Adaptive Robust Control Algorithm for Enhanced Path-Tracking Performance of Automated Driving in Critical Scenarios

A profound concern dwelling on developing practical control algorithms for autonomous cars is to guarantee robustness and resilience in harsh driving situations. Extraneous environmental factors coupled with structured and unstructured uncertainties have always posed concerns about the proposed algorithms’ effectiveness. This paper presents an improved control algorithm based on a robust adaptive neural network trained with integral sliding mode (NN-ISMC) with the capacity for state estimation and auxiliary control inputs. Additionally, the paper considers the contribution of active front steering (AFS) integrated with direct yaw moment control (DYC) to employ during critical driving scenarios. Moreover, a super-twisting ESO disturbance observer (STESO-DO) was developed to estimate disturbances imposed on the car during an emergency maneuver, such as double-lane change, in terms of successive strong gusts of crosswind. Additional uncertainties were introduced to the system to evaluate the algorithm’s robustness, such as the tire cornering stiffness and traveling speed. The performance of the designed framework was further assessed against two documented comparable methods in the literature using high-fidelity MATLAB/Simulink–CarSim co-simulations. The findings from various driving conditions and speeds indicate that the proposed controller successfully stabilizes the handling dynamics and thus enhances the path-tracking performance compared to the previously reported methods.