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

Hamid Taghavifar, Chuan Hu, Yechen Qin, Chongfeng Wei

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.
Original languageEnglish
Article number9067077
Pages (from-to)4788-4800
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number8
Early online date14 Apr 2020
Publication statusPublished - Aug 2021


National Science Foundation of China under Grant 51805028 and in part by the Beijing Institute of Technology Research Fund Program for Young Scholars.


  • Autonomous vehicles
  • Path-following
  • indirect adaptive control
  • type-2 Fuzzy neural network

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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