Neural predictive controller of nonlinear systems based on quasi-ARX neural network

Imam Sutrisno; Mohammad Abu Jami'in; Jinglu Hu

Neural predictive controller of nonlinear systems based on quasi-ARX neural network

Imam Sutrisno^*, Mohammad Abu Jami'in, Jinglu Hu

^*Corresponding author for this work

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

This paper present a neural predictive controller (NPC) based on improved quasi-ARX neural network (IQARXNN) for nonlinear dynamical systems. The IQARXNN is used as a model identifier with switching algorithm and switching stability analysis. The primary controller is designed based on a modified Elman neural network (MENN) controller using back-propagation (BP) learning algorithm with modified particle swarm optimization (MPSO) to adjust the learning rates in the BP process to improve the learning capability. The adaptive learning rates of the controller are investigated via Lyapunov stability theorem, which are respectively used to guarantee the convergences of the predictive controller. Performance of the proposed MENN controller with MPSO is verified by simulation results to show the effectiveness of the proposed method both on stability and accuracy.

Original language	English
Title of host publication	ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing
Subtitle of host publication	Integration of Design and Engineering
Pages	78-83
Number of pages	6
Publication status	Published - 2012 Nov 26
Event	18th International Conference on Automation and Computing, ICAC 2012 - Loughborough, Leicestershire, United Kingdom Duration: 2012 Sept 7 → 2012 Sept 8

Publication series

Name	ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering

Conference

Conference	18th International Conference on Automation and Computing, ICAC 2012
Country/Territory	United Kingdom
City	Loughborough, Leicestershire
Period	12/9/7 → 12/9/8

Keywords

improved quasi-ARX neural network (IQARXNN)
modified Elman neural network (MENN)
modified particle swarm optimization (MPSO)
neural predictive controller (NPC)
stability and accuracy

ASJC Scopus subject areas

Artificial Intelligence
Software

Cite this

Sutrisno, I., Jami'in, M. A., & Hu, J. (2012). Neural predictive controller of nonlinear systems based on quasi-ARX neural network. In ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering (pp. 78-83). Article 6330529 (ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering).

Neural predictive controller of nonlinear systems based on quasi-ARX neural network. / Sutrisno, Imam; Jami'in, Mohammad Abu; Hu, Jinglu.
ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering. 2012. p. 78-83 6330529 (ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sutrisno, I, Jami'in, MA & Hu, J 2012, Neural predictive controller of nonlinear systems based on quasi-ARX neural network. in ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering., 6330529, ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering, pp. 78-83, 18th International Conference on Automation and Computing, ICAC 2012, Loughborough, Leicestershire, United Kingdom, 12/9/7.

Sutrisno I, Jami'in MA, Hu J. Neural predictive controller of nonlinear systems based on quasi-ARX neural network. In ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering. 2012. p. 78-83. 6330529. (ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering).

Sutrisno, Imam ; Jami'in, Mohammad Abu ; Hu, Jinglu. / Neural predictive controller of nonlinear systems based on quasi-ARX neural network. ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering. 2012. pp. 78-83 (ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering).

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abstract = "This paper present a neural predictive controller (NPC) based on improved quasi-ARX neural network (IQARXNN) for nonlinear dynamical systems. The IQARXNN is used as a model identifier with switching algorithm and switching stability analysis. The primary controller is designed based on a modified Elman neural network (MENN) controller using back-propagation (BP) learning algorithm with modified particle swarm optimization (MPSO) to adjust the learning rates in the BP process to improve the learning capability. The adaptive learning rates of the controller are investigated via Lyapunov stability theorem, which are respectively used to guarantee the convergences of the predictive controller. Performance of the proposed MENN controller with MPSO is verified by simulation results to show the effectiveness of the proposed method both on stability and accuracy.",

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AB - This paper present a neural predictive controller (NPC) based on improved quasi-ARX neural network (IQARXNN) for nonlinear dynamical systems. The IQARXNN is used as a model identifier with switching algorithm and switching stability analysis. The primary controller is designed based on a modified Elman neural network (MENN) controller using back-propagation (BP) learning algorithm with modified particle swarm optimization (MPSO) to adjust the learning rates in the BP process to improve the learning capability. The adaptive learning rates of the controller are investigated via Lyapunov stability theorem, which are respectively used to guarantee the convergences of the predictive controller. Performance of the proposed MENN controller with MPSO is verified by simulation results to show the effectiveness of the proposed method both on stability and accuracy.

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Neural predictive controller of nonlinear systems based on quasi-ARX neural network

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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