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To solve the problems of serious buffeting in traditional sliding mode control and difficulty in obtaining the derivative information of the system sliding mode surface, a distributed drive electric vehicles active front steering (AFS) control method based on adaptive super-twisting sliding mode control (ASTSMC) is proposed. Taking the yaw rate deviation as the state quantity, the stable and convergent sliding mode surface is designed to obtain the equivalent control input of the front wheel angle. The sliding mode function information is substituted into the parameters of the super-twisting algorithm; the discontinuous term is kept in the integrand function to keep the control signal continuous and weaken the system chattering; the adaptive control law is added to design the AFS controller. The co-simulation results of Matlab/Simulink and Carsim show that ASTSMC can reduce the yaw rate by 40.59% compared with no control under step steering condition. Compared with the sliding mode controller, ASTSMC has optimized the sideslip angle by 5.41% under the double line shifting condition. [ABSTRACT FROM AUTHOR]

To cope with the challenges of inadequate precision and weak robustness of tire–road friction coefficient (TRFC) and vehicle state estimation under mixed Gaussian noise circumstances, this research puts forward a method combining maximum correntropy criterion (MCC) and generalized high-degree cubature Kalman filter (GHCKF). A coupled lateral and longitudinal vehicle model and the Dugoff tire model are established. The vehicle mass is estimated using the recursive least squares approach based on a forgetting factor (FFRLS). Low-cost onboard sensors are utilized to design observers for vehicle state and TRFC. The observers’ effectiveness is tested by Carsim/Simulink co-simulation tests under the conditions of sine steering input and steering wheel angle step input. The research results indicate that in handling mixed Gaussian noise environments, maximum correntropy generalized high-degree cubature Kalman filter (MCGHCKF) method outperforms maximum correntropy cubature Kalman filter (MCCKF), GHCKF, and cubature Kalman filter (CKF) methods concerning robustness and estimation accuracy, providing stable and reliable support for systems of vehicle active safety control. [ABSTRACT FROM AUTHOR]

To coordinate the accuracy and driving stability of intelligent automobile in the path tracking process and improve the adaptive capability of the control algorithm to different working conditions, an intelligent automobile path tracking control method based on T-S fuzzy is proposed. First, the lateral deviation and heading angle deviation during tracking are considered, and the path tracking error equation is established using a 2 degree-of-freedom single-track dynamic model. Second, an adaptive preview algorithm based on vehicle speed, reference path curvature and heading angle deviation is designed, and feedforward control is designed based on the results of the algorithm. Then, the T-S fuzzy control method with fast decision-making capability is utilized to realize the adaptive adjustment of the weight coefficients of the linear quadratic regulation (LQR) controller to adapt to the variable weight path tracking control under different working conditions. Finally, the designed control method is tested on a double-lane road condition using the Carsim-Simulink co-simulation platform. The results show that the designed controller has high tracking accuracy, and can maintain good accuracy and driving stability under different working conditions. [ABSTRACT FROM AUTHOR]