The development of intelligent driving and steer-by-wire (SBW) technologies has increased the computational complexity and the number of CAN bus nodes in SBW vehicles, leading to the presence of significant time-varying CAN network time delay (TV-CAN-TD) in the control inputs of the SBW system. For dual-motor steer-by-wire (DMSBW) vehicles, TV-CAN-TD not only severely affects vehicle stability but also further deteriorates the synchronization performance of two motors, potentially causing accidents. To ensure vehicle safety under TV-CAN-TD conditions, a hierarchical time delay control strategy is proposed. For the upper-level controller, a TD-H₂/H∞ mixed sensitivity stability controller is designed. Based on the operational mechanism of the CAN network, the random time delay uncertainties in the vehicle's lateral dynamics model are modeled in detail using a polytope model. Combined with H₂/H∞ robust control, the controller mitigates the adverse effects of time delay uncertainties on vehicle stability. For the lower-level controller, a synchronization strategy that combines the novel reaching law sliding mode control with a cross-coupling synchronization structure is designed to ensure synchronization performance under TV-CAN-TD conditions. Simulations and hardware-in-the-loop experiments demonstrate that the proposed hierarchical control strategy effectively enhances the stability and synchronization performance of the DMSBW vehicle under TV-CAN-TD.

To enhance path-tracking accuracy for four-wheel independent steering and drive (4WIS-4WID) vehicles on high-curvature paths under model mismatch uncertainties and potential actuator faults, this paper proposes a hierarchical robust control strategy consisting of a pseudo-control law solution layer and an over-actuated robust control allocation layer. The pseudo-control layer develops an enhanced tube MPC framework with a novel state-error-driven adaptive terminal constraint set, deriving a robust control law resistant to state matrix uncertainties. Addressing deviations induced by unknown real-time perturbations in the control matrix during angle and torque allocation, the subsequent allocation layer introduces an innovative convex quadratic cone optimization (CQCO) methodology. This approach reformulates the uncertain control allocation problem into a deterministic linear cone quadratic optimization problem, effectively suppressing time-varying perturbations and accommodating partial actuator faults to minimize deviation from the desired pseudo-control law. Hardware-in-the-loop experiments demonstrate that proposed method achieves significantly superior tracking performance under uncertainty and fault conditions compared to conventional approaches.

The dual-motor steer-by-wire (DMSBW) system significantly enhances the reliability of vehicle steering systems due to its high redundancy. However, in practical steering control, the limited bandwidth of the in-vehicle controller area network (CAN) communication network often leads to issues such as packet loss and congestion. These issues can cause asynchronous time delays (ATDs) in the control signals transmitted to the dual motors, resulting in inconsistent motor angles, which severely affects the tracking accuracy of the vehicle. To address the problem, this article proposes an event-triggered neural network (NN) dynamic surface consistency tracking control strategy for DMSBW systems. First, a nonlinear model of the DMSBW system with ATD is established, with the system’s nonlinearities approximated using NNs. Then, an event-triggered dynamic surface consistency controller is designed, incorporating an auxiliary variable and event-triggering mechanism within the backstepping control framework to mitigate the effects of ATD and limited bandwidth. Additionally, the use of dynamic surface and error compensation techniques reduces the computational burden and accuracy degradation caused by the iterative derivation in backstepping control. The introduction of the dual-motor consistency errors and finite-time terms ensures fast convergence and tracking consistency of the system. Finally, hardware-in-the-loop experiments demonstrate that the proposed control strategy improves the DMSBW consistency by 45% and reduces communication load by 53.7%, effectively ensuring consistency tracking accuracy under ATD while alleviating communication burdens.

During high-speed platooning, if the leading vehicle experiences sudden failure (e.g., tire blowout or braking loss), the following vehicles must execute simultaneous deceleration and obstacle avoidance within a minimal timeframe. This process induces significant load transfer, leading to drastic variations in vertical load distribution and tire cornering stiffness, thereby critically compromising lateral stability. Conventional planning methods, reliant on static stability boundaries and fixed rollover thresholds, fail to adapt to such dynamic perturbations, resulting in degraded control performance. This paper proposes an intelligent trajectory planning framework for emergency obstacle avoidance in heavy-duty truck platoons. The framework incorporates a physics-informed neural network to predict real-time load transfer effects on tire cornering stiffness, combined with an Energy-based Rollover Index (ERI) for dynamic stability boundary assessment. Innovatively integrating the nonlinear effects of braking-induced load transfer into coordinated trajectory-speed optimization, the method enhances platoon maneuverability while ensuring safety. Simulation results demonstrate that the proposed approach increases maximum safe lane-change speeds by 0.5%-4.3% while reducing false rollover alarms to below 2%. Co-simulation using MATLAB/Simulink and TruckSim verifies a 4.2% improvement in overall planning performance while maintaining dynamic safety standards.

The integrated wheel-end module (IWEM) incorporates both driving and steering functions at the wheel end, enabling independent control of each wheel and significantly enhancing the vehicle’s agility and stability. However, during steering maneuvers, road excitations and variations in wheel motion may lead to air-gap eccentricity (AGE) in the in-wheel motor, resulting in the unbalanced radial force and torque (URF/URT). These disturbances degrade the tracking accuracy of the wheel-end module and subsequently impair the vehicle’s steering performance. To address this issue, a control-oriented model of the IWEM is developed, incorporating the coupled characteristics of longitudinal slip and lateral slip angle of the tire. Furthermore, an interval type-II fuzzy logic (IT2FL) driven self-tuning tube model predictive control strategy is proposed. First, the generation mechanism of URF/URT under AGE is analyzed in depth. To mitigate the adverse effects of these uncertain disturbances on the wheel-end dynamics, an online observation method based on IT2FL systems is designed to enable real-time estimation of URF/URT and its perturbation bounds. On this basis, a self-tuning tube model predictive controller is developed to dynamically adjust the Tube boundaries according to the real-time observed disturbance range, effectively reducing control conservatism and improving tracking accuracy. Hardware-in-the-loop simulations and real-vehicle experiments validate that the proposed method can effectively suppress the adverse effects caused by AGE and significantly enhance the tracking control performance of the integrated wheel-end system.

Four-Wheel Independent Steering (4WIS) vehicles can independently control each wheel angle to achieve multiple steering modes, thereby expanding their motion space. However, existing trajectories do not match the multiple steering modes, making it difficult to leverage the stability of same-direction steering and the agility of opposite-direction steering of the front and rear wheels. This results in 4WIS vehicles facing challenges in generating trajectories that balance flexibility and stability under emergency obstacle avoidance scenarios. To address this, this manuscript proposes a multi-mode trajectory planning method for 4WIS vehicles under emergency obstacle avoidance scenarios. It decomposes the multiple steering modes of 4WIS vehicles into stable side-move motion and flexible yaw motion. By integrating these two motions, we establish a comprehensive kinematic model for 4WIS multi-mode operation. Based on this, the method combines side-move and yaw motions to construct a multi-mode risk field. The field is then iteratively optimized by the ILQR-MPC method to generate obstacle avoidance trajectories with sideslip and yaw angles, providing reference for the side-move and yaw motions across the multiple steering modes. Results show that the proposed method can generate trajectories that balance flexibility and stability, providing a foundation for safe and efficient obstacle avoidance.

We will apply dual three-phase permanent magnet synchronous motors (DTP-PMSM) to the road sensing unit of the steer-by-wire system to improve redundancy and fault tolerance performance, meeting the requirements of intelligent transportation systems. However, open circuit faults inevitably lead to periodic torque ripple, parameter mismatch, and current harmonics, which pose a huge threat to drivers and traffic participants. Therefore, this article proposes a dynamic balancing strategy that can operate stably under both motor health and fault conditions. Firstly, this strategy does not require diagnostic fault information, thereby fundamentally avoiding performance degradation caused by diagnostic accuracy, time, model reconstruction, and strategy switching. The torque control module (TCM) is based on adaptive iterative learning control with a forgetting factor, which achieves torque tracking and suppression of periodic ripple after faults. The current control module (CCM) is based on ultra local model free predictive current control and extended state observer to track current and suppress the effects of parameter mismatch and current harmonics. Based on the multi-variable multi-objective sliding mode extreme value search strategy, the internal parameters and external weights of the two modules are dynamically adjusted to achieve dynamic balance between health and fault operation of TCM and CCM. The experimental results show that this strategy effectively improves the robustness of the system in a healthy state and the fault tolerance performance in a faulty state, effectively suppressing the periodic torque ripple and current harmonics caused by faults. Compared with the dynamic balancing strategy based on NSGA-II, this strategy increases the THD of torque by 13.45% and reduces the THD of current harmonics by 9.83%.

In order to promote the safety redundancy of steer-by-wire system, a dual-motor steer-by-wire (DMSBW) system is designed in this paper. However, when one of the steering motors fails seriously, the vehicle's angle tracking performance is affected, which is exacerbated by load fluctuation and limited CAN communication bandwidth in the control system. Therefore, we innovatively establish DMSBW switching control model and propose an event-triggered adaptive fuzzy switching fault-tolerant control strategy. Firstly, the interval type-2 fuzzy state observer is used to estimate the state of the nonlinear DMSBW system and the time-varying fluctuation of the load torque before and after switching. Then, the prescribed performance adaptive backstepping controller is designed to solve the control output by fusing the error and fault event-triggered controller to realize the unification of switching fault tolerance and accurate tracking while reducing communication resources. And the switching fault-tolerant performance under both transient and steady state is guaranteed by proving Lyapunov stability and boundedness. Finally, hardware-in-the-loop experiments show that the proposed method is effective.