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Embedded intelligent adaptive PI controller for an electromechanical system.
ISA Trans 2016; 64:314-327IT

Abstract

In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties.

Authors+Show Affiliations

Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menofia University, Menuf 32852, Egypt. Electronic address: Ahmed_elnagar@menofia.edu.eg.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27342993

Citation

El-Nagar, Ahmad M.. "Embedded Intelligent Adaptive PI Controller for an Electromechanical System." ISA Transactions, vol. 64, 2016, pp. 314-327.
El-Nagar AM. Embedded intelligent adaptive PI controller for an electromechanical system. ISA Trans. 2016;64:314-327.
El-Nagar, A. M. (2016). Embedded intelligent adaptive PI controller for an electromechanical system. ISA Transactions, 64, pp. 314-327. doi:10.1016/j.isatra.2016.06.006.
El-Nagar AM. Embedded Intelligent Adaptive PI Controller for an Electromechanical System. ISA Trans. 2016;64:314-327. PubMed PMID: 27342993.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Embedded intelligent adaptive PI controller for an electromechanical system. A1 - El-Nagar,Ahmad M, Y1 - 2016/06/21/ PY - 2016/01/20/received PY - 2016/05/02/revised PY - 2016/06/07/accepted PY - 2016/6/26/entrez PY - 2016/6/28/pubmed PY - 2016/6/28/medline KW - Adaptive PI controller KW - Interval type-2 fuzzy neural networks KW - Lyapunov theorem KW - Nonlinear DC motor SP - 314 EP - 327 JF - ISA transactions JO - ISA Trans VL - 64 N2 - In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties. SN - 1879-2022 UR - https://www.unboundmedicine.com/medline/citation/27342993/Embedded_intelligent_adaptive_PI_controller_for_an_electromechanical_system_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0019-0578(16)30114-8 DB - PRIME DP - Unbound Medicine ER -
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