An innovative localisation algorithm for railway vehicles

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An innovative localisation algorithm for railway vehicles ab

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B. Allotta , P. D'Adamio , M. Malvezzi , L. Pugi , A. Ridolfi , A. ab

Rindi

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& G. Vettori

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Department of Industrial Engineering, University of Florence, via di Santa Marta 3, 50139 Florence, Italy b

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Mechatronics and Dynamic Modelling Lab (MDM Lab), University of Florence @Pistoia, via Panconi 12, 51100 Pistoia, Italy c

Department of Information Engineering, University of Siena, via Roma 56, 53100 Siena, Italy Published online: 05 Aug 2014.

To cite this article: B. Allotta, P. D'Adamio, M. Malvezzi, L. Pugi, A. Ridolfi, A. Rindi & G. Vettori (2014) An innovative localisation algorithm for railway vehicles, Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, 52:11, 1443-1469, DOI: 10.1080/00423114.2014.943928 To link to this article: http://dx.doi.org/10.1080/00423114.2014.943928

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Vehicle System Dynamics, 2014 Vol. 52, No. 11, 1443–1469, http://dx.doi.org/10.1080/00423114.2014.943928


An innovative localisation algorithm for railway vehicles B. Allottaa,b , P. D’Adamioa,b , M. Malvezzic , L. Pugia,b , A. Ridolfia,b∗ , A. Rindia,b and G. Vettoria,b a Department of Industrial Engineering, University of Florence, via di Santa Marta 3, 50139 Florence, Italy; b Mechatronics and Dynamic Modelling Lab (MDM Lab), University of Florence @Pistoia, via Panconi 12, 51100 Pistoia, Italy; c Department of Information Engineering, University of Siena, via

Roma 56, 53100 Siena, Italy (Received 24 January 2014; accepted 5 July 2014 ) In modern railway automatic train protection and automatic train control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. The aim of this work has been developing an innovative localisation algorithm for railway vehicles able to enhance the performances, in terms of speed and position estimation accuracy, of the classical odometry algorithms, such as the Italian Sistema Controllo Marcia Treno (SCMT). The proposed strategy consists of a sensor fusion between the information coming from a tachometer and an Inertial Measurements Unit (IMU). The sensor outputs have been simulated through a 3D multibody model of a railway vehicle. The work has provided the development of a custom IMU, designed by ECM S.p.a, in order to meet their industrial and business requirements. The industrial requirements have to be compliant with the European Train Control System (ETCS) standards: the European Rail Traffic Management System (ERTMS), a project developed by the European Union to improve the interoperability among different countries, in particular as regards the train control and command systems, fixes some standard values for the odometric (ODO) performance, in terms of speed and travelled distance estimation. The reliability of the ODO estimation has to be taken into account basing on the allowed speed profiles. The results of the currently used ODO algorithms can be improved, especially in case of degraded adhesion conditions; it has been verified in the simulation environment that the results of the proposed localisation algorithm are always compliant with the ERTMS requirements. The estimation strategy has good performance also under degraded adhesion conditions and could be put on board of high-speed railway vehicles; it represents an accurate and reliable solution. The IMU board is tested via a dedicated Hardware in the Loop (HIL) test rig: it includes an industrial robot able to replicate the motion of the railway vehicle. Through the generated experimental outputs the performances of the innovative localisation algorithm have been evaluated: the HIL test rig permitted to test the proposed algorithm, avoiding expensive (in terms of time and cost) on-track tests, obtaining encouraging results. In fact, the preliminary results show a significant improvement of the position and speed estimation performances compared to those obtained with SCMT algorithms, currently in use on the Italian railway network. Keywords: odometry algorithms; sensor fusion; degraded adhesion conditions; Kalman filtering; localisation; railway engineering

1.

Introduction

The state of the art of the localisation algorithms for railway vehicles show that they are currently mainly based on tachometers installed on independent wheel sets (e.g. the ∗ Corresponding

author. Email: [email protected]

c 2014 Taylor & Francis

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Italian Sistema Controllo Marcia Treno (SCMT)).[1,2] These algorithms are able to reach an accuracy, on position and speed estimation, considered sufficient for practical purposes. But these solutions show the following drawbacks: • the wheel diameter greatly affects the accuracy. It varies over time due to the removal of material (wear), needing operations of recalibration of the algorithm parameters; • odometric (ODO) algorithms are very sensitive to sliding phenomena, and thus to the adhesion conditions. When the adhesion coefficient is low (degraded adhesion conditions), errors in the speed estimation process increase; • installation and maintenance of tachometers may be complicated and expensive. Furthermore, also more advanced solutions which exploit other kind of sensors, such as radar Doppler or mono-axial accelerometer [3–5] have the following drawbacks: • the accuracy of the radar Doppler deeply depends on the conditions of the surface which the sensor point on; • the measurements of the train longitudinal acceleration carried out by a mono-axial accelerometer can be affected by lateral or vertical components due to the assembly errors; and • a non reliable estimation of the line gradient causes non negligible errors in the evaluation of the longitudinal acceleration. To overcome the drawbacks of each type of sensor and thus improve the quality of the position and speed estimation the use of sensor fusion techniques can be suitable. These data fusion strategies try to combine measurements from various types of independent sensors, so as to extract the best information in terms of accuracy and reliability.[6–8] The use of multiple types of sensors also reduces the vulnerability of the system with respect to faults of each individual component. All navigation system have drift of integration: little errors in acceleration and angular speed measurements generate in bigger errors in speed, that are aggravated in bigger errors in the position.[9–11] According to the basic principles of the system, what is done is to compare the data output signals from the inertial navigation system (INS) and a navigation system independently, e.g. GPS,[12–14] and their difference is sent in input to a filter that process the corrections applicable to the two navigation systems. The availability of the GPS signal is not always guaranteed,[15] so it is necessary to consider alternative solutions.[16–20] To overcome these limits, in the proposed paper the sensor fusion between the information coming from a tachometer and an ‘Inertial Measurements Unit’ (IMU) is studied.[21–24] The sensor outputs have been simulated through a 3D multi-body model of a railway vehicle, able to reproduce also the critical conditions under degraded adhesion.[25– 27] In particular, to reproduce the motion of the railway vehicle an industrial robot with a custom IMU on its end-effector has been used: the performance of the proposed localisation algorithm have been thus evaluated. This approach permits to avoid expensive on track tests and the preliminary achieved results show a significant improvement of the position and speed estimation performances compared to those obtained with classical ODO algorithms, e.g. the current ones in use on the Italian railway network.

2.

Innovative localisation algorithm theory

At first is defined the reference system used in the INS (Figure 1). • Inertial frame (i-frame): has the origin in the centre of the earth and its axes do not rotate with respect to the fixed stars;


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Figure 1. Reference system used in the INS.

• Earth frame (e-frame): has the origin in the centre of the earth and axes fixed a to the earth itself. The e-frame rotates with respect to the i-frame with angular velocity along the z-axis; • Navigation frame (n-frame): it is a local geographic with origin coincident with the location of the origin of the INS and the axes defined by the direction of North, East and the local vertical (towards the centre of the earth); and • Body frame (b-frame): it has its origin at the point of the vehicle on which the inertial sensors are installed; its axes are aligned with the roll, pitch and yaw axes of the vehicle itself. The proposed algorithm is summarised by the block diagram in Figure 2, where Rˆ bn represents the rotation from n-frame (defined as the initial body frame of the vehicle) to b-frame and gz is the gravitational vector. Two Kalman Filters are implemented in this diagram: • Orientation Kalman filter estimates the orientation of the train from b-frame to n-frame in terms of Euler angles (roll-pitch-yaw) fusing the information of angular rate coming from the gyroscope with the wheel peripheral acceleration, derived from the tachometer. • INS-ODO Kalman filter estimates speed and travelled distance, fusing the gravity compensated body longitudinal acceleration with the wheel peripheral speed. The algorithm provides four diamond boxes, related to some relevant working conditions Coasting, Straight, Adhesion, Balise. 2.1.

Orientation Kalman filter

The state of equation of the Orientation Kalman filter in block matrix form is xGi (k + 1) = FGi xGi (k),

(1)


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Figure 2. Block diagram of the localisation algorithm.

where i = x, y, z and k represents the discrete time sample: ˙ T, xGx = [ψ ψ] xGy = [θ θ˙ ]T ,

(2)

˙ , xGz = [φ φ] T

ψ, θ and φ are, respectively, the roll, pitch, and yaw angles,ψ˙ θ˙ and φ˙ are their derivatives with respect to time, and the matrix FG is defined as follows: 1 Ts (3) FGi = 0 1 with i = x, y, z, Ts is the sampling time Process noise covariance matrix QG is assumed as ⎡

QG = diag(QGx , QGy , QGz ),

QGi

Ts3 ⎢ 3 = σi2 ⎢ ⎣ T2 s 2

⎤ Ts2 2⎥ ⎥. Ts1 ⎦ 1

(4)

In this equation σi values represents the experimentally determined standard deviations of the components of the state vector. The observation array is ⎛ b⎞ ω¯ x ⎜ ω¯ b ⎟ ⎜ y⎟ zG = ⎜ b ⎟ (5) ⎝ ω¯ z ⎠ a¯ bx 0 The first three components are the b-frame angular rates measured by the gyroscope; the fourth, instead, is the longitudinal component of the b-frame acceleration obtained by the


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finite derivatives of the subsequent tachometer measures.[1]


a¯ bx (k) =

v¯ bx (k) − v¯ bx (k − 1) TS

(6)

The last component is the zero value for the reset of the roll angle which can occur in the particular condition of straight track. The HG matrix,[21] which correlates observations with the state is ⎛

0 ⎜0 ⎜ HG = ⎜ ⎜0 ⎝0 1

1 0 0 0 0

0 0 0 cos ψ 0 − sin ψ 0 gz 0 0

⎞ 0 − sin ψ 0 cos θ sin ψ ⎟ ⎟ 0 cos θ cos ψ ⎟ ⎟, ⎠ 0 0 0 0

(7)

where gz = −9.81 m/s2 Since pitch angles are small, the following approximation can be assumed: ax = gz sin(θ ) ≈ gz θ .

(8)

Sensor noise covariance matrix RG is assumed as RG = diag(σω2x , σω2y , σω2z , σa2b , σ02 ),

(9)

where the elements in the diagonal matrix are the standard deviations of sensor measures, in particular σωx,y,z are the standard deviations of the gyroscope, σab value represents the standard deviation of the longitudinal component of the b-frame acceleration and σ0 represents the standard deviation of the event of reset of the roll angle. The RG matrix is adaptive with respect to the conditions Coasting and Straight represented in the diamond boxes in. The Coasting is the phase where neither traction and braking occur: in this case the wheel peripheral acceleration is equal to the longitudinal acceleration, since in this phase phenomena of degraded adhesion do not have effect. σω2y σa2b

b if |f¯x | < ηfx ,

σω2y σa2b

otherwise.

(10)

The equation explains how the Coasting phase is handled by the Orientation Kalman filb ter: f¯x represents the longitudinal acceleration provided by IMU accelerometer [11] and ηfx is a threshold experimentally determined. When ‘zero’ longitudinal acceleration occurs, sensor noise covariance values are set so as to enable the contribution of the tachometer and disable the component y of the gyroscope. The complementary case, instead, occurs when longitudinal component of the accelerometer is far from zero (traction and braking phase). The Straight condition states the condition of reset of the roll angle: in fact, neglecting roll variations due to the suspensions, relevant values of this angle occur when the track is curvilinear in the presence of cant angles. The requirements of ‘zero’ lateral acceleration is not sufficient to state the absence of curved track: in fact the compensation of the total lateral acceleration can subsist when the centrifugal acceleration, due to the train motion in curve, is compensated by the ‘gravity lateral acceleration’, due to the presence of cant in the curves of

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the railway track. It is, thus, necessary to include conditions on the gyroscope, that is ‘zero’ angular rates over x and z axes: σω2x σ02

if |f¯yb |) < ηfy with |ω¯ xb | < ηωx with |ω¯ zb | < ηωz ,

σω2x σ02

otherwise.

(11)

This condition allows turning on the contribution of the roll reset when the three requirements explained occur at the same time. The thresholds ηfy , ηωx , ηωy and ηωz have been experimentally tuned. INS-ODO Kalman filter. The state equations of the INS-ODO Kalman filter, expressed in block matrix form,[11] are ⎤ ⎡ ⎡ b ⎤ ⎡pb (k)⎤ px (k + 1) 1 Ts 21 Ts 2 x ⎢ b ⎥ ⎥ ⎢ b (12) ⎣vx (k + 1)⎦ = ⎣0 1 Ts ⎦ ⎣vx (k)⎦ b b 0 0 1 ax (k + 1) ax (k) where pbx (k) is the distance travelled by the train, vbx (k) is the train speed, and abx (k) is the b-frame acceleration. Through the integration, the accelerometer noise will be propagated: process noise covariance matrix QA is defined and initialised as follows to allow to estimate and reduce the white noise propagation to the velocity and position (obtaining a correctly estimation). To calculate the noise covariance matrix QA , the transition matrix A need to be introduced: supposing that X˙ = AX where X = [x x˙ x¨ ] and x represents the position of vehicle. The discrete-time process noise covariance matrix QA is calculated as[28] ⎡ ⎤ ⎡ ⎤⎡ ⎤ x˙ 0 1 0 x ⎣x¨ ⎦ = ⎣0 0 1⎦ ⎣x˙ ⎦ , (13) 0 0 0 0 x¨ ⎡ ⎤ Ts 0 0 0 T eAτ Qt eA τ dτ , Qt = ⎣0 0 0 ⎦ , (14) QA = 0 0 0 qc where QA , Qt are the time-discrete and continue noise covariance matrix, respectively, and qc represents the process noise covariance for continue-time (set equal to one) ⎛ 5 ⎞ Ts Ts2 Ts3 ⎜ 20 8 6⎟ ⎜ ⎟ 4 3 ⎜ Ts Ts2 ⎟ 2 ⎜ Ts ⎟ QA = σa ⎜ (15) ⎟ 3 2⎟ ⎜8 ⎝ T3 T3 ⎠ s s Ts 6 2 and where σa represents the standard deviation of the state vector components. The observation array is ⎛ b ⎞ fx − Rˆ bn gn ⎜ ⎟ (16) zA = ⎝ vbx ⎠. 0 The first component is the compensated longitudinal gravity acceleration, in which gn represents the gravity acceleration expressed in the navigation reference system (n-frame),[11]


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while the second is the speed of the train measured by the tachometer, the third is the zero point of the position reset sent by the balise, which is supposed to occur each 1000 m. The HA matrix, which correlates the observations with the state, is HA = I3×3 .

(17)


Sensor noise covariance matrix RA is assumed as RA = diag(σa2x , σv2x , σp2x ),

(18)

where the elements in the diagonal are the standard deviation values of the sensor measures. The RA matrix is adaptive with respect to the conditions Adhesion and Balise represented in the diamond boxes in. The Adhesion condition is determined through the Accelerometric criterion if the difference between the wheel peripheral acceleration abx and the gravity compensated body longitudinal acceleration |f¯xb − Rˆ bn gn | is less than a threshold (ηad ), the adhesion is considered good. In order to avoid that ‘fake’ good adhesion conditions are considered, a slave Tachometric criterion has been implemented: it allows the speed reset only if the difference between the actual estimated speed vb and the wheel peripheral speed vbx is lower than a threshold ηv . It is worth pointing out that, compared to the classical SCMT solutions, only one tachometer is sufficient for the detection of the wheel–rail adhesion condition. σa2x σv2x

if |f¯x − Rˆ bn gn − abx | < ηad with |¯vb − vbx | < ηv ,

σa2x σv2x

otherwise.

b

(19)

This formulation states that, when good adhesion between the wheel and the rail occurs, the measurement update of the Kalman filter can rely on the contribution of the speed measure provided by the tachometer. Moreover, although degraded adhesion conditions occur, the longitudinal acceleration signal provides anyway an estimate of speed and travelled distance. The thresholds ηad , ηv have been experimentally tuned in the testing phase. The Balise condition allows to reset the travelled distance estimate, recognising the occurrence of a Balise. During the operative conditions a logic signal reveals the presence of a Balise. In a simulated scenario it has been hypothesised the occurrence of a Balise every 1000 m, assuming a five meters bidirectional error (expressed by ηb ) on its positioning along the track. This value of uncertainty is obtained considering empirical knowhow about the tolerances linked to the balise positioning along the line. σa2x σr2x

if |¯pbx − 1000| < ηba ,

σa2x σr2x

otherwise.

(20)

These equations provide the better estimation of the process state, composed by the vehicle position and speed. In particular, Equations (19) and (20) impose the major or minor accuracy of each sensor measure, by tuning the corresponding values of standard deviation. This way the Kalman filter gain and its correction action are weighted as a function of the standard deviation of tachometer and tri-axial gyroscope (Equations (9) and (18)).[6]

3.

Testing

In this section, the testing procedure, sketched in Figure 3, is described in detail.

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Figure 3. Testing procedure.

3.1.

Multibody model of the railway vehicle

The development and calibration of the ODO algorithm, described in the section on sensor fusion between odometers and INS, involves the availability of coherent kinematical inputs (wheel angular speed, acceleration, and angular orientation) and the simulation of a wide range of working conditions, whose realisation by means of experimental test runs is difficult and expensive. On the other hand, for this type of application, the use of commercial multibody software is quite difficult. In order to overcome all these problems a complete 3D multibody model of a railway vehicle has been developed using Matlab-SimulinkTM ,[25,29] which is able to reproduce different working conditions, with arbitrary tracks. In particular, the 3D multibody model of a high-speed train was implemented.[29–32] Using a multibody approach, the system was divided into one coach, two bogie frames, eight axle boxes, and four wheel sets: these bodies were supposed to be rigid. The rail vehicle has a B0 − B0 wheel arrangement. The coach is held by a rear and front bogie with a two-stage suspension system (Figure 4). Force elements have been modelled by means of springs and dampers, with opportunely defined non linear characteristics reproducing the real component behaviour. The used data reproduce typical properties of high-speed trains (e.g. the EMU V250 high-speed train by AnsaldoBreda). The implemented 3D multibody model has been also thought to simulate degraded adhesion conditions,[33–35] which are critical conditions for the localisation algorithm. This is an important aspect and in-time adhesion model law were used for the simulations.[26,27,36] (Figure 4)

Figure 4. Two-stage suspension bogie model.


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Table 1. Main characteristics of the vehicle.


Parameter Total mass Wheel arrangement Bogie wheelbase Bogie distance Wheel diameter Primary suspended masses own frequency Secondary suspended masses (carbody) own frequency

Units

Value

kg – m m m Hz Hz

≈ 56, 000 B0 − B0 2.42 16.9 0.92 ≈ 4.5 ≈ 0.8

Table 2. Elastic characteristics of the two-stage suspension. Element Primary suspensions Secondary suspension Axlebox bushing Anti roll bar

Transl. stiff. x Transl. stiff. y Transl. stiff. z Rotat. stiff. x Rotat. stiff.y Rotat. stiff. z (N/m) (N/m) (N/m) (Nm/rad) (Nm/rad) (Nm/rad) 844, 000 124, 000 40, 000, 000 0

84, 400 124, 000 6, 500, 000 0

790, 000 340, 000 40, 000, 000 0

10, 700 0 45, 000 0

10, 700 0 9700 0

0 0 45, 000 0

In Table 1, the main properties of the rail vehicle are shown. In Table 2 the elastic characteristics of the connection elements are displayed. The simulated tests have the following characteristics: • • • •

long time running: in order to simulate high INS integration errors; degraded adhesion: in order to stress tachometer measures; line gradient: it significantly affects the accelerometer error; curves and cant angles: a good estimation of the line gradient in the Attitude Kalman filter is influenced by the good estimation of yaw and roll angles; and • patterns of irregularities of the rail line (rail gauge irregularities, cant, etc.). 3.2.

Simulated sensor mask

The accelerations and angular rates reproduced by the Matlab-SimulinkTM model have been processed by a mask that simulates the sensor errors.[11,37] The quantification of the errors was carried out by the experimentation of a custom IMU designed by ECM S.p.A. based on low assumptions have been checked by the IMU testing:[38] • Random noise has been simulated, both for the accelerometer and for the gyroscope, as a Gaussian white noise with zero mean and standard deviation, ηa and ηg , as in Table 3; Table 3. Errors of sensors-IMU. Parameter

Units

Value

ηa ηg ba bg ηm

m/s2 rad/s m/s2 rad/s rad

2.2e−3 7.8e−4 4.1e−3 2.5e−5 2.2e−4


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• Deterministic biases have not been simulated, since they are supposed to be removed during the calibration phase;[39] • Random biases, ba and bg , have been simulated with the values given in Table 3; • Scale-factor errors have not been simulated, since they do not influence in the output is not relevant, and no error of this kind appeared during the tests;[40] and • A two degrees assembly error has been simulated for each Euler angle. This value has been obtained from the experience of the manufacturers. This error could be removed for roll and pitch angles through an initial gravity calibration. The inaccuracy which persists after this procedure has been simulated with a zero-mean Gaussian noise with standard deviation ηm . The values ηa , ba and ηg , bg are obtained, respectively, from the data sheet of the accelerometer and the gyroscope used in the custom IMU board. 3.3.

Post-processing and evaluation of the performances of the algorithm

Monte Carlo runs are made to obtain an estimation of the expected value of the performance from a sample average of independent realisations. A large number of runs increases the power of the hypothesis testing. The performance estimation relative to N independent runs is the mean of the N cost values. The performance parameter used to evaluate the advantages of the localisation algorithm in terms of reliability is the percentage of time the signal error does not meet the European Train Control System (ETCS) requirements.[41–44] The proposed localisation algorithm allows speed and travelled distance errors much lower than the values imposed by the ETCS requirements to be obtained, and the results are compared with error limits stricter than the ETCS values, equal, respectively, to one half, one quarter and one eighth of the ETCS limit. These reduced performance thresholds are shown in Figures 5 and 6.

Figure 5. ETCS speed requirements: reference (blue line) and reduced values.



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Figure 6. ETCS position requirements: reference (blue line) and reduced values.

3.4.

Simulated results

The testing procedure has been applied to 10 worst-case-design paths, whose features are summarised in Table 4. Every path consists of three phases of traction and braking, affected by degraded adhesion and interspersed by a phase of coasting. A degree of criticality (high or very high) is assigned to each testing path, in relation to the features that highlight the weakness of the sensors. The first five are characterised by a high degree of criticality, since the changes of slopes and the curves are faced at such a speed that the angular rate are greater than the noise of the gyroscope. The last five paths are characterised by a very high degree of Table 4. Testing path: characteristics. ID Degree of Weakness 1 2

High High

3

High

4

High

5

High

6 7

Very High Very High

8

Very High

9 Very High 10 Very High

Characteristics Articulated altimetry, with uphills and downhills up to 30, without any curves Curved track with a radius of curvature of 18,000 m, but level (there are no uphills or downhills) Combination of curves (radius of curvature of 1800 m) and slopes (uphills and downhills up to 30) Curves (radius of curvature of 1800 m) and uphills (downhills) with mixed slopes (10, 20, 20) Very similar to the previous ones, but the curves are faced at a such speed and with a radius of curvature that the lateral acceleration is zero Very long (nearly 30 km) with slopes in the coasting phase, too The first uphill, with slope of 30, is faced at a speed of about 15 km/h (testing if the gyroscope can read the angular rates over y-axis) The first uphill, with slope of 10/1000, is faced at a speed of 35 km/h (same objective of the previous) Very similar to the seventh, but the facing speed is about 8 km/h The first curve (radius of curvature of 10,000 m) is faced at speeds below 40 km/h (testing if the gyroscope can read the angular rates over z-axis)


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Figure 7. Comparison between true and estimated speeds.

Figure 8. Comparison between true and estimated speeds: zoom on traction phase.



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Figure 9. Comparison between true and estimated speeds: zoom on braking phase.

critically due to the high level of stress imposed on the sensors: it is checked if the gyroscopes are able to detect the angular rate in very extreme conditions due to very low train speeds and very limited slope transients of the line gradient. To be short, the following graphs are reported only for path1. The comparison between the true speed of the train and the speed estimated by SCMT and INS-ODO algorithms with a zoom on the traction and braking phase. In Figures 7–9 the accuracy enhancement provided by the innovative algorithm, compared to the SCMT solution, is manifest: although the braking conditions imposed in the simulation are critical, in terms of adhesion, the wheel peripheral speed is very far from the ‘true’ one,

Figure 10. Path 1 ∝ 5: comparison between the speed errors and the ETCS requirements.


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Figure 11. Path 6 ∝ 10: comparison between the speed errors and the ETCS requirements.

and the contribution of the INS algorithm allows a good estimation with a very low drift. The speed errors of the 10 paths are shown in Figures 10 and 11. The results show that the localisation algorithm estimates are good, since the speed and the position errors are always much smaller than the speed and position requirement thresholds. Figures 12 and 13 show particulars of the critical starting phase: in fact, since the train speed is low, the angular rate measured over the y-axis, although the line slope is changing, may be so small as to be incorporated into the noise. From the picture, it is obvious that

Figure 12. Path 1 ∝ 5: speed errors in the starting phase.



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Figure 13. Path 6 ∝ 10: speed errors in the starting phase.

paths 7, 8 and 9, which are characterised by a slow initial acceleration ramp, have worse performances in terms of accuracy, even though the error meets the ETCS requirements.

4.

Testing of the innovative localisation algorithm through the dynamic simulator

This paragraph is focused on the testing activity of the innovative localisation algorithm through the dynamic simulator. Figure 14 explains the testing procedure of the algorithm with HIL. The 3D railway multibody model brings out the ideal paths, in terms of simulated profiles and kinematic parameters into the dynamic simulator, represented by the 6 degree of freedom robot COMAU Smart Six. IMU board on the end-effector of the robot measures the acceleration and angular speed [45] reproducing the experimental results and these, with simulated GIT measure (coming from the 3D multibody model) are put into the innovative localisation algorithm that responds with vehicle speed and position estimation that are finally compared with the corresponding simulated ones. The first task is the project of a custom IMU which could fit both the technical and the business requirements. The IMU board, supplied by ECM, has been designed as a piggyback board to be assembled into the subsisting SCMT odometry module. The piggyback is divided into two perfectly reflecting sections, in order to meet the reliability requirements which impose the redundancy of all the hardware and software structure (Figure 15). In each sections the following components lie: a triaxial accelerometer, two dualaxis gyroscopes, a temperature sensor, three amplifiers (one for each inertial sensor) and a controller. The assembly of the module on the robot is such that its orientation is the same as in the train cabinet (Figure 16). The amplifier is aimed to the pre-amplification of the analog output of the sensors.


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Figure 14. Testing of the algorithm with Hardware in the Loop (HIL).

Figure 15. Piggyback module.



Figure 16. Custom IMU board.

Figure 17. Climate chamber tests – inside view (left) and outside view(right).

Figure 18. Trend of the accelerometer offset w.r.t temperature: xoff (left) and yoff (right).

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Figure 19. Trend of the gyroscope offset w.r.t temperature: xoff (top left), yoff (top right), and zoff (bottom).

Figure 20. Path 42 – Accelerometer.


4.1.

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Calibration and thermal analysis

This section deals with the procedure for the calibration of the custom IMU board for the removal of the sensor errors. The sensor measure is composed by three error sources caused by assembly misalignment, drift based on the temperature and error given by intrinsic biases.[46]


( 0g )imu = η + 0g (T) + 0¯ g , • η: error caused by assembly misalignment; • 0g (T): drift based on the temperature; and • 0¯ g : error given by intrinsic biases.

Figure 21. Path 42 – Gyroscope.

(21)


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Figure 22. Path 59 – Accelerometer.

The strategy exploits a commercial IMU placed at the rear of the board, as reference for the output measurements. In order to characterise the behaviour of the acceleration in temperature and determinate the drift 0g (T) several tests in the climatic chamber were carried out: the temperature has been varied from −25◦ C to +55◦ C with gradient of 2◦ C (Figure 17). The following graphs report the trend of the 0g (T) for longitudinal and lateral components of the accelerometer and for the three components of the gyroscope Figures 18 and 19. 4.2.

Testing of a custom IMU

In this section the graphs of the longitudinal and lateral accelerations measured by the custom IMU board are compared to the reference signals: two paths, as example,are reported to show the good performances of accelerometer and gyroscopes in terms of matching with the simulated signals. For each figure the following signals are reported: • the reference signal generated by the DM (3D multibody model); • the raw measurement of the custom IMU board; • the calibrated measurement of the custom IMU board, i.e. the raw one compensate through the temperature-based offset. Figures 20–23 show the acceleration and gyroscope measurements for paths 42 and 59. In particular, it is visible that an offset, based on temperature drift, is present and affects the raw measurement amplitude: this error assumes positive values for the longitudinal acceleration both for the 42 and 59 paths, instead it assumes negative values for the other cases (lateral acceleration and tri-axial gyroscope measure). There is a good matching between the IMU



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Figure 23. Path 59 – Gyroscope.

calibrated measures (outputs) and the kinematic input data (from the DM): the HIL approach is thus a feasible solution to test the innovative localisation algorithm. 4.3.

Testing of the innovative localisation algorithm

The 10 paths represented in Table 4 are used for the HIL testing. Below the graphs of the speed and travelled error estimation of a few paths are sketched, in comparison to the corresponding ETCS requirements. The corresponding errors are very low (less than 1% in the worst cases): more precisely, the speed errors are always less than 0.5 m/s and distance error less than 2 m. When the speed error is different from zero (Figures 24–29), it assumes positive values and so it represents an overestimation of the vehicle speed that implies a safety (cautionary) situation. The achieved results show the INS-ODO algorithm performances are better than the SCMT algorithm ones; on-track tests will give further confirmation of the proposed strategy.


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Figure 24. Path 1 – distance error.

Figure 25. Path 1 – speed error.

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Figure 27. Path 5 – speed error.

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Figure 29. Path 8 – Speed Error.

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5.

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Conclusions

The aim of this work has been developing an innovative localisation algorithm for railway vehicles, able to enhance the performance, in terms of speed and position estimation accuracy, of the classical odometry algorithms, such as the Italian SCMT. The proposed solution consists of sensor fusion techniques fusing the information coming from an odometer and an IMU. The sensor output signals have been simulated through a 3D multibody model of a railway vehicle: a huge number of simulated paths with a wide range of working conditions and track configurations have been used to test the proposed algorithm. The work has provided the development of a custom IMU board, designed by ECM S.p.a.. The IMU board is tested via a dedicated HIL test rig developed as a dynamic simulator for the testing of the inertial sensors and navigation algorithms. The HIL test rig includes an industrial robot devoted to replicate the motion of the railway vehicle on the IMU to be tested, according to suitable washout filters design: real test runs have been reproduced through the HIL approach. Thus, it has been possible to apply the testing procedure of the innovative localisation algorithm to a set of 10 worst-case-design paths, all characterised by a high degree of criticality, since the high level of stress imposed to the sensors. The obtained results confirm the good prospects of the innovative localisation algorithm: the speed and the position errors are always much smaller than the speed and position ERTMS requirement thresholds. At the same time the accuracy of the estimation is more reliable than the one provided by the classical algorithms. On-tracks tests will be performed soon to evaluate the feasibility of the proposed strategy.

Funding This work was supported by ECM S.p.A. within the projects COINS (Cooperative Odometry-Inertial Navigations System), funded by Regione Toscana under the program ‘BANDO UNICO R&§anno 2008’ linea AG, and TRACEIT (Train Control Enhancement via Information Technology) funded by Regione Toscana - PAR FAS 2007-2013, Azione 1.1. P.I.R. 1.1.B - POR CReO fesr 2007-2013.

References [1] Allotta B, Colla V, Malvezzi M. Train position and velocity estimation using wheel velocity measurements. Proc. of the IMechE, J Rail Rapid Transit. 2002;216:207–225. [2] Malvezzi M. Odometry algorithms for railway applications [Ph.D. diss.]. Università degli Studi di Bologna; 2003. [3] Mirabadi A, Mort N, Schmid F. Multisensor integration methods in the development of a fault-tolerant train navigation system. J Navig. 2003;56(3):385–398. [4] Stadlmann B. Automation of operational train control on regional branch lines by a basic train control. Proceedings of the IEEE Intelligent Transportation Systems Conference, Toronto, Canada; 2006 September 17–20. p. 50–54. [5] Ramsak T. Deployment of the ETCS system in Slovenia. ICTS – International Conference on Traffic Science, Toronto, Canada; 2006. [6] Jwo DJ, Pin Weng T. An adaptive sensor fusion method with applications in integrated navigation. J Navig. 2008;61:705–721. [7] Malvezzi M, Allotta B, Rinchi M. Odometric estimation for automatic train protection and control systems. Veh Syst Dyn. 2010;491:723–739. [8] Shin EH. Estimation techniques for low-cost inertial navigation [Ph.D.diss.]. Calgary (AL): University of Calgary; 2005. [9] Nassar S. Improving the inertial navigation system (INS) error model for INS and INS/DGPS applications [Ph.D.diss.]. Calgary (AL): University of Calgary; 2003. [10] Petovello M. Real time integration of a tactical-grade IMU and GPS for high-accuracy positioning and navigation [Ph.D.diss.]. Calgary (AL): University of Calgary; 2003. [11] Titterton DH, Weston JL. Strapdown inertial navigation technology. 2nd ed. IEE Radar, Sonar, Navigation and Avionics Series 17. London: The Institution of Electrical Engineers; 2004.


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B. Allotta et al.

[12] Phuong NHQ, Kang HJ, Suh YS, Ro YS, Lee KC. A GPS/INS integration system with new orientation IEEE Industrial Electronics, IECON 2006, Paris, France, 2006 November 6–10. p. 3099–3104. [13] Zhang H, Rong J, Zhong X. Research of the INS/GPS integrated navigation system for high speed trains. Paper presented at: The 9th International Conference on Young Computer Scientists, 2008. ICYCS 2008, Hunan, China, 2008 November 18–21. p. 1659–1663. [14] Carona F, Duos E, Pomorski D, Vanheeghe P. On-track braking tests. Internal Report of Trenitalia; 2006. p. 221–230. [15] Mazl R, PfeuEil L. Sensor Data Fusion for Inertial Navigation of Trains in GPS-dark areas. Department of Cybemetics, Faculty of Electrical Engineering Czech Technical University in Prague; 2003. [16] Ernest P, Mazl R, PfeuEil L. Train locator using inertial sensors and odometer. IEEE Intelligent Vehicles Symposium University of Parma; 2004. p. 860–865. [17] Godha S. Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application. UCGE Reports Number 20239; 2006. [18] Acharya A, Sadhu S, Ghoshal TK. State inequality constraint based method for rail navigation. Annual IEEE India Conference (INDICON), Kolkata, India, 2010 December 17–19. p. 1–6. [19] Acharya A, Sadhu S, Ghoshal TK. Train localization and parting detection using data fusion. Transp Res Part C 2011;19:75–84. [20] Liu J, Cai B, Wang Y, Wang J, Shangguan W. A GPS/compass based train integrated positioning method for high-speed railways. Antennnas and Propagation in Wireless Communications (APWC), 2012 IEEE-APS Topical Conference, Cape Town, South Africa; 2012 September 2–7. p. 1201–1204. [21] Wang M, Yang Y, Hatch RR, Zhang Y. Adaptive filter for a miniature MEMS based attitude and heading reference system. Position Localization and Navigation Symposium; 2004 April 26–29. p. 193–200. [22] Ding W, Wang J, Rizos C. Improving adaptive Kalman estimation in GPS/INS integration. J. Navig. 2007;60:517–529. [23] Jwo DJ, Chung FC. A fuzzy adaptive fading Kalman filter for GPS navigation. In: Huang DS, Heutte L, Loog M, editors. Advanced intelligent computing theories and applications, with aspects of theoretical and methodological issues. Lecture notes in computer science, Vol. 4681; Heidelberg: Springer; 2007. p. 820–831. [24] Amirsadri A, Kim J, Petersson L, Trumpf J. Practical considerations in precise calibration of a low-cost MEMS IMU for road-mapping applications. American Control Conference Fairmont Queen Elizabeth, Montreal, Canada; 2012 June 27–29. p. 881–888. [25] Falomi S, Malvezzi M, Meli E. Multibody modeling of railway vehicles: innovative algorithms for the detection of wheel-rail contact points. Wear. 2011;271(1–2):453–461. [26] Meli E, Ridolfi A, Rindi A. An innovative degraded adhesion model for railway vehicles: development and experimental validation. Meccanica. 2014;49(4):919–937. [27] Meli E, Ridolfi A. An innovative wheel–rail contact model for railway vehicles under degraded adhesion conditions. Multibody Syst Dyn. 2014. doi:10.1007/s11044-013-9405-4. [28] Tsang T, Chiu C. Error reduction techniques for a MEMS accelerometer-based digital input device. The Chinese University of Hong Kong; March 2008. [29] Allotta B, Pugi L, Ridolfi A, Malvezzi M, Vettori G, Rindi A. Evaluation of odometry algorithm performances using a railway vehicle dynamic model. Veh Syst Dyn. 2012;50(5):699–724. [30] Brakes – Specifications for the Construction of Various Brake Parts – Wheel Slide Protection Device (WSP), UIC 541-05, November 2005. [31] Brakes – Electropneumatic brake (ep brake) – electropneumatic emergency brake override (EBO), UIC 541-5; May 2006. [32] Iwnicki S. Handbook of Railway Vehicle Dynamics. Boca Raton, FL: Taylor & Francis; 2006. [33] Cai W, Liao W, Li D, Song Y. Observer based traction/braking control design for high speed trains considering adhesion nonlinearity. Abstr Appl Anal. 2014. doi:10.1155/2014/968017. [34] Conti R, Meli E, Ridolfi A, Rindi A. An innovative hardware in the loop architecture for the analysis of railway braking under degraded adhesion conditions through roller-rigs. Mechatronics. 2014;24:139–150. [35] Allotta B, Conti R, Meli E, Ridolfi A. Modeling and control of a full-scale roller-rig for the analysis of railway braking under degraded adhesion conditions. IEEE Trans Contr Syst Technol. 2014. doi:10.1109/TCST.2014.2320672. [36] Voltr P, Lata M, Cerny O. Measuring of wheel–rail adhesion characteristics at a test stand. Proceeding of the 18th Conference Engineering Mechanics; 2012. May 14–15; Svratka, Czech Republic. [37] Allotta B, Costanzi R, Meli E, Pugi L, Ridolfi A, Vettori G. Cooperative localization of a team of AUVs by a tetrahedral configuration. Rob Auton Syst. 2014. doi:10.1016/j.robot.2014.03.004. [38] De Agostino M. Stima degli errori nei sensori inerziali a basso costo, Dipartimento di Ingegneria del Territorio, dell’Ambiente e delle Geotecnologie, Politecnico di Torino (Italy). [39] El-Sheimy N, Nassar S, Noureldin A. Wavelet de-noising forIMU alignment. Aerosp Electron Syst Mag IEEE. 2004;19:32–39. [40] IEEE Standard. Standard for Inertial Sensor Terminology. IEEE. 2001 [41] Performance Requirements for STMs. In: EEIG ERTMS users Group – Reference EEIG Subset 059 Issue 2.0.0 (28/03/00) [42] Performance Requirements for STMs. In: EEIG ERTMS users Group – Reference EEIG Subset 041 Issue 2.0.0 (30/03/00) [43] Odometer FFFIS. In: EEIG 97E2675B Version5 (31/07/98)


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[44] Specific Transmission Module FFFIS. In: EEIG ERTMS Users-Group – Reference EEIG Subset 035 Issue 2.0.0 (30/03/00) [45] Allotta B, Becciolini L, Costanzi R, Giardi F, Ridolfi A, Vettori G. Design and implementation of dynamic simulators for the testing of inertial sensors. IEEE International Conference on Robotics and Automation (ICRA 2012), Saint Paul, Minnesota, USA; 2012 May 14–18. p. 5024–5029. [46] Shen SC, Chen CJ, Huang HJ. A new calibration method for low cost MEMS inertial sensor module. J Marine Sci Technol. 2010;18:818–824.

An innovative localisation algorithm for railway vehicles

An innovative localisation algorithm for railway vehicles

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