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Both active front wheel steering (AFS) and differential braking control (DBC) can improve the vehicle handling and stability. In this article, an AFS strategy and a DBC strategy are proposed and compared. The strategies are as follows: A yaw instability judging module and a rollover instability judging module are put forward to determine whether the coach is in a linear state and whether the additional torque/angle module should be actuated. The additional torque module based on linear quadratic regulator (LQR) and the additional steering wheel angle module based on adaptive proportion integral differential (PID) fuzzy controller are designed to make the actual yaw rate and sideslip angle track the reference yaw rate and sideslip angle. Under some typical driving conditions such as sinusoidal, J-turning, crosswind, and straight-line brake maneuver on the μ-split road, simulation tests are carried out for the coach with no control, DBC strategy, and AFS control, respectively.

With the popularity of electrification and driver assistance systems on vehicle dynamics and controls, the steering performance of the vehicle put forward higher requirements. Thus, the steer-by-wire technology is becoming particularly important. Through specific control algorithm, the steer-by-wire system electronic control unit can receive signals from other sensors on the vehicle, realize the personalized vehicle dynamics control on the basis of understanding the driver’s intention, and grasp the vehicle movement state. At the same time, to make these driver assistance systems better cooperate with human drivers, reduce system frequent false warning, full consideration of mutual adaptation for the systems and the driver’s characteristics is critical. This paper focuses on the steering performance of steer-by-wire vehicle. Feature parameters are obtained from the virtual turning experiment designed on the driving simulator experimental platform.

Aiming at estimating the vehicle mass and the position of center of gravity, an on-line two-stage estimator, based on the recursive least square method, is proposed for buses in this paper. Accurate information of the center of gravity position is crucial to vehicle control, especially for buses whose center of gravity position can be varied substantially because of the payload onboard. Considering that the buses start and stop frequently, the first stage of the estimator determines the bus total mass during acceleration, and the second stage utilizes the recursive least-square methods to estimate the position of the center of gravity during braking. The proposed estimator can be validated by the co-simulation with MATLAB/Simulink and TruckSim software, simulation results exhibit good convergence and stability, so the center of gravity position can be estimated through the proposed method in a certain accuracy range.

At present, the active safety and stability of heavy vehicles have becoming big concern among the road transportation industry. The purpose of this paper is to specify the research stability and safety of heavy vehicles those set up the accurate and reliable dynamic vehicle reference model and search the method to improve the stability and safety of tractor and semitrailer. A Multi-objective control algorithm was studied to differential braking based on linear quadratic regulator (LQR) control method. Simulation results show that the multi-objective control algorithm can effectively improve the vehicle driving stability and safety.

The paper focus on enhancing the braking safety and improving the braking performance of the tractor/trailer vehicle. A slip-rate-based braking force distribution algorithm is proposed for the electronic braking system of tractor/trailer combination vehicle. The algorithm controls the slip-rates of the tractor's rear wheels and the semi-trailer's wheels changing with the slip-rate of tractor's front wheels, making tractor's front wheels lock up ahead of the tractor's rear wheels and the semi-trailer's wheels. The algorithm protects the combination vehicle from jackknifing and swing, guaranteeing that the combination vehicle has better driving stability and steering capability. The algorithm can be tested by co-simulation with MATLAB/Simulink and TruckSim software both on high adhesion and low adhesion roads.