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.

For ordinary drivers, stabilizing drift during collision avoidance is challenging, potentially leading to serious accidents. To enhance driving safety and human-machine cooperative efficiency under such extreme conditions, this paper presents a human-centered drift-curbing (HCDC) control strategy considering the driver’s acceptance of the driving assistance system’s intervention. Specifically, the acceptance domain (AD), a framework for capturing and describing the driver’s intervention tolerance characteristics, is presented. To constrain the additional steering intervention within the driver’s AD, a soft-intervention (SI) strategy based on steering ratio control is formulated. On this basis, an HCDC controller is developed using nonlinear model predictive control (NMPC), with individualized constraints designed by referring to the driver’s AD. Driver- and controller-in-the-loop verification experiments are conducted in a low adhesion highway collision avoidance scenario prone to drift. The experimental results indicate that the proposed HCDC control strategy provides an acceptable “soft” intervention, enabling drivers to stabilize drift quickly while reducing steering effort.

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.

The increased number of controller area network bus nodes in the four-wheel independent steer-by-wire (4WISBW) system introduces uncertain network communication delays in the steering mechanism’s control inputs, reducing tracking accuracy and synchronization performance. To address this issue, we propose a multi-agent adaptive formation control strategy, comprising a nonlinear time-delay estimator (NTDE) and a multi-agent formation controller (MAFC). The NTDE reduces high-frequency oscillations and steady-state errors in delay estimation using a nonlinear integral sliding surface and a chatter-free supertwisting delay equation, while deriving the implicit time-delay system of the steering mechanism through nonsingular transformations. The MAFC constructs a leader-follower formation topology for the 4WISBW implicit time-delay system and designs adaptive coupling coefficients to dynamically adjust the time-varying formation of steering mechanisms, compensating for tracking and synchronization errors caused by network delays. Hardware-in-the-loop testing validates the proposed strategy’s effectiveness.

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.

The electro-mechanical braking (EMB) system, known for its integration and intelligence, is considered an ideal actuator for vehicles. However, accurate slip ratio tracking is hindered by uncertainties in the road adhesion coefficient and communication delays, which adversely affect anti-lock braking performance. This study proposes an interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy robust$H_{\mathrm {\infty }}$anti-lock braking control strategy to address these challenges. An IT2 T-S fuzzy anti-lock braking system (ABS) model is designed to enhance ABS linearization accuracy under uncertainties in the road adhesion coefficient. The vehicle speed is observed using an IT2 T-S fuzzy observer. A delay-product-type Lyapunov-Krasovskii (L-K) functional robust$H_{\mathrm {\infty }}$anti-lock braking controller is designed. The delay-product-type L-K functional effectively utilizes delay information to reduce the conservatism of the controller, improving the slip ratio tracking performance of the EMB system under input delay conditions. Additionally, a motor torque compensation strategy is introduced to optimize slip ratio control on different roads. Simulations verify that the proposed strategy improves the EMB system’s slip ratio control performance under conditions of uncertainty and input delay.

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 high-level autonomous driving, high-precision map construction is crucial, and map construction based on laser SLAM is one of the mainstream methods. Existing laser SLAM technology usually assumes that the environment is static, ignores dynamic targets, or removes their influence on map construction by directly removing dynamic targets. This paper proposed a factor graph optimization SLAM mapping method for autonomous vehicles considering dynamic target motion, aiming to improve the accuracy and reliability of map construction. First, the kinematic model of the autonomous vehicle was established, and the inertial measurement unit (IMU) and laser radar (LiDAR) were jointly calibrated. The laser point cloud distortion was corrected using the IMU and kinematic model. Then, a semantic spatiotemporal consistency method for dynamic target detection was proposed, and dynamic targets were effectively detected from the laser point cloud through a fully convolutional neural network (FCN), and the estimation of the motion pose of the dynamic target was optimized by combining the improved unscented Kalman filter (UKF). Finally, based on the motion estimation of the dynamic target, the laser point cloud was divided into static and dynamic parts, and the static part was directly registered, while the dynamic part was registered by introducing motion pose compensation. A multi-source asynchronous factor model was constructed through semantic segmentation results, IMU and global positioning system (GPS) data, and a graph optimization method was used for global optimization, which significantly improved the accuracy of map construction and eliminated the negative impact of dynamic targets on map construction.

Compared with other steering systems, Four-Wheel Independent Steering (4WIS) can control not only yaw motion but also their unique side-move motion to provide intelligent vehicles with stronger trajectory tracking capabilities. However, existing 4WIS wheel angle control often aims at zeroing the sideslip angle of the Center of Gravity (CoG), which inhibits the unique side-move motion of 4WIS vehicles. Especially under large-curvature turning conditions, relying only on yaw motion, the system is prone to entering a state of nonlinear instability; its trajectory tracking accuracy and stability are difficult to guarantee. In response, this paper proposes a 4WIS vehicle instantaneous center of rotation (ICR) tracking control method under large-curvature turning conditions. It converts traditional wheel angle control into ICR control. Through the coordinated control of side-move and yaw motion, trajectory tracking accuracy and stability are ensured under large-curvature turning conditions. The proposed ICR tracking control includes two parts: ICR control and trajectory decomposition. ICR control constructs a polar coordinate system with the vehicle CoG as the pole and establishes a decoupled mapping of side-move and yaw motion to the ICR. By predicting and optimizing the deviation between the ICR and its reference, the side-move and yaw motions are coordinated to achieve effective tracking of large curvature trajectories. Trajectory decomposition mainly converts the trajectory tracking target into the ICR control reference. By combining the particle model and trajectory curvature, polar coordinates are used to decompose the trajectory curvature into side-move and yaw curvatures, and through rolling optimization of lateral and heading errors, the target trajectory is decomposed into side-move and yaw control reference. HIL experimental results show that the proposed ICR tracking control strategy can effectively improve the trajectory tracking performance of 4WIS vehicles under large-curvature turning conditions.