Conference proceeding
Online Takagi-Sugeno Fuzzy Identification of a Quadcopter Using Experimental Input-Output Data
2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp.527-533
2019 IEEE Symposium Series on Computational Intelligence (SSCI) (Xiamen, China, 06/12/2019 - 09/12/2019)
12/2019
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Abstract
This paper presents a sequential learning machine based on the Takagi-Sugeno (TS) fuzzy inference system to model the dynamics of a MIMO nonlinear quadcopter using experimental data. Unlike conventional TS-fuzzy systems, all the antecedent and consequent parameters of our proposed TS-fuzzy model are updated using the gradient descent-based back-propagation algorithm. After extensive numerical simulations, the accuracy of the proposed model is validated and compared with the Fuzzy C-Means clustering (FCM) algorithm and also with the ARMAX linear model identification technique. This paper leverages the advantages of model-free systems, which can incorporate various uncertainties such as noise, wind gusts, etc. The learning capability using back-propagation method is also suitable to represent the nonlinear dynamics of our quadcopter.
Details
- Title
- Online Takagi-Sugeno Fuzzy Identification of a Quadcopter Using Experimental Input-Output Data
- Creators
- Ayad Al-Mahturi - University of New South WalesFendy Santoso - University of New South WalesMatthew A. Garratt - University of New South WalesSreenatha G. Anavatti - University of New South WalesMd Meftahul Ferdaus - University of New South Wales
- Publication Details
- 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp.527-533
- Conference
- 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (Xiamen, China, 06/12/2019 - 09/12/2019)
- Publisher
- IEEE
- Identifiers
- 991013092523602368
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Conference proceeding