State-dependent RBF-ARX model based nonlinear predictive control and application

被引：0
|
作者：
Zeng, Xiao-Yong [1 ,2 ,3 ]
Peng, Hui [1 ,3 ]
Wei, Ji-Min [1 ,3 ]
机构：
[1] School of Information Science and Engineering, Central South University, Changsha 410083, China
[2] School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410076, China
[3] Hunan Engineering Laboratory for Advanced Control and Intelligent Automation, Changsha 410083, China
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2012年 / 34卷 / 10期
关键词：
D O I：
10.3969/j.issn.1001-506X.2012.10.23
中图分类号：
学科分类号：
摘要：
引用
下载
收藏
页码：2110 / 2116
相关论文
共 50 条
  • [41] Learning-based Stochastic Model Predictive Control with State-Dependent Uncertainty
    Bonzanini, Angelo D.
    Mesbah, Ali
    LEARNING FOR DYNAMICS AND CONTROL, VOL 120, 2020, 120 : 571 - 580
  • [42] Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty
    Soloperto, Raffaele
    Mueller, Matthias A.
    Trimpe, Sebastian
    Allgoewer, Frank
    IFAC PAPERSONLINE, 2018, 51 (20): : 442 - 447
  • [43] Nonlinear system modeling and predictive control using the RBF nets-based quasi-linear ARX model
    Peng, Hui
    Wu, Jun
    Inoussa, Garba
    Deng, Qiulian
    Nakano, Kazushi
    CONTROL ENGINEERING PRACTICE, 2009, 17 (01) : 59 - 66
  • [44] Event-Triggered Nonlinear Model Predictive Control with Bounded Disturbances and State-dependent Uncertainties
    Wang, Mengzhi
    Sun, Jian
    IFAC PAPERSONLINE, 2017, 50 (01): : 9308 - 9314
  • [45] Stability analysis of RBF network-based state-dependent autoregressive model for nonlinear time series
    Gan, Min
    Peng, Hui
    APPLIED SOFT COMPUTING, 2012, 12 (01) : 174 - 181
  • [46] A synthesis approach of fast robust MPC with RBF-ARX model to nonlinear system with uncertain steady status information
    Xiaoying Tian
    Hui Peng
    Feng Zhou
    Xiaoyan Peng
    Applied Intelligence, 2021, 51 : 19 - 36
  • [47] A synthesis approach of fast robust MPC with RBF-ARX model to nonlinear system with uncertain steady status information
    Tian, Xiaoying
    Peng, Hui
    Zhou, Feng
    Peng, Xiaoyan
    APPLIED INTELLIGENCE, 2021, 51 (01) : 19 - 36
  • [48] Nonlinear Model Predictive Control for Systems with State-Dependent Switches and State Jumps Using a Penalty Function Method
    Katayama, Sotaro
    Satoh, Yasuyuki
    Doi, Masahiro
    Ohtsuka, Toshiyuki
    2018 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA), 2018, : 312 - 317
  • [49] Nonlinear Model Predictive Control based on State Dependent Riccati Equation
    Belarbi, K.
    Boumaza, H.
    Boutamina, B.
    201415TH INTERNATIONAL CONFERENCE ON SCIENCES & TECHNIQUES OF AUTOMATIC CONTROL & COMPUTER ENGINEERING (STA'2014), 2014, : 65 - 69
  • [50] A deep reinforcement learning control method guided by RBF-ARX pseudo LQR
    Peng, Tianbo
    Peng, Hui
    Liu, Fang
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2024, : 5839 - 5861
12345