Search Results (85)

When in operation, ultra-high-speed elevators encounter transverse vibrations due to uneven guide rails and airflow disturbances, which can greatly undermine passenger comfort. To alleviate these adverse effects and boost passenger comfort, a gas–solid coupling dynamic model for ultra-high-speed elevator cars is constructed, and a vibration suppression approach is proposed. To start with, the flow field model of the elevator car-shaft under different motion states is simulated, and the calculation formula of air excitation is derived. Next, by incorporating the flow field excitation into the four degrees of freedom dynamic model of the separation between the car and the frame, a transverse vibration model of the elevator car based on gas–solid coupling is established. Finally, an LQR controller is used to suppress elevator transverse vibration, and a multi-objective optimization algorithm is applied to optimize the parameters of the weight matrix to obtain the optimal solution of the LQR controller. A set of controllers with moderate control cost and system performance meeting the requirements was selected, and the effectiveness of the controller was verified. Compared with other methods, the proposed LQR-based method has greater advantages in suppressing the transverse vibration of ultra-high-speed elevators. This work provides an effective solution for enhancing the ride comfort of ultra-high-speed elevators and holds potential for application in the vibration control of high-speed transportation systems. Full article

Especially in real-world circumstances with uneven road surfaces and impulsive shocks, nonlinear dynamic effects in vehicle systems can greatly skew biometric data utilized to track passenger and driver physiological states. By creating a thorough multibody human–seat–chassis model, this work tackles the effect of vehicle-induced vibrations on the accuracy and dependability of biometric measures. The model includes external excitation from road-induced inputs, nonlinear damping between structural linkages, and vertical and angular degrees of freedom in the head–neck system. Motion equations are derived using a second-order Lagrangian method; simulations are run using representative values of a typical car and human body segments. Results show that higher vehicle speed generates more vibrational energy input, which especially in the head and torso enhances vertical and angular accelerations. Modal studies, on the other hand, show that while resonant frequencies stay constant, speed causes a considerable rise in amplitude and frequency dispersion. At speeds ≥ 50 km/h, RMS and VDV values exceed ISO 2631 comfort standards in the body and head. The results highlight the need to include vibration-optimized suspension systems and ergonomic design approaches to safeguard sensitive body areas and preserve biometric data integrity. This study helps to increase comfort and safety in both traditional and autonomous car uses. Full article

Self-driving technology aims to minimize human error and improve safety, efficiency, and mobility through advanced autonomous driving algorithms. Among these, Model Predictive Control (MPC) is highly valued for its optimization capabilities and ability to manage constraints. However, its effectiveness depends on an accurate system model, as modeling errors and disturbances can degrade performance, making uncertainty management crucial. Learning-based MPC addresses this challenge by adapting the predictive model to changing and unmodeled conditions. However, existing approaches often involve trade-offs: robust methods tend to be overly conservative, stochastic methods struggle with real-time feasibility, and deep learning lacks interpretability. Sparse regression techniques provide an alternative by identifying compact models that retain essential dynamics while eliminating unnecessary complexity. In this context, the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm is particularly appealing, as it derives governing equations directly from data, balancing accuracy and computational efficiency. This work investigates the use of SINDy for learning and adapting vehicle dynamics models within an MPC framework. The methodology consists of three key phases. First, in offline identification, SINDy estimates the parameters of a three-degree-of-freedom single-track model using simulation data, capturing tire nonlinearities to create a fully tunable vehicle model. This is then validated in a high-fidelity CarMaker simulation to assess its accuracy in complex scenarios. Finally, in the online phase, MPC starts with an incorrect predictive model, which SINDy continuously updates in real time, improving performance by reducing lap time and ensuring a smoother trajectory. Additionally, a constrained version of SINDy is implemented to avoid obtaining physically meaningless parameters while aiming for an accurate approximation of the effects of unmodeled states. Simulation results demonstrate that the proposed framework enables an adaptive and efficient representation of vehicle dynamics, with potential applications to other control strategies and dynamical systems. Full article

The design of intelligent vehicle trajectory tracking controllers still has some problems, such as parameter uncertainty and time consumption. To improve the tracking accuracy of the trajectory tracking controller and reduce its computational complexity, an adaptive MPC trajectory tracking control method with a variable prediction horizon is proposed. Firstly, a three-degree-of-freedom vehicle dynamics model is constructed, and the design is improved based on the ordinary MPC controller. Secondly, several groups of different constant vehicle speeds are selected to compare the tracking effect of the ordinary MPC and the improved controller. Then, low speed (30 km/h) and high speed (100 km/h) are selected as representative speeds to solve the calculation time of the controller. The relationship between vehicle speed and prediction horizon is analyzed, and curve fitting is carried out. An adaptive trajectory tracking controller is designed. Finally, it is verified by CarSim and MATLAB/Simulink co-simulation. The results show that compared with ordinary MPC, the improved adaptive trajectory tracking controller can maintain good tracking accuracy and stability according to the speed change and improve the computational efficiency of the controller. Full article

During the operation of outdoor heavy-duty Automated Guided Vehicle (AGV), the stability and safety of AGV are easily reduced due to load transfer. In order to solve this problem, a trajectory tracking control strategy considering load transfer is proposed to realize the trajectory tracking of AGV and the adaptive distribution of driving torque. The three-degree-of-freedom (3-DOF) kinematics model and pose error model of heavy-duty AGV vehicles are established. The lateral load transfer and longitudinal load transfer rules are analyzed. The vehicle trajectory tracking control strategy is composed of an improved model predictive controller (IMPC) and drive motor torque adaptive distribution controller considering load transfer. By optimizing the lateral acceleration of the vehicle body, the IMPC controller improves the problem of large driving force difference between the left and right sides of the wheel caused by the lateral transfer of the load and the problem of large wheel adhesion rate caused by the longitudinal transfer of the load is improved by the speed controller and the torque proportional distribution controller. The joint simulation platform of MATLAB/Simulink and CarSim is built to simulate and analyze the trajectory tracking of heavy-duty AGV under different pavement adhesion coefficients. The simulation results have shown that compared with the control strategy without considering load transfer, on the two types of pavements with different adhesion coefficients, the maximum lateral acceleration is reduced by 19.7%, and the maximum tire adhesion rate is reduced by 11.5%. Full article

This study proposes an advanced intelligent vehicle path-following control system using deep reinforcement learning, with a particular focus on the role of road geometry perception in motion planning and control. The system is structured around a three-degree-of-freedom (3-DOF) vehicle model, which facilitates the extraction of critical dynamic features necessary for robust control. The longitudinal control architecture integrates a Deep Deterministic Policy Gradient (DDPG) agent to optimise longitudinal velocity and acceleration, while lateral vehicle control is handled by a Deep Q-Network (DQN). To enhance situational awareness and adaptability, the system incorporates key input variables, including ego vehicle speed, speed error, lateral deviation, lateral error, and safety distance to the preceding vehicle, all in the context of road geometry and vehicle dynamics. In addition, the influence of road curvature is embedded into the control framework through perceived acceleration (sensed by vehicle occupants), allowing for more accurate and responsive adaptation to varying road conditions. The vehicle control system is tested in a simulated environment with a lead car in front with realistic speed profiles. The system outputs continuous values for acceleration and steering angle. The results of this study suggest that the proposed intelligent control system not only improves driver assistance but also has potential applications in autonomous driving. This framework contributes to the development of more autonomous, efficient, safety-aware, and comfortable vehicle control systems. Full article

This study examines the smoothness of an intelligent chassis for electric vehicles, analyzes the chassis structure and configuration, and considers the impacts of the primary energy subsystem, electric drive subsystem, and auxiliary control subsystem on smoothness. The influence of suspension parameters on smoothness is examined, highlighting the significance of elastic element stiffness and the shock absorber damping ratio. Dynamic models of quarter- and half-car suspension systems, as well as a comprehensive nine-degree-of-freedom vehicle model, are developed to examine the vibration characteristics under varying road conditions. The chassis suspension dynamic model is developed, simulated, and analyzed using ADAMS/View software 2024. The suspension damping value is optimized with the ADAMS/PostProcessor tool, revealing that smoothness can be enhanced by judiciously decreasing the damping value. The article discusses the human body’s reaction to vibration and assessment metrics, referencing worldwide standards to establish a foundation for evaluation. The study offers theoretical backing for the design and optimization of an intelligent chassis, hence advancing the technological development of electric vehicles. Full article

To meet the active avoidance requirements of intelligent vehicles, this paper proposes an efficient hierarchical control system. The upper layer generates a safe avoidance trajectory through an optimized path planning algorithm, while the lower layer precisely controls the vehicle to follow the planned path. In the upper layer design, an improved quintic polynomial method is employed to generate the baseline trajectory. By dynamically adjusting lane change duration and utilizing an improved dual-quintic algorithm, collisions with preceding vehicles are effectively avoided. Additionally, a genetic algorithm is applied to automatically optimize parameters, ensuring both driving comfort and planning efficiency. The lower layer control is based on a three-degree-of-freedom monorail vehicle model and the Magic Formula tire model, employing a model predictive control (MPC) approach to continuously correct trajectory deviations in real time, thereby ensuring stable path tracking. To validate the proposed system, a co-simulation environment integrating CarSim, PreScan, and MATLAB was established. The system was tested under various vehicle speeds and road conditions, including wet and dry surfaces. Experimental results demonstrate that the proposed system achieves a path tracking error of less than 0.002 m, effectively reducing accident risks while enhancing the smoothness of the avoidance process. This hierarchical design decomposes the complex avoidance task into planning and control, simplifying system development while balancing safety and real-time performance. The proposed method provides a practical solution for active collision avoidance in intelligent vehicles. Full article

In order to solve the problem of the insufficient vibration isolation performance of passenger cars in the suspension matching process, the six-degree-of-freedom (6-DOF) model, including three translational (x, y, z) and three rotational (roll, pitch, yaw) degrees of freedom, is established to comprehensively analyze the dynamic behavior of the powertrain mounting system. A 6-DOF dynamic model was established to analyze the decoupling rate and frequency distribution in its inherent characteristics, calculate the dynamic reaction of the suspension system, set the decoupling rate and the dynamic reaction of the suspension as optimization objectives, and use the NSGA II (Non-dominated Sorting Genetic Algorithm II) optimization algorithm to optimize the stiffness of the suspension. The 6-DOF decoupling of the whole suspension system is optimized and the dynamic reaction transmitted to the body is minimized. At the same time, this ensures that each suspension has enough static load support stiffness, and that its static deformation and amplitude are within the limit allowed under various working conditions, avoiding premature fatigue damage. The vibration isolation capability of the optimized system has been significantly improved, and the centroid acceleration has been significantly reduced under start–stop and road excitation conditions. The optimization method was effectively verified. Compared with existing studies focusing on single-objective optimization, the proposed NSGA II-based approach achieves a 93.4% decoupling rate in the critical Rx direction (vs. 59% pre-optimization) and reduces dynamic reaction forces by 8.3% (from 193 N to 177 N), demonstrating superior engineering applicability compared with traditional methods. Finally, the robustness analysis of the optimized stiffness met the requirements of production and manufacturing, indicating that the improvement of the decoupling rate of the suspension system and the optimization of the dynamic reaction force can effectively improve the vibration isolation performance, thereby improving the ride comfort of the vehicle. Full article

Multiple-Input Multiple-Output (MIMO) radar enjoys the advantages of a high degree of freedom and relatively large virtual aperture, so it has various forms of applications in several aspects such as remote sensing, autonomous driving and radar imaging. Among all multiplexing schemes, Time-Division Multiplexing (TDM)-MIMO radar gains a wide range of interests, as it has a simple and low-cost hardware system which is easy to implement. However, the time-division nature of TDM-MIMO leads to the dilemma between the lower Pulse Repetition Interval (PRI) and more transmitters, as the PRI of a TDM-MIMO system is proportional to the number of transmitters while the number of transmitters significantly affects the resolution of MIMO radar. Moreover, a high PRI is often needed to obtain unambiguous imaging results for MIMO-SAR 3D imaging applications on a fast-moving platform such as a car or an aircraft. Therefore, it is of vital importance to develop an algorithm which can achieve unambiguous TDM-MIMO-SAR 3D imaging even when the PRI is low. Inspired by the motion compensation problem associated with TDM-MIMO radar imaging, this paper proposes a novel imaging algorithm which can utilize the phase shift induced by the time-division nature of TDM-MIMO radar to achieve unambiguous MIMO-SAR 3D imaging. A 2D-Compressed Sensing (CS)-based method is employed and the proposed method, which is called HPC-2D-FISTA, is verified by simulation data. Finally, a real-world experiment is conducted to show the unambiguous imaging ability of the proposed method compared with the ordinary matched-filter-based method. The effect of velocity error is also analyzed with simulation results. Full article

In order to improve the driving stability of distributed-drive intelligent electric vehicles under different roadway attachment conditions, this paper proposes a multi-parameter control algorithm based on the estimation of road adhesion coefficients. First, a seven-degree-of-freedom (7-DOF) vehicle dynamics model is established and optimized with a layered control strategy. The upper-level control module calculates the desired yaw rate and sideslip angle using the two-degree-of-freedom (2-DOF) vehicle model and estimates the road adhesion coefficient by using the singular-value optimized cubature Kalman filtering (CKF) algorithm; the middle-level utilizes the second-order sliding mode controller (SOSMC) as a direct yaw moment controller in order to track the desired yaw rate and sideslip angle while also employing a joint distribution algorithm to control the torque distribution based on vehicle stability parameters, thereby enhancing system robustness; and the lower-level controller performs optimal torque allocation based on the optimal tire loading rate as the objective. A Speedgoat-CarSim hardware-in-the-loop simulation platform was established, and typical driving scenarios were simulated to assess the stability and accuracy of the proposed control algorithm. The results demonstrate that the proposed algorithm significantly enhances vehicle-handling stability across both high- and low-adhesion road conditions. Full article

The technical condition of the shock absorbers used in automotive suspension systems is important with respect to vehicle occupants’ comfort and traffic safety. Therefore, much effort has been made for many years to find diagnostic methods that would be more effective. There is a preference for those methods where the shock absorbers do not have to be dismounted from the vehicle. Among those being in use, the ‘forced vibration methods’ have earned the widest acceptance. One of them is the solution where the angle of phase shift between the vertical displacement of the vibration plate and the tyre–plate interaction force is measured. The authors decided to assess this method’s usability by comparing simulation results with the results of experiments run on a prototype diagnostic test stand. They used two ‘quarter-car’ simulation models (linear and non-linear) and experimentally tested suspension systems of two medium-class cars. In the first stage, computations were made in the frequency domain for the linear model with two degrees of freedom, followed by simulations in the time domain, where an analogous but strongly non-linear model was used. In the latter model, the actual characteristic curves (determined during the laboratory measurements) of shock absorber damping, tyre and suspension elasticity, sliding friction in the suspension system, and tyre bouncing were taken into account. The authors have presented the computation results in the form of curves representing the phase angle as a function of the relative damping in the suspension system under test for the two medium-class cars. The suspensions of the cars had similar inertia properties but different characteristics of the spring and damping forces. The cars also differed from each other in the observed and measured level of the friction forces (twice bigger). The computation results obtained for the linear and non-linear model and the experiment results show a similar qualitative nature. In quantitative terms, however, they differ significantly from each other. The role of non-linearities is important. Nevertheless, the results show monotonicity and noticeable sensitivity to changes in the technical condition of the shock absorbers, which is an essential and desirable feature in diagnostics. Full article

Road networks are crucial infrastructures that play a significant role in the progress and advancement of societies. However, roads deteriorate over time due to regular use and external environmental factors. This deterioration leads to discomfort for road users as well as the generation of noise and vibrations, which negatively impact nearby structures. Therefore, it is essential to regularly maintain and monitor road networks. The International Roughness Index (IRI) is commonly used to quantify road roughness and serves as a key indicator for assessing road condition. Traditionally, obtaining the IRI involves manual or automated methods that can be time-consuming and expensive. This study explores the potential of using artificial neural networks (ANNs) and dynamic vehicle accelerations from two simulated car models to reconstruct road roughness profiles. These models include a simplified quarter-car (QC) model with two degrees of freedom, valued for its computational efficiency, and a more intricate full-car (FC) model with seven degrees of freedom, which replicates real-life vehicle behavior. This study also examines the ability of ANNs to predict the mechanical properties of the FC model from dynamic vehicle responses to obstacles. We compare the accuracy and computational efficiency of the two models and find that the QC model is almost 10 times faster than the FC model in reconstructing the road roughness profile whilst achieving higher accuracy. Full article

The objective of continuous increase in the evolution of autonomous and intelligent vehicles is to attain a trustworthy, economical, and safe transportation system. Four-wheel steering (4WS) vehicles are favored over traditional front-wheel steering (FWS) vehicles because they have excellent dynamic characteristics. This paper exhibits the trajectory tracking task of a two degree of freedom (2DOF) underactuated 4WS Autonomous Vehicle (AV). Because the system is underactuated, MIMO, and has a nontriangular form, the traditional adaptive backstepping control scheme cannot be utilized to control it. For the purpose of rectifying this issue, two-state feedback-based methods grounded on the hierarchical steps of the block backstepping controller are proposed and compared in this paper. In the first strategy, a modified block backstepping is applied for the entire dynamic system. Global stability of the overall system is manifested by Lyapunov theory and Barbalat’s Lemma. In the second strategy, a block backstepping controller has been applied after a reduction of the high-order model into various first-order subsystems, consisting of Lyapunov-based design and stability warranty. A trajectory tracking controller that can follow a double lane change path with high accuracy is designed, and then simulation experiments of the CarSim/Simulink connection are carried out against various vehicle longitudinal speeds and road surface roughness to demonstrate the effectiveness of the presented controllers. Furthermore, a PID driver model is introduced for comparison with the two proposed controllers. Simulation outcomes show that the proposed controllers can attain good response implementation and enhance the 4WS AV performance and stability. Indeed, enhancement of the stability and efficacy of 4WS autonomous vehicles would afford a sustainable transportation system by lessening fuel consumption and gas emissions. Full article

Paper presents an effective technique for developing reduced-order models to predict the dynamic responses of systems using the receptance coupling and frequency-based substructuring (RCFBS) method. The proposed approach is particularly suited for reconfigurable dynamic systems across various applications, like cars, robots, mechanical machineries, and aerospace structures. The methodology focuses on determining the overall system receptance matrix by coupling the receptance matrices (FRFs) of individual subsystems in a disassembled configuration. Two case studies, one with distributed parameters and the other with lumped parameters, are used to illustrate the application of this approach. The first case involves coupling three substructures with flexible components under fixed–fixed boundary conditions, while the second case examines the coupling of subsystems characterized by multiple masses, springs, and dampers, with various internal and connection degrees of freedom. The accuracy of the proposed method is validated against a numerical finite element analysis (FEA), direct methods, and a modal analysis. The results demonstrate the reliability of RCFBS in predicting dynamic responses for reconfigurable systems, offering an efficient framework for reduced-order modeling by focusing on critical points of interest without the need to account for detailed modeling with numerous degrees of freedom. Full article