allow setting the graphs of transient processes.