Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Sahoo, Amit Kumara; * | Mishra, Sudhansu Kumarb | Acharya, Deep Shekharc | Sahu, Sitanshu Sekhard | Paul, Sanchitae | Gupta, Vikash Kumarf
Affiliations: [a] Department of EEE, Centurion University of Technology and Management, Odisha, India | [b] Department of EEE, Birla Institute of Technology, Mesra, Ranchi, India | [c] Department of EEE, BIT Mesra, Off-Campus Deoghar, Deoghar, Jharkhand, India | [d] Department of ECE, Birla Institute of Technology, Mesra, Ranchi, India | [e] Department of CSE, Birla Institute of Technology, Mesra, Ranchi, India | [f] Department of Applied Sciences and Humanities, NIAMT, Hatia. Ranchi
Correspondence: [*] Corresponding author. Amit Kumar Sahoo, Department of EEE, Centurion University of Technology and Management, Odisha, India. E-mail: amitkumar2687@gmail.com.
Abstract: System identification techniques have proved to be the most effective methodologies for the modeling highly non-linear and system. For the purpose of real-time parameter estimation of a Maglev system, a Teaching Learning Based Optimization (TLBO) for updating the weights of Functional Link Artificial Neural Network (FLANN) model is proposed and implemented in this research. Moreover, we proposed a one & two-Degree of Freedom (one-DOF & two-DOF) Fractional Order PID (FOPID) controller, where the parameters are optimized by using the Teaching Learning Based Optimization (TLBO) and the recently proposed Black Widow Optimization (BWO) algorithm. To investigate the robustness of the proposed controller, a pulse signal disturbance is added at equal intervals of the output of the identified model of the Maglev system. It is found that the suggested two-DOF FOPID controller with TLBO performs better than its counterpart in terms of both in time domain specifications (i.e., maximum overshoot = 1.2648%, settling time = 1.3884 sec and rise time = 0.8685 sec) and in robustness analysis (i.e., system is sufficiently robust, because the infinity norms of the sensitivity and the complementary sensitivity functions of the system are less than two). The TLBO algorithm has performed better for both identification and optimization of controller parameter due to very less number of algorithmic parameter is as compared to other algorithm.
Keywords: System identification, fractional calculus, FOPID, IOPID, MAGLEV system
DOI: 10.3233/JIFS-222238
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7277-7289, 2023
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl