Please use this identifier to cite or link to this item:
Title: Online identification of a rotary wing Unmanned Aerial Vehicle from data streams
Authors: Ferdaus, Md Meftahul
Pratama, Mahardhika
Anavatti, Sreenatha G.
Garratt, Matthew A.
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Ferdaus, M. M., Pratama, M., Anavatti, S. G. & Garratt, M. A. (2019). Online identification of a rotary wing Unmanned Aerial Vehicle from data streams. Applied Soft Computing, 76, 313-325.
Journal: Applied Soft Computing 
Abstract: Until now the majority of the neuro and fuzzy modeling and control approaches for rotary wing Unmanned Aerial Vehicles (UAVs), such as the quadrotor, have been based on batch learning techniques, therefore static in structure, and cannot adapt to rapidly changing environments. Implication of Evolving Intelligent System (EIS) based model-free data-driven techniques in fuzzy system are good alternatives, since they are able to evolve both their structure and parameters to cope with sudden changes in behavior, and performs perfectly in a single pass learning mode which is suitable for online real-time deployment. The Metacognitive Scaffolding Learning Machine (McSLM) is seen as a generalized version of EIS since the metacognitive concept enables the what-to-learn, how-to-learn, and when-to-learn scheme, and the scaffolding theory realizes a plug-and-play property which strengthens the online working principle of EISs. This paper proposes a novel online identification scheme, applied to a quadrotor using real-time experimental flight data streams based on McSLM, namely Metacognitive Scaffolding Interval Type 2 Recurrent Fuzzy Neural Network (McSIT2RFNN). Our proposed approach demonstrated significant improvements in both accuracy and complexity against some renowned existing variants of the McSLMs and EISs.
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2018.12.013
Rights: © 2018 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Page view(s)

Updated on Jul 3, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.