An adaptive predictive control based on a quasi-ARX neural network model

Mohammad Abu Jami'In, Imam Sutrisno, Takayuki Furuzuki, Norman Bin Mariun, Mohd Hamiruce Marhaban

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

A quasi-ARX (quasi-linear ARX) neural network (QARXNN) model is able to demonstrate its ability for identification and prediction highly nonlinear system. The model is simplified by a linear correlation between the input vector and its nonlinear coefficients. The coefficients are used to parameterize the input vector performed by an embedded system called as state dependent parameter estimation (SDPE), which is executed by multi layer parceptron neural network (MLPNN). SDPE consists of the linear and nonlinear parts. The controller law is derived via SDPE of the linear and nonlinear parts through switching mechanism. The dynamic tracking controller error is derived then the stability analysis of the closed-loop controller is performed based Lyapunov theorem. Linear based adaptive robust control and nonlinear based adaptive robust control is performed with the switching of the linear and nonlinear parts parameters based Lyapunov theorem to guarantee bounded and convergence error.

本文言語English
ホスト出版物のタイトル2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
出版社Institute of Electrical and Electronics Engineers Inc.
ページ253-258
ページ数6
ISBN(印刷版)9781479951994
DOI
出版ステータスPublished - 1997 3 19
イベント2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore
継続期間: 2014 12 102014 12 12

Other

Other2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
CountrySingapore
CitySingapore
Period14/12/1014/12/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Artificial Intelligence
  • Control and Systems Engineering

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