Search Results (373)

Roll motion induced by steering maneuvers and vertical vibration excited by road unevenness are strongly coupled in active suspension vehicles. Neglecting this coupling may deteriorate the performance of coordinated chassis control and compromise both roll stability and ride comfort. To improve roll stability and ride comfort under combined steering and road excitation conditions, this paper develops a roll–vertical coupled control framework. First, a nine-degree-of-freedom roll–vertical coupled vehicle model is established by integrating lateral–yaw dynamics, sprung mass heave motion, roll and pitch motion, and four unsprung mass vertical dynamics. Second, an adaptive square root cubature Kalman filter (ASRCKF) is designed to estimate key roll states, including the roll angle and roll rate. The square root structure improves numerical stability, while the Sage–Husa adaptive estimator updates the measurement noise covariance online using the innovation sequence. Third, a load transfer ratio-based rollover risk assessment method and a model predictive control (MPC)-based active suspension controller are introduced to realize coordinated roll–vertical control. Finally, the proposed framework is validated using a MATLAB/Simulink–CarSim co-simulation platform. The results demonstrate that the proposed method effectively improves vehicle roll stability and vertical ride performance under complex driving conditions. Full article

This study proposes a dual-modal preview-control framework that combines two filtered-x least-mean-square (FxLMS) algorithms with a Lotus-type modal-force transformation for active suspension systems. Using previewed road information as a common reference, the heave- and pitch-mode FxLMS controllers independently generate a generalized vertical force and pitch moment to reduce sprung-mass vertical acceleration and pitch rate, respectively. These modal commands are mapped to the front and rear actuator forces through a full-rank modal-force transformation defined from the half-car geometry. Although the baseline and proposed architectures use the same two physical actuators, the proposed controller replaces the baseline’s single adaptive rear-force correction with two independently adapted generalized commands, thereby separating the prescribed heave and pitch commands at the command-allocation level. Performance was assessed using conventional ride-comfort and motion-sickness dose measures together with supplementary visual-task-weighted indices. CarSim–MATLAB/Simulink co-simulations were conducted under four selected road-input conditions. Compared with the selected baseline, the proposed architecture produced lower vertical-motion indices in most cases, whereas the relative pitch-response benefit depended on the road input and evaluation metric. These results support further investigation under constrained and experimentally validated conditions. Full article

Autonomous vehicles (AVs) increasingly rely on accurate steering dynamics models and high-precision steering angle tracking to achieve safe and reliable control. Additionally, the increased attention from automakers and academia emphasizes the potential of AVs in improving transportation safety, convenience, energy efficiency, and ride comfort. This paper presents a data-driven modeling framework using both simulated and real-world driving data collected from a 2025 Nissan Leaf SV Plus. This test vehicle is equipped with an in-house developed drive-by-wire system. Besides the data-driven steering dynamics model, this paper also presents a data-driven steering angle tracking control and a proportional-integral-derivative control. Simulation and on-vehicle tests demonstrate that the proposed data-driven model and controllers can be deployed on the vehicle: the PID controller achieves a steady-state tracking root-mean-square error of 1.23° across the full ±450° operating range, and the neural controller tracks in closed loop with a characterized direction asymmetry identified for future refinement. The proposed data-driven steering dynamics model and control can be used in the development of next-generation autonomous driving systems for their accuracy and simplicity. Full article

The first natural frequency is the most important vibration parameter during the design of suspensions. It has a major impact on vehicle ride comfort and handling. This paper presents the results of the effects of different bushings’ elastic characteristics of the natural frequencies of a front-independent quarter MacPherson suspension system. The natural frequencies and mode shapes were obtained using Finite Element Analysis (FEA). A simulation study was conducted, taking into account the elastic characteristics of bushings and an analysis with two rubber bushings within the mounting of the arm (Case I), and a rubber bushing and a polyurethane bushing (Case II). The natural frequencies were also determined by Frequency Response Function (FRF) analysis. FRF analysis was performed using experimentally obtained acceleration and time data of the body. The experiment was conducted using a suspension tester and a measuring system. FEA was performed using SolidWorks 2023. The results were compared and analyzed. Full article

The development of suspension systems for urban micro-mobility vehicles, such as bicycles and e-bikes, requires balancing effective road filtering with structural simplicity. Traditional solutions, such as telescopic forks and rear shock absorbers, face significant challenges related to weight, bulk, and mechanical complexity, which increase production and maintenance costs. The integration of in-wheel motors into wheel hubs further complicates the design by increasing unsprung mass and vertical vibrations, negatively affecting ride comfort. The In-Wheel Suspension (IWS) system offers an innovative solution by incorporating elastic and damping elements directly into the wheel rim, eliminating the need for frame modifications and reducing overall weight. This study proposes an integrated approach to optimising the internal geometries of IWS elastic elements, using structural analyses with LS-Dyna and dynamic simulations in the Simulink environment for comfort assessment. Results demonstrate that optimising the geometry of spokes and rims significantly reduces stiffness variations and self-induced vibrations, with enhancements in ride comfort and resistance to fatigue. The optimized IWS design minimises discomfort peaks at critical speeds and improves vibration attenuation. However, the high average stiffness limits filtering performance at speeds above 10 km/h. While IWS systems represent a promising alternative to traditional suspensions due to their advantages in weight reduction, compactness, and construction simplicity, further improvements—such as the use of composite materials and alternative geometries—are necessary to further increase comfort and to ensure structural resistance to variable loads. Full article

Semi-active suspension systems combine low power consumption, rapid response, and fail-safe operation by reverting to passive mode after control failure, making them important for intelligent chassis and vibration-control systems. With the development of intelligent actuators, nonlinear modeling, and advanced control methods, this technology is expanding from conventional road vehicles to off-road vehicles and agricultural machinery. Compared with passenger cars, agricultural machinery faces stronger random excitation, time-varying loads, muddy environments, resource-constrained controllers, and requirements for operational accuracy. This review focuses on semi-active damping and vibration-isolation systems for off-road and agricultural applications. Mainstream actuators, control-oriented nonlinear damper models, classical, robust, and adaptive control methods, MPC, DRL, and mechanism–data fusion control are compared in terms of hardware constraints, model accuracy, real-time computation, and agricultural adaptability. Applications in seat/cab isolation, tractor and tracked chassis systems, rollover prevention, and precision implements are summarized. The review shows that semi-active suspension in agricultural machinery is evolving beyond the conventional trade-off between ride comfort and handling stability toward multi-objective coordination of safety, ground-contact stability, operational accuracy, operator protection, and energy consumption. Full article

Vehicle energy-regenerative suspension can convert part of the vibration energy induced by road excitation into electrical energy. However, there are coupled performance conflicts among energy recovery, ride comfort, and suspension safety, and a fixed-weight LQR controller finds it difficult to maintain a reasonable performance compromise under different road conditions. To address this problem, this paper proposes an AWG-NN-LQR control method based on an Adaptive Weight Generation neural network. First, a quarter-car energy-regenerative suspension model, an electromagnetic actuator model, and a random road model are established, and the vertical vehicle responses and energy-regeneration characteristics under different road classes are analyzed. Second, vehicle speed, road roughness coefficient, and statistical features of vehicle responses are used as inputs. LQR weight labels are generated through offline closed-loop simulation and candidate-weight search, and the AWG-NN is trained to learn the nonlinear mapping relationship between road conditions and weight parameters. Finally, closed-loop comparative validation is conducted for the passive suspension, fixed-weight LQR, and AWG-NN-LQR under a typical class-C road condition. The results show that, compared with the fixed-weight LQR, AWG-NN-LQR reduces the RMS of body acceleration from $1.7041{\mathrm{m}/\mathrm{s}}^2$ to $1.6527{\mathrm{m}/\mathrm{s}}^2$, and reduces the RMS of suspension deflection from $0.00863\mathrm{m}$ to $0.00844\mathrm{m}$, while achieving an average regenerated power of $9.41\mathrm{W}$. The proposed method can improve the objective-bias problem of the fixed-weight LQR under a typical operating condition while maintaining a certain energy-regeneration capability, providing a feasible approach for multi-objective adaptive control of energy-regenerative suspension. Full article

Electric vehicles equipped with in-wheel motors provide packaging, controllability, and drivetrain-simplification advantages; however, the increase in wheel-side unsprung mass can intensify vibration transmission to the chassis, seat, and occupant. This paper presents a coordinated active seat and active suspension control strategy for an integrated 8-DOF quarter-car model that includes an in-wheel motor, an active seat suspension, and a 4-DOF seated driver body model. The proposed controller combines a Harmony Search (HS)-optimized proportional–integral–derivative (PID) feedback baseline with a repeatable-disturbance feedforward compensation term. The HS-PID loop provides baseline transient attenuation, while the feedforward term compensates the repeatable component of the bump-induced disturbance transmitted through the coupled seat–vehicle system. The controller is evaluated against passive suspension, active-seat-only control, active-vehicle-suspension-only control, and an HS-PID baseline under repeated bump/shock excitation. The results show that coordinated actuation reduces occupant displacement and acceleration responses relative to the benchmark cases. The discussion explains the active-seat-only peak-acceleration amplification, the different magnitudes of displacement and acceleration improvements, and the practical implications of suspension stroke and actuator-force limits. The reported conclusions are therefore confined to the repeated bump/shock condition considered in this numerical study; broader ride-comfort generalization requires standardized whole-body vibration metrics, random-road validation, speed variation, parametric uncertainty analysis, and drivetrain energy evaluation. Full article

Despite the presence of multi-axis vibration in many vehicles, biodynamic responses of the human body are mostly studied under single-axis conditions. In this study, the fore-aft, lateral, and vertical apparent masses were measured under tri-axis translational excitation with various in-line (“primary-axis”) and cross-axis (“secondary-axes”) magnitudes and backrest inclinations (0°, 10°, and 20°). The softening effect of increasing the primary-axis magnitude was reduced by higher secondary-axes magnitudes, and the effect of secondary-axes magnitudes was similarly attenuated by higher primary-axis magnitudes. With tri-axis vibration, the fore-aft peak modulus at the seat pan first fell, then rose with the increasing backrest inclination as under single-axis conditions, whereas the resonance frequency lost the non-monotonic pattern. Backrest effects on fore-aft and lateral apparent masses were insignificant with single-axis vibration but became significant with tri-axis vibration. These findings suggest that incorporating multi-axis vibration and backrest inclination may improve the accuracy of ride comfort evaluation for realistic driving conditions. Full article

Adaptive cruise control (ACC) performance is affected by multiple coupled factors, including safety margin, dynamic response, spacing regulation, target-transition behavior, and ride comfort. A single indicator is therefore insufficient for comprehensive ACC evaluation. This study proposes an adaptive cruise control comprehensive performance evaluation index model (ACC-CPEIM) for scenario-oriented ACC testing and diagnosis. The model links functional objectives, six typical ACC scenarios, measurable longitudinal indicators, hierarchical weights, and scenario-specific scoring rules into a unified evaluation chain. Time-domain response data are converted into scenario-level, criterion-level, and overall performance scores, while the results remain traceable to specific weak scenarios and performance dimensions. The proposed model was evaluated using a CarSim/Simulink co-simulation platform and further applied to vehicle-test data. The co-simulation results yielded an overall score of approximately 3.32 and identified weak acceleration-following response, insufficient spacing reserve during deceleration, and limited cut-in safety margin as the main limitations. The vehicle-test application produced an overall score of approximately 2.98 and showed that comfort and steady-state control were relatively stronger, whereas target-transition adaptability, safety margin, and dynamic response remained limiting dimensions. The results indicate that the ACC-CPEIM can provide quantitative, interpretable, and engineering-oriented support for ACC performance testing and diagnosis. Full article

The ride stability and driving comfort of vehicles are highly dependent on the performance of the damping system. The fatigue life prediction of damping components using rubber as the core damping material has become a research hotspot in the field of vehicle vibration isolation. Taking an automotive engine rubber isolator as the research carrier, this paper jointly carries out finite element simulation analysis and structural component fatigue life tests. A dual-parameter mapping framework is proposed, which integrates maximum strain energy density and cyclic energy dissipation instead of using a single damage indicator. This approach comprehensively accounts for the coupling effect of energy storage and energy dissipation coexisting under actual service conditions. Through uniaxial tensile tests on rubber specimens, combined with finite element simulations and physical model parameters, a quantitative mapping relationship between laboratory specimens and full-scale engine rubber isolators is established. Based on this mapping, the fatigue life curve of the isolator is derived from the specimen-based failure characteristics. Validation tests under two randomly selected operating conditions yield prediction errors of 7.5% and 6.9%, demonstrating that the proposed model can accurately achieve equivalent fatigue life transformation from small specimens to actual components. Unlike conventional direct extrapolation methods, this approach does not require complex multiaxial fatigue tests on the component itself; it only needs simple specimen fatigue data, significantly reducing development costs, while providing a reliable theoretical basis for material selection, fatigue performance optimization, and forward structural design of rubber isolators. Full article

This paper presents an LMI-based discrete PI–P cascade control method for active quarter-car suspension systems. The quarter-car dynamics are reformulated into a modified cascade sampled-data model that retains the body–wheel coupling between the sprung-mass and unsprung-mass dynamics. Based on this structure, a finite-memory discrete PI–P controller is developed, where the primary PI loop regulates the body-side response and the secondary proportional loop shapes the wheel–actuator-side dynamics. A Lyapunov stability condition and a tractable LMI synthesis method are derived for controller gain co-design. Numerical simulations show that the proposed controller improves sprung-mass acceleration attenuation while keeping the suspension deflection, tire-load-related response, and actuator effort bounded. The study is positioned for vertical ride comfort control rather than full-vehicle handling evaluation. Full article

The two-speed transmission system significantly enhances the powertrain matching performance of heavy-duty electric military armored vehicles by optimizing high-torque output at low speed and energy efficiency at high speed. However, most existing electric vehicles do not incorporate driving styles or real-time driving condition prediction into their shift control strategies, resulting in suboptimal gear shift timing and smoothness that fail to align with driver expectations and operational requirements. To address these limitations, this study focuses on the two-speed automated manual transmission (AMT) system in heavy-duty electric military armored vehicles. Firstly, a comprehensive shift control model is established, integrating key components such as the drive motor and power battery. Furthermore, a shift control strategy based on vehicle speed prediction and driving style recognition is proposed. The operational logic of this strategy is systematically analyzed under various driving cycles. Simulation and hardware-in-the-loop (HIL) results confirm the performance gains. Simulation and hardware-in-the-loop (HIL) results indicate that the proposed approach improves vehicle power performance by 21.36%, increases energy efficiency by 3.94%, and reduces powertrain shock by 31.81% compared to the conventional vehicle-speed-based gear shifting method. Compared to the adaptive shift schedule design method, the proposed approach reduces shifting frequency by 21.43% and improves ride comfort by at least 19.17% while maintaining comparable dynamic performance and energy efficiency. Full article

Active seat suspensions are a potential approach for reducing vertical vibration exposure in vehicle and construction-machinery seats. In most existing studies on active seat-suspension control, acceleration signals are rarely used as direct feedback because of their high noise sensitivity. However, acceleration can be measured at low cost and directly reflects ride comfort, which makes it attractive for prototype-level vibration control. This paper proposes an acceleration-feedback-based adaptive weighted control strategy for a motor-driven active seat-suspension prototype with input delay. A 2-DOF driver-seat model is employed to describe the dominant vertical dynamics. An auxiliary virtual state variable is introduced to embed a deformation-dependent weighting mechanism into the control objective, allowing the controller to coordinate ride-comfort improvement and suspension-stroke safety according to real-time suspension deformation. Based on the Linear Matrix Inequality (LMI) method, a state-feedback H-infinity controller is synthesized while considering actuation delay and input saturation. The stability of the controlled system is proved under the stated model assumptions, and the controller performance is examined through numerical simulation and laboratory prototype experiments. The acceleration transmissibility from the vibration-platform floor to the driver is evaluated experimentally in the frequency domain, and random-excitation responses are investigated through both simulation and experimentation. The results show that the proposed strategy can reduce the dominant vibration responses and satisfy the imposed stroke and actuation constraints on the laboratory test rig. Full article

Road quality monitoring is necessary for safety, ride comfort, and driver-assistance systems. The knowledge of road features enables preventive and corrective actions at vehicle and infrastructure levels. While deep learning models are effective for surface classification, transitioning them to real-time embedded environments requires optimization. This study deploys a model based on convolutional and long short-term memory neural networks to classify five road conditions using continuous vibration data from multiple inertial measurement units. Executed on a MicroAutoBox III Embedded PC, the system preprocesses data at vehicle speeds between 5.0 and 25.0 km/h. Compared to the offline baseline deployment, this edge-optimized architecture reduced inference latency by 88% (from 33.8 ms to 4.05 ms) while maintaining a fair weighted-average F1-score of 0.8751 in real-world, cross-platform conditions (against the offline baseline average F1-score of 0.9338). This processing time operates within the 11.6 ms limit required by the 86 Hz sensor polling rate. Additionally, geospatial mapping was able to localize structural anomalies, showing robustness to environmental lighting conditions, which frequently affect vision-based systems. This cyber-physical deployment suggests the feasibility of executing temporal deep learning real-time models. Future work will target highway-speed validation and domain adaptation to assess transferability across diverse vehicle suspensions. Full article