Vehicle Dynamics Model Research Articles

Adaptive backstepping control for nonlinear CAVs platoon with input delays and disturbances

In recent years, the platoon control of connected autonomous vehicles (CAVs) has attracted a lot of attention in intelligent transport research. The third-order nonlinear vehicle dynamics model, due to its practicality, has been adopted in many research projects. In platoon control, controller input delays and disturbances are the significant issues that cannot be ignored. To this end, this paper investigates the cooperative control problem of third-order nonlinear CAVs platoon with controller input delays and unknown external disturbances. First, the neural network (NN) is used to approximate external disturbances with unknown bounds. Then, with the help of Pade approximation, a new variable is introduced to deal with the input delays. Subsequently, a corresponding compensation system is constructed based on the introduced variable. At last by using backstepping approach, a distributed adaptive control strategy based on vehicle-to-vehicle (V2V) communication is proposed in this paper. Additionally, Lyapunov functions and the uniformly ultimately bounded stability theorem are used to demonstrate the stability of the closed-loop systems, and the effectiveness of the control strategy is demonstrated by a simulation example.

Wheel wear of mountain metro vehicles with complex line conditions

Purpose Mountain metro vehicles have unique wheel wear characteristics due to the complex flat and longitudinal lines. With a combination of flat and longitudinal curved tracks, the traction and braking conditions are more frequent in mountain metro vehicles. This paper aims to analyze the wheel wear characteristics of mountain metro vehicles in complex flat and longitudinal lines. Design/methodology/approach A dynamic model of the mountain metro vehicle and a wear model are established to analyze the dynamic and wheel wear characteristics of mountain metro vehicles. The wheel wear law of mountain metro vehicles under complex track conditions is analyzed, and the suppression measure based on variable stiffness rotary arm nodes of mountain metro vehicles is proposed. Findings The results showed that the maximum wheel wear depth without considering the ramp track and considering the ramp track are 3.283 mm and 3.717 mm, respectively; the maximum wheel wear depth increases by 13.2%. Wheel wear can be effectively suppressed by the variable stiffness rotary arm model, and the maximum wear depth of the wheel profile is 3.316 mm, which is reduced by 10.79% compared with the constant stiffness model. Originality/value A dynamic model of a mountain metro vehicle is established, and the metro vehicle wheel wear under the large ramps under the traction and braking conditions is analyzed, and the metro vehicle wheel wear suppression measure based on variable stiffness rotary arm nodes is proposed. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0247

Efficient Nonlinear Model Predictive Path Tracking Control for Autonomous Vehicle: Investigating the Effects of Vehicle Dynamics Stiffness

Motion control is one of the three core modules of autonomous driving, and nonlinear model predictive control (NMPC) has recently attracted widespread attention in the field of motion control. Vehicle dynamics equations, as a widely used model, have a significant impact on the solution efficiency of NMPC due to their stiffness. This paper first theoretically analyzes the limitations on the discretized time step caused by the stiffness of the vehicle dynamics model equations when using existing common numerical methods to solve NMPC, thereby revealing the reasons for the low computational efficiency of NMPC. Then, an A-stable controller based on the finite element orthogonal collocation method is proposed, which greatly expands the stable domain range of the numerical solution process of NMPC, thus achieving the purpose of relaxing the discretized time step restrictions and improving the real-time performance of NMPC. Finally, through CarSim 8.0/Simulink 2021a co-simulation, it is verified that the vehicle dynamics model equations are with great stiffness when the vehicle speed is low, and the proposed controller can enhance the real-time performance of NMPC. As the vehicle speed increases, the stiffness of the vehicle dynamics model equation decreases. In addition to the superior capability in addressing the integration stability issues arising from the stiffness nature of the vehicle dynamics equations, the proposed NMPC controller also demonstrates higher accuracy across a broad range of vehicle speeds.

Adaptive sensor attack detection and defense framework for autonomous vehicles based on density

The security of autonomous vehicles heavily depends on localization systems that integrate multiple sensors, which are vulnerable to sensor attacks and increase the risk of accidents. Given the diversity of sensor attacks and the dynamic changing of driving scenarios of autonomous vehicles, an adaptive and effective attack detection and defense framework faces a considerable challenge. This paper proposes a novel real-time adaptive attack detection and defense framework based on density, which can detect and identify attacked sensors and effectively recover data. We first develop a reinforcement learning multi-armed Bandit-based Density-Based Spatial Clustering of Applications with Noise (BDBSCAN) algorithm that selects hyperparameters adaptively. The Adaptive Extended Kalman Filter (AEKF) combines with the vehicle dynamic model on the localization system and extracts data features used for the BDBSCAN algorithm to monitor potential sensor attacks. If attack detection indicates possible system compromise, AEKF is further employed on localization sensors with anomalies identified through the BDBSCAN algorithm of the attacked sensors. To ensure precision and reliability, the data recovery incorporates a redundancy mechanism to apply a decision tree to select the optimal state estimation between AEKF and Extended Kalman Filter (EKF) to replace corrupted sensor data. To evaluate the effectiveness and adaptability of the proposed framework, we conducted 15,000 experiments using the real-world KITTI and V2V4Real datasets across various driving and sensor attack scenarios. The results demonstrate that our proposed framework achieves 100% accuracy and 0% false alarm rate in various driving scenarios for attack detection within 0.15 s, with a recovery time of 0.08 s.

RRT-guided experience generation for reinforcement learning in autonomous lane keeping

Reinforcement Learning has emerged as a significant component of Machine Learning in the domain of highly automated driving, facilitating various tasks ranging from high-level navigation to control tasks such as trajectory tracking and lane keeping. However, the agent’s action choice during training is often constrained by a balance between exploitation and exploration, which can impede effective learning, especially in environments with sparse rewards. To address this challenge, researchers have explored combining RL with sampling-based exploration methods such as Rapidly-exploring Random Trees to aid in exploration. This paper investigates the effectiveness of classic exploration strategies in RL algorithms, particularly focusing on their ability to cover the state space and provide a quality experience pool for learning agents. The study centers on the lane-keeping problem of a dynamic vehicle model handled by RL, examining a scenario where reward shaping is omitted, leading to sparse rewards. The paper demonstrates how classic exploration techniques often cover only a small portion of the state space, hindering learning. By leveraging RRT to broaden the experience pool, the agent can learn a better policy, as exemplified by the dynamic vehicle model’s lane-following problem.

Simulation of Advanced Driving Assistance Systems for a Dynamic Vehicle Model

Simulation of Advanced Driving Assistance Systems for a Dynamic Vehicle Model

Research on the potential of integrated vehicle control with closed-loop phase portrait analysis

The phase portrait is an important tool for analyzing vehicle stable region widely utilized in vehicle dynamic control systems. At handling limits, traditional open-loop vehicle stable region constraints will restrict the vehicle control ability, while the closed-loop actuator interventions can help states outside the open-loop stable region converge back to stable origin, which is more suitable for constraining vehicle states under extreme working conditions. This article systematically analyzes the potential of integrated vehicle control with closed-loop phase portrait. Firstly, a vehicle dynamic model considering actuator dynamics and tire transient characteristics is established. Then, a numerical optimal control method for calculating closed-loop controllability region is introduced. The controllability region under different friction coefficients, vehicle speeds, convergence time, vehicle steering response characteristics, actuator time constants, and tire transient characteristics are discussed and analyzed. In terms of different control inputs combinations, this article displays the controllability regions under different front steering angles, rear steering angles, direct yaw moments, and their synergistic performances. Control input laws derived from numerical optimization are summarized in two specific directions. Finally, a comparative analysis of open-loop stable regions and closed-loop controllability regions confirms the superiority of the proposed method. Potential applications of the systematic analysis are also discussed.

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.

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

Coupling Dynamics Study on Multi-Body Separation Process of Underwater Vehicles

Based on the Newton-Euler method, a coupling rigid-body dynamics model of a Trans-Medium Vehicle (TMV) separating from an Unmanned Underwater Vehicle (UUV) has been established. The modeling is based on the “holistic method” and “Kane” ideas respectively, so that most of the equations can be derived without considering the internal forces between the two bodies. The separation propulsion force, which is an internal force, only appears in the relative glide dynamics equation of the TMV along the axis of the separation tube that is installed on the UUV. This greatly reduces the workload of modeling and derivation. The UUV works entirely underwater, while the hydrodynamic shape of the TMV changes continuously during the process of the TMV separating from the UUV. Therefore, accurate hydrodynamic calculations for the UUV and TMV are the basis of numerical resolution for the two rigid bodies’ coupling dynamics model in water. A large number of numerical simulations was conducted using CFD methods to investigate the hydrodynamic performance of the UUV and TMV under various conditions. These simulations aim to establish a hydrodynamic database, and accurate hydrodynamic models were developed through fitting methods and online interpolation. In the process of solving the coupling dynamics of two bodies, the hydrodynamic model is used to calculate the hydrodynamic force experienced by the UUV and TMV. This balances the accuracy and efficiency of a numerical simulation. Finally, numerous simulations and comparative analyses were conducted under various operational conditions and separation parameters. The simulation results indicate that the impact of TMV separation on the motion state of the UUV becomes more prominent with smaller UUV to TMV mass ratios or deeper TMV separation depths. This effect can further influence the stability control of the UUV. The coupling rigid body dynamics analysis method established in this paper provides a fast and effective prediction method for use during the scheme design and separation safety evaluation phases of creating UUV-TMV systems.

Finite-time formation trajectory tracking control for unmanned underwater vehicles with time delays and Markov-switch topology

ABSTRACT The formation trajectory tracking control strategy of unmanned underwater vehicles is proposed for communication time delay and communication topology random switching. The methods ensure that formation systems are finite-time bounded under discrete time. Firstly, based on the consistency theory and the exact feedback linearised dynamics model of unmanned underwater vehicle, a control strategy is proposed for discrete formation system with communication time delay. Secondly, a cooperative control method is designed for communication problems of Markovian switching topology and time delay. Then, definition of finite-time bounded and linear matrix inequality technique are applied to sufficient conditions of system stability for the above problem. Finally, the formation motion of discrete unmanned underwater vehicle system in three-dimensional space is simulated, fully demonstrating the feasibility of the method.

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.

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.

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.

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.

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.

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.

An Enhanced GNSS/MINS Integrated Navigation System With a Wheel Velocity Predictor Based on Vehicle Dynamics Model

An Enhanced GNSS/MINS Integrated Navigation System With a Wheel Velocity Predictor Based on Vehicle Dynamics Model