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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages253-258
Number of pages6
ISBN (Print)9781479951994
DOIs
Publication statusPublished - 1997 Mar 19
Event2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore
Duration: 2014 Dec 102014 Dec 12

Other

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

Fingerprint

Parameter estimation
Robust control
Neural networks
Controllers
Multilayer neural networks
Embedded systems
Nonlinear systems
Identification (control systems)

ASJC Scopus subject areas

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

Cite this

Jami'In, M. A., Sutrisno, I., Furuzuki, T., Mariun, N. B., & Marhaban, M. H. (1997). An adaptive predictive control based on a quasi-ARX neural network model. In 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 (pp. 253-258). [7064314] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICARCV.2014.7064314

An adaptive predictive control based on a quasi-ARX neural network model. / Jami'In, Mohammad Abu; Sutrisno, Imam; Furuzuki, Takayuki; Mariun, Norman Bin; Marhaban, Mohd Hamiruce.

2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. Institute of Electrical and Electronics Engineers Inc., 1997. p. 253-258 7064314.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jami'In, MA, Sutrisno, I, Furuzuki, T, Mariun, NB & Marhaban, MH 1997, An adaptive predictive control based on a quasi-ARX neural network model. in 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014., 7064314, Institute of Electrical and Electronics Engineers Inc., pp. 253-258, 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, Singapore, Singapore, 14/12/10. https://doi.org/10.1109/ICARCV.2014.7064314
Jami'In MA, Sutrisno I, Furuzuki T, Mariun NB, Marhaban MH. An adaptive predictive control based on a quasi-ARX neural network model. In 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. Institute of Electrical and Electronics Engineers Inc. 1997. p. 253-258. 7064314 https://doi.org/10.1109/ICARCV.2014.7064314
Jami'In, Mohammad Abu ; Sutrisno, Imam ; Furuzuki, Takayuki ; Mariun, Norman Bin ; Marhaban, Mohd Hamiruce. / An adaptive predictive control based on a quasi-ARX neural network model. 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. Institute of Electrical and Electronics Engineers Inc., 1997. pp. 253-258
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