Remarks on Adaptive-type Hypercomplex-valued Neural Network-based Feedforward Feedback Controller

被引：13
|
作者：
Takahashi, Kazuhiko [1 ]
机构：
[1] Doshisha Univ, Fac Sci & Engn, Informat Syst Design, Kyoto 6100321, Japan
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (CIT) | 2017年
关键词：
feedforward feedback controller; complex neural network; bicomplex neural network; quaternion neural network;
D O I：
10.1109/CIT.2017.16
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this study, we investigate the control performance of an adaptive-type feedforward feedback controller using multilayer hypercomplex-valued neural network. The control system consists of a neural network and a feedback controller, whereby the control input of a plant is synthesised online by using the sum of the multilayer hypercomplex-valued neural network and the feedback controller to track the plant output to the desired output generated by a reference model. Computational experiments to control a multiple-input and multiple-output discrete-time nonlinear plant are conducted to evaluate the capability and characteristics of the hypercomplex-valued neural network-based feedforward feedback controller. Experimental results show the feasibility and effectiveness of the proposed controller.
引用
收藏
页码：151 / 156
页数：6
相关论文
共 50 条
  • [1] Remarks on feedback loop gain characteristics of adaptive type neural network feedforward feedback controller
    Yamada, Takayuki
    [J]. 2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7, 2008, : 2159 - 2164
  • [2] Self-organizing hypercomplex-valued adaptive network
    Hazubski, Simon
    Hoppe, Harald
    [J]. NEUROCOMPUTING, 2024, 607
  • [3] Remarks on tracking method of neural network weight change for adaptive type neural network feedforward feedback controller
    Yamada, Takayuki
    [J]. ARTIFICIAL LIFE AND ROBOTICS, 2008, 13 (01) : 286 - 289
  • [4] Remarks on an Adaptive-Type Parallel Controller Using Quantum Neural Network with Qubit Neurons
    Takahashi, Kazuhiko
    [J]. 2012 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA), 2012, : 871 - 875
  • [5] Remarks on Quaternion Neural Network-Based Controller Trained by Feedback Error Learning
    Takahashi, Kazuhiko
    Isaka, Ayana
    Fudaba, Tomoki
    Hashimoto, Masafumi
    [J]. 2017 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII), 2017, : 875 - 880
  • [6] Remarks on Feedforward-Feedback Controller Using Simple Recurrent Quaternion Neural Network
    Takahashi, Kazuhiko
    [J]. 2018 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA), 2018, : 1246 - 1251
  • [7] A NEURAL-NETWORK-BASED FEEDFORWARD ADAPTIVE CONTROLLER FOR ROBOTS
    CARELLI, R
    CAMACHO, EF
    PATINO, D
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1995, 25 (09): : 1281 - 1288
  • [8] A neural network-based adaptive algorithm on the single EWMA controller
    C.-C. Hsu
    C.-T. Su
    [J]. The International Journal of Advanced Manufacturing Technology, 2004, 23 : 586 - 593
  • [9] Adaptive critic neural network-based controller for nonlinear systems
    Jagannathan, S
    [J]. PROCEEDINGS OF THE 2002 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL, 2002, : 303 - 308
  • [10] A neural network-based adaptive algorithm on the single EWMA controller
    Hsu, CC
    Su, CT
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2004, 23 (7-8): : 586 - 593
12345