Vehicle Dynamics Research Articles

A heavy-duty tracked vehicle model with a reduced feasible domain for motion tracking control considering dynamic characters of hybrid powertrain

Motion tracking control is an essential part of heavy-duty unmanned tracked vehicles to realize autonomous mobility, which adjusts the vehicle motion in real-time by controlling the driving torques. Achieving accurate motion tracking strongly depends on the use of a precise vehicle dynamic model. To support the motion tracking ability, a vehicle dynamic model with a reduced feasible domain is proposed for heavy-duty unmanned tracked vehicles. Firstly, a vehicle dynamic model is established based on the vehicle kinematics and dynamics. Building upon the established vehicle dynamic model, a graph neural network (GNN) is employed to reduce the feasible domain of the vehicle dynamic model, specifically focusing on the limitation of the powertrain. The powertrain components and their interconnections are abstracted into a graph, which is analyzed to study the interactions between the components using the GNN. Finally, the proposed vehicle dynamic model with a reduced feasible domain is verified through actual heavy-duty tracked vehicle experiments in various cases. Compared with the back propagation neural network, the evaluation error of the driving torque decreased by 25.86%. Results show that the proposed vehicle dynamic model with a reduced feasible domain is closer to the performance of the actual heavy-duty tracked vehicle and their powertrain. And the vehicle dynamic model accurately reduces the solution time of the motion tracking control, resulting in improved accuracy and efficiency of the motion tracking control.

Road Geometry Feasibility for Automated Vehicles with ACC Systems: Vehicle Dynamics on Curved Roads

Road Geometry Feasibility for Automated Vehicles with ACC Systems: Vehicle Dynamics on Curved Roads

PERTURBED MOTIONS OF A NEARLY DYNAMICALLY SPHERICAL RIGID BODY WITH A MOVABLE MASS SUBJECT TO CONSTANT BODY-FIXED TORQUE

The problem of motion of a rigid body about a fixed point is one of the classical problems of mechanics. The interest to the problems of the rigid body dynamics has increased in the second half of the XX century in connection with the development of rocket and space technologies. A spacecraft or satellite, while orbiting about its center of mass, experiences torques from forces of diverse physical nature. This includes torques generated by the motion of internal masses, which can arise from factors such as presence of rotating components (like rotors or gyroscopes), and the activities of crew members aboard the crew vehicle. The dynamics of rigid body incorporated moving masses is a significant focal point in classical mechanics. Extensive research is dedicated to investigating the rotation of a rigid body featuring motion of internal masses. It is assumed that the body contains a viscoelastic element that is modeled by a moving mass connected to the body by a strong damper. The moving mass model loosely attached elements in a space vehicles, which can significantly affect the vehicle’s motion about its center of mass during a long period of time. Some cases are considered of the motion of a rigid body containing internal masses connected to the body by means of elastic and dissipative elements. A number of works are devoted to the analysis of various problems of the dynamics of space vehicles containing internal movable masses. The paper develops an approximate solution by means of averaging method to the system of Euler’s equation terms for a nearly dynamically spherical rigid body containing a viscoelastic element under the action of constant body-fixed torque. We obtained the system of motion equations in the standard form which refined in square-approximation by small parameter. Asymptotic approach permits to obtain some qualitative results and to describe evolution of angular motion using simplified averaged equations and numerical solution. The main objective of this paper is to extend the previous results for the problem of motion about a center of mass of a rigid body under the influence of small internal torque (cavity filled with a fluid of high viscosity) or external torque (resistive medium). The importance of the results is in progress of moving mass control motion of spinning projectiles.

Precise motion control for a space mining robot based on improved African vultures optimization

Space mining robots can develop and utilize abundant mineral resources in the outer space to solve the problem of the depletion of earth mineral resources. However, the motion errors of space mining robots in complex environments on the outer space surface of asteroids greatly hinder its applications. To bridge this research gap, a precise motion controller of the space mining robot is proposed in this study. Firstly, the kinematics model of robot’s wheels and the whole vehicle dynamics model in soft soil are established. Then, the optimal PID motion controller is developed based on the improved African vultures optimization algorithm (IAVOA). The Henon chaotic mapping, nonlinear adaptive incremental inertial weight factor and reverse learning competition strategy are introduced to optimize the initial population position, position update method and optimal solution output, respectively. Meanwhile, the excellent optimization performance of IAVOA is also validated by test results of nine benchmark functions. Lastly, the performance of controller is verified by simulations and experiments. The simulation results show that the proposed IAVOA-PID controller is superior to the original PID and AVOA-PID controller with faster response time and smaller overshoot. The experiment results show the space mining robot with the assistance of proposed motion controller can move with a trajectory error of less than 0.2 m and a motion error of less than 0.01 m.

Research on Maximum Adhesion Control Technique Based on Polach Adhesion Model and Vehicle Dynamics Model

Research on Maximum Adhesion Control Technique Based on Polach Adhesion Model and Vehicle Dynamics Model

Numerical Investigation of the Fuel Tank Sloshing Condition of a Commercial Vehicle

This study investigates the impact of baffles on fuel sloshing behavior within truck fuel tanks using numerical simulations. The volume of the fluid multiphase model is employed to analyze the flow dynamics of 25% diesel fuel in a 250 L tank, modeled in a 3D domain. Two configurations were compared: a tank with baffles and one without. The primary focus is to analyze fuel distribution within the intake port region during vehicle acceleration and deceleration maneuvers. The simulated scenario mimics a realistic driving situation. The vehicle accelerates from 0 km/h to 60 km/h over 10 s, followed by a 3-s braking period to reach a complete stop (0 km/h) at the 13-s mark. The simulation then observes the fuel behavior within the tank for an addi-tional 7 s while the vehicle remains stationary. Results reveal significant differences in fuel behavior between baffled and unbaffled tanks. In the absence of baffles, the sloshing motion is substantial, leading to a complete depletion of fuel in the intake port region for a duration of 3 s during both the acceleration and deceleration phases (between 10 and 13 s). Compared to a standard tank, the presence of baffles significantly reduced the sloshing amplitude by approximately 70%. Furthermore, baffles led to a 50% decrease in pressure variations on the tank walls. Temporary fuel starvation can negatively impact engine performance and combustion efficiency. Conversely, the presence of baffles within the tank effectively mitigates sloshing and ensures continuous fuel presence at the intake port the entire simulation. This suggests that baffles play a crucial role in maintaining a stable and consistent fuel supply to the engine, even during dynamic vehicle maneuvers.

Construction and simulation of a decision model for anti-slip restoration technology of asphalt pavement surface based on vehicle dynamics

Construction and simulation of a decision model for anti-slip restoration technology of asphalt pavement surface based on vehicle dynamics

Intelligent vehicle lateral control strategy research based on feedforward + predictive LQR algorithm with GA optimisation and PID compensation

Targeting the lateral motion control problem in the intelligent vehicle autopilot structural system, this paper proposes a feedforward + predictive LQR algorithm for lateral motion control based on Genetic Algorithm (GA) parameter optimisation and PID steering angle compensation. Firstly, based on the vehicle dynamics tracking error model, the intelligent vehicle LQR lateral motion controller as well as the feedforward controller are designed, and upon which the predictive controller is added to eliminate the system lag.Subsequently, exploiting the advantage that the PID algorithm is not model-based, a PID steering angle compensation controller that can directly control and correct the lateral error is designed. Second, a LQR controller based on path tracking deviation is designed by using the parameter rectification method of genetic algorithm (GA), which optimizes the control parameters of the lateral motion controller and improves the adaptivity of the control accuracy. Finally, Based on the Carsim-Simulink co-simulation platform, the simulation validation and analysis of double lane change (DLC) test and circular condition test (CCT) are carried out, and the results indicate that compared with the other two LQR controllers, the optimised controllers improved more than 50% in lateral error and heading error control, and the vehicle sideslip angle and vehicle yaw rate are in the range of −0.05° to 0.05° and − 0.15 rad/s to 0.10 rad/s, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

How optimal is the minimum-time manoeuvre of an artificial race driver?

Minimum-lap-time optimal control problems (MLT-OCPs) are a popular tool to assess the best lap time of a vehicle on a racetrack. However, MLT-OCPs with high-fidelity dynamic vehicle models are computationally expensive, which limits them to offline use. When using autonomous agents in place of an MLT-OCP for online trajectory planning and control, the question arises of how far the resulting manoeuvre is from the maximum performance. In this paper, we improve a recently proposed artificial race driver (ARD) for online trajectory planning and control, and we compare it with a benchmark MLT-OCP. The novel challenge of our study is that ARD controls the same high-fidelity vehicle model used by the benchmark MLT-OCP, which enables a direct comparison of ARD and MLT-OCP. Leveraging its physics-driven structure and a new formulation of the g-g-v performance constraint, ARD achieves lap times comparable to the offline MLT-OCP (few milliseconds difference). We analyse the different trajectories resulting from the ARD and MLT-OCP solutions, to understand how ARD minimises the effect of local execution errors in search of the minimum-lap-time. Finally, we show that ARD consistently maintains its performance when tested on unseen circuits, even with unmodelled changes in the vehicle's mass.

Ride Test on Vehicles Travelling Over Speed Bumps: Simulation with CarSim Software

This study explores the effects of different speed bump geometries—flat-topped, sinusoidal, and parabolic—on vehicle dynamics and ride comfort using CarSim simulations. The analysis focuses on key parameters such as vertical forces on the suspension, vertical acceleration, and the wheel surface adhesion index. The results show that flat-topped bumps generate the highest vertical forces, reaching peaks of up to 6,000 N on the front suspension, leading to increased discomfort. Sinusoidal bumps, in contrast, generate smoother transitions, with vertical forces peaking at approximately 3,500 N, improving ride comfort. At vehicle speeds of 30 km/h, the vertical forces on the suspension increase significantly, with flat-topped bumps reducing the wheel surface adhesion index to as low as 0.6, indicating a higher risk of wheel slip and compromised vehicle stability. In contrast, sinusoidal bumps maintain a more favorable adhesion index of 0.85 at similar speeds. These reductions in adhesion elevate the risk of loss of control, especially at higher speeds. The findings suggest that adaptive suspension systems, capable of adjusting damping and stiffness based on the bump geometry and vehicle speed, would enhance ride quality and stability. Additionally, smoother bump designs, such as sinusoidal profiles, are recommended to reduce the impact on vehicle dynamics, particularly in urban environments. These insights contribute to improving both vehicle design and road safety, ensuring safer and more comfortable driving experiences.

Optimization design of unmanned mining truck path tracking controller based on road adhesion coefficient estimation

Abstract In response to the issues of inaccurate tracking precision caused by continuous turning and variable road adhesion coefficients on complex roads in mining areas for unmanned mining trucks, this paper adopts unscented Kalman filter (UKF) to estimate the road adhesion coefficient. Based on this, an adaptive preview feedforward linear quadratic regulator (LQR) control algorithm is designed using genetic algorithm, feedforward control, and linear quadratic optimal path tracking control methods. Firstly, a nine degree of freedom vehicle dynamics model and a two degree of freedom vehicle lateral dynamics model are established, and the Dugoff model to calculate tire forces is introduced; secondly, utilizing UKF to estimate the road adhesion coefficient, and leveraging active disturbance rejection control for rapid tracking of road curvature, an optimized design of the LQR controller is carried out. Then, the effectiveness of the designed controller was verified through Trucksim/Simulink joint simulation testing. Finally, the hardware in the loop test results showed that the designed controller had good tracking accuracy and strong adaptability in complex road and special working conditions in mining areas.

Data-Driven Modeling of Tire-Soil Interaction with POD-Based Model Order Reduction

Abstract A data-driven model capable of predicting time-domain solutions of a high-fidelity tire-soil interaction model is developed to enable quick prediction of mobility capabilities on deformable terrain. The adaptive model order reduction based on the proper orthogonal decomposition (POD), for which the high-dimensional equations are projected onto the reduced subspace, is utilized as the basis for predicting the time-domain tire-soil interaction behavior. The projection-based model order reduction, however, requires many online matrix operations due to the successive updates of the nonlinear functions and Jacobians at every time step, thereby hindering the computational improvement. Therefore, a data-driven approach using a Long Short Term Memory (LSTM) neural network is introduced to predict the reduced order coordinates without the projection and time integration processes for computational speedup. With this model, a hybrid data-driven/physics-based off-road mobility model is proposed, where four separate LSTM-POD data-driven tire-soil interaction models are integrated into the physics-based multibody dynamics (MBD) vehicle model through a force-displacement coupling algorithm. By doing so, the individual data-driven tire-soil interaction model can be constructed efficiently, and the MBD and LSTM models are assembled as a single off-road mobility model and analyzed with existing off-road mobility solvers. The predictive ability and computational benefit of the proposed data-driven tire-soil interaction model with the POD-based model order reduction are examined with several numerical examples.

Nonlinear scenario‐based model predictive control for quadrotors with bidirectional thrust

AbstractThe control of quadrotor vehicles under state and parameter uncertainty is a well studied problem that is vitally important to the deployment of these systems under real world conditions. In this article, we propose a linearization‐based extension to nonlinear systems of the existing scenario model predictive control (MPC) framework, which quantifies the impact of uncertainty on the vehicle dynamics through repeated sampling of the uncertainty space. Given the computational costs of such an approach, we also propose two simplifications of the scenario MPC algorithm that are significantly more tractable. In order to evaluate the performance of the algorithms, the specific problem of the control of a bidirectionally actuated quadrotor vehicle is considered. Simulations are carried out for each scenario MPC scheme as well as for a reference deterministic MPC scheme. When a sufficiently large sample count is considered, each of the scenario MPC algorithms achieves safer performance than the deterministic formulation without sacrificing any optimality. Additionally, the approximate solution techniques conclusively outperform the original nonlinear scenario MPC formulation for the same computational cost.

Research on Trajectory Planning and Tracking Algorithm of Crawler Paver

The implementation of unmanned intelligent construction on the concrete surfaces of an airport effectively improves construction accuracy and reduces personnel investment. On the basis of three known common tracked vehicle dynamics models, reference trajectory planning and trajectory tracking controller algorithms need to be designed. In this paper, based on the driving characteristics of the tracked vehicle and the requirements of the stepping trajectory, a quartic polynomial trajectory planning algorithm was selected with the stability of the curve as a whole and the end point as the optimization goal, combining the tracked vehicle dynamics model, collision constraints, start–stop constraints and other boundary conditions. The objective function of trajectory planning was designed to effectively plan the reference trajectory of the tracked vehicle’s step-by-step travel. In order to realize accurate trajectory tracking control, a nonlinear model predictive controller with transverse-longitudinal integrated control was designed. To improve the real-time performance of the controller, a linear model predictive controller with horizontal and longitudinal decoupling was designed. MATLAB 2023A and CoppeliaSim V4.5.1 were used to co-simulate the two controller models. The experimental results show that the advantages and disadvantages of the tracked vehicle dynamics model and controller design are verified.

Path tracking control of high‐speed intelligent vehicles considering model mismatch

AbstractThe precision of path tracking in high‐speed intelligent vehicles is significantly influenced by model mismatch arising from factors like parameter uncertainty, model simplification, external disturbances, and other sources. In this paper, we propose a novel robust control strategy that integrates the compensation function observer (CFO) with the model predictive control (MPC) method, utilizing an optimized vehicle dynamics model (opt‐model) to address this challenge, called OCMPC. Initially, we establish the opt‐model to design predictive model by leveraging suspension kinematics and compliance (K&C) data collected from a miniature pure electric vehicle. Remarkably, the opt‐model exhibits improved accuracy compared to the conventional vehicle dynamics model (con‐model) while preserving the same degrees of freedom (DOF). Next, we incorporate CFO into the path tracking process of high‐speed intelligent vehicles, enabling dynamic real‐time observation of the model mismatch between the prediction model and the actual vehicle. CFO can capture the dynamics of the vehicle, including nonlinearities and uncertainties, without placing a heavy computing burden on the controller. This observed mismatch is subsequently employed for feed‐forward compensation, facilitating the attainment of optimal control values. Ultimately, we validate the effectiveness of our proposed method in enhancing path tracking accuracy for high‐speed intelligent vehicles through co‐simulation using Simulink and Carsim.

A study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation

This paper is a study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation. It aims to develop a prediction model for the energy efficiency of electric vehicles and compare it with the results of chassis dynamometer tests. Driving tests were conducted on a chassis dynamometer, which included various test cycles of the 5-cycle test. Using MATLAB Simulink, a vehicle dynamics analysis model for electric vehicles was constructed, and analytical research was conducted focusing on light-duty electric vehicles. By comparing and analyzing the experimental data obtained from the chassis dynamometer with the data calculated through simulation, it is possible to verify the accuracy of the electric vehicle energy efficiency prediction model. And the reliability of the simulation model was secured by comparing the experimental data obtained through chassis dynamometer test with the simulation results of the developed electric vehicle energy efficiency prediction model. There is good agreement between the experimental and simulation results. In most areas, the error rate remains below 3 %, indicating that the simulation model closely follows the experimental results from the chassis dynamometer. Based on these results, it is expected that the developed simulation model can be utilized for measuring the energy efficiency of electric vehicles.

Road adhesion coefficient Estimation: Physics-informed deep learning method with vehicle dynamics model

Road adhesion coefficient (RAC) is not only a significant basis for trajectory planning and decision-making of autonomous vehicles but also a critical parameter to determine the control potential of autonomous vehicles under complex operating conditions. Accurate, timely, and reliable estimation of RAC facilitates many function units of autonomous vehicles and improves driving safety. To address the issues of low accuracy and transparency of RAC estimation method based on road surface image recognition, a vehicle dynamics model-informed deep learning (VDMIDL) method for RAC estimation is proposed in this work. The physics information related to vehicle dynamics is integrated into a CNN-based deep learning model via the fusion of vehicle physical features and high-dimensional road surface image features to realize accurate estimation of RAC. The results of the model training show that the proposed VDMIDL model for RAC estimation possesses higher prediction accuracy compared with the conventional deep learning (CDL) model. The vehicle test results indicate that the proposed VDMIDL model can provide more prospective and precise RAC for trajectory planning, decision-making, and control of the vehicle in response to sudden variations in road conditions. The VDMIDL model is applicable to a wide range of road conditions. RAC estimated by the VDMIDL model is accurate and stable under poor road condition in particular. The proposed fusion estimation model incorporates more physics information into deep learning model, endowing more interpretability and transparency to the black-box model.

Integrating machine learning and bee algorithms with multi-agent systems for dynamic vehicle routing problem with time windows

Integrating machine learning and bee algorithms with multi-agent systems for dynamic vehicle routing problem with time windows

Design and research of face gear central limited slip differential and analysis of its influence on vehicle performance

In this paper, a new design scheme of face gear central limited-slip differential (F-LSD) is proposed, which aims to unveil the technical veil of central limited-slip differential and fill the research blank of face gear application in the field of central limited-slip differential. Through the analysis of structural principle and motion characteristics, the dynamic equilibrium equation and locking coefficient model of F-LSD are established. The Simulink simulation model based on F-LSD dynamics and the torque distribution model of the whole transmission system are established. A 7-DOF vehicle dynamics model was established, and the performance of F-LSD vehicle in four conditions of acceleration, climbing, chassis twist sine wave and double line change was studied by using simulation software. At the same time, four groups of four-wheel drive vehicles with different central differential speeds were set as the control test group. The simulation results show that F-LSD system can effectively improve the vehicle's dynamic performance and extrication performance, and will not reduce the vehicle's handling stability, which provides a reference for the practical application of the face gear central limited slip differential.

Optimization of Power Control for Autonomous Hybrid Electric Vehicles with Flexible Power Demand

Abstract Technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics and powertrain dynamics, with one key aspect often overlooked. This aspect, known as flexible power demand, recognizes that the powertrain control system does not necessarily have to precisely match the power requested by the vehicle motion controller at all times. Leveraging this feature can lead to control designs achieving improved fuel economy by adding an extra degree of freedom to the powertrain control while maintaining safety and drive comfort. The present research investigates the use of an Approximate Dynamic Programming (ADP) approach to develop a powertrain controller, which takes into account the flexibility in power demand within the ADP framework. The concept of reachable sets is incorporated into the ADP framework to ensure safety, improve ride comfort, and enhance the accuracy of the optimization solution. The formulation is based on an autonomous hybrid electric vehicle (HEV), while the methodology can also be applied to other types of vehicles. It is also found that necessary customization of the ADP algorithm is needed for this particular control problem to prevent convergence issues. Finally, a case study is presented to evaluate the effectiveness of flexible power demand, as addressed by the ADP method. The experiment demonstrates a 14.1% improvement in fuel economy compared to a scenario without flexible power demand.