In order to improve the lateral and directional performance of vehicles, this paper proposes a novel rear-steering based decentralized control (RDC) algorithm for four-wheel-steering (4WS) vehicles. Based on a linearized single-track vehicle model, a prototype of the RDC algorithm for linear conditions is derived. Linear analysis in frequency domain shows that the RDC can significantly reduce the magnitude and increase the phase lag of sideslip response as well as enlarge the bandwidth of yaw response in a broad frequency range. Through the incorporation of a novel sliding mode controller, the RDC algorithm is further extended for nonlinear applications. Analysis based on nonlinear model shows that, the RDC can guarantee the performance of the closed-loop (CL) system in the presence of uncertainties in system parameters and state feedback values. Moreover, the RDC is robust to arbitrary lateral disturbances and can guarantee that the vehicle converges to reference yaw rate and zero sideslip. Simulation results show that the RDC can effectively improve the lateral and directional performance of the vehicle in comparison with previous control methods. Excellent robustness to external disturbance and road friction variation is reported as well.

This article proposes an innovative controllable seat suspension with a long stroke negative stiffness (NS) spring for reducing the whole-body vibration of heavy vehicle drivers. The proposed seat suspension is mainly composed of a NS magnetic spring (NSMS), a coil spring, and an electromagnetic damper for semiactive vibration control. In this way, the system characteristic induced by the NSMS can be maximally utilized to improve ride comfort. Property tests are conducted to validate the mathematical modeling and the constructed system stiffness characteristic. Based on the derived dynamic model, a prescribed performance control technique with closed-loop stability is employed to guarantee both the transient and steady-state responses under the predefined levels. Experiments are implemented where the transient and random vibration signals are applied on the seat suspension base. The results illustrate that when compared with a commercial seat product, an increasing convergence rate by 16% and a decreasing frequency-weighted root-mean-square value by 34% are achieved.

This paper presents a kinetic dynamic suspension (KDS) system to achieve enhanced cooperative control of the roll and warp motion modes for on-road and off-road sports utility vehicles (SUV). The proposed KDS system consists of two hydraulic circuits acting on two pairs of torsional rods and levers, which can be treated as novel anti-roll bars. Hence, these anti-roll bars do not work independently, but are coupled to merely respond to particular motion modes. To verify the handling and ride performance of the system, a 14-DOF model of a SUV and a “magic formula” tire model are developed. The dynamic responses of the vehicle model with KDS suspension are obtained through half-sine bump, asynchronous sine road, and fishhook maneuver simulations. The responses of the KDS equipped vehicle are compared to those of one with anti-roll bars to demonstrate its improved performance and also illustrate the side-effects. The results show that the KDS system considerably improves the vehicle’s anti-roll ability. Furthermore, the vehicle’s warp stiffness is significantly reduced by the KDS system, which enhances the vertical load distribution of each wheel when driving off-road.

This paper proposes an innovative hierarchical direct yaw moment control strategy consisting of upper, middle and lower controllers. In the upper layer, a linear quadratic regulator metric based on current side-slip angle and predicted yaw rate is established to generate the controlled yaw moment. The middle layer determines the actuating tyre forces and allocates the required longitudinal forces for each tyre according to the current tyre–road contact condition. Furthermore, the desired longitudinal slip ratios for each tyre are calculated in the middle layer. Finally, a suitable brake pressure is achieved by the sliding mode controller in the lower layer. The simulation results of sine with dwell and double lane change verify the effectiveness of the proposed method. Compared with a traditional direct yaw moment control strategy that preferentially brakes the priority wheel, the proposed novel strategy is able to keep the longitudinal force of the tyre working in a linear region and has better robustness response when the tyre–road contact condition encounters sudden change.

Precisely assessing driving threat of road segments could significantly enhance the driving efficiency of intelligent connected vehicles (ICV) within mixed traffic scenarios. Existing methods primarily concentrate on collision probabilities, resulting in an insufficient appraisal of asymmetrical hazard levels attributed to the various interactions. Meanwhile, the uncertainty and communication delay have great influence on ICV, and it is an issue that must be addressed when designing decision-making and planning model. Thus, this study proposes and formulates a new driving aggressiveness model after analyzing asymmetric interactions behaviors among vehicles with different types. Subsequently, aims to verify the capability of generating asymmetric interaction, the driving aggressiveness model is applied on lane-change decision-making and planning. Concretely, the aggressiveness-sensitive lanes-selection model is designed based on game theory, and the uncertainty-aware trajectory planning is developed by utilizing stochastic model predictive control (MPC) and the asymmetric driving aggressiveness. Finally, two naturalistic driving scenarios are utilized to verify the performance of the decision-making and planning model. The outcomes of simulations illustrate that the driving aggressiveness model introduces a novel perspective to assess the asymmetric driving threat. Meanwhile, the uncertainty-aware decision making and planning model can reduce the influence on interactive vehicles, and it has superior adaptability for dynamic and connected traffic environments.

To enhance riding comfort for drivers of heavy-duty vehicles, this paper introduces a novel adaptive prescribed performance control (APPC) for active seat suspension systems. The model incorporates dynamic friction and hysteresis damping effects to capture the complex behavior of the seat suspension. The accuracy of the proposed model is validated through experimental data. The controller utilizes a prescribed performance function (PPF) to regulate the dynamic response of the system, combined with an adaptive backstepping control (ABC) method to account for system uncertainties, such as variations in driver weight, friction, suspension stiffness, and damping coefficients. A set of parameter estimators, governed by innovative adaptive laws, compensates for estimation errors. Furthermore, the stability of the controlled system is rigorously demonstrated. Both simulation and experimental tests, including bump and random excitation tests, are conducted to assess the controller performance in both time and frequency domains. The results confirm that the proposed controller effectively mitigates vibrations in the driver–seat system and demonstrates robustness against system uncertainties.