Online system identification for nonlinear uncertain dynamical systems using recursive interval type-2 TS fuzzy C-means clustering
Date
2020
Authors
Al Mahturi, A.
Santoso, F.
Garratt, M.A.
Anavatti, S.G.
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Conference paper
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2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020, pp.1695-1701
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2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2020) (1 Dec 2020 - 4 Dec 2020 : Virtual event)
Abstract
This paper presents a novel online system identification technique based on a recursive interval type-2 Takagi-Sugeno fuzzy C-means clustering technique (IT2-TS-FC) for modeling nonlinear uncertain dynamics of autonomous systems. The construction of the fuzzy antecedent parameters is based on the type-2 fuzzy C-means clustering (IT2FCM) technique, while the Weighted Least Square (WLS) algorithm is utilized to determine the upper and lower fuzzy consequent parameters. Moreover, a scaling factor to represent the footprint of uncertainties (FoU) is introduced to convert type-l and type2 fuzzy systems. The efficiency of our proposed algorithm has been validated using two benchmark system datasets, flight test data from a quadcopter and Mackey-Glass time series data. We also compare our proposed technique with a type-l fuzzy C-means technique. The robustness of our proposed identification is investigated by means of a noisy dataset.
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Copyright 2020 IEEE