Application of feedforward and recurrent neural networks for model-based control systems
Marek Krok
Wojciech P.Hunek
Szymon Mielczarek
Filip Buchwald
Adam Kolender
Abstract：In this paper, a new study concerning the usage of artificial neural networks in the control application is given. It is shown, that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern. Interestingly, the instances driven by neural networks have the ability to outperform the original analytically driven scenarios. Three different control schemes, namely perfect, linear-quadratic, and generalized predictive controllers were used in the theoretical study. In addition, the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.
Keywords：Predictive controlLinear-quadratic controlInverse problemsFeedforward networkRecurrent neural networkOptimization
Publication Date：2025-02-04
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：14( 91-104 )