High Speed Railway: Collecting Pantographs Active Control and Overhead Lines Diagnostic Solutions

SUMMARY

The catenary-pantograph system and its dynamic behaviour play a decisive role for high speed trains from the power collecting point of view. The decisive criterion for assessing the contact quality is to reduce the contact force variability as far as possible. In this paper active pantograph elements are introduced in order to improve the system performance. The control strategy is based on Extended Kalman Filter technique, used to get a contact force estimation available for control feedback. The same estimation procedure based on the Extended Kalman Filter is also used to obtain some information useful to overhead line diagnostic purposes.     

Estimation of the Non-Linear Suspension Tyre Cornering Forces from Experimental Road Test Data

The paper discusses the application to real data of an identification procedure based on an Extended Kalman Filter, for estimating the equivalent non-linear suspension tyre cornering forces of a road vehicle from a single standard manoeuvre. In particular, both the steady-state and the dynamic handling characteristics can be evaluated.     

车辆操纵稳定性状态估计算法比较研究

建立了基于运动学的车辆3自由度状态估计模型,分别将扩展卡尔曼滤波（EKF）、无迹卡尔曼滤波（UKF）和粒子滤波（PF）应用到车辆状态估计中,通过仿真试验比较了3种算法的估计效果。结果表明,车辆工作在线性稳定区域时,EKF算法效果最优,而车辆工作在强非线性区域并处于失稳状态时,PF算法效果最优。     

Robust road friction estimation during vehicle steering

Automated vehicles require information on the current road condition, i.e. the tyre–road friction coefficient for trajectory planning, braking or steering interventions. In this work, we propose a framework to estimate the road friction coefficient with stability and robustness guarantee using total aligning torque in vehicle front axle during steering. We first adopt a novel strategy to estimate the front axle lateral force which performs better than the classical unknown input observer. Then, combined with an indirect measurement based on estimated total aligning torque and front axle lateral force, a non-linear adaptive observer is designed to estimate road friction coefficient with stability guarantee. To increase the robustness of the estimation result, criteria are proposed to decide when to update the estimated road conditions. Simulations and experiments under various road conditions validate the proposed framework and demonstrate its advantage in stability by comparing it with the method utilising the wide-spread Extended Kalman Filter.     

Hierarchical Direct Yaw-Moment Control System Design for In-Wheel Motor Driven Electric Vehicle

In this study, a hierarchical structured direct yaw-moment control (DYC) system, which consists of a main-loop controller and a servo-loop controller, is designed to enhance the handling and stability of an in-wheel motor driven driven electric vehicle (IEV). In the main loop, a Fractional Order PID (FO-PID) controller is proposed to generate desired external yaw moment. A modified Differential Evolution (M-DE) algorithm is adopted to optimize the controller parameters. In the servo-loop controller, the desired external yaw moment is optimally distributed to individual wheel torques by using sequential quadratic programming (SQP) approach, with the tire force boundaries estimated by Unscented Kalman Filter (UKF) based on a fitted empirical tire model. The IEV prototype is virtually modelled by using Adams/Car collaborating with SolidWorks, validated by track tests, and serves as the control plant for simulation. The feasibility and effectiveness of the designed control system are examined by simulations in typical handling maneuver scenarios.     

Vehicle Dynamics Estimation Using Kalman Filters

This paper deals with the application of stochastic state estimators in vehicle dynamics control. It is often unrealistic to assume that all vehicle states and the disturbances acting on it can be measured. System states that cannot be measured directly, can be estimated by a Kalman Filter. The idea of the Kalman filter is to implement a model of the real system in an on-board computer in parallel with the system itself. This paper will give 3 examples of this principle applied to automotive systems.     

Vehicle Dynamics Estimation Using Kalman Filters

This paper deals with the application of stochastic state estimators in vehicle dynamics control. It is often unrealistic to assume that all vehicle states and the disturbances acting on it can be measured. System states that cannot be measured directly, can be estimated by a Kalman Filter. The idea of the Kalman filter is to implement a model of the real system in an on-board computer in parallel with the system itself. This paper will give 3 examples of this principle applied to automotive systems.     

Suspension system state estimation using adaptive Kalman filtering based on road classification

This paper provides a new method to solve the problem of suspension system state estimation using a Kalman Filter (KF) under various road conditions. Due to the fact that practical road conditions are complex and uncertain, the influence of the system process noise variance and measurement noise covariance on the estimation accuracy of the KF is first analysed. To accurately estimate the road condition, a new road classification method through the vertical acceleration of sprung mass is proposed, and different road process variances are obtained to tune the system’s variance for the application of the KF. Then, road classification and KF are combined to form an Adaptive Kalman Filter (AKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under various road conditions. Simulation results show that the proposed AKF algorithm can obtain a high accuracy of state estimation for a suspension system under varying International Standards Organisation road excitation levels.     

Inverse identification of wheel–rail contact forces based on observation of wheel disc strains: an evaluation of three numerical algorithms

In this paper, three numerical algorithms for the identification of wheel–rail contact forces based on measured wheel disc strains on an instrumented railway wheelset are discussed and compared. The three algorithms include one approach resting on static calibration, one that is applying a Kalman filter and the third is exploiting an inverse identification scheme. To demonstrate and evaluate the alternative methods, two load cases including periodic excitation by sinusoidal wheel–rail irregularities and transient excitation by an insulated rail joint are considered. Based on a previously presented vehicle–track interaction model in the time domain, load scenarios are defined by taking the calculated vertical wheel–rail contact forces as the reference force to be re-identified by the proposed algorithms. The reference contact forces are applied on a finite element model of the wheel to generate synthetic observation data, that is, radial strains at the positions of the strain gauges, serving as input to the identification procedures. It is concluded that the inverse identification scheme leads to superior accuracy at higher computational cost. If on-line implementation and evaluation is required, the Kalman filter generates better accuracy than the static calibration approach.     

An Active Pantograph with Shaped Frequency Response Employing Linear Output Feedback Control

In pantographs used for current collection on high speed electric trains it is desirable to minimise the fluctuations in the contact force between the collector head and the catenary. A simple two-mass linear model is employed for the pantograph and the design of the proposed control system is based on the input admittance at low frequencies. Frequency shaping is incorporated in the performance index, and a simple dynamic controller is employed to achieve optimality in an equivalent transformed system, while minimising the number of feedback quantities to be measured. A significant reduction in the average contact force appears possible.     

Innovative Solutions for Overhead Catenary-Pantograph System: Wire Actuated Control and Observed Contact Force

In this paper an innovative active pantograph for high-speed trains is proposed. The results presented are based on extensive simulation tests. The parameters used in the simulation are those of a real pantograph for high-speed trains: the pantograph model is modified by adding a wire actuation, in order to exert a constant contact force between the moving pantograph and the overhead contact wire. A wire-actuated control and contact force observers are proposed as effective solutions in the case of a possible implementation.     

Innovative Solutions for Overhead Catenary-Pantograph System: Wire Actuated Control and Observed Contact Force

In this paper an innovative active pantograph for high-speed trains is proposed. The results presented are based on extensive simulation tests. The parameters used in the simulation are those of a real pantograph for high-speed trains: the pantograph model is modified by adding a wire actuation, in order to exert a constant contact force between the moving pantograph and the overhead contact wire. A wire-actuated control and contact force observers are proposed as effective solutions in the case of a possible implementation.     

An Active Pantograph with Shaped Frequency Response Employing Linear Output Feedback Control

SUMMARY

In pantographs used for current collection on high speed electric trains it is desirable to minimise the fluctuations in the contact force between the collector head and the catenary. A simple two-mass linear model is employed for the pantograph and the design of the proposed control system is based on the input admittance at low frequencies. Frequency shaping is incorporated in the performance index, and a simple dynamic controller is employed to achieve optimality in an equivalent transformed system, while minimising the number of feedback quantities to be measured. A significant reduction in the average contact force appears possible.     

Fault detection and isolation for a nonlinear railway vehicle suspension with a Hybrid Extended Kalman filter

Fault detection is considered to be one way to improve system reliability and dependability for railway vehicles. The secondary lateral and anti-yaw dampers are the most critical parts in railway suspension systems. So far, the dampers have been modelled as linear components in the fault detection and isolation observer design. In this work, a Hybrid Extended Kalman filter is used to capture the nonlinear characteristics of the dampers. In order to detect and isolate faults, a nonlinear residual generator is developed, which can distinguish clearly between different types of faults. A lateral half train model serves as an example for the proposed technique. The results show that failures in the nonlinear suspension system can be detected and isolated accurately.     

Nonlinear ACC in Simulation and Measurement

In this paper an adaptive cruise control (ACC) of a convoy consisting of two passenger cars is designed and tested. For the ACC only on board sensors in the following vehicle are used, communication within the convoy or between the controlled vehicle and electronic systems on the roadside is not assumed. A laser scanner is applied for range measurements, derived from the complete vision data of the area in front of the car. Since the scanner provides the range only, a Kalman Filter is used to estimate the velocity and acceleration of the car. For controller design the concept of flat outputs in connection with the exact state linearization is applied. Moreover, the exact state linearization is combined with a sliding mode control. The control parameters are obtained by an optimization algorithm using optimal tracking formulation. The optimization also guarantees individual vehicle stability as well as string stability of the convoy. It is shown how the convoy is responding to disturbances resulting from initial errors or from velocity steps by the leading vehicle at lower speed in simulation and experiment.     

Nonlinear ACC in Simulation and Measurement

In this paper an adaptive cruise control (ACC) of a convoy consisting of two passenger cars is designed and tested. For the ACC only on board sensors in the following vehicle are used, communication within the convoy or between the controlled vehicle and electronic systems on the roadside is not assumed. A laser scanner is applied for range measurements, derived from the complete vision data of the area in front of the car. Since the scanner provides the range only, a Kalman Filter is used to estimate the velocity and acceleration of the car. For controller design the concept of flat outputs in connection with the exact state linearization is applied. Moreover, the exact state linearization is combined with a sliding mode control. The control parameters are obtained by an optimization algorithm using optimal tracking formulation. The optimization also guarantees individual vehicle stability as well as string stability of the convoy. It is shown how the convoy is responding to disturbances resulting from initial errors or from velocity steps by the leading vehicle at lower speed in simulation and experiment.     

Expanding transmission capacity of can systems using dual communication channels with kalman prediction

The controller area network (CAN) protocol is widely used for in-vehicle network (IVN) systems, and many automotive companies also use the CAN in chassis network systems. However, the increasing number of electronic control units (ECUs) dictated by the need for more intelligent and fuel-efficient functions requires an IVN system with a greater transmission capacity and less network delay. Automotive companies have tried several approaches such as segmenting CAN systems and developing time-triggered protocols. This paper presents a practical method for increasing the transmission capacity and reducing the network delay in CAN systems using dual communication channels with a traffic-balancing algorithm based on Kalman prediction to forecast the traffic on each channel and allocate frames to the one that is most appropriate. An experimental testbed using commercial microcontrollers with two or more CAN protocol controllers was used to demonstrate the feasibility of the Kalman traffic-balancing algorithm. Experimental results show that the traffic-balancing CAN system with Kalman prediction reduced the transmission delay of all priority messages compared to that of a simple method, such as a channel-switching CAN, without sacrificing the performance for high-priority messages.     

Customer loads of two-wheeled vehicles

Customer usage profiles are the most unknown influences in vehicle design targets and they play an important role in durability analysis. This publication presents a customer load acquisition system for two-wheeled vehicles that utilises the vehicle's onboard signals. A road slope estimator was developed to reveal the unknown slope resistance force with the help of a linear Kalman filter. Furthermore, an automated mass estimator was developed to consider the correct vehicle loading. The mass estimation is performed by an extended Kalman filter. Finally, a model-based wheel force calculation was derived, which is based on the superposition of forces calculated from measured onboard signals. The calculated wheel forces were validated by measurements with wheel–load transducers through the comparison of rainflow matrices. The calculated wheel forces correspond with the measured wheel forces in terms of both quality and quantity. The proposed methods can be used to gather field data for improved vehicle design loads.     

Robust estimation of maximum tire-road friction coefficient considering road surface irregularity

An accurate estimation of the maximum tire-road friction coefficient may provide higher performance in a vehicle active safety control system. Unfortunately, real-time tire-road friction coefficient estimation is costly and necessitates additional sensors that must be installed and maintained at all times. This paper proposes an advanced longitudinal tire-road friction coefficient estimation method that is capable of considering irregular road surfaces. The proposed algorithm uses a stiffness based estimation method, however, unlike previous studies, improvements were made by suggesting a third order model to solve problems related to nonlinear mu-slip curve. To attain the tire-road friction coefficient, real-time normalized force is obtained from the force estimator as exerted from the tire in the low slip region using the recursive least squares method. The decisive aspect of using the suggested algorithm lies in its low cost and versatility. It can be used under irregular road conditions due to its capability of easily obtaining wheel speed and acceleration values from production cars. The newly improved algorithm has been verified to computer simulations as well as compact size cars on dry asphalt conditions.