The effect of wheel set gyroscopic action on the hunting stability of high-speed trains

This study explores the effects of wheel set gyroscopic action on hunting stability by calculating linear and nonlinear critical speeds. First, a dynamic model for a high-speed vehicle with 23 degrees of freedom is developed by considering wheel set gyroscopic action. The linear and nonlinear critical speeds are calculated by eigenvalue analysis and drawing a bifurcation map, respectively. Two computer programs for linear and nonlinear stability analysis are developed. Second, based on an actual high-speed vehicle in China, the effects of wheel set gyroscopic action on hunting stability are quantitatively investigated using computer simulation. Furthermore, the difference between the effects of gyroscopic moments about the x-axis and z-axis is discussed. The results show that the moment about the x-axis is harmful to hunting stability, but the moment about the z-axis is beneficial to hunting stability. However, the integrated effect of these two moments can enhance the critical speeds and suppress the hunting motion.     

融合预瞄与势场栅格法的紧急避撞驾驶人模型

紧急避障工况下的驾驶人操作具有响应快且动作幅值较大的特点，传统预瞄驾驶人模型已不能适应紧急避障工况的需求，故考虑实际避撞场景开发相应的驾驶人模型就显得尤为必要。针对此种状况，基于驾驶模拟器，结合紧急避撞工况实际驾驶人操纵数据，提出了一种融合预瞄与势场栅格法的紧急避撞驾驶人模型。首先针对紧急避撞工况下车辆运动特点，建立车辆横、纵向耦合非线性动力学模型，并给出其状态空间方程描述；其次，离线仿真分析紧急避撞系统特征，并结合线性二次型最优控制，建立最优曲率预瞄+跟踪误差反馈驾驶人模型；再者，基于紧急避撞工况下真实驾驶人经验转向行为数据，开发基于势场栅格法的驾驶人模型，为进一步提高驾驶人模型对避障行驶工况的适应性，将基于势场栅格法的驾驶人模型与最优曲率预瞄+跟踪误差反馈驾驶人模型进行融合，并基于Sigmoid函数实现两者输出的权重分配；最后，针对所提出的融合预瞄与势场栅格法的驾驶人模型，开展基于避撞台架的驾驶人在环仿真试验以及实车试验。研究结果表明：在紧急避撞工况下，对比最优曲率预瞄+跟踪误差反馈驾驶人模型，融合预瞄与势场栅格法的驾驶人模型输出的转向动作与实际驾驶人行为较为接近，可在保证避障安全性的前提下，兼顾避障路径跟踪精度与车辆行驶的稳定性。     

eTS fuzzy driver model for simultaneous longitudinal and lateral vehicle control

In this paper, evolving Takagi-Sugeno (eTS) fuzzy driver model is proposed for simultaneous lateral and longitudinal control of a vehicle in a test track closed to traffic. The developed eTS fuzzy driver model can capture human operator’s driving expertise for generating desired steering angle, throttle angle and brake pedal command values by processing only information which can be supplied by the vehicle’s on-board control systems in real time. Apart from other fuzzy rule based (FRB) models requiring human expert knowledge or off-line clustering, the developed eTS driver model can adapt itself automatically, even ‘from scratch’, by an on-line learning process using eTS algorithm while human driver is supervising the vehicle. Proposed eTS fuzzy driver model’s on-line human driver identification capability and autonomous vehicle driving performance were evaluated on real road profiles created by digitizing two different intercity express ways of Turkey in IPG© CarMaker® software. The training and validation simulation results demonstrated that eTS fuzzy driver model can be used in product development phase to speed up different tests via realistic simulations. Furthermore eTS fuzzy driver model has an application potential in the field of autonomous driving.     

Adaptive Throttle Control for Speed Tracking

SUMMARY

Electronic throttle control is an important part of every advanced vehicle control system. In this paper we design an adaptive control scheme for electronic throttle that achieves good tracking of arbitrary constant speed commands in the presence of unknown disturbances. The design is based on a simplified linear vehicle model which is derived from a validated nonlinear one. The designed control scheme is simulated using the validated full order nonlinear vehicle model and tested on an actual vehicle. The simulation and vehicle test results are included in this paper to show the performance of the controller. Due to the learning capability of the adaptive control scheme, changes in the vehicle dynamics do not affect the performance of the controller in any significant manner.     

Dynamic model for automotive side impact crashes

A rigid body model to represent a side impact crash is constructed using five degrees-of-freedom (dof) for the vehicle and three dof for each occupant in the vehicle. Nonlinear stiffness and damping elements and the presence of physical gaps between several components make the model highly nonlinear. The model is validated using experimental crash test data from a National Highway Traffic Safety Administration (NHTSA) database. To simplify the parameter identification process and reduce the number of parameters to be identified at each stage, a two-step process is adopted in which the vehicle is first assumed to be unaffected by the presence of the occupants, and its model parameters are identified. Subsequently, the parameters in the occupant models are identified.

The active set method with a performance index that includes both the L₂ and L_∞ norms is used for parameter identification. A challenge is posed by the fact that the optimisation problem involved is non-convex. To overcome this challenge, a large set of random initial values of parameter estimates is generated and the optimisation method is applied with all these initial conditions. The values of parameters that provide the minimal performance index from the entire set of initial conditions are then chosen as the best parameter values. The optimal parameters values thus identified are shown to significantly improve the match between the model responses and the experimentally measured sensor signals from the NHTSA crash test.     

Nonlinear robust control of integrated vehicle dynamics

A new methodology to design the vehicle GCC (global chassis control) nonlinear controller is developed in this paper. Firstly, to handle the nonlinear coupling between sprung and unsprung masses, the vehicle is treated as a mechanical system of two-rigid-bodies which has 6 DOF (degree of freedom), including longitudinal, lateral, yaw, vertical, roll and pitch dynamics. The system equation is built in the yaw frame based on Lagrange's method, and it has been proved that the derived system remains the important physical properties of the general mechanical system. Then the GCC design problem is formulated as the trajectory tracking problem for a cascade system, with a Lagrange's system interconnecting with a linear system. The nonlinear robust control design problem of this cascade interconnected system is divided into two H _∞ control problems with respect to the two sub-systems. The parameter uncertainties in the system are tackled by adaptive theory, while the external uncertainties and disturbances are dealt with the H _∞ control theory. And the passivity of the mechanical system is applied to construct the solution of nonlinear H _∞ control problem. Finally, the effectiveness of the proposed controller is validated by simulation results even during the emergency manoeuvre.     

Development of route information based driving control algorithm for a range-extended electric vehicle

A route information based driving control algorithm was developed for an RE-EV which consists of two motorgenerators, MG1 and MG2. A threshold power which controls the engine on/off to charge the battery was obtained by an optimization process using route information, such as the vehicle velocity and altitude. The threshold power allows the vehicle to travel to the final destination while making the final battery SOC close to SOC _low. Using the threshold power, route based control (RBC) was proposed by considering the driver’s characteristics and traffic conditions using the driving data base. In addition, a relationship between the threshold power and various initial battery SOC was obtained by off-line optimization. The performance of the RBC was evaluated by simulation and human-in-the-loop simulation (HILS) for city driving. It was found from the simulation and HILS results that the RBC achieved approximately 4 % to 12 % reduction in fuel consumption compared to the existing charge depleting/charge sustaining (CD/CS) driving control.     

Robust control and actuator dynamics compensation for railway vehicles

A robust controller is designed for active steering of a high speed train bogie with solid axle wheel sets to reduce track irregularity effects on the vehicle’s dynamics and improve stability and curving performance. A half-car railway vehicle model with seven degrees of freedom equipped with practical accelerometers and angular velocity sensors is considered for the H_∞ control design. The controller is robust against the wheel/rail contact parameter variations. Field measurement data are used as the track irregularities in simulations. The control force is applied to the vehicle model via ball-screw electromechanical actuators. To compensate the actuator dynamics, the time delay is identified online and is used in a second-order polynomial extrapolation carried out to predict and modify the control command to the actuator. The performance of the proposed controller and actuator dynamics compensation technique are examined on a one-car railway vehicle model with realistic structural parameters and nonlinear wheel and rail profiles. The results showed that for the case of nonlinear wheel and rail profiles significant improvements in the active control performance can be achieved using the proposed compensation technique.     

车辆转弯制动横向轨迹控制驾驶员模型研究

为了较为真实地反映车辆转弯制动工况,建立了含Pacejka"魔术公式"非线性联合工况轮胎模型的4轮8自由度车辆系统模型,并基于预瞄跟随理论、加速度反馈控制和模糊PID控制技术建立了车辆转弯制动横向轨迹控制驾驶员模型。针对不同初始速度和制动强度,利用MATLAB/Simulink进行了横向轨迹控制仿真分析。分析结果表明,驾驶员控制模型能很好地跟踪横向轨迹,模型的可行性和有效性得到验证,同时不同仿真条件下结果的一致性也说明该控制方法具有较强的自适应能力和鲁棒性,为进一步研究复杂工况下的驾驶员模型及横向轨迹控制提供了一条可行的途径。     

Black-box modelling of nonlinear railway vehicle dynamics for track geometry assessment using neural networks

ABSTRACT

The use of vehicle dynamics simulation for the track geometry assessment gives rise to new demands. In order to analyse the responses of the vehicles to the measured track geometry defects, the integration of the simulation process in the measurement chain of the track geometry recording car is envisaged. Fast and reliable simulation results are required. This work studies the use of black-box modelling approaches as an alternative to multi-body simulation. The performances of different linear and nonlinear black-box models for the simulation of the vertical and lateral bogie accelerations are compared. While linear transfer function models give good results for the simulation of the vertical responses, their use is not suitable for the highly nonlinear lateral vehicle dynamics. The lateral accelerations are best represented by recurrent neural networks. For the training and validation on high-speed lines using measured vehicle responses, the performance of the black-box simulation outperforms the multi-body simulation. Due to the larger variability of track design and track quality conditions on conventional lines, the model performance degrades and depends significantly on the analysed vehicle type and the track characteristics.     

Vehicle Handling Improvement by Active Steering

Summary This paper first analyses some stability aspects of vehicle lateral motion, then a coprime factors and linear fractional transformations (LFT) based feedforward and feedback H 8 control for vehicle handling improvement is presented. The control synthesis procedure uses a linear vehicle model which includes the yaw motion and disturbance input with speed and road adhesion variations. The synthesis procedure allows the separate processing of the driver reference signal and robust stabilization problem or disturbance rejection. The control action is applied as an additional steering angle, by combination of the driver input and feedback of the yaw rate. The synthesized controller is tested for different speeds and road conditions on a nonlinear model in both disturbance rejection and driver imposed yaw reference tracking maneuvers.     

A lateral driver model for vehicle–driver closed-loop simulation at the limits of handling

This paper presents a lateral driver model for vehicle–driver closed-loop simulation at the limits of handling. An appropriate driver model can be used to evaluate the performance of vehicle chassis control systems via computer simulations before vehicle tests which incurs expenses especially at the limits of handling. The driver model consists of two parts. The first part is an upper-level controller employing force-based approach to reduce the number of unknown vehicle parameters. The feedforward part of the upper controller has been designed by using the centre of percussion. The feedback part aims to minimise ‘tangential error’, defined as the sum of body slip angle and yaw error, to match vehicle direction and road heading angle. The part is designed to regenerate an appropriate skid motion similar to that of a professional driver at the limits. The second part is a lower-level controller which converts the desired front lateral force to steering wheel angle. The lower-level controller also consists of feedforward and feedback parts. A two-degree-of-freedom bicycle model-based feedforward part provides nominal steering wheel angle, and the feedback part aims to eliminate unmodelled error. The performance of the lateral driver model has been investigated via computer simulations. It has been shown that the steering behaviours of the proposed driver model are quite close to those of a professional driver at the limits. Compared with the previously developed lateral driver models, the proposed lateral driver model shows good tracking performance at the limits of handling.     

Vehicle Handling Improvement by Active Steering

Summary This paper first analyses some stability aspects of vehicle lateral motion, then a coprime factors and linear fractional transformations (LFT) based feedforward and feedback H 8 control for vehicle handling improvement is presented. The control synthesis procedure uses a linear vehicle model which includes the yaw motion and disturbance input with speed and road adhesion variations. The synthesis procedure allows the separate processing of the driver reference signal and robust stabilization problem or disturbance rejection. The control action is applied as an additional steering angle, by combination of the driver input and feedback of the yaw rate. The synthesized controller is tested for different speeds and road conditions on a nonlinear model in both disturbance rejection and driver imposed yaw reference tracking maneuvers.     

Durability prediction for automobile aluminum front subframe using nonlinear models in virtual test simulations

This research work presents fatigue life evaluation techniques for an automotive vehicle aluminum front subframe using virtual test simulation technology with nonlinear suspension components model. The technology was used for improving the accuracy of the polynomial model used in conventional analysis. The proposed nonlinear suspension components models were developed using direct approach. The effects of the nonlinear elements on the prediction of the fatigue life were also analyzed. Actual aluminum front subframe was tested using half-car road test simulator to verify the accuracy of the models. It was found that the proposed nonlinear models yield more accurate results than conventional polynomial models. The proposed virtual test simulation technology with nonlinear suspension components model can be used to predict fatigue life for vehicle chassis structures more accurately.     

Linear matrix inequality based control of vehicle active suspension system

Linear matrix inequality (LMI) methods, novel techniques in solving optimisation problems, were introduced as a unified approach for vehicle's active suspension system controller design. LMI methods were used to provide improved and computationally efficient controller design techniques. The active suspension problem was formulated as a standard convex optimisation problem involving LMI constraints that can be solved efficiently using recently developed interior point optimisation methods. An LMI based controller for a vehicle system was developed. The controller design process involved setting up an optimisation problem with matrix inequality constraints. These LMI constraints were derived for a vehicle suspension system. The resulting LMI controller was then tested on a quarter-car model using computer simulations. The LMI controller results were compared with an optimal PID controller design solution. The LMI controller was further tested by incorporating a nonlinear term in the vehicle's suspension model; the LMI's controller degraded response was enhanced by using gain-scheduling techniques. The LMI controller with gain-scheduling gave good results in spite of the unmodelled dynamics in the suspension system, which was triggered by large deflections due to off-road driving.     

An adaptive integrated algorithm for active front steering and direct yaw moment control based on direct Lyapunov method

In this article, an adaptive integrated control algorithm based on active front steering and direct yaw moment control using direct Lyapunov method is proposed. Variation of cornering stiffness is considered through adaptation laws in the algorithm to ensure robustness of the integrated controller. A simple two degrees of freedom (DOF) vehicle model is used to develop the control algorithm. To evaluate the control algorithm developed here, a nonlinear eight-DOF vehicle model along with a combined-slip tyre model and a single-point preview driver model are used. Control commands are executed through correction steering angle on front wheels and braking torque applied on one of the four wheels. Simulation of a double lane change manoeuvre using Matlab^®/Simulink is used for evaluation of the control algorithm. Simulation results show that the integrated control algorithm can significantly enhance vehicle stability during emergency evasive manoeuvres on various road conditions ranging from dry asphalt to very slippery packed snow road surfaces.     

Nonlinear H∞ control for semi-active suspension

This paper describes the development of a damping control system for semi-active suspension which is based on nonlinear H_∞ control theory instead of conventional linear control theory. A two degrees of freedom system is used as the structure for the vehicle suspension model. Since the structure is bilinear, it's not easy to design the controller. We designed the controller based on the Hamilton-Jacobi inequality by solving a linear Riccati equation. We were able to verify by simulation that nonlinear H_∞ control theory made it possible to control vehicle vibration optimally and smoothly.     

Effects of Model Complexity on the Performance of Automated Vehicle Steering Controllers: Controller Development and Evaluation

SUMMARY

Due to increased traffic congestion and travel times, research in Advanced Vehicle Control Systems (AVCS) has focused on automated lateral and headway control. Automated vehicles are seen as a way to increase freeway capacity and vehicle speeds while reducing accidents due to human error. Recent research in automated lateral control has focused on vehicle control during low-g maneuvers. To increase safety, automated lateral controllers will need to recognize and react to emergency situations.

This paper investigates the effects of vehicle and tire model order on the response of automated vehicles to an emergency step lane change using a controller based on linear vehicle and tire models. From these studies it is concluded that control strategies based solely on linear vehicle and tire models are inadequate for emergency vehicle maneuvers.

A strategy is then proposed to automatically control vehicles through emergency maneuvers. Here the response of a nonlinear vehicle model is used with a linear state model to optimize controller gains for nonlinear maneuvers. An emergency step lane change is used as a preliminary test of the method.     

Modeling Human Vehicle Driving by Model Predictive Online Optimization

A driver model is designed which relates the driver's action to his perception, driving experience, and preferences over a wide range of possible traffic situations. The basic idea behind the work is that the human uses his sensory perception and his expert knowledge to predict the vehicle's future behavior for the next few seconds (prediction model). At a certain sampling rate the vehicle's future motion is optimized using this prediction model, in order to meet certain objectives. The human tries to follow this optimal behavior using a compensatory controller. Based on this hypothesis, human vehicle driving is modeled by a hierarchical controller. A repetitive nonlinear optimization is employed to plan the vehicle's future motion (trajectory planning task), using an SQP algorithm. This is combined with a PID tracking control to minimize its deviations. The trajectory planning scheme is experimentally verified for undisturbed driving situations employing various objectives, namely ride comfort, lane keeping, and minimized speed variation. The driver model is then applied to study path planning during curve negotiation under various preferences. A highly dynamic avoidance maneuver (standardized ISO double lane change) is then simulated to investigate the overall stability of the closed loop vehicle/driver system.     

Designing H ∞/GH 2 static-output feedback controller for vehicle suspensions using linear matrix inequalities and genetic algorithms

This paper presents an approach to design the H _∞/GH ₂ static-output feedback controller for vehicle suspensions by using linear matrix inequalities (LMIs) and genetic algorithms (GAs). Three main performance requirements for an advanced vehicle suspension are considered in this paper. Among these requirements, the ride-comfort performance is optimized by minimizing the H _∞ norm of the transfer function from the road disturbance to the sprung mass acceleration, while the road-holding performance and the suspension deflection limitation are guaranteed by constraining the generalized H ₂ (GH ₂) norms of the transfer functions from the road disturbance to the dynamic tyre load and the suspension deflection to be less than their hard limits, respectively. At the same time, the controller saturation problem is considered by constraining its peak response output to be less than a given limit using the GH ₂ norm as well. A four-degree-of-freedom half-car model with active suspension system is applied in this paper. Several kinds of H _∞/GH ₂ static-output feedback controllers, which use the available sprung mass velocities or the suspension deflections as feedback signals, are obtained by using the GAs to search for the possible control gain matrices and then resolving the LMIs together with the minimization optimization problem. These designed H _∞/GH ₂ static-output feedback controllers are validated by numerical simulations on both the bump and the random road responses which show that the designed H _∞/GH ₂ static-output feedback controllers can achieve similar or even better active suspension performances compared with the state-feedback control case in spite of their simplicities.