EP2331381A1

EP2331381A1 - Method for determining a characteristic of a track position parameter

Info

Publication number: EP2331381A1
Application number: EP09783334A
Authority: EP
Inventors: Peter Häse
Original assignee: Bombardier Transportation GmbH
Current assignee: Alstom Transportation Germany GmbH
Priority date: 2008-09-23
Filing date: 2009-09-23
Publication date: 2011-06-15
Anticipated expiration: 2029-09-23
Also published as: AU2009295943A1; WO2010034744A1; EP2331381B1; DE102008048601A1; US20110276203A1; CA2737419A1

Abstract

The present invention relates to a method for determining a characteristic of at least one position parameter, in particular of a disruption in the position parameter, of a track for a vehicle, in which detection values of at least one detection parameter affected by the position parameter are detected on a vehicle (101) driving along a section of the track (105), and wherein the at least one position parameter for the track section is determined from the detection values. The current value of the position parameter is determined from the detection values as a function of the model data of a previously determined vehicle model of the vehicle, wherein a relationship between the position parameter and the at least one detection parameter affected by the position parameter is generated by way of the vehicle model.

Description

A method for determining a property of a route location parameter

The present invention relates to a method for determining a property of at least one position parameter, in particular a position parameter disturbance, of a travel path for a vehicle, in which detection values of at least one detection parameter influenced by the position parameter are detected at a vehicle traveling on a route section of the travel path and from the detection values of at least one position parameter for the route section is determined. The present invention further relates to a method for controlling a vehicle and a vehicle for carrying out the method according to the invention.

In vehicles, especially in rail vehicles, the dynamic load of the vehicle components (especially of course the suspension components) in operation depends heavily on the condition of the traveled route. This condition of the route is represented, inter alia, by so-called position parameters, which in the case of a rail vehicle are usually subsumed under the term "track position". The track position refers to the rule, the location of a railway track in the horizontal and / or vertical direction and, where appropriate, the mutual altitude of both rails of the track.

The smaller the deviation of a track from its desired track position, the lower the track position errors, the higher the quality of the track and the lower are the resulting from such track position errors dynamic loads on the vehicle. With the progressing economic separation between the operators of the infrastructure (rail network, etc.) and the operators of the vehicles used thereon, the condition of the tracks gains ever greater economic importance. In particular, the train path price achievable by the infrastructure manager (ie the fee for the use of the infrastructure) will be more geared to the quality of the route, so that reliable information about the condition of the track will become more and more important.

So far, the track position of a particular section of the route is elaborately determined by so-called measuring vehicles, which directly acquire, store and, if appropriate, make available in the form of suitable data sets via a corresponding complex sensor system the properties of the position parameters of the track. Problematic here is that the measuring vehicles on the one hand (in the purchase and use) are relatively expensive and on the other hand (due to the low realizable speeds during the test drive) only at certain times with a low utilization of the route (for example, at night, at weekends, etc.) can be used to prevent the regular traffic on this route. Especially on heavily traveled sections, where a rapid deterioration of the track condition is to be expected, this leads to insufficiently long intervals between the test drives.

From the article Charles, G.A., Goodall, R.M. Dixon, R .: "Wheel-Rail Profile Estimation", (Proceedings of IET International Conference on Railway Condition Monitoring, The International Conference on Railway Condition Monitoring 2006, Birmingham, November 2006, pp. 32-37, ISBN 0 86341 732 9) it is known, via appropriate sensors on the vehicle and corresponding calculation algorithms (in particular a known from control technology so-called observer algorithm in the form of a so-called Kalman filter) conclusions on the actual state of the current wheel-rail pairing, in particular the effective conicity of the wheel Rail mating, pull. Here, however, only knowledge about the current state of the wheel-rail pairing are obtained, which are also significantly influenced by the state of the wheel used. An isolated view of the rail, which could provide information about the current condition of the track, but does not.

The present invention is therefore based on the object to provide a method and a vehicle of the type mentioned above, which does not have the abovementioned disadvantages or at least to a lesser extent and in particular in a simple and cost-effective manner a detection and use of the properties of the position parameters a section of track allows.

The present invention solves this problem starting from a method according to the preamble of claim 1 by the features stated in the characterizing part of claim 1.

The present invention is based on the technical teaching that a simple and cost-effective detection and use of the properties of the positional parameters of a route section is possible if the current value of the attitude parameter is determined from the detection values as a function of the model data of a previously determined vehicle model of the vehicle. being related to the vehicle model between the positional parameter and the at least one detection parameter influenced by the positional parameter. About such a (previously established) vehicle model, it is possible in an advantageous manner (possibly in real time) to draw conclusions about the sought location parameters. This can be any suitable mathematical model via which a connection can be established between the acquisition variable and the location parameter.

According to one aspect, the present invention therefore relates to a method for determining a property of at least one position parameter, in particular a position parameter disturbance, of a travel path for a vehicle, in which detection values of at least one detection parameter influenced by the position parameter are detected on a vehicle traveling on a route section of the travel path, and from the detection values, the at least one position parameter for the route section is determined. The current value of the position parameter is determined from the detection values as a function of the model data of a previously determined vehicle model of the vehicle, wherein a relationship between the vehicle model and the vehicle model is established

Position parameter and the at least one influenced by the position parameter detection variable is made.

Under certain conditions, it can be provided that the current value of the position parameter is calculated in a recursive method from the detection values as a function of the model data of the vehicle model. The vehicle model is in this case designed such that it directly reflects the relationship between the detection values of the detection variable and the desired location parameter (s) or (with sufficient precision, for example in sufficiently good approximation) one to the desired location parameter ( n) leading recalculation on the basis of the acquisition values of the entry size.

In further preferred and comparatively universally applicable variants of the present invention, an observer algorithm well known from the field of control technology is used which, depending on a current detection value, outputs an associated current estimate of at least one state variable of the vehicle, which is influenced by the position parameter, and it is then determined in dependence on the model data of a previously determined vehicle model of the vehicle, the current value of the position parameter. As mentioned, the vehicle model may be any suitable mathematical model that determines the relationship between the attitude parameter and the at least one State variable represents. Such models are well known in the field of vehicle dynamics.

With the present method it is possible, inter alia, to draw sufficiently reliable conclusions on the desired position parameter or the desired position parameters, ie, therefore, the current state of the traveled section on the basis of the detection values anyway already present on the vehicle detection devices (for example, the measured values on the vehicle sensors). By suitable modeling of the vehicle (ie a suitable choice of the vehicle model) and a suitable design of the observer algorithm, it is possible with the present invention to quickly and with sufficient accuracy from detection variables which do not allow any direct conclusions about the positional parameters To draw conclusions.

In particular, it is possible to carry out the determination of the properties of the position parameter in real time during the drive of the vehicle on the route section. Another aspect in connection with such a real-time determination of the state of the current rail track is the possibility of control of the vehicle depending on this condition. Especially in the case of rail vehicles, trailing wheels can be tracked using the information about the position parameter (obtained, for example, on a leading chassis). Thus, the state of the guideway, be actively influenced, for example, to achieve a particularly quiet vehicle running and / or a desired, optionally optimized wear behavior of the vehicle components, in particular the suspension components.

The properties of the respective position parameter can be both the current (possibly absolute) value of the position parameter. Likewise, it may additionally or alternatively also be a deviation of the position parameter from a predefined setpoint value, that is to say a position parameter disturbance or a position parameter error.

According to a further aspect, the present invention therefore relates to a method for determining a property of at least one position parameter, in particular a position parameter disturbance, of a driving path for a vehicle, in which detection values of at least one detection variable influenced by the position parameter are detected on a vehicle traveling on a route section of the driving path and the at least one position parameter for the route section is determined from the detection values becomes. The at least one attitude parameter is determined for the route segment using an observer algorithm, wherein the observer algorithm is designed to output, depending on a current detection value, an associated current estimate of at least one state variable of the vehicle which is influenced by the attitude parameter. Depending on the model data of a previously determined vehicle model of the vehicle, the current value of the position parameter is determined, the vehicle model representing the relationship between the position parameter and the at least one state variable.

As already mentioned, any suitable mathematical model can be used for the vehicle model, which the different bodies of the

Vehicle and their coupling represents. Preferably, the vehicle model was determined using a, in particular non-linear, dynamic multi-body model. Such multi-body models are well known in the field of vehicle dynamics and are often used to predict the driving safety and running quality of vehicles.

These (sometimes also referred to as dynamic multi-body systems) are currently in the regime! around non-linear models. In order to simplify the calculations to be performed (in particular with regard to a real-time determination of the position parameter), the model is linearized by a (well-known) suitable procedure, so that a linear state space model is obtained as the vehicle model. The inputs of the vehicle model then form the properties to be determined of the desired position parameter or parameters (in the case of a rail vehicle, for example, the track position or the track position interference), while the outputs represent the relevant detection variable or detection variables. The states of the modeled system are then, for example, the position or the speed of certain vehicle components of interest (in a rail vehicle, for example, the wheels or wheelsets, the chassis frame and other vehicle components such as the car body, etc.).

The present inventive method can be carried out in principle with an arbitrarily complex or complex modeling of the vehicle. In particular, one or more degrees of freedom up to all six possible degrees of freedom (translation in and rotation about all three spatial directions) can be taken into account for the movement of a vehicle component. However, in order to reduce the calculation effort, it is preferably provided that for the Vehicle model, only those degrees of freedom of the components of the multi-body system are taken into account, which have a primary influence on the detection size and / or which are primarily influenced by the attitude parameter.

It has been shown that sufficiently precise results can be achieved if movements in degrees of freedom are neglected, which only have a small influence on the detection variable of interest or the position parameter of interest. If, for example, the detection variable of interest involves the axial change in length of a spring, then movements in degrees of freedom which merely cause a deflection of the spring transversely to its spring axis can be disregarded. Although these movements may also bring about a certain axial length change, their contribution is generally negligible.

The observer algorithm can in principle also have been generated in any suitable manner. Preferably, the observer algorithm was determined using the vehicle model, since this can be achieved in a particularly simple way sufficiently precise results. Depending on the nature and design of the observer algorithm as well as on the nature of the determining property of the position parameter, only the vehicle model may have been included in the determination of the observer algorithm.

In advantageous variants of the invention, a consideration of the properties of the position parameter to be determined and the observer algorithm already takes place when the observer algorithm is determined. If, for example, position parameter perturbations are to be determined whose type does not coincide with the type of perturbation typically detected via the observer algorithm, an adaptation is preferably made via the vehicle model used in the determination of the observer algorithm. In preferred variants of the method according to the invention, it is therefore provided that a position parameter disturbance is determined, wherein the vehicle model was determined by linearization of the multi-body system and a suitable shape filter was used to take into account the real noise behavior of the position parameter disturbance in determining the observer algorithm at at least one input of the vehicle model. Such shape filters are well known in vehicle technology (see, for example, Laun, R .: Active vibration damping by adhesion control based on a state controller, University of Applied Sciences Offenburg, DE, 1996). For a rail vehicle, the corresponding parameters of such shape filters For example, the publication "ORE Question B 176 - Bends with radially adjustable wheelsets" (Railway Engineering Publications - ETF, Paris, FR) are to be taken.

Any suitable mathematical algorithms may be used for the observer algorithm. For example, it may be a so-called Luenberg observer, as described in the publication: Geering, Hans Peter, Control Engineering (5th, Überarb, u., Ed., Springer Verlag, Berlin, 2001,! SBN 3-540 -41264-6) is known. A Kalman filter is particularly well suited in the present case as an observer algorithm, since these are preferably used when the input variables of the system and / or the measured variables are corrupted by stochastic variables ("noise") take advantage of that

Track interference as the input variables distorting noise is considered.

The method according to the invention can only be used after passing through the section of track using the values of the relevant detection variable recorded thereby. Preferably, however, it is provided that it is carried out while the vehicle is traveling on the route section, in particular in real time.

The detection of the detection variable (s) can basically take place at any suitable location in or on the vehicle. However, the detection variable is preferably determined on a chassis of the vehicle, in particular measured, since in this way particularly good results are possible in determining the properties of the position parameter.

In principle, any detection variables can be detected, which allow a conclusion on the properties of the position parameter via a coupling manifested in the vehicle model used. Preferably, a spring travel of at least one spring unit supported on a wheel of the chassis is determined as the detection variable. This has the advantage that corresponding sensors are often provided in such undercarriages (usually for other purposes) and generally provide reliable measured values in a simple manner, which can be further processed without difficulty.

As already mentioned, the method according to the invention can basically be used for any desired vehicles. It can be used particularly advantageously in connection with rail vehicles, so that it is preferably provided that the vehicle is a rail vehicle and a property of the track position is determined as a position parameter, in particular a track position disturbance. In principle, any suitable modeling of the rail vehicle can also be selected here. Preferably, the vehicle model has been determined on the basis of an arrangement with two wheelsets, one supported on the wheelsets chassis frame and supported on the chassis frame car body, since this can be achieved with particularly good results.

It has been shown that basically any number of degrees of freedom can be taken into account for the components of the model. As already mentioned, however, preferably only those degrees of freedom are taken into account for determining the track position, which are primarily influenced by the track position. Therefore, as the degrees of freedom of the two wheelsets, a translation in the direction of the vertical axis of the vehicle and a rotation about the longitudinal axis of the vehicle are preferably taken into account and as degrees of freedom of the chassis frame and the car body a translation in the direction of the vertical axis of the vehicle, a rotation about the longitudinal axis of the vehicle and a rotation about the transverse axis of the vehicle considered.

In preferred variants with still further reduced calculation effort, the geometric relationships of the components of the vehicle are taken into account. Thus, for example, be taken into account the fact that the track position disturbances acting on a trailing wheelset are different from those of the first wheel set only by a time shift corresponding to the driving speed. This can be done by inserting corresponding delay elements at the inputs of the second wheel set in the linearized model. In advantageous variants of the method according to the invention, it is accordingly provided that in the vehicle model the time delay dependent on the driving speed is taken into account for the effect of a position parameter between the leading wheelset and the trailing wheelset, in particular via a travel-speed-dependent delay element.

Among other things, the modeling of the wheel-rail contact has a significant influence on the design of the method. In certain variants of the method according to the invention, the wheel-rail contact via a spring-damper arrangement is taken into account for the vehicle model, wherein in particular a high rigidity of the spring-damper arrangement in the direction of the vertical axis of the vehicle is assumed. In this case, an adaptive so-called extended Kalman filter is preferably used as the observer algorithm, since this makes possible a particularly simple determination of the position parameter. In particular, it is possible, to a good approximation to use as attitude parameter the corresponding displacement of the respective wheel set provided by the Kalman filter, since this estimate has proved to be sufficiently precise. Accordingly, in preferred variants of the method according to the invention, the observer algorithm is designed such that a current estimated value of at least one state variable of the vehicle is used as the current value of the position parameter.

In other variants of the method according to the invention, the wheel-rail contact is assumed to be infinitely stiff for the vehicle model. In this case, an immediate use of the estimates provided by the observer algorithm for the desired property of the position parameter is not readily possible and it is preferably determined using a current estimate based on the model data of the vehicle model, the associated current value of the position parameter.

The detected values of the at least one position parameter can basically only be used up-to-date in the vehicle. Preferably, however, it is provided that the at least one position parameter for the traveled section is logged in order to make it accessible for later use.

The present invention further relates to a method for controlling a vehicle, in particular a rail vehicle, in which a method according to the invention on a leading chassis of the vehicle at least one property of a position parameter of a currently traveled section is determined and a trailing chassis of the vehicle using the determined property the position parameter is controlled. This can realize the advantages already described above in the control of a vehicle.

Finally, the present invention relates to a vehicle, in particular a rail vehicle, having a processing unit, which is designed to carry out the method according to the invention, and a detection unit, which is designed to detect the detection values. With this vehicle, the advantages and variants described above in connection with the method according to the invention can be realized to the same extent, so that reference should be made only to the above statements. Further preferred embodiments of the invention will become apparent from the dependent claims and the following description of preferred embodiments, which refers to the accompanying drawings. Show it:

Figure 1 is a schematic view of a preferred embodiment of the vehicle according to the invention;

FIG. 2 is a flowchart of a preferred variant of the invention

Method that can be performed with the vehicle of Figure 1;

FIG. 3 shows a diagram which illustrates the signal flow when carrying out the method from FIG.

First embodiment

A preferred exemplary embodiment of the vehicle according to the invention in the form of a rail vehicle 101 will be described below with reference to FIGS. 1 to 3. For a better understanding of the following explanations, a coordinate system is indicated in FIGS. 1 and 2, in which the x-coordinate is the longitudinal direction of the rail vehicle 101, the y-coordinate is the transverse direction of the

Rail vehicle 101 and the z-coordinate the height direction of the rail vehicle 101 denote.

1 shows a schematic side view of a part of the vehicle 101, which has a vehicle longitudinal axis 101.1. The vehicle 101 includes a car body 102, which in the region of both ends each on a chassis in the form of a

Bogie 103 and 104 is supported. The bogies 103 and 104 are in turn supported on a track 105.

The preceding in the direction of travel bogie 103 comprises two sets of wheels 106 and 107, on the two wheel bearings via a respective primary suspension 108, a bogie frame 109 is supported. The car body 102 is in turn supported on the bogie frame 109 via a secondary suspension 110.

Each of the four primary suspensions 108 is assigned as a detection device a sensor 111 which measures the change in length of the primary suspension 108 in the axial direction (here: z-direction) of the primary suspension 108. The measurement signals of the four sensors 111 are fed to a central processing unit 112, and processed in this manner in the manner described below according to the inventive method to determine the track position disturbances of the track 105.

In this case, the sequence of the method is first started in a step 1 13.1 while the vehicle 101 is traveling on a predetermined section of the track 105 to be examined. In a step 113.2, the current measured values of the four sensors 111 are then detected and forwarded to the processing unit 112. In a step 113.3, the deviations of the track 105 at the respective wheel contact point of the wheels of the wheelsets 106 and 107 from a target track position in the z-direction are then determined in the processing unit 112 as track position disturbances and stored in a memory of the processing unit 112 for logging (and optionally later processing). In a step 113.4 it is then checked whether a further determination of the track position disturbances should be carried out. If this is the case, jump back to the step 113.2. Otherwise, the process flow is terminated in a step 113.5.

FIG. 3 shows the signal flow during execution of the method from FIG. 2. As can be seen from FIG. 3, the vehicle 101 traveling on the track 105 is acted upon with the real track position as an input variable, whereby the real track position results from the desired track position and the superimposed trackside disturbances. When

Starting output, the sensors 11 1 on the vehicle 101 each deliver a measuring signal which is superimposed on them by the noise of the sensors and with which the processing unit 112 is fed.

To determine the track position faults, the processing unit 112 uses a previously determined and stored in the memory of the processing unit 112

Observer algorithm in the form of an extended Kalman filter, as is well known in the field of control engineering.

The Kalman filter was previously determined on the basis of a vehicle model in the form of a mathematical model of the vehicle 101. The vehicle model was determined using a non-linear, dynamic multi-body model, as they are well known in the field of vehicle dynamics and often used to predict the driving safety and running quality of vehicles. In such vehicle models, the state space of the system is often modeled by linear differential equations that describe the dynamic properties of the system in question and typically have the following form in time-continuous models:

- = Ax + Bu, (1) dy ^{~ ~}

y = Cx + you, (2)

where x denotes the state vector of the system and y the output vector or u the input vector of the system, and A, B, C, D denote the state space matrices characterizing the system. For time-discrete vehicle models, these differential equations are replaced by differential equations of the following form:

x " _{+ i} = Aκ" + Bμ _π , (3)

V _n = Cx _n + you _n , (4)

where n denotes the nth sampling cycle.

The multi-body model has been linearized to simplify the calculations to be performed by the processing unit 112 (particularly with regard to real-time detection of track-bearing disturbances) by a suitable procedure (also well-known) such that a linear state space model was obtained as the vehicle model.

In the present example, the vehicle model! based on a multi-body arrangement with the two sets of wheels 106, 107, the bogie frame 109 supported on the wheelsets 106, 107, and the body 102 supported on the bogie frame 109 (which is modeled as a point mass in the model for simplicity).

As has already been mentioned, an arbitrarily complex or complex modeling of the vehicle 101 is fundamentally suitable for the method according to the invention. However, in order to reduce the calculation effort for the processing unit 112, in the present example only the degrees of freedom of the above-mentioned vehicle model are used Components 106, 107, 109 and 102 of the multi-body system taken into account, which have a primary influence on the travel (ie the detection size) and / or which are primarily influenced by the track position disturbances (ie the property of the position parameter to be determined).

In the present example, a translation in the direction of the vertical axis of the vehicle 101 (z-direction) and a rotation about the longitudinal axis of the vehicle (x-direction) are taken into account as degrees of freedom of the vehicle frame 109, and as degrees of freedom of the chassis frame 109 for the vehicle mode and the car body 102 takes into account a translation in the direction of the vertical axis of the vehicle (z-direction), a rotation about the longitudinal axis of the vehicle (x-direction) and a rotation about the transverse axis of the vehicle (y-direction).

Among other things, the modeling of the wheel-rail contact has a significant influence on the design of the method. In the present example, the wheel-rail contact for the vehicle model continues to be considered via a spring-damper arrangement, wherein a high rigidity of this spring-damper arrangement in the direction of the vertical axis of the vehicle (z-axis) is assumed.

In determining the Kalman filter from this vehicle model, consideration is also given to the properties of the Kalman filter. Thus, the Kalman filter is usually suitable for the processing of signals that are subject to a so-called white noise. As a rule, corresponding track position disturbances of the track 105 may not be sufficiently accurate for such white noise, so that in the present example a suitable form filter is used to take into account the expected real noise behavior of the track position disturbances in determining the observer algorithm at at least one input of the vehicle model, as described above already described. It goes without saying, however, what use of such shape filters may possibly also be omitted in other variants of the invention.

The Kalman filter modeled in this manner provides as output a state vector which, in addition to reevaluating the spring travel as discrete states of the vehicle model, provides a sufficiently accurate estimate of the position and velocity of the modeled components of the vehicle 101 in the degrees of freedom considered. In the present case, these are thus 20 discrete states, namely for the two sets of wheels 106, 107 the amount and the speed of translation in the direction of the vertical axis of the vehicle 101 (z-direction) and the amount and the speed of rotation about the longitudinal axis of the vehicle (x-direction) and for the chassis frame 109 and the body 102 respectively the amount and speed of translation in the direction of the vertical axis of the vehicle (z-direction), the amount and the speed of Rotation about the longitudinal axis of the vehicle (x-direction) and the amount and speed of rotation about the transverse axis of the vehicle (y-direction).

In the present example, the calculation effort for the processing unit 112 is further reduced by taking into account the geometric relationships of the components of the vehicle 101 in subsequent repetitions of steps 113.2 and 113.3, taking into account that the track position disturbances acting on the trailing axle 107 are limited only by one of the driving speed of the vehicle 101 corresponding time shift of which are different on the leading wheel 106. This consideration is made in the present example by inserting corresponding vehicle speed-dependent delay elements at the inputs of the modeled second wheel set 107 in the linearized model.

Because of the above-described modeling of the wheel-rail contact as a spring-damper arrangement with a finite rigidity an adaptive so-called extended Kalman filter is used in the present case, since this is a particularly simple determination of the track position disturbances possible. Thus, in the present example, it is possible, to a good approximation, to use as the value for the real cast layer the values supplied by the Kalman filter for the corresponding displacement of the relevant wheel set, since this estimate has proved to be sufficiently precise. In order to determine the track position disturbances, a desired track track (previously determined for the track section) can then be used at the location of the current measurement, as indicated by the dashed contour 114 in FIG. 3, so that the output state vector already represents the track track disturbances.

The processing unit 112 performs the determination of the track position disturbances during the travel of the vehicle 101 on the track in real time and uses the information obtained about the track position disturbances to control the trailing chassis 104 by transmitting corresponding control commands to the corresponding controls 104.1 of the chassis 104. It is understood, however, that in other variants of the invention, only a corresponding logging of the Gieislagestörungen can be done. Second embodiment

In the following, a further preferred exemplary embodiment of the method according to the invention will be described, which can be carried out with the vehicle 101. The procedure basically corresponds to the procedure of FIG. 2 in its sequence and its mode of operation, so that here mainly the differences should be discussed.

The essential difference from the first embodiment is that the wheel-rail contact is assumed to be infinitely stiff for the vehicle model, in which case immediate use of the state vector estimates provided by the Kalman filter is not immediately used for the track attitude disturbances can. Rather, in this example, preferably using a current estimate on the basis of the model data of the vehicle model (which reflects just the relationship between the represented by the state vector states of the vehicle and the track position errors) determines the associated current value of the track position disturbances.

The following equation can be used for this:

u (t) = D ^ι - y (t) + D ^{~ l} 'C - x (t), (5)

if the matrix D is square (ie the number of inputs is equal to the number of outputs) and their norm is not equal to zero (eg the outputs are independent).

A more general approach to determining the current values of the inputs (ie, the attitude parameters) can be derived from equation (5) in the case of an empty matrix D. Thus, the following equation can be used (inter alia for time-discrete models):

U _n = (C - By ¹ - Cy _{n + 1} - C - A - X _n ), (6)

if the matrix (C-B) is square (eg the number of inputs and outputs is the same). In this case, by suitably selecting the input and the detection variables, it is preferable to ensure that the matrix C and the matrix B are designed such that the matrix (C-B) has full rank. If the matrix D from the equation (5) or the matrix (C-β) from equation (6) is not square or the matrix (C-B) is not full rank, their inverses can not be calculated directly in each case. In this case, so-called (well-known) algorithms can be used to form so-called pseudo-inverses.

It goes without saying that the procedure just described for determining the current values of the inputs (ie the positional parameters) using equations (5) or (6) also applies to the vehicle model with the finite stiffness wheel-rail contact from the first exemplary embodiment can be used.

Third embodiment

In the following, a further preferred embodiment of the method according to the invention is described, which can be performed with the vehicle 101. The procedure basically corresponds to the procedure of FIG. 2 in its sequence and its mode of operation, so that here mainly the differences should be discussed.

In this variant, the use of an observer algorithm is dispensed with.

Rather, the vehicle model is designed as a time-discrete model such that the current value of the position parameter is calculated in a recursive method from the detection values as a function of the model data of the vehicle model. Here, in addition to the above equation (3), the following equations are used:

X ₁ = X ₀ , (7)

= ST ¹ -y "- D ^'1 - C- x" (8)

if the matrix D is square and its norm is nonzero. If the matrix D is not quadratic, its inverse can not be calculated directly. In this case, so-called (well-known) algorithms can again be used to form so-called pseudo-sneaks.

It is understood that the track can be modeled in other variants of the method according to the invention, regardless of the representation of the wheel-rail contact as elastic or elastically mounted component. In this case, it is possible to find the desired input variable, ie the position parameter directly by the observer algorithm estimate without further calculation steps. It should be noted at this point that this represents a self-protected thought.

The present invention has been described above solely by means of examples of a rail vehicle. It is understood, however, that the invention may also be used in connection with any other vehicles.

_{* * * * ** * * * *}

Claims

claims

1. Method for determining a property of at least one position parameter, in particular a position parameter disturbance, of a travel path for a vehicle, in which a vehicle driving on a route section of the travel path (105)

(101) detection values of at least one detection variable influenced by the position parameter are detected, and

is determined from the detection values of the at least one position parameter for the route section, characterized in that

- From the detection values in dependence on the model data of a previously determined vehicle model of the vehicle, the current value of the position parameter is determined, wherein

- Based on the vehicle model, a relationship between the attitude parameter and the at least one by the attitude parameters afffected detection size is made.

2. The method according to claim 1, characterized in that

the at least one attitude parameter for the stretch is determined using an observer algorithm, wherein the observer algorithm is configured to output an associated current estimate of at least one state variable of the vehicle influenced by the attitude parameter depending on a current detection value; and

- Is determined in dependence on the model data of a previously determined vehicle model of the vehicle, the current value of the position parameter, wherein

- The vehicle model represents the relationship between the position parameter and the at least one state variable.

3. The method according to claim 1 or 2, characterized in that - The vehicle model was determined using a, in particular non-linear, dynamic Mehrkörpermodelis, wherein

- the vehicle fashion !! was determined in particular by linearization of the multi-body model.

4. The method according to claim 3, characterized in that for the vehicle model only those degrees of freedom of the components of the multi-body system are taken into account, which have a primary influence on the detection size and / or which are primarily influenced by the position parameter.

5. The method according to any one of claims 2 to 4, characterized in that the observer algorithm has been determined using the vehicle model.

6. The method according to claim 3 and 5, characterized in that

- A position parameter disturbance is determined, wherein

- The vehicle model was determined by linearization of the multi-body system and - a suitable shape filter was used to take into account the real noise behavior of the position parameter disturbance in the determination of the observer algorithm at least one input of the vehicle model.

7. The method according to any one of claims 2 to 6, characterized in that a Kalman filter is used as the observer algorithm.

8. The method according to any one of the preceding claims, characterized in that it is performed while driving the vehicle (101) on the route section, in particular in real time.

9. The method according to any one of the preceding claims, characterized in that the detection variable on a chassis (103) of the vehicle (101) is determined, in particular is measured.

10. The method according to claim 9, characterized in that a spring travel of at least one on a wheel of the chassis (103) supported spring unit (108) is determined as the detection variable.

11. The method according to any one of the preceding claims, characterized in that the vehicle (101) is a rail vehicle and a property of

Track position is determined as a location parameter, in particular a trackside failure.

12. The method according to claim 11, characterized in that the vehicle model based on an arrangement with two wheelsets (106, 107), one supported on the wheelsets (106, 107) chassis frame (109) and one on the chassis frame (109) supported Car body (102) was determined.

13. The method according to claim 12, characterized in that for the vehicle model

- as degrees of freedom of the two wheelsets (106, 107) a translation in the direction of the vertical axis of the vehicle (101) and a rotation about the longitudinal axis of the vehicle (101) are taken into account and - as degrees of freedom of the chassis frame (109) and the car body (102) a translation in the direction of the vertical axis of the vehicle (101), a rotation about the longitudinal axis of the vehicle (101) and a rotation about the transverse axis of the vehicle (101) are taken into account.

14. The method of claim 12 or 13, characterized in that in the vehicle model dependent on the driving speed time delay of the effect of a position parameter between the leading wheelset (106) and the trailing wheelset (107), in particular via a driving speed-dependent delay element is taken into account ,

15. The method according to any one of claims 1 1 to 14, characterized in that - for the vehicle model of the wheel-rail contact via a spring-damper

Arrangement is taken into account, wherein

- In particular, a high stiffness of the spring-damper assembly in the direction of the vertical axis of the vehicle (101) is assumed.

16. The method according to claim 15, characterized in that as an observer algorithm, a so-called extended Kalman Fiiter is used.

17. The method according to claim 15 or 16, characterized in that the observer algorithm is designed such that a current estimate of at least one state variable of the vehicle (101) as the current value of

Position parameter is used.

18. The method according to any one of claims 11 to 14, characterized in that for the vehicle model of the wheel-rail contact is assumed to be infinitely stiff.

19. Method according to claim 18, characterized in that the associated current value of the position parameter is determined using a current estimated value on the basis of the model data of the vehicle model.

20. The method according to any one of the preceding claims, characterized in that the at least one position parameter for the section is logged.

21. The method according to claim 1, characterized in that the current value of the position parameter in a recursive method from the detection values in

Depending on the model data of the vehicle model is calculated.

22. A method for controlling a vehicle, in particular a rail vehicle, in which

- With a method according to one of the preceding claims on a leading chassis (103) of the vehicle (101) at least one property of a position parameter of a currently traveled section is determined and

- A trailing chassis (104) of the vehicle (101) is controlled using the determined property of the position parameter.

23. vehicle, in particular rail vehicle, with

- A processing unit (112), which is designed for carrying out the method according to one of the preceding claims, and a detection unit (111), which is designed to detect the detection values.

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