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.
|Number of pages||13|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Early online date||14 Apr 2020|
|Publication status||Published - Aug 2021|
FunderNational Science Foundation of China under Grant 51805028 and in part by the Beijing Institute of Technology Research Fund Program for Young Scholars.
- Autonomous vehicles
- indirect adaptive control
- type-2 Fuzzy neural network
ASJC Scopus subject areas
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications