Implementation of neural network based non-linear predictive control

被引：45
|
作者：
Sorensen, PH
Norgaard, M
Ravn, O
Poulsen, NK
机构：
[1] Tech Univ Denmark, Dept Automat, DK-2800 Lyngby, Denmark
[2] Tech Univ Denmark, Dept Math Modelling, DK-2800 Lyngby, Denmark
来源：
NEUROCOMPUTING | 1999年 / 28卷
关键词：
predictive control; quasi-Newton optimization; implementation issues;
D O I：
10.1016/S0925-2312(98)00114-3
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system. (C) 1999 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：37 / 51
页数：15
相关论文
共 50 条
  • [41] Non-linear predictive control for a distillation column
    Rueda, A.
    Cristea, S.
    de Prada, C.
    De Keyser, R.
    2005 44th IEEE Conference on Decision and Control & European Control Conference, Vols 1-8, 2005, : 5156 - 5161
  • [42] Dynamic neural network control for non-linear systems: Optimal neural network structure and stability analysis
    Earth Sci. Div. Lawrence Berkeley N., University of California at Berkeley, Berkeley, CA 94720, United States
    不详
    不详
    Chemical Engineering Journal, 1997, 68 (01) : 41 - 50
  • [43] Dynamic neural network control for non-linear systems: optimal neural network structure and stability analysis
    Nikravesh, M
    Farell, AE
    Stanford, TG
    CHEMICAL ENGINEERING JOURNAL, 1997, 68 (01): : 41 - 50
  • [44] Artificial neural network based adaptive linear model predictive control
    Cetin, Meric
    Beyhan, Selami
    Bahtiyar, Bedri
    PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI, 2016, 22 (08): : 650 - 658
  • [45] Neural network based blind source separation of non-linear mixtures
    Koutras, A
    Dermatas, E
    Kokkinakis, G
    ARTIFICIAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS, 2001, 2130 : 561 - 567
  • [46] Development of deep artificial neural network controller based on non-linear model predictive control data for real-time autonomous driving
    Lee T.
    Kang Y.
    Journal of Institute of Control, Robotics and Systems, 2020, 26 (05) : 302 - 311
  • [47] TurboBrain: A neural network with direct learning based on linear or non-linear threshold logics
    Dubois, D.M.
    Resconi, G.
    Raymondi, A.
    Lecture Notes in Computer Science, 1105
  • [48] Global adaptive neural network control for a class of uncertain non-linear systems
    Chen, P.
    Qin, H.
    Sun, M.
    Fang, X.
    IET CONTROL THEORY AND APPLICATIONS, 2011, 5 (05): : 655 - 662
  • [49] CBA-neural network control of a non-linear full vehicle model
    Gulez, Kayhan
    Guclu, Rahmi
    SIMULATION MODELLING PRACTICE AND THEORY, 2008, 16 (09) : 1163 - 1176
  • [50] Optimized Fuzzy-Based Wavelet Neural Network Controller for a Non-Linear Process Control System
    Deepa, S. N.
    Jayalakshmi, N. Yogambal
    IETE JOURNAL OF RESEARCH, 2023, 69 (03) : 1363 - 1372
12345