Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles

Man-Wen Tian, Shu-Rong Yan, Ardashir Mohammadzadeh, Jafar Tavoosi, Saleh Mobayen, Rabia Safdar, Wudhichai Assawinchaichote, Mai The Vu and Anton Zhilenkov
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Man-Wen Tian: National Key Project Laboratory, Jiangxi University of Engineering, Xinyu 338000, China
Shu-Rong Yan: National Key Project Laboratory, Jiangxi University of Engineering, Xinyu 338000, China
Ardashir Mohammadzadeh: Electrical Engineering Department, University of Bonab, Bonab 5551395133, Iran
Jafar Tavoosi: Department of Electrical Engineering, Ilam University, Ilam 69315516, Iran
Saleh Mobayen: Future Technology Research Center, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Rabia Safdar: Department of Mathematics, Lahore College Women University, Lahore 54000, Pakistan
Wudhichai Assawinchaichote: Department of Electronic and Telecommunication Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand
Mai The Vu: School of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
Anton Zhilenkov: Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, 190121 Saint-Petersburg, Russia

Mathematics, 2021, vol. 9, issue 21, 1-17

Abstract: Economic efficient Autonomous Road Vehicles (ARVs) are invariably subjected to uncertainties and perturbations. Therefore, control of vehicle systems requires stability to withstand the effect of variations in the nominal performance. Lateral path-tracking is a substantial task of ARVs, especially in critical maneuvering and cornering with variable speed. In this study, a new controller on the basis of interval type-3 (T3) fuzzy logic system (FLSs) is designed. The main novelties and advantages are as follows. (1) The uncertainty is a main challenge in the path-following problem of ARVs. However, in the fuzzy-based approaches, the bounds of uncertainty are assumed to be known. However, in the our suggested approach, the bounds of uncertainties are also fuzzy sets and type-3 FLSs with online adaptation rules are suggested to handle the uncertainties. (2) The approximation errors (AEs) and perturbations are investigated and tackled by the compensators. (3) The bounds of estimation errors are also uncertain and are estimated by the suggested adaptation laws. (4) The stability is ensured under unknown dynamics, perturbations and critical maneuvers. (5) Comparison with the benchmarking techniques and conventional fuzzy approaches verifies that the suggested path-following scheme results in better maneuver performance.

Keywords: fuzzy system; autonomous vehicles; machine learning; path-following; stability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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