A non-linear fractional-order type-3 fuzzy control for enhanced path-tracking performance of autonomous cars

Path-tracking and lane-keeping efficiency of driverless cars remain critical characteristics of the efficient and safe deployment of such vehicles in future intelligent transportation systems. This study introduces a robust type-3 (T3) fuzzy controller implementation for the path-tracking task of driverless cars during critical driving conditions and subject to exogenous disturbances. Unlike many existing control paradigms, the proposed scheme is independent of the parameter information and assumes the system dynamics are unknown and non-linear. Control inputs are constructed to improve robustness by eliminating the error bounds while ensuring stability by leveraging the Lyapunov stability theorem and Barbalat's lemma. Also, a predicate scheme based on non-linear predictive control technique is introduced to enhance the lateral displacement. Based on the obtained results, the schemed controller exhibits competitive effectiveness in path-tracking tasks, and strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances. This study introduces a robust type-3 fuzzy neural controller implementation for the path-tracking task of driverless cars during critical driving conditions and subject to exogenous disturbances.image