This paper presents a fault-tolerant control (FTC) strategy to improve lateral stability and maneuverability of a four-wheel independently actuated (FWIA) electric ground vehicle with active front steering (AFS). Front wheel steering angle and external yaw moment generated by the tire force difference between the two sides of the motors are adopted to simultaneously regulate the vehicle yaw rate and slip angle. Considering the high cost of currently available sensors for vehicle slip angle measurement, a robust H_∞ dynamic output-feedback controller is designed to control the vehicle, which can be implemented without the slip angle or lateral speed measurement. Both vehicle parameter uncertainties and actuators faults are considered, making the proposed control method robust to the uncertainties and actuator faults. Simulation results based on full-vehicle model in Carsim validate the effectiveness of the proposed control approach.

This paper investigates the experimental study of differential steering control of a four-wheel independently driven (FWID) electric vehicle (EV) based on the steer-by-wire (SBW) system. As each wheel of FWID vehicle can be independently driven, differential steering is realized by applying different driven torques to the front-two wheels. Firstly, the principle of the differential steering is analyzed based on the SBW system. When the differential steering is activated, the driver’s steering request is sent to the vehicle’s ECU. Then, the ECU gives different control signals to the front-left and front-right wheels, generating an external steering force on the steering components. The external steering force pushes the steering components to turn corresponding to the driver’s request. Secondly, to test the feasibility of differential steering, a FWID EV is assembled and the vehicle is equipped with four independently driven in-wheel motors. The corresponding control system is designed. Finally, the field test of the vehicle based on the proposed differential steering control strategy is performed. In the experiment, the fixed yaw rate tracking and varied yaw rate tracking maneuvers are employed. In the fixed yaw rate tracking, the vehicle can track the desired yaw rate well with differential steering. In addition, the vehicle can track the varied yaw rate with proposed differential steering. The test results confirm the feasibility and effectiveness of the differential steering. By using the differential steering, a backup steering is established without additional components; thus, the costs can be reduced and the reliability of the vehicle steering system can be enhanced, significantly.

Four-wheel independently driven electric vehicles are prone to rollover when driving at high speeds on high-adhesion roads and to sideslip on low-adhesion roads, increasing the risks associated with such vehicles. To solve this problem, this study proposes a path tracking and stability-integrated controller based on a model predictive control algorithm. First, a vehicle planar dynamics model and a roll dynamics model are established, and the lateral velocity, yaw rate, roll angle, and roll angle velocity of the vehicle are estimated based on an unscented Kalman filter. The lateral stiffness of the tires is estimated online according to the real-time feedback state of the vehicle. Then, the path tracking controller, roll stability controller, and lateral stability controller are designed. An integrated control strategy is designed for the path tracking and stability, and the conditions and coordination strategies for the vehicle roll and lateral stability state in the path tracking are studied. The simulation results show that the proposed algorithm can effectively limit the lateral load transfer rate on high-adhesion roads and the sideslip angle on low-adhesion roads at high speeds. Hence, the driving stability of the vehicle under different road adhesion coefficients can be ensured and the path tracking performance can be improved.

Accuracy and nonlinear processing capabilities are critical to the positioning and navigation of autonomous vehicles in visual–inertial odometry (VIO). Existing filtering-based VIO methods struggle to deal with strongly nonlinear systems and often exhibit low precision. To this end, this paper proposes a VIO method based on the Schur complement and Iterated Extended Kalman Filtering (IEKF). The algorithm first enhances ORB (Oriented FAST and Rotated BRIEF) features using Multi-Layer Perceptron (MLP) and Transformer architectures to improve feature robustness. It then integrates visual information and Inertial Measurement Unit (IMU) data through IEKF, constructing a complete residual model. The Schur complement is applied during covariance updates to compress the state dimension, improving computational efficiency and significantly enhancing the system’s ability to handle nonlinearities while maintaining real-time performance. Compared to traditional Extended Kalman Filtering (EKF), the proposed method demonstrates stronger stability and accuracy in high-dynamic scenarios. The experimental results show that the algorithm achieves superior state estimation performance on several typical visual–inertial datasets, demonstrating excellent accuracy and robustness.

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