EKF-Neural Network Observer Based Type-2 Fuzzy Control of Autonomous Vehicles

Taghavifar, Hamid, Hu, Chuan, Qin, Yechen and Wei, Chongfeng (2021) EKF-Neural Network Observer Based Type-2 Fuzzy Control of Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems, 22 (8). pp. 4788-4800. ISSN 1524-9050

Full text not available from this repository. (Request a copy)
Official URL: https://doi.org/10.1109/TITS.2020.2985124


This paper proposes a novel robust path-following strategy for autonomous road vehicles based on type-2 fuzzy PID neural network (PIDT2FNN) method coupled to an Extended Kalman Filter-based Fuzzy Neural Network (EKFNN) observer. Uncertain Gaussian membership functions (MFs) are employed to self-adjust the universe of discourse for MFs using the adaptation mechanism derived from Lyapunov stability theory and Barbalat's lemma. External disturbances are significant in autonomous vehicles by changing the driving condition. Furthermore, parametric uncertainties related to the physical limits of tires and the change of the vehicle mass may significantly affect the desired performance of autonomous vehicles. The robustness of the proposed controller against the parametric uncertainties and external disturbances is compared with one active disturbance rejection control (ADRC) algorithm, and a linear-quadratic tracking (LQT) method. The obtained results in terms of the maximum error and root mean square error (RMSE), demonstrate the effectiveness of the proposed control algorithm to reach the minimized path-tracking error.

Item Type: Article
Uncontrolled Keywords: Autonomous vehicles, path-following, indirect adaptive control, type-2 Fuzzy neural network
Subjects: G400 Computer Science
H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 04 Jun 2020 13:22
Last Modified: 11 Aug 2021 15:50
URI: http://nrl.northumbria.ac.uk/id/eprint/43343

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics