Lateral Dynamic Model Research Articles

Sensor fault detection in autonomous mining trucks with unknown varying gross weight.

This paper focuses on the problem of the sensor fault detection in Autonomous Mining Trucks (AMTs) with the unknown varying gross weight and measurement noise. The dust and extreme temperatures in strip mines can lead to bias and drift faults in the sensors of AMTs. Besides, due to the bumpy roads in mining areas, the longevity and accuracy of the weight sensors cannot be guaranteed, which makes the weight sensor useless for AMTs. Therefore, the gross weight is treated as an unknown parameter in the lateral dynamics model of AMTs. The emphasis or difficulty lies in obtaining the state estimation and reducing the false alarm rate under the unknown variations in gross weight. This paper proposes an interval observer with the zonotope method to estimate the AMT state under the condition of unknown variations in gross weight. An interval residual generator with the generalized likelihood ratio test and the zonotope method is proposed for the sensor fault detection in AMTs. Finally, the effectiveness of the proposed approach is validated through the simulations of an AMT.

Conceptual Design of a Novel Autonomous Water Sampling Wing-in-Ground-Effect (WIGE) UAV and Trajectory Tracking Performance Optimization for Obstacle Avoidance

As a fundamental part of water management, water sampling treatments have recently been integrated into unmanned aerial vehicle (UAV) technologies and offer eco-friendly, cost-effective, and time-saving solutions while reducing the necessity for qualified staff. However, the majority of applications have been conducted with rotary-wing configurations, which lack range and sampling capacity (i.e., payload), leading scientists to search for alternative designs or special configurations to enable more comprehensive water assessments. Hence, in this paper, the conceptual design of a novel long-range and high-capacity WIGE UAV capable of autonomous water sampling is presented in detail. The design process included a vortex lattice solver for aerodynamic investigations, while analytical and empirical methods were used for weight and dimensional estimations. Since the mission involved operation inside maritime traffic, potential obstacle avoidance scenarios were discussed in terms of operational safety, and the aim was for autonomous trajectory tracking performance to be improved by means of a stochastic optimization algorithm. For this purpose, an artificial intelligence-integrated concurrent engineering approach was applied for autonomous control system design and flight altitude determination, simultaneously. During the optimization, the stability and control derivatives of the constituted longitudinal and lateral aircraft dynamic models were predicted via a trained artificial neural network (ANN). The optimization results exhibited an aerodynamic performance enhancement of 3.92%, which indicates a remarkable improvement in trajectory tracking performance for both the fly-over and maneuver obstacle avoidance modes, by 89.9% and 19.66%, respectively.

Analysis of coupled suspension lateral dynamics characteristics based on suspension moment centre and non-steady tyre model

This paper presents an analytical approach for developing a coupled suspension lateral dynamics model to investigate lateral vibration characteristics. The concept of the suspension moment centre was introduced. The direction of the constraint reaction force from the independent suspension control arm is perpendicular to the tyre contact speed. This arrangement leads to a larger lateral movement of the suspension hub as the height of the moment centre increases. According to the coupled suspension lateral dynamics model, experimental results were used to validate the relationship between wheel camber, lateral displacement of wheel centre and wheel travel. The non-steady state lateral suspension force was calculated based on the tyre slip ratio, defined as the lateral speed relative to the rolling speed, and the system dynamics characteristics was analysed. The results show that under excitation from an uneven road surface, the root mean square value of the suspension lateral force initially increases and then decreases as vehicle speed increases. Additionally, the root mean square value of the suspension lateral force increases with the increase in tyre relaxation length.

Dynamic Analysis and Equivalent Modeling for a Four-Axle Vehicle

This paper focuses on a comprehensive study of a four-axle vehicle, including dynamics analysis, equivalent modeling methods, and their comparison. Firstly, a linear two-degree lateral dynamic model is established, which has four drive axles and two steer axles. Secondly, the mathematical transfer function expressions for the yaw rate and the centroid sideslip angle were derived on the basis of the model. The steady-state parameters, such as yaw rate gain Gγss, centroid sideslip angle gain Gβss, stability factor Kn, equivalent axial distance ln, and equivalent centroid sideslip angle coefficient Kn’ were obtained by using the transfer functions. Then, the steady-state and transient characteristics are roundly discussed, including steady-state parameters, system root trajectory, frequency domain, and time domain. Some recommendations for the four-axle vehicle’s parameter design are also given. Finally, for a more simple and efficient analysis of response characteristics of four-axle vehicles and even n (n > 4) axle vehicles, the equivalent model is developed for the four-axle vehicle, and comprehensive analyses are presented with four equalization methods, which are based on the inner heart of the approximation triangle, the outer heart of the approximation triangle, the center of gravity of the approximation triangle and the compensation point. Following a thorough analysis of the four, it is determined that the inner approximation triangle solution approach is most suited for four-axle vehicles.

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.

Dynamic modelling and experimental validation of a dynamic track stabiliser vehicle–track spatially coupling dynamics system

A dynamic track stabiliser vehicle is an unconventional vehicle for maintaining the quality of ballasted tracks, and its dynamic performance has a significant impact on its working efficiency. For the first time, this paper proposes a comprehensive dynamic track stabiliser vehicle–track coupled vertical and lateral multi-body dynamics model based on classical vehicle–track coupling dynamics theory. In this model, detailed attention is given to the vertical, lateral, roll, yaw, and pitch motions of the components, including the vehicle body, bogies, wheelsets and stabilisers. The Shen–Hedrick–Elkins theory is employed to describe non-linear wheel–rail contact relationships between the bogie wheelsets and the rails, and three various methodologies are adopted to address complicated wheel–rail interaction problems between the stabiliser wheelsets and the rails. This comprehensive multi-body dynamics model enables a detailed investigation of the dynamic performance of the system under complex excitations, such as the lateral excitation of the stabilisers and vertical downward pressure exerted by hydraulic cylinders, especially the dynamic response of the stabilisers in the vibration environment of the complete vehicle. The dynamics model was fully validated by comparing its results with time- and frequency-domain data obtained from field tests. The results indicate that the model is capable of being used for analysis of the dynamic performance of dynamic track stabiliser vehicles.

An asymmetrically variable wingtip anhedral angles morphing aircraft based on incremental sliding mode control: Improving lateral maneuver capability

This paper presents the design of an asymmetrically variable wingtip anhedral angles morphing aircraft, inspired by biomimetic mechanisms, to enhance lateral maneuver capability. Firstly, we establish a lateral dynamic model considering additional forces and moments resulting during the morphing process, and convert it into a Multiple Input Multiple Output (MIMO) virtual control system by importing virtual inputs. Secondly, a classical dynamics inversion controller is designed for the outer-loop system. A new Global Fast Terminal Incremental Sliding Mode Controller (NDO-GFTISMC) is proposed for the inner-loop system, in which an adaptive law is implemented to weaken control surface chattering, and a Nonlinear Disturbance Observer (NDO) is integrated to compensate for unknown disturbances. The whole control system is proven semi-globally uniformly ultimately bounded based on the multi-Lyapunov function method. Furthermore, we consider tracking errors and self-characteristics of actuators, a quadratic programming-based dynamic control allocation law is designed, which allocates virtual control inputs to the asymmetrically deformed wingtip and rudder. Actuator dynamic models are incorporated to ensure physical realizability of designed allocation law. Finally, comparative experimental results validate the effectiveness of the designed control system and control allocation law. The NDO-GFTISMC features faster convergence, stronger robustness, and 81.25% and 75.0% reduction in maximum state tracking error under uncertainty compared to the Incremental Nonlinear Dynamic Inversion Controller based on NDO (NDO-INDI) and Incremental Sliding Mode Controller based on NDO (NDO-ISMC), respectively. The design of the morphing aircraft significantly enhances lateral maneuver capability, maintaining a substantial control margin during lateral maneuvering, reducing the burden of the rudder surface, and effectively solving the actuator saturation problem of traditional aircraft during lateral maneuvering.

Hybrid modeling for vehicle lateral dynamics via AGRU with a dual-attention mechanism under limited data

A precise vehicle dynamics model is critical for simulation and algorithm testing. Neural networks have been widely used to build high-fidelity vehicle dynamics models due to the excellent learning ability. However, data starvation is a common phenomenon in neural networks. With limited data, it is difficult for neural networks to achieve precise predictions. To address these problems, a hybrid model combining physics and dual attention neural networks is developed to model vehicle lateral dynamics. First, due to the interpretability of the physical model, linear lateral dynamics model is regarded as a prior model. However, due to the imperfect prior knowledge, there are residuals between the prior and the actual vehicle dynamics. Therefore, neural networks are used to characterize the residuals to achieve recalibration of vehicle dynamics model. Modeling vehicle residual dynamics with neural networks is a time series forecasting problem. The GRU with a dual attention mechanism and adaptive initial hidden states (DA-AGRU) is designed to capture spatial and temporal correlations in the vehicle dynamics data. In particular, considering the unique auto regressive structure of the hybrid model, a spatial attention mechanism with a feature fusion module is designed, so as to globally compute the weights of different channel features. The dataset used to train and validate the model is recorded from a vehicle platform, and the experimental results show that the proposed hybrid model can accurately predict vehicle dynamics states in a data-scarce environment.

Modal coupling characteristics of primary hunting in metro vehicles: Carbody suspension modes and hunting modes

Due to coupling with carbody suspension modes in railway vehicles, primary hunting can lead to significant low frequency swaying of the carbody, severely deteriorating ride quality. In order to address this issue, this paper conducts a detailed investigation into the coupling effect between suspension modes and hunting modes of metro vehicles. Initially, a simplified lateral dynamics model is established, and a continuous modal tracking method is adopted for track suspension modes and hunting modes. The results indicate that when the modal frequency of one hunting mode approaches that of corresponding suspension mode, the damping ratio of the two modes exhibits abnormal variation, and the modal frequency curve veers or is slightly disturbed. In exploring the coupling characteristics of hunting modes and suspension modes, revealing the variation trends and similarity of modal shapes, polar diagrams of modal shapes are presented, and the correlation distance is calculated. Equivalent conicity exerts a substantial influence on the effect of modal coupling, influencing minimum damping ratio and modal shape similarity within the modal coupling zone. To ensure sufficient stability margin for metro vehicles, it is necessary to avoid excessively low equivalent conicity. Finally, the phenomenon of vibration response deterioration within the modal coupling zone is confirmed through both a simplified model and a full DOF model.

Integration of bio-inspired limb-like structure damping into motor suspension of high-speed trains to enhance bogie hunting stability

Hunting stability is an important performance criterion in railway vehicles. This study proposes an incorporation of a bio-inspired limb-like structure (LLS)-based nonlinear damping into the motor suspension system for traction units to improve the nonlinear critical speed and hunting stability of high-speed trains (HSTs). Initially, a vibration transmission analysis is conducted on a HST vehicle and a metro vehicle that suffered from hunting motion to explore the effect of different motor suspension systems from on-track tests. Subsequently, a simplified lateral dynamics model of an HST bogie is established to investigate the influence of the motor suspension on the bogie hunting behavior. The bifurcation analysis is applied to optimize the motor suspension parameters for high critical speed. Then, the nonlinear damping of the bio-inspired LLS, which has a positive correlation with the relative displacement, can further improve the modal damping of hunting motion and nonlinear critical speed compared with the linear motor suspension system. Furthermore, a comprehensive numerical model of a high-speed train, considering all nonlinearities, is established to investigate the influence of different types of motor suspension. The simulation results are well consistent with the theoretical analysis. The benefits of employing nonlinear damping of the bio-inspired LLS into the motor suspension of HSTs to enhance bogie hunting stability are thoroughly validated.

The Two-Parameter Bifurcation and Evolution of Hunting Motion for a Bogie System

The complex service environment of railway vehicles leads to changes in the wheel–rail adhesion coefficient, and the decrease in critical speed may lead to hunting instability. This paper aims to reveal the diversity of periodic hunting motion patterns and the internal correlation relationship with wheel–rail impact velocities after the hunting instability of a bogie system. A nonlinear, non-smooth lateral dynamic model of a bogie system with 7 degrees of freedom is constructed. The wheel–rail contact relations and the piecewise smooth flange forces are the main nonlinear, non-smooth factors in the system. Based on Poincaré mapping and the two-parameter co-simulation theory, hunting motion modes and existence regions are obtained in the parameter plane consisting of running speed v and the wheel–rail adhesion coefficient μ. Three-dimensional cloud maps of the maximum lateral wheel–rail impact velocity are obtained, and the correlation with the hunting motion pattern is analyzed. The coexistence of periodic hunting motions is further revealed based on combined bifurcation diagrams and multi-initial value phase diagrams. The results show that grazing bifurcation causes the number of wheel–rail impacts to increase at a low-speed range. Periodic hunting motion with period number n = 1 has smaller lateral wheel–rail impact velocities, whereas chaotic motion induces more severe wheel–rail impacts. Subharmonic periodic hunting motion windows within the speed range of chaotic motion, pitchfork bifurcation, and jump bifurcation are the primary forms that induce the coexistence of periodic motion.

Study of the influence of lateral forces and velocity on the lateral dynamics of automobile

Stability of a car's trajectory of motion is the ability of a car to retain its required state of motion, under different conditions of motion. Depending on different states of motion, cars can lose direction of motion instability when there is a lateral force, due to vehicle speed, due to turning or braking the road with different grip coefficients on the sides of the wheel. In such complex moving conditions, cars need to keep a stable trajectory of movement under the driver's control. The paper examines the stability of the car's trajectory in linear motion conditions with the influence of lateral forces and the velocity of the vehicle, these two factors act to change the state of movement of the vehicle, which significantly affects the safety of the vehicle's movement. The authors looked at the effect of vehicle velocity and lateral forces on vehicle transverse dynamics: lateral forces caused by horizontal winds, inclination of the road surface, speed of movement of the vehicle acting on the entire body create centrifugal inertial forces; this force acts to cause the tires to deform. At this point the vehicle is shifted from its original trajectory. In the research content, the authors built a lateral Dynamics Model, in which the influencing factors are set as inputs to the model including vehicle velocity and transverse wind forces, output is the rotation angle of the body, transverse acceleration and horizontal displacement of the vehicle. The simulation results showed that the effect of vehicle speed was relatively large: lateral displacement increased by 228 % at a speed of 120 km/h. Therefore, when driving on the highway, there should always be driver control to ensure that the car does not deviate from the lane

Online Modeling of Lateral Vehicle Dynamics via Recursive Integrated Physics-Data-Based Method

Online Modeling of Lateral Vehicle Dynamics via Recursive Integrated Physics-Data-Based Method

A Direct Yaw Moment Control Framework Through Robust T-S Fuzzy Approach Considering Vehicle Stability Margin

The direct yaw moment control system of distributed drive electric vehicles provides a flexible control scheme to enhance the vehicle stability through four independently driven in-wheel motors. However, the vehicle nonlinearities may lead to the invalidation of control. To this end, a Takagi–Sugeno (T-S) fuzzy-based robust <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H∞</i> control method is proposed to ensure the vehicle performance while addressing the nonlinear challenge. First, thanks to the T-S fuzzy modeling technology, the tire nonlinear characteristics are described by fuzzy rules, based on which the vehicle lateral dynamics model is established. Next, the safety region represented by the tire slip angles phase plane is presented to evaluate the vehicle stability performance. Considering the control priority of vehicle handling performance and stability control with different stability margins, a multiobjective optimization function is transformed into a standard robust performance optimization problem with dynamic weight coefficients. A T-S fuzzy-based robust <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H∞</i> state feedback controller is then designed to ensure the system stability and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H∞</i> performance. Finally, the hardware-in-the-loop tests are conducted to validate the proposed controller. Comparative results show the effectiveness to improve the vehicle handling performance while ensuring the stability.

Physics-Informed Data-Based LPV Modeling and Validations of Lateral Vehicle Dynamics

Physics-Informed Data-Based LPV Modeling and Validations of Lateral Vehicle Dynamics

Trajectory Tracking for Autonomous Vehicles Using Robust Model Predictive Control

This paper proposes a novel trajectory tracking control method using a robust model predictive control (RMPC) design technique based on matrix inequality and the theoretical approach of the hybrid & switched system. Firstly, considering bounded model uncertainties and norm-bounded external disturbances, the system states and control matrices are modified. In addition, vehicle time-varying longitudinal speed is considered, and a vehicle lateral dynamic model based on Linear Parameter Varying (LPV) is established by utilizing a polytope with finite vertices. Then the Min-Max robust MPC state feedback control law is obtained at every timestamp by solving a set of matrix inequalities which are derived from Lyapunov stability and the minimization of the worst-case in infinite-horizon quadratic objective function. The numerical simulation results demonstrated that the proposed robust LPV MPC can effectively track vehicle lateral dynamics on both straight and curved roads with varying longitudinal speeds.

Impacts of Tapered Wingtip on Lateral-Directional Stability Coefficients of a Morphing Fixed-wing UAV

Recent developments in unmanned aerial vehicle (UAV) technologies have shown the possibility of morphing applications to provide improvement in various performance metrics in the desired manner. In rotary-wing UAVs, applications mainly focused on propeller blades and rotor arms, while efforts on fixed-wing UAVs mainly concentrated on the main wing and tail geometries. Although every morphing design has its own advantages and disadvantages, all of the applications have similar common purposes to have improved aerodynamics, flight performance, control responses, or a combination of such objectives. In that context, new morphing design attempts require a precise investigation of their pros and cons. Thus, in this study, a new morphing scenario of tapering morphing wingtip is applied to ZANKA-I fixed-wing UAV and investigated in terms of lateral-directional stability considerations. The lateral dynamic model of the aircraft is constituted and necessary aerodynamic, geometric, and inertial assessments are numerically and analytically performed. The lateral-directional stability coefficients are discussed and an improvement in lateral stability is obtained, while directional stability is found to be affected negatively by the morphing application.

Development and performance evaluation of lateral control simulation-based multi-body dynamics model for autonomous agricultural tractor

Development and performance evaluation of lateral control simulation-based multi-body dynamics model for autonomous agricultural tractor

Adaptive heading control strategy for unmanned ground vehicle with variable wheelbase based on robust-active disturbance rejection control

In order to fully exploit the variable wheelbase characteristics of the unmanned ground vehicle (UGV) with variable configuration, this paper proposes an adaptive heading tracking control strategy based on wheelbase changes. This strategy involves adjusting the wheelbase according to different working conditions to optimize various driving performance aspects. Firstly, the impact of changing wheelbase on the response characteristics of sideslip angle and heading angle relative to the front-wheel steering angle is analyzed by using a lateral vehicle dynamic model. The configurations where the 2nd axle moves forward by 0.5 m, remains unchanged, and moves backward by 0.5 m are defined as the ‘stable configuration’, ‘balanced configuration’, and ‘maneuverable configuration’, respectively. Considering time lag effects of hydraulic systems and external random noise interference, a robust-active disturbance rejection control (r-ADRC) method is developed by incorporating a robust term into active disturbance rejection control (ADRC) in order to achieve desired front-wheel steering angle. Additionally, a torque distribution method based on the tire load rate and the real-time vertical load of each wheel is applied to ensure uniform tire wear and enhance longitudinal driving stability. Finally, simulations under various typical working conditions as well as scaled prototype experiments are conducted, and both the theory behind the changing wheelbases and the effectiveness of proposed control strategy are verified. According to different vehicle configurations and mission requirements, the maneuverability and stability of the ground unmanned vehicle are ensured respectively.

Planning and tracking control of full drive-by-wire electric vehicles in unstructured scenario

Full drive-by-wire electric vehicles (FDWEV) equipped with X-by-wire technology can enable independent driving, braking, and steering of each wheel, making them an ideal platform for developing autonomous driving technology. However, designing a robust control algorithm that comprehensively integrates vehicle path planning in a complex and unstructured scenario is a challenging task for FDWEV. To address this issue, this paper (1) proposes the artificial potential field (APF) method for path planning in the prescribed park with different static obstacles to generate the reference path information, where speed planning is incorporated considering kinematics and dynamic constraints; (2) designs curvature calculation (CC-based) and model predictive control (MPC-based) tracking methods with the lateral dynamics model to track the desired path under different driving conditions, in which a forward-looking behavior model of the driver with variable preview distance is designed based on the fuzzy control theory; (3) conducts CarSim-AMESim-Simulink co-simulation with the existence of obstacles. The simulation results show that the proposed two control approaches are practical for classical driving scenarios. Especially the MPC-based path-tracking controller enhances dynamic tracking performance and ensures good maneuverability under high-dynamic driving conditions.