Article information
2020 , Volume 25, ¹ 3, p.111-118
Voevoda A.A., Romannikov D.O.
The synthesis method of regulators for multichannel systems using neural networks
The problem for synthesis of automatic control systems is hard, especially for multichannel objects. One of the approaches is the use of neural networks. For the approaches that are based on the use of reinforcement learning, there is an additional issue — supporting of range of values for the set points. The method of synthesis of automatic control systems using neural networks and the process of its learning with reinforcement learning that allows neural networks learning for supporting regulation is proposed in the predefined range of set points. The main steps of the method are 1) to form a neural net input as a state of the object and system set point; 2) to perform modelling of the system with a set of randomly generated set points from the desired range; 3) to perform a one-step of the learning using the Deterministic Policy Gradient method. The originality of the proposed method is that, in contrast to existing methods of using a neural network to synthesize a controller, the proposed method allows training a controller from an unstable initial state in a closed system and set of a range of set points. The method was applied to the problem of stabilizing the outputs of a two-channel object, for which stabilization both outputs and the first near the input set point is required
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Keywords: neural network, control, regulator, multichannel system, closed system
doi: 10.25743/ICT.2020.25.3.012
Author(s): Voevoda Aleksandr Aleksandrovich Dr. , Professor Position: Professor Office: Novosibirsk State Technical University Address: 630087, Russia, Novosibirsk, Karl Marx Ave. 20
E-mail: voevoda@ucit.ru Romannikov Dmitry Olegovich PhD. , Associate Professor Position: Associate Professor Office: Novosibirsk State Technical University Address: 630087, Russia, Novosibirsk, Karl Marx Ave. 20
E-mail: dmitry.romannikov@gmail.com
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