Development of C-Means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle
Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous MAVs. Some desiring features of the FW MAV are quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced mano...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2020
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| Online Access: | https://hdl.handle.net/10356/142322 |
| _version_ | 1826128909735821312 |
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| author | Md Meftahul Ferdaus Anavatti, Sreenatha G. Garratt, Matthew A. Pratama, Mahardhika |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Md Meftahul Ferdaus Anavatti, Sreenatha G. Garratt, Matthew A. Pratama, Mahardhika |
| author_sort | Md Meftahul Ferdaus |
| collection | NTU |
| description | Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous MAVs. Some desiring features of the FW MAV are quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability contrasted with similar-sized fixed and rotary wing MAVs. Inspired by the FW MAV’s advanced features, a four-wing Nature-inspired (NI) FW MAV is modelled and controlled in this work. The Fuzzy C-Means (FCM) clustering algorithm is utilized to construct the data-driven NIFW MAV model. Being model free, it does not depend on the system dynamics and can incorporate various uncertainties like sensor error, wind gust etc. Furthermore, a Takagi-Sugeno (T-S) fuzzy structure based adaptive fuzzy controller is proposed. The proposed adaptive controller can tune its antecedent and consequent parameters using FCM clustering technique. This controller is employed to control the altitude of the NIFW MAV, and compared with a standalone Proportional Integral Derivative (PID) controller, and a Sliding Mode Control (SMC) theory based advanced controller. Parameter adaptation of the proposed controller helps to outperform it static PID counterpart. Performance of our controller is also comparable with its advanced and complex counterpart namely SMC-Fuzzy controller. |
| first_indexed | 2024-10-01T07:32:35Z |
| format | Journal Article |
| id | ntu-10356/142322 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:32:35Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1423222020-06-19T03:47:02Z Development of C-Means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle Md Meftahul Ferdaus Anavatti, Sreenatha G. Garratt, Matthew A. Pratama, Mahardhika School of Computer Science and Engineering Engineering::Computer science and engineering Adaptive Fuzzy Clustering Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous MAVs. Some desiring features of the FW MAV are quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability contrasted with similar-sized fixed and rotary wing MAVs. Inspired by the FW MAV’s advanced features, a four-wing Nature-inspired (NI) FW MAV is modelled and controlled in this work. The Fuzzy C-Means (FCM) clustering algorithm is utilized to construct the data-driven NIFW MAV model. Being model free, it does not depend on the system dynamics and can incorporate various uncertainties like sensor error, wind gust etc. Furthermore, a Takagi-Sugeno (T-S) fuzzy structure based adaptive fuzzy controller is proposed. The proposed adaptive controller can tune its antecedent and consequent parameters using FCM clustering technique. This controller is employed to control the altitude of the NIFW MAV, and compared with a standalone Proportional Integral Derivative (PID) controller, and a Sliding Mode Control (SMC) theory based advanced controller. Parameter adaptation of the proposed controller helps to outperform it static PID counterpart. Performance of our controller is also comparable with its advanced and complex counterpart namely SMC-Fuzzy controller. Published version 2020-06-19T03:47:02Z 2020-06-19T03:47:02Z 2018 Journal Article Md Meftahul Ferdaus, Anavatti, S. G., Garratt, M. A., & Pratama, M. (2019). Development of C-Means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle. Journal of Artificial Intelligence and Soft Computing Research, 9(2), 99-109. doi:10.2478/jaiscr-2018-0027 2083-2567 https://hdl.handle.net/10356/142322 10.2478/jaiscr-2018-0027 2-s2.0-85060116660 2 9 99 109 en Journal of Artificial Intelligence and Soft Computing Research © 2018 The Author(s) (published by Sciendo). This is an open-access article distributed under the terms of the Creative Commons Attribution License. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Adaptive Fuzzy Clustering Md Meftahul Ferdaus Anavatti, Sreenatha G. Garratt, Matthew A. Pratama, Mahardhika Development of C-Means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle |
| title | Development of C-Means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle |
| title_full | Development of C-Means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle |
| title_fullStr | Development of C-Means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle |
| title_full_unstemmed | Development of C-Means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle |
| title_short | Development of C-Means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle |
| title_sort | development of c means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle |
| topic | Engineering::Computer science and engineering Adaptive Fuzzy Clustering |
| url | https://hdl.handle.net/10356/142322 |
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