Conference proceeding
Online System Identification for Nonlinear Uncertain Dynamical Systems Using Recursive Interval Type-2 TS Fuzzy C-means Clustering
2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp.1695-1701
2020 IEEE Symposium Series on Computational Intelligence (SSCI) (Canberra, ACT, Australia, 01/12/2020 - 04/12/2020)
12/2020
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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 Cmeans technique. The robustness of our proposed identification is investigated by means of a noisy dataset.
Details
- Title
- Online System Identification for Nonlinear Uncertain Dynamical Systems Using Recursive Interval Type-2 TS Fuzzy C-means Clustering
- Creators
- Ayad Al-Mahturi - UNSW SydneyFendy Santoso - UNSW AustraliaMatthew A. Garratt - UNSW AustraliaSreenatha G. Anavatti - UNSW Australia
- Publication Details
- 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp.1695-1701
- Conference
- 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (Canberra, ACT, Australia, 01/12/2020 - 04/12/2020)
- Publisher
- IEEE
- Identifiers
- 991013092521702368
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Conference proceeding