Identification Methods for Vehicle System Dynamics

SUMMARY

The paper presents a survey on parameter identification techniques for complex vehicle models. In order to cope with the complexity of the model, the information on the system available from the equations of motion has to be included in the identification process. Basic methods for the solution of this problem are shown. The application of the approach is demonstrated by identification of the vertical automobile dynamics. It is concluded that the presented techniques will become more important with increasing applications of theoretical modeling in vehicle system dynamics.     

智能车辆转向变结构控制方法的研究

从车辆的转向动力学和预瞄运动学特性出发，结合转向系统的系统辨识，建立了车辆转向系统的线性模型。运用变结构滑模控制理论和多模型切换，使车辆转向控制具有良好的跟踪性能和鲁棒性。     

Vehicle handling dynamics modelling by a data-driven identification method

Modelling of vehicle handling dynamics has received a renewed attention in recent years. Different from traditional vehicle modelling, a novel data-driven identification method for vehicle handling dynamics is proposed, which can avoid the problems of the under-modelling and parameter uncertainties in the first-principle modelling process. By first-order Taylor expansion, the nonlinear vehicle system can be linearised as a slowly linear time-varying system with fourth-order. In order to identify the derived identifiable model structure, a recursive subspace method is presented. Derived by optimal version of predictor-based subspace identification (PBSID_opt) and projection approximation subspace tracking (PAST), the identification method is numerical stability and gives an unbiased estimation for the closed-loop system. Based on standard road tests, the proposed modelling method is proven effective and the obtained model has good predictive ability. Additionally, it is noted that the model obtained from the initial phase of straight driving is just a mathematical model to describe the relationship between input and output. And when the vehicle is steering, the model can converge to a stable phase quickly and represent vehicle dynamic performance.     

Effect of vehicle model on the estimation of lateral vehicle dynamics

A methodology is presented for estimating vehicle handling dynamics, which are important to control system design and safety measures. The methodology, which is based on an extended Kalman filter (EKF), makes it possible to estimate lateral vehicle states and tire forces on the basis of the results obtained from sinusoidal steering stroke tests that are widely used in the evaluation of vehicle and tire handling performances. This paper investigates the effect of vehicle-road system models on the estimation of lateral vehicle dynamics in the EKF. Various vehicle-road system models are considered in this study: vehicle models (2-DOF, 3-DOF, 4-DOF), tire models (linear, non-linear) and relaxation lengths. Handling tests are performed with a vehicle equipped with sensors that are widely used by vehicle and tire manufacturers for handling maneuvers. The test data are then used in the estimation of the EKF and identification of lateral tire model coefficients. The accuracy of the identified values is validated by comparing the RMS error between experimentally measured states and regenerated states simulated using the identified coefficients. The results show that the relaxation length of the tire model has a notable impact on the estimation of lateral vehicle dynamics.     

智能交通系统中基于机器视觉的数字车辆控制

在描述成像模型与视觉坐标系的基础上，给出了基于计算机视觉的车道与障碍物辨识检测算法，应用预瞄转向以及车辆控制等技术手段来实现数字车辆的道路跟踪。     

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic ‘input–output’ model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilised as ‘virtual measurements’ considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.     

Application of observer-based fault detection in vehicle roll control

This paper describes how observer-based techniques for intelligent fault detection were applied to monitoring an active suspension control system in an experimental articulated heavy vehicle. The aim was to define a practical method for detecting faults, taking into account the nonlinearities of the vehicle. The experimental vehicle was divided conceptually into subsystems, namely the passive dynamics of the trailer, the dynamics of the hydraulic actuators, and the expected response of the closed-loop system. A linear dynamic model was designed for each subsystem. A fault detection observer was then designed for each dynamic model. The observer feedback gains were chosen to optimise estimation by the observer residual of specified errors on the output measurements. The observer residuals were then normalised and combined logically to provide a fault diagnosis. The performance of the fault detection scheme is demonstrated in the case of sensor faults and changes in the operation of the active control system.     

Aerodynamics of Road- and Rail Vehicles

The technical state-of-the-art of aerodynamics of ground transportation vehicles is reviewed. Currently available theoretical calculation methods and experimental simulation techniques as well as typical results illustrating the impact of aerodynamics on vehicle performance and running characteristics are summarized and the interactions between vehicle system dynamics and aerodynamics are adressed. Correlation of theoretical and experimental data show the present potential of vehicle aerodynamics and point to fields in which further research work is necessary.     

A new method for determining wheel–rail multi-point contact

A new method for wheel–rail multi-point contact is presented in this paper. In this method, the first- and the second-order derivatives of the wheel–rail interpolation distance function and the elastic wheel–rail virtual penetration are used to determine multiple contact points. The method takes account of the yaw angle of the wheelset and allows the identification of all possible points of contact between wheel and rail surfaces with an arbitrary geometry. Static contact geometry calculations are first carried out using the developed method for both new and worn wheel profiles and with a new rail profile. The validity of the method is then verified by simulations of a coupled vehicle and track system dynamics over a small radius curve. The simulation results show that the developed method for multi-point contact is efficient and reliable enough to be implemented online for simulations of vehicle–track system dynamics.     

Aerodynamics of Road- and Rail Vehicles

SUMMARY

The technical state-of-the-art of aerodynamics of ground transportation vehicles is reviewed. Currently available theoretical calculation methods and experimental simulation techniques as well as typical results illustrating the impact of aerodynamics on vehicle performance and running characteristics are summarized and the interactions between vehicle system dynamics and aerodynamics are adressed. Correlation of theoretical and experimental data show the present potential of vehicle aerodynamics and point to fields in which further research work is necessary.     

Flexible multibody simulation of automotive systems with non-modal model reduction techniques

The stiffness of the body structure of an automobile has a strong relationship with its noise, vibration, and harshness (NVH) characteristics. In this paper, the effect of the stiffness of the body structure upon ride quality is discussed with flexible multibody dynamics. In flexible multibody simulation, the local elastic deformation of the vehicle has been described traditionally with modal shape functions. Recently, linear model reduction techniques from system dynamics and mathematics came into the focus to find more sophisticated elastic shape functions. In this work, the NVH-relevant states of a racing kart are simulated, whereas the elastic shape functions are calculated with modern model reduction techniques like moment matching by projection on Krylov-subspaces, singular value decomposition-based reduction techniques, and combinations of those. The whole elastic multibody vehicle model consisting of tyres, steering, axle, etc. is considered, and an excitation with a vibration characteristics in a wide frequency range is evaluated in this paper. The accuracy and the calculation performance of those modern model reduction techniques is investigated including a comparison of the modal reduction approach.     

Fault detection and isolation for a full-scale railway vehicle suspension with multiple Kalman filters

Reliability and dependability in complex mechanical systems can be improved by fault detection and isolation (FDI) methods. These techniques are key elements for maintenance on demand, which could decrease service cost and time significantly. This paper addresses FDI for a railway vehicle: the mechanical model is described as a multibody system, which is excited randomly due to track irregularities. Various parameters, like masses, spring- and damper-characteristics, influence the dynamics of the vehicle. Often, the exact values of the parameters are unknown and might even change over time. Some of these changes are considered critical with respect to the operation of the system and they require immediate maintenance. The aim of this work is to detect faults in the suspension system of the vehicle. A Kalman filter is used in order to estimate the states. To detect and isolate faults the detection error is minimised with multiple Kalman filters. A full-scale train model with nonlinear wheel/rail contact serves as an example for the described techniques. Numerical results for different test cases are presented. The analysis shows that for the given system it is possible not only to detect a failure of the suspension system from the system's dynamic response, but also to distinguish clearly between different possible causes for the changes in the dynamical behaviour.     

Nonlinear Design Approach to Four-Wheel-Steering Systems Using Neural Networks

Four-wheel-steering (4WS) systems have been studied and developed with remarkable success from the viewpoint of vehicle dynamics. Most of the control methods require a linearized bicycle model of the actual vehicle system which is however strongly influenced by tire nonlinearity. This paper proposes a new method to design the 4WS system taking into account the nonlinear characteristics of tires and suspensions. For this purpose integration of artificial neural network and linear control theory is introduced for the identification and control of a nonlinear vehicle model structured using a software for multi-body dynamic analysis (ADAMS). This model takes into account the nonlinear characteristics of actual vehicles with tires modeled by “magic formula“. The results of computer simulations show that the proposed nonlinear approach is efficient in improving the handling and stability of vehicles.     

Nonlinear dynamics and stability analysis of vehicle plane motions

In this article, the problems of dynamics and stability for vehicle planar motion systems have been investigated. By introducing a so-called joint-point locus approach, equilibria of the system and their associated stability properties are given geometrically. With this method, it is discovered that the difference between the front and the rear steering angles plays a key role in vehicle system dynamics and that the topological structure of the phase portrait and the types of bifurcations are different from those published previously. In particular, the vehicle system could still be stabilized even when pushed to work in a certain severely nonlinear region, by applying extremely large steering angles. However, it is worth noticing that the attractive domain of the stable equilibrium is very narrow. These developments might prove to be important in active steering control design. Numerical experiments are carried out to illustrate the potentials of the proposed techniques.     

Dynamics of Automated Guideway Transit Vehicle with Single-axle Bogies

Summary The steering type of a mechanical guidance system has been used for Automated Guideway Transit (AGT) system in Japan. Recently, the single-axle bogie system has developed for AGT vehicle and applied to Yurikamome 7200 type vehicle. This paper describes dynamic characteristics of AGT vehicle with single-axle bogies. Introducing a nonlinear, 15 degree-of-freedom dynamic model, a computer simulation study on the lateral motion of the AGT vehicle with single-axle bogies are carried out. In order to show the dynamic characteristics of the single-axle bogie clearly, it is compared to that of the AGT vehicle with conventional steering system. The simulation study with actual vehicle parameters shows that single-axle bogie has suitable characteristics for AGT system. The multi-body dynamics modeler, DADS, is used to build the dynamic model of AGT vehicle with single-axle bogies and this is used to demonstrate the vehicle motion in actual guideway. Obtained results are compared to that of the field test. It is shown that the vehicle dynamic response can be obtained in realistic situation by using multibody dynamics code, that is useful for designing both vehicle and guideway.     

Dynamics of Automated Guideway Transit Vehicle with Single-axle Bogies

Summary The steering type of a mechanical guidance system has been used for Automated Guideway Transit (AGT) system in Japan. Recently, the single-axle bogie system has developed for AGT vehicle and applied to Yurikamome 7200 type vehicle. This paper describes dynamic characteristics of AGT vehicle with single-axle bogies. Introducing a nonlinear, 15 degree-of-freedom dynamic model, a computer simulation study on the lateral motion of the AGT vehicle with single-axle bogies are carried out. In order to show the dynamic characteristics of the single-axle bogie clearly, it is compared to that of the AGT vehicle with conventional steering system. The simulation study with actual vehicle parameters shows that single-axle bogie has suitable characteristics for AGT system. The multi-body dynamics modeler, DADS, is used to build the dynamic model of AGT vehicle with single-axle bogies and this is used to demonstrate the vehicle motion in actual guideway. Obtained results are compared to that of the field test. It is shown that the vehicle dynamic response can be obtained in realistic situation by using multibody dynamics code, that is useful for designing both vehicle and guideway.     

基于工作模态法的动力总成刚体参数识别

介绍一种基于多参考点最小二乘频域法(一种工作模态法)的刚体参数识别方法。它适用于具有阻尼和非线性刚度支承的系统(如汽车动力总成),可在实车工作情况下对动力总成的模态参数和惯性参数进行识别。通过实验验证了该方法的有效性。     

A novel method for identifying inertial parameters of electric vehicles based on the dual H infinity filter

ABSTRACT

Accurate identification of vehicle inertial parameters is essential to the design of vehicle dynamics control systems. In this paper, a novel vehicle inertial parameter identification method based on the dual H infinity filter (DHIF) for electric vehicles (EVs) is proposed. The filter algorithm employs a nonlinear longitudinal vehicle model with three vehicle states. A hierarchical framework is engaged by the DHIF to estimate the vehicle states and inertial parameters concurrently. In order to minimise the disturbance of unknown noise, the vehicle states are estimated by using the linear H infinity filter (LHIF), while the nonlinear H infinity filter (NHIF) utilises the observed states to identify the vehicle inertial parameters. Finally, the proposed estimation method is verified and compared through the dSPACE based hardware-in-the-loop (HIL) simulation experiments. The results indicate that the DHIF-based estimation method is effective to identify the vehicle inertial parameters with high precision, remarkable robustness, and quick convergence.     

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.     

Nonlinear dynamics and stability analysis of vehicle plane motions

In this article, the problems of dynamics and stability for vehicle planar motion systems have been investigated. By introducing a so-called joint-point locus approach, equilibria of the system and their associated stability properties are given geometrically. With this method, it is discovered that the difference between the front and the rear steering angles plays a key role in vehicle system dynamics and that the topological structure of the phase portrait and the types of bifurcations are different from those published previously. In particular, the vehicle system could still be stabilized even when pushed to work in a certain severely nonlinear region, by applying extremely large steering angles. However, it is worth noticing that the attractive domain of the stable equilibrium is very narrow. These developments might prove to be important in active steering control design. Numerical experiments are carried out to illustrate the potentials of the proposed techniques.