Abstract
In this paper, the teaching–learning-based optimization-based functional link artificial neural network (FLANN) has been proposed for the real-time identification of Maglev system. This proposed approach has been compared with some of the other state-of-the-art approaches, such as multilayer perceptron–backpropagation, FLANN least mean square, FLANN particle swarm optimization and FLANN black widow optimization. Further, the real-time Maglev system and the identified model are controlled by the Fuzzy PID controller in a closed loop system with proper choice of the controller parameters. The efficacy of the identified model is investigated by comparing the response of both the real-time and identified Fuzzy PID-controlled Maglev system. To validate the dominance of the proposed model, three nonparametric statistical tests, i.e., the sign test, Wilcoxon signed-rank test and Friedman test, are also performed.
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Sahoo, A.K., Mishra, S.K., Majhi, B. et al. Real-Time Identification of Fuzzy PID-Controlled Maglev System using TLBO-Based Functional Link Artificial Neural Network. Arab J Sci Eng 46, 4103–4118 (2021). https://doi.org/10.1007/s13369-020-05292-x
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DOI: https://doi.org/10.1007/s13369-020-05292-x