Car rollovers are a class of serious traffic accidents that can easily cause heavy casualties and property damage, particularly for special operation vehicles. To enhance the driving stability of vehicles on forest roads, we developed a control strategy for wire-controlled auxiliary braking based on body-attitude detection and the overall design of the system. Moreover, the control system was further investigated and developed. A three-degrees-of-freedom (3-DOF) vehicle dynamics model with longitudinal, lateral, and lateral tilt was developed based on actual-vehicle test data. The lateral load-transfer rate (LTR) of the vehicle was selected as the early warning algorithm for vehicle rollover; the differential braking of the vehicle was realized by adjusting the pressure of the wheel cylinders; and automatic speed reduction was achieved according to the rollover attitude of the vehicle by combining the fuzzy-PID control algorithm. Finally, a vehicle dynamics model was developed, and the results verified the effectiveness of the anti-rollover control strategy under extreme operating conditions.

Harmonic force and torque, which are caused by rotor imbalance and sensor runout, are the dominant disturbances in active magnetic bearing (AMB) systems. To eliminate the harmonic force and torque, a novel control method based on repetitive control and notch filters is proposed. Firstly, the dynamics of a four radial degrees of freedom AMB system is described, and the AMB model can be described in terms of the translational and rotational motions, respectively. Next, a closed-loop generalized notch filter is utilized to identify the synchronous displacement resulting from the rotor imbalance, and a feed-forward compensation of the synchronous force and torque related to the AMB displacement stiffness is formulated by using the identified synchronous displacement. Then, a plug-in repetitive controller is designed to track the synchronous feed-forward compensation adaptively and to suppress the harmonic vibrations due to the sensor runout. Finally, the proposed control method is verified by simulations and experiments. The control algorithm is insensitive to the parameter variations of the power amplifiers and can precisely suppress the harmonic force and torque. Its practicality stems from its low computational load.

This paper presents a theoretical and experimental study conducted on the rollover warning of wheeled off-road operating vehicles. The time to rollover warning algorithm was studied with real-time vehicle roll angle and roll angle velocity as the input variables, and lateral load transfer ratio was used as the rollover determination index. Subsequently, a vehicle dynamics model was built using CarSim software, and a warning algorithm was established in the MATLAB/Simulink environment. The rollover joint simulation in CarSim and MATLAB/Simulink was conducted under typical working conditions. Finally, combined with inertial measurements, a rollover warning system was independently developed. In addition, the rollover warning system was installed on a light forest firefighting truck to verify the feasibility of the system via a real vehicle experiment, and the law of vehicle rollover motion was also studied. The serpentine experiment and steady-state rotation experiment were conducted. The experimental results showed that at identical front-wheel steering angles, the roll angle and lateral acceleration increased with an increase in the vehicle speed. Furthermore, for identical vehicle speeds, the roll angle and lateral acceleration of the vehicle increased with an increase in the front-wheel steering angle. The dangerous vehicle speed was 50 km h ⁻¹ in the serpentine condition and 40 km h ⁻¹ in the steady-state rotation condition. The risk trend and alarm signal obtained by the rollover warning system were consistent with the actual situation. Thus, this can assist drivers in judging the rollover risk and effectively improve the active safety of special vehicles. Furthermore, it also provides a reference for further research on active rollover control technology of special vehicles.

High-accuracy heading angle is significant for estimating autonomous vehicle attitude. By integrating GNSS (Global Navigation Satellite System) dual antennas, INS (Inertial Navigation System), and a barometer, a GNSS/INS/Barometer fusion method is proposed to improve vehicle heading angle accuracy. An adaptive Kalman filter (AKF) is designed to fuse the INS error and the GNSS measurement. A random sample consensus (RANSAC) method is proposed to improve the initial heading angle accuracy applied to the INS update. The GNSS heading angle obtained by a dual-antenna orientation algorithm is additionally augmented to the measurement variable. Furthermore, the kinematic constraint of zero velocity in the lateral and vertical directions of vehicle movement is used to enhance the accuracy of the measurement model. The heading errors in the open and occluded environment are 0.5418° (RMS) and 0.636° (RMS), which represent reductions of 37.62% and 47.37% compared to the extended Kalman filter (EKF) method, respectively. The experimental results demonstrate that the proposed method effectively improves the vehicle heading angle accuracy.

The track shoe is the key component of the tracked chassis in contact with the ground. In order to improve the passing capacity (traction force) of the tracked chassis in sand, we designed a track shoe for running on sand, inspired by the ostrich-foot. Focusing on the structural parameters of the track shoe, we developed a traction model of the track shoe behavior based on the theory of vehicle terra-mechanics. The finite element analysis of different forms of track shoes was carried out combined with orthogonal experiments, and verified by the soil bin test. The results show that the width of the track shoe and the height of the track grouser are positively correlated with the traction performance of the chassis, and each additional track shoe increases its traction by 30%. The No.1 sand-special track shoe (the design inspiration comes from the sand-gathering function of ostrich-foot.) can improve the traction of the tracked chassis by 41.7%. The tractive force model can well predict the tractive force of the tracked chassis in sand, and the track shoe designed in this paper is helpful to improve the tractive force of the tracked chassis in sandy land.

In the field of autonomous driving progresses, obtaining the precise position and attitude of vehicles through measurement technology is vital. An integrated GNSS/INS/Barometer (Global Navigation Satellite System/Inertial Navigation System/Barometer) fusion method is proposed to improve vehicle positioning and heading angle accuracy. An extended Kalman filter (EKF) based on horizontal dilution of precision (HDOP) is designed to fuse the INS error and the GNSS measurement. Furthermore, GNSS dual antennas are utilized to increase a state variable in EKF based on HDOP in addition to position and velocity. The kinematic constraint of zero velocity in the lateral and vertical directions of vehicle movement is used to measure the mounting angle to reduce error. The position and heading angle accuracy of the proposed method are 0.483 m and 1.358°, which represent reductions of 37.03% and 43.86% compared to the conventional EKF method, respectively. The experimental results demonstrate that the proposed method can measure accurate position and heading angle in both open and occluded environments.