A coordinated strategy is proposed to prevent interference between trajectory tracking control and stability control in wheel-driven autonomous vehicles. A tire cornering stiffness estimate model is developed using the recursive least squares approach with a forgetting factor (FFRLS), resulting in precise estimation of tire cornering stiffness. An adaptive trajectory tracking control is developed, utilizing real-time updates of tire cornering stiffness; for the direct yaw moment required for stability control, an integral sliding-mode control is adopted, and the chatter problem of the integral sliding-mode controller is optimized by a fuzzy controller. The coordinated control of trajectory tracking and vehicle stability is ultimately attained through the application of the normalized stability index. The method’s practicality is validated by the hardware-in-the-loop simulation platform.

Precise and real-time estimation of vehicle mass and road slope plays a pivotal role in attaining accurate vehicle control. Currently, road slope estimation predominantly emphasizes longitudinal slopes, with limited research on intricate slopes that include both longitudinal roads and continuous turning up-and-down slopes. To address the limitations in existing road slope estimation research, this paper puts forward a novel joint-estimation approach for vehicle mass and road slope. Vehicle mass is initially estimated via M-estimation and recursive least squares with a forgetting factor (FFRLS). A road slope estimate approach, which utilizes interacting multiple models (IMM) and cubature Kalman filtering (CKF), is proposed for complex road slope scenarios. This algorithm integrates kinematic and dynamic vehicle models within the multi-model (MM) ensemble of the IMM filter. The kinematic vehicle model is appropriate for longitudinal road gradients, whereas the dynamic vehicle model is better suited for continuous turning up-and-down slope conditions. The IMM filter employs a stochastic process to weight the appropriate vehicle model according to the driving conditions. Consequently, the weights assigned by the IMM filter enable the algorithm to adaptively select the most suitable vehicle model, leading to more accurate slope estimates under complex conditions compared to single-model-based algorithms. Simulations were carried out using Matlab/Simulink2020-Trucksim2020 to verify the effectiveness of the proposed estimation approach. The results demonstrate that, compared with existing methods, the proposed estimation approach has achieved an improvement in the precision of evaluating vehicle mass and road gradient, thus confirming its superiority.

To address the poor stability of four-wheel steering vehicles under extreme conditions, this paper proposes a coordinated control strategy for vehicles with four-wheel independent drive. The strategy combines the Active Four-Wheel Steering system with the Direct Yaw Moment Control system. First, a shared steering control model is constructed by considering both the vehicle’s path-tracking performance and handling stability. Based on this model, a control strategy for the four-wheel steering system is proposed using a non-cooperative Nash game. Next, a direct yaw moment controller is designed to improve vehicle lateral stability under dangerous driving conditions. To achieve synergy between rear-wheel steering and direct yaw moment control, a rule-based coordination strategy is introduced to optimize the working intervals of each sub-controller. Finally, experimental verification is performed under double-lane-change and slalom conditions using the CarSim/Simulink hardware-in-the-loop platform. All computations were done in MATLAB R2024a, using specific m-files and Simulink functions for implementation, and the controller was implemented using the Micro-Autobox tool. The results demonstrate that the proposed control strategy significantly enhances vehicle path-tracking accuracy and handling stability under extreme driving conditions.

To address the coupled challenges of tire dynamic load regulation and ride comfort improvement in hydrogen-powered heavy-duty trucks (HPHDTs) under non-stationary road excitations, this study evaluates a magnetorheological (MR) damper-based semi-active front suspension system. A vehicle–road coupled dynamic simulation model was developed in MATLAB/Simulink (R2025b) using a Class C road profile, and three representative driving conditions, namely acceleration, deceleration, and constant-speed driving, were considered. Four control strategies, namely, interval type-2 (IT2) fuzzy control, type-1 (T1) fuzzy control, skyhook control, and PID control, were comparatively investigated. The results indicate that deceleration is the most critical operating condition, resulting in more severe tire–road interactions and poorer ride comfort than the other scenarios. Among the evaluated strategies, IT2 fuzzy control provides the best overall performance. Compared with the passive suspension, it reduces the front-wheel RMS dynamic load by 63.39% and improves ride comfort by 64.67% under deceleration. The T1 fuzzy and PID controllers provide moderate improvements, whereas skyhook control exhibits relatively limited effectiveness. These findings demonstrate that combining MR dampers with IT2 fuzzy control provides a feasible and robust approach for improving road friendliness, ride quality, and operational stability in advanced heavy-duty vehicle suspension design.

To enhance the trajectory tracking accuracy and lateral stability of distributed-drive electric vehicles, a hierarchical cooperative control strategy optimized by the Genetic–Firefly Algorithm (G-FA) is proposed. First, bottom-level controllers for trajectory tracking utilizing a Linear Quadratic Regulator (LQR) and stability relying on Sliding Mode Control (SMC) are jointly optimized offline using the G-FA to address the limitations of empirical parameter tuning and effectively mitigate chattering. Compared to traditional Nonlinear Model Predictive Control (NMPC), which relies on computationally demanding dynamic programming, the proposed G-FA acts as an efficient approximate optimization method that significantly reduces the online computational burden while maintaining high control accuracy. Second, an adaptive cooperative mechanism based on desired yaw rate correction is introduced. By constructing two reference benchmarks—“tracking-oriented” and “stability-oriented”—a cooperative weighting coefficient adapts the fusion of control objectives based on the vehicle’s stability state. Hardware-in-the-loop (HIL) simulation results demonstrate that, under high-adhesion double lane change maneuvers, the proposed strategy reduces peak lateral error and sideslip angle by 31.53% and 28.08%, respectively, compared to traditional LQR. In low-adhesion S-curve limit maneuvers, where traditional LQR fails, the proposed strategy outperforms the NMPC benchmark, further reducing these indices by 61.98% and 8.33%, respectively, significantly improving control performance under extreme conditions.

Achieving simultaneous trajectory accuracy and dynamic stability is challenging for four-wheel independent drive (4WID) electric vehicles under near-limit conditions. To effectively resolve this internal control conflict, this paper proposes a novel normalized stability index that accurately quantifies real-time instability risks. Based on this index, a hierarchical adaptive coordinated control architecture is developed, utilizing sliding-mode control for active front-wheel steering to follow trajectories and a fuzzy-logic yaw moment controller to maintain stability. To prevent over-control in safe driving regions, an adaptive weighting mechanism seamlessly adjusts the stability interventions according to the proposed index. Hardware-in-the-loop (HIL) experiments demonstrate that the proposed method lowers sideslip risks on low-adhesion tracks. During a variable-curvature slalom, it reduces the lateral RMSE by 15.08% and decreases the maximum additional yaw moment from 118 N·m to 32 N·m, thereby mitigating excessive control effort, minimizing steering conflicts, and structurally improving the actuation efficiency of the 4WID system.