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

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.

Automated summary

This research proposes a novel robust path-following strategy for autonomous road vehicles using a type-2 fuzzy PID neural network and an Extended Kalman Filter-based Fuzzy Neural Network observer with uncertain Gaussian membership functions. The effectiveness of the proposed control algorithm in minimizing path-tracking errors is demonstrated through comparison with other control methods.