Application of feedforward and recurrent neural networks for model-based control systems  

作　　者：Marek Krok Wojciech P.Hunek Szymon Mielczarek Filip Buchwald Adam Kolender 

机构地区：[1]Department of Control Science and Engineering,Opole University of Technology,Opole,Poland

出　　处：《Control Theory and Technology》2025年第1期91-104,共14页控制理论与技术(英文版)

摘　　要：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.

关 键 词：Predictive control Linear-quadratic control Inverse problems Feedforward network Recurrent neural network OPTIMIZATION 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]