Search Results (107)

Brake-by-wire (BBW) systems face challenges such as structural complexity, high energy consumption, and control inaccuracies induced by nonlinear factors. This study develops a novel self-locking dual-side synchronously clamping electronic wedge brake (EWB) system as an advanced BBW architecture. This novel design consists of a single screw with opposite-handed threads to drive the wedge mechanism bidirectionally, leveraging the self-energizing effect and the self-interlocking effect to significantly reduce energy consumption while achieving hydraulic-free synchronous braking. Additionally, the inherent precise displacement control of the screw transmission offers a simplified solution for air gap management. A multi-domain coupled model integrating mechanical dynamics and control algorithms is developed based on the proposed architecture, with finite element analysis (FEA) validating the mechanical strength and thermal degradation resistance of key components under extreme conditions. A hybrid control algorithm combining model predictive current control (MPCC) and a PI controller is developed. Compared with the active disturbance rejection control (ADRC), the proposed method achieves a 55% improvement in dynamic response and a $69.1$% reduction in steady-state error. The vehicle braking performance is validated through a CarSim–Simulink co-simulation, while the rapid dynamic response and precise clamping force control of the key actuator are verified via bench testing, demonstrating the effectiveness of the proposed EWB system architecture and its control strategy, thereby laying a solid theoretical foundation for its future industrial implementation. Full article

To address the issue of vehicle instability and veering during braking when a single wheel fails in an electric vehicle’s electromechanical braking (EMB) system, an integrated application-oriented control framework based on adaptive sliding mode control (ASMC) is proposed. To address the shortcomings of SMC—such as difficulty in suppressing oscillations and the high workload associated with parameter tuning—a novel composite reaching law function was designed, and the TD3 algorithm was employed to optimize the sliding mode control parameters. When a failure in the EMB system is detected, the upper-layer control uses an improved ASMC algorithm to calculate the vehicle’s additional yaw moment. The lower-layer control employs an optimal control algorithm to distribute braking force, taking into account braking intensity, yaw moment, and tire utilization. This approach is integrated with sliding mode steering control to enhance vehicle stability during braking. To meet the driver’s braking requirements, a backpropagation (BP) neural network is first employed to identify braking intent. Based on this, the additional yaw moment is calculated by the upper-layer controller, and the brake force distribution is optimized through the lower-layer controller, thereby improving the vehicle’s stability. Through co-simulation analysis using Simulink-2024a and CarSim-2019.1, the results show that, compared to traditional algorithms, the proposed hierarchical control strategy reduced the maximum sideslip angle by 51.4%, decreased the maximum yaw rate by 47.2%, and reduced the maximum lateral offset by 45.6%. This control strategy enables enhanced stability across various braking intensity conditions. Full article

To address the emergency obstacle-avoidance problem of intelligent vehicles on structured roads, this paper proposes an integrated planning and control method that combines an improved Artificial Potential Field (APF) with fuzzy Model Predictive Control (MPC). Different from a direct APF + MPC combination, the planning layer introduces a braking-distance threshold, an effective obstacle-influence boundary, and sinusoidal shape factors to reshape the obstacle repulsive field and alleviate local-minimum behavior. A seventh-order polynomial smoothing strategy is then adopted to generate a reference path with higher-order continuity. For trajectory tracking, a fuzzy adaptive MPC controller adjusts the prediction horizon and control horizon online according to lateral error, while a fuzzy PID controller regulates longitudinal speed. MATLAB/Simulink and CarSim co-simulation results in single-static, double-static, and double-dynamic obstacle scenarios show that the proposed method can generate smoother trajectories and achieve more stable tracking, thereby improving obstacle-avoidance safety and ride comfort. In the double-static scenario, the peak lateral error is reduced from about 0.7 m to within 0.1 m, while in the double-dynamic scenario the longitudinal speed is maintained within 78–80 km/h instead of dropping to about 67 km/h under the baseline controller. The study provides a practical technical framework for integrated decision-planning-control design in structured-road intelligent vehicles. Full article

To ensure reliable lane change behavior in-wheel motor-driven electric vehicles (IWM-EVs) under steer-by-wire (SBW) failure, this paper presents an integrated lateral–longitudinal lane change control strategy based on differential steering. The control framework and relevant models are first established. An upper-layer model predictive control (MPC) controller is then designed to simultaneously achieve lateral path tracking and longitudinal speed regulation, outputting the desired front-wheel steering angle and acceleration. Finally, a model-free adaptive control (MFAC)-based lower-layer lateral controller transforms the desired steering angle into differential driving torques for the front wheels, while a feedforward–feedback lower-layer longitudinal controller (incorporating drive/brake switching and PI control) computes the required driving torque or braking pressure. Co-simulation in Matlab/Simulink R2022b and CarSim R2020 reveals that the MPC controller designed in this study outperforms the LQR-PID controller, reducing the maximum absolute values of lateral error, heading error, front-wheel steering angle, yaw rate and sideslip angle by 42.9%, 50.0%, 7.8%, 2.8% and 10.3%. The proposed hierarchical control strategy outperforms the compared hierarchical controller, reducing the maximum absolute values of the lateral displacement error, heading error and yaw rate by 17.9%, 6.7%, and 33.3%. These results verify that the strategy can improve trajectory tracking accuracy and achieve basic differential steering functionality in specific scenarios. Full article

Considering the problems that the inner wheel load decreases due to centrifugal force during the steering of differential steering autonomous vehicles, which may result in differential steering failure or even vehicle rollover in severe cases, a path-tracking strategy for differential steering autonomous vehicles considering active body inward tilt is proposed. Aiming at the problems of fixed parameters and insufficient adaptability of model predictive control in the path-tracking process of autonomous vehicles, this paper proposes a collaborative adaptive model predictive controller (MPC) with preview time and weight matrix based on fuzzy inference as the upper control, so as to realize the tracking control of the reference path by conventionally steered autonomous vehicles. In the lower control, an H_∞/H₂ hybrid controller with particle swarm optimization (PSO)-based parameter self-tuning is employed to control the differential steering autonomous vehicle (DSAV) to track the reference model, achieving differential steering and active body inward tilt simultaneously. Co-simulation results of CarSim and Simulink show that the proposed method outperforms the fixed-preview-time MPC and the manually tuned H_∞/H₂ hybrid controller. Compared with the latter, the maximum absolute values of lateral deviation and yaw angle deviation are reduced by 17.9% and 14.5%, respectively; the maximum deviation in the reference yaw rate is decreased by 21.2%; the maximum absolute value of the inward tilt angle is reduced by 53.4%; and the maximum values of LTR and occupant-perceived lateral acceleration are lowered by 57.1% and 44.2%, respectively. Full article

Potholes are typical negative road obstacles that can significantly compromise vehicle safety and ride comfort when traversed at inappropriate speeds. To address this issue, this paper proposes a pothole-detection-based, comfort-oriented pothole traversal algorithm that integrates multi-sensor fusion perception, comfort-constrained speed planning, and fuzzy control. A camera and a single-point ranging LiDAR are first fused to extract key geometric features of potholes, including contour, area, and depth. Based on these features, a vehicle–pothole dynamic model is developed in ADAMS to quantify the influence of pothole area and depth on vehicle vertical vibration. The vertical frequency-weighted root-mean-square (RMS) acceleration is adopted as the ride comfort indicator, based on which the maximum allowable traversal speed under different pothole geometries is determined. Furthermore, a longitudinal pothole traversal control strategy based on fuzzy theory is designed to regulate vehicle acceleration, enabling the vehicle to reach the comfort-constrained limiting speed within a finite preview distance while ensuring braking safety. The proposed method is validated through multi-scenario co-simulations using MATLAB/Simulink and CarSim, as well as real-vehicle experiments. Results demonstrate that the proposed strategy can effectively adjust vehicle speed before pothole traversal, satisfying comfort constraints and improving ride comfort without sacrificing driving safety. Full article

This paper proposes a hierarchical control framework designed to enhance the path tracking accuracy of intelligent electric vehicles under diverse operating conditions. For lateral control, an improved model predictive control strategy is developed, utilizing a fuzzy inference system for parameter initialization and a Deep Deterministic Policy Gradient algorithm for online adaptive tuning. For longitudinal control, a proportional–integral–derivative controller is optimized via a hybrid genetic algorithm–particle swarm optimization method. Co-simulations conducted in CarSim/Simulink under straight-line, double-lane-change, and double-sine-wave maneuvers demonstrate that the proposed framework significantly reduces lateral deviation and heading error while ensuring smoother actuator response. Compared to conventional MPC and PID controllers, the proposed method reduces maximum lateral error by over 50% and settling time by 60%, confirming its effectiveness and robustness in complex tracking scenarios. Full article

Single-wheel brake failure in electromechanical brake (EMB) systems breaks the left-right symmetry of wheel forces and yaw moments, creating a critical conflict between emergency braking effectiveness and lateral stability. To address this symmetry-breaking condition, this paper proposes a bimodal, adaptive, coordinated fault-tolerant control strategy that integrates dynamic brake torque redistribution with active front steering (AFS). A novel dynamic interaction model linking deceleration demand with tire adhesion utilization enables real-time assessment and optimization of the balance between longitudinal braking performance and yaw stability. Braking forces are allocated based on adhesion utilization through a layered two-mode strategy—balanced distribution prioritizing lateral stability and compensatory distribution engaging the healthy front wheel when rear axle capacity is exceeded. An integral sliding-mode controller computes the additional yaw moment needed to suppress yaw-rate deviation, with rigorous Lyapunov stability analysis confirming closed-loop stability. AFS is triggered only when yaw-rate deviation exceeds 0.05 rad/s or adhesion utilization reaches 90%, incorporating hysteresis to ensure smooth transitions and minimize unnecessary steering intervention. Comprehensive co-simulations using Carsim and MATLAB/Simulink under diverse failure locations (left-front and right-rear wheels), road adhesion levels ($\mu$ = 0.85 and 0.5), and braking intensities (0.2 g–0.6 g) demonstrate that the proposed strategy reduces lateral displacement by up to 85.3% compared to full-time AFS control while maintaining over 99% deceleration satisfaction. The results establish an effective dual-objective fault-tolerant framework that enhances both robustness and functional safety of EMB systems under symmetry-breaking faults, offering a physically interpretable, computationally efficient solution well-suited for real-time automotive applications. Full article

High-performance speed regulation of interior permanent magnet synchronous motor (IPMSM) drives in electric vehicle (EV) applications becomes particularly challenging in the field-weakening region, where voltage constraints, parameter variations, and nonlinear aerodynamic loads significantly affect the closed-loop stability. To address these challenges, this paper proposes a stability-aware adaptive fractional-order speed control framework for EV traction systems. The framework integrates a fractional-order PI (FOPI) core to provide iso-damping robustness, a bounded fuzzy gain-scheduling mechanism for real-time adaptation, and an offline multi-objective optimization layer for systematic parameter tuning. A Lyapunov-based qualitative analysis is provided to justify closed-loop ultimate boundedness under adaptive gain modulation and field-weakening constraints. The fuzzy scheduler is explicitly structured to regulate the error energy dissipation rate by modulating the proportional and integral gains while preserving the gain boundedness. The controller parameters are optimized using a diversity-driven fractional-order multi-objective PSO algorithm to balance the tracking accuracy and control effort. The proposed framework was validated using a high-fidelity MATLAB/Simulink–CarSim 2023 co-simulation platform under the aggressive US06 driving cycle. The results demonstrated a zero-overshoot transient response, robustness against a 2.5× inertia mismatch, and sustained performance under flux-linkage and inductance variations in deep field-weakening operation. Compared with conventional PI-based strategies, the proposed approach reduced the speed RMSE by 82%, lowered the current THD from 18.5% to 3.2%, and reduced the cumulative DC-link current-squared index by 6.7%. These results validate the practical robustness and computational feasibility of the proposed stability-aware framework for EV traction control. Full article

High-performance speed control of Permanent Magnet Synchronous Motor (PMSM) drives in Electric Vehicle (EV) applications faces significant challenges due to inherent nonlinearities, parameter variations, and signal non-idealities such as sensor noise and measurement latency. To address these issues, this paper proposes a robust PI-based Fractional-Order PSO-Fuzzy Weight Controller (PI-FOPSOFWC). The proposed strategy integrates a fractional-order PI (FOPI) core to ensure iso-damping robustness, a fuzzy inference mechanism for online gain scheduling against nonlinear load dynamics, and a novel Fractional-Order Particle Swarm Optimization (FOPSO) algorithm for optimal parameter tuning. A key contribution of this study is the validation of the control strategy within a high-fidelity co-simulation framework coupling MATLAB/Simulink with CarSim 2023, which incorporates realistic vehicle dynamics and time-varying road loads unavailable in conventional simplified simulations. Co-simulation results demonstrate that the proposed controller effectively eliminates overshoot in step responses and maintains stability under significant parameter mismatches ($2.0\times$ inertia). Furthermore, under the EPA urban driving cycle, the proposed method reduces the speed tracking Root Mean Square Error (RMSE) by 75.0% compared to the standard PI controller. Computational complexity analysis further confirms the feasibility of the proposed algorithm for real-time implementation in commercial EV traction drives. Full article

With the rapid development of autonomous driving, path tracking has emerged as a pivotal research direction. Model predictive control (MPC) has become one of the most prevailing approaches for path tracking, owing to its superior capacity in dealing with multi-constrained control problems and compatibility with the symmetry of vehicle dynamic systems. Nevertheless, conventional MPC suffers from performance degradation in path tracking when vehicle localization drift occurs, referring to the noticeable deviation between sensor-measured position and actual physical position over time, which is mainly induced by sensor noise and outliers. To overcome these limitations and enhance the accuracy and stability of path tracking, this paper presents a location-adaptive model predictive control framework. Specifically, a supervisor is designed to detect localization drift, and a Runge–Kutta-based location estimator is activated to predict the current vehicle state once drift is identified. Furthermore, a linear time-varying MPC is utilized to compute the desired control input for real-time multi-objective optimization. A set of co-simulations based on Simulink and CarSim are conducted to validate the effectiveness of the proposed strategy. Numerical results demonstrate that the presented method outperforms traditional MPC in terms of tracking accuracy and stability under localization drift conditions. Full article

Accurate identification of road roughness is pivotal for optimizing vehicle suspension control and enhancing passenger comfort. However, existing data-driven methods often struggle to balance classification accuracy with the strict computational constraints of real-time onboard monitoring. To address this challenge, this paper proposes a lightweight and robust road roughness classification framework utilizing a single sprung mass accelerometer. First, to overcome the scarcity of labeled real-world data and the limitations of linear models, a high-fidelity co-simulation platform combining CarSim and Simulink is established. This platform generates physically consistent vibration datasets covering ISO A–F roughness levels, effectively capturing nonlinear suspension dynamics. Second, we introduce DB-MLP, a novel Dual-Branch Multi-Layer Perceptron architecture. In contrast to computationally intensive Transformer or RNN-based models, DB-MLP employs a dual-branch strategy with multi-resolution temporal projection to efficiently capture multi-scale dependencies, and integrates dual-domain (time and position-wise) feature transformation blocks for robust feature extraction. Experimental results demonstrate that DB-MLP achieves a superior accuracy of 98.5% with only 0.58 million parameters. Compared to leading baselines such as TimeMixer and InceptionTime, our model reduces inference latency by approximately 20 times (0.007 ms/sample) while maintaining competitive performance on the specific road classification task. This study provides a cost-effective, high-precision solution suitable for real-time deployment on embedded vehicle systems. Full article

Connected and autonomous vehicle (CAV) platoons face the dual challenge of maintaining longitudinal formation stability while ensuring lateral safety in dynamic traffic environments, yet existing control approaches often address these objectives in isolation. This paper proposes a hierarchical cooperative control framework that integrates a differential game-based longitudinal controller with a risk potential field-driven model predictive controller (MPC) for lateral motion. At the coordination control layer, a differential game formulation models inter-vehicle interactions, with analytical solutions derived for both open-loop Nash equilibrium under predecessor-following (PF) topology and an estimated Nash equilibrium under two-predecessor-following (TPF) topology. The motion control layer employs a risk potential field model that quantifies collision threats from surrounding obstacles and road boundaries, guiding the MPC to perform real-time trajectory optimization. A comprehensive co-simulation platform integrating MATLAB/Simulink, Prescan, and CarSim validates the proposed framework across three representative scenarios: ramp merging with aggressive cut-in maneuvers, emergency braking by a preceding obstacle vehicle, and multi-lane cooperative obstacle avoidance involving multiple dynamic obstacles. Across all scenarios, the CAV platoon achieves safe obstacle avoidance through autonomous decision-making, with spacing errors converging to zero and smooth velocity adjustments that ensure both formation stability and ride comfort. The results demonstrate that the proposed framework effectively adapts to diverse and complex traffic conditions. Full article

Partial demagnetization of multiple in-wheel motors changes torque distribution characteristics and can reduce vehicle stability, which poses a challenge for in-wheel motor drive electric vehicles (IWMDEVs) to maintain a balance between safety and efficiency. To address this issue, a hierarchical multi-objective adaptive fault-tolerant control (FTC) strategy based on wheel terminal torque compensation is developed. In the upper layer, a nonlinear model predictive controller (NMPC) generates the desired total driving force and corrective yaw moment according to vehicle dynamics and driving conditions. The lower layer employs a quadratic programming (QP) scheme to allocate the wheel torques under actuator and tire constraints. Two adaptive coefficients—the stability–efficiency weighting factor and the current compensation factor—are updated through a randomized ensembled double Q-learning (REDQ) algorithm, enabling the controller to adaptively balance yaw stability preservation and energy optimization under different fault scenarios. The proposed method is implemented and verified in a CarSim–Simulink–Python co-simulation environment. The simulation results show that the controller effectively improves yaw and lateral stability while reducing energy consumption, validating the feasibility and effectiveness of the proposed strategy. This approach offers a promising solution to achieve reliable and energy-efficient control of IWMDEVs. Full article

The sideslip angle is a key state for evaluating the lateral stability of a vehicle. Its accurate estimation is crucial for active safety and intelligent driving assistance systems. To improve the estimation accuracy and robustness of the sideslip angle for distributed drive electric vehicles (DDEV) under extreme maneuvering conditions, this paper proposes a redundant estimation scheme based on multi-source sensor information fusion. Firstly, a dynamic model of the DDEV is established, including the vehicle body dynamics model, wheel rotation dynamics model, tire model, and hub motor model. Subsequently, robust unscented particle filtering (RUPF) and backpropagation (BP) neural network algorithms are developed to estimate the sideslip angle from both the vehicle dynamics and data-driven perspectives. Based on this, a redundant estimation scheme for the sideslip angle is developed. Finally, the effectiveness of the redundant estimation scheme is validated through the Matlab/Simulink-CarSim co-simulation platform using MATLAB R2022b and CarSim 2020.0. Full article