Patent ID: 12208829

It should first of all be noted that while the figures set out the invention in detail for its implementation, they can of course be used to better define the invention if necessary. It should also be noted that, in all the figures, elements that are similar and/or fulfill the same function are indicated by the same numbering.

FIG.1shows a transport installation100using the control method200according to the invention. The transport installation100comprises a continuously moving mobile cable20, guided and supported by a plurality of pylons1. In an embodiment which is not shown, the transport installation100comprises a plurality of cables20.

The cable20is a traction and carrying cable, that is to say, the cable20provides both a traction and support function for the vehicles2attached to the cable20. The cable20is continuous in a closed loop. The cable20is a metal cable comprising a plurality of metal wire strands which are twisted into a helix forming a composite “rope,” in a pattern called a “laid rope,” or comprises a plurality of metal cables having a laid rope pattern arranged according to a pattern called “laid cable.” The cable is mainly made of steel.

The transport installation100also comprises vehicles2. The vehicles2can be used to transport people and/or equipment. The vehicles2are configured to be coupled to the cable20and towed by the latter. More particularly, the transport installation100can be a cableway, and in this case the vehicles2are closed, or a chairlift where the vehicles2are open. In an embodiment which is not shown, the transport installation100may comprise other traction and/or carrying cables, in particular when the vehicles2are large and heavy. These other cables can be traction or carrying cables

The transport installation100also comprises two end stations3a,3bfor the embarkation and/or disembarkation of people and/or equipment in vehicles2. A first station3ais located at a first end100aof the transport installation100and a second station3bis located at a second end100bof the transport installation. The stations3a,3bare also called terminals. Thus, as illustrated inFIG.1, the cable20makes it possible to move the vehicles2by traction from the first station3ato the second station3b, and vice versa.

In an embodiment which is not shown, the transport installation can comprise at least one intermediate station located along the cable. It is also intended for the embarkation and/or disembarkation of passengers and/or the transshipment of equipment depending on the type of use of the transport installation100.

The cable2is driven by a driving pulley17, which in turn is driven via a shaft connected to an electric motor16of the first end station3a, also called the driving station. The second end station3bin turn comprises a non-motorized return pulley19and a tensioning system18for the cable20. The return pulley19is therefore not driven by the electric motor16or another motor. The return pulley19is free to rotate. The return pulley is also called an idler pulley.

In another embodiment which is not shown, the tensioning system18is located in the driving station3a. The tensioning system18comprises a mobile carriage for tensioning the cable20, the carriage supporting the driving pulley17. The driving pulley17then plays a tensioning and driving role.

As shown inFIG.1, the transport installation100furthermore comprises a learning and correction device4which acts in direct interaction with a control device5of the transport installation100. The learning and correction device4comprises at least one computing system, not shown, which is configured to implement the method which is described later in the disclosure. The computing system can be a central processing unit comprising at least one single-core or multi-core processor. In another embodiment, the computing system is a remote computer server to which the learning and correction device4has access by a wired connection or by a wireless connection via a communication network, such as the Internet. The connection to the communication network can be secured.

The control device5is configured to act on at least one parameter of the transport installation100as a function of at least one instruction15sent by the learning and correction device4. The control device5furthermore comprises a user interface coupled to a processor to allow an operator14to give setpoint values to at least one of the parameters of the transport installation100.

In the example described and shown inFIG.1, the parameters of the transport installation100are an average tension T of the cable20, an average speed v of the cable20, a distance d between two successive vehicles2, and an average mass m carried by the vehicles2.

The learning and correction device4is configured to receive information6on the parameters of the transport installation100, this information6being at least in part collected by a set of sensors located in the transport installation100. The set of sensors comprises measurement sensors7arranged on the pylons1, measurement sensors8placed on the vehicles2, counting sensors9configured to count the passengers and/or the quantity of equipment entering the transport installation100and arranged in stations3a,3b, counting sensors10configured to count the passengers and/or the quantity of equipment leaving the transport installation and arranged in the stations3a,3b, a measuring sensor11to carry out measurements on the cable20, a measuring sensor12to carry out measurements on the electric motor16, a measuring sensor13to carry out measurements on the tensioning system18of the cable20.

The learning and correction device4can also receive information6given by the operator14via the user interface of the control device5. Thus, the operator14can set a setpoint for at least one of the parameters of the transport installation100.

The collected information6is then processed by a computer program product comprising portions of program codes which, once loaded on the computing system, allow the implementation of the control method200of the transport installation100. After processing, the learning and correction device4sends a set of instructions15to act on at least one of the parameters of the transport installation100even if a setpoint has been given to at least one of the parameters. By acting for example on the electric motor16, the driving pulley17and/or the tensioning equipment18, the learning and correction device4will make it possible to control the dynamic behavior of the transport installation100such as low-frequency vibratory phenomena, also called pumping phenomena, in particular taking the form of at least one oscillation of the cable20and/or of at least one of the vehicles2. In the remainder of the disclosure, the oscillation is considered to be an established periodic oscillatory movement.

The motor16is controlled and speed-regulated on the transport installation100. The motor16acts on the driving pulley17. As the low-frequency vibratory phenomena take the form of at least one oscillation of the cable20and/or at least one of the vehicles2, the forces generated on the cable20have a direct effect on the instantaneous change of the torque on the shaft linked to the driving pulley17and therefore, consequently, on the value of the instantaneous torque delivered by the motor16. Knowing the value of torque variations AC at the driving pulley17therefore provides direct information on the value of the amplitude of the vibratory phenomena which affect the transport installation100, for example the value of the amplitude of at least one oscillation of the cable20and/or at least one of the vehicles2of the transport installation100.

In order to improve the precision of the measurement of the amplitude of the vibratory phenomena, the measurement of the torque variations AC can be supplemented by measurements done by sensors placed directly in the cable spans20and along the cable line20. Thus, feeler rollers can be placed on the pylon supports1. The feeler rollers are fitted with force sensors which deliver a signal whose variation is an image of the amplitude of the oscillations of the cable20and/or of at least one of the vehicles2. In addition or alternatively, accelerometers are available in at least one of the vehicles2, preferably in several vehicles2. Complementarily or alternatively, it is possible to measure the instantaneous speed of rotation at the return pulley19, the dynamic change of which takes place according to the tensions of the cable20. A measurement done at the return pulley gives a characterization of the overall dynamics of the transport installation100.

These additional measures to improve the measurement precision of the amplitude of the vibratory phenomena make it possible to take into account part of the vibratory phenomena which do not return to the driving pulley17and therefore which do not significantly affect the change of the instantaneous torque of the motor16. Indeed, several reasons explain why some of the vibratory phenomena which are present on the line may not perceptibly affect the torque at the driving pulley17. For example, a span of cable20subjected to an oscillation and which is too far from the driving pulley17causes the variations in cable tension induced by the oscillation to be “smoothed” by the dissipative effects present along the cable20at the supports of the pylons1, that is to say, the friction between the cable20and the linings of the rollers at the supports of the pylons1.

The synthesis of the measurements carried out at these different points characterizes the vibratory phenomena affecting the transport installation, and in particular characterizes an overall amplitude of the oscillations of the cable20and/or of at least one of the vehicles2of the transport installation100.

In the remainder of the description, only the torque variations AC are considered to reflect the value of the amplitude of at least one oscillation of the cable20and/or of at least one of the vehicles2of the transport installation100.

The control method200of the vehicle transport installation100making it possible in particular to control the vibratory phenomena of the transport installation100, such as at least one oscillation of the cable20and/or of at least one of the vehicles2, will now be described. A flowchart of the control method200is illustrated inFIG.3.

In the following, the learning and correction device4acts directly on the speed setpoint of the cable20which is transmitted to a drive control variator of the cable20via the control device5. The drive control variator is a module located just before the motor which controls the energy flows sent to it. In addition, it is a control component of the motor16.

The control method200comprises a step of determining201at least one oscillation of the cable20and/or of at least one of the vehicles2as a function of at least one parameter chosen from an average tension T of the cable20, an average speed v of the cable20, a distance d between two successive vehicles2, and an average mass m carried by the vehicles2, a step of generating a database202which relates a value of the amplitude of the oscillation to a combination of the average tension T of the cable20, the average speed v of the cable20, the distance d between two successive vehicles2, and the average mass m carried by the vehicles2and a step of reducing the oscillation203by varying at least any one of the parameters.

The value of the torque variations AC is considered to correctly reflect the value of the amplitude of at least one oscillation of the cable20and/or of at least one of the vehicles2.

The step of generating the database can be part of a learning cycle. The learning cycle is generally initiated before the commercial commissioning of the transport installation and/or during the commercial operation of the transport installation.

The step of generating the database202comprises a first oscillation sampling sub-step202.1. During this step, each oscillation of the cable20and/or of at least one of the vehicles2, via the value of the torque variations AC, is associated with the measurement of the average tension T of the cable20, the average speed v of the cable20, the distance d between two successive vehicles2, and the average mass m carried by the vehicles2.

The learning and correction device4carries out measurement cycles, each of a duration DT and in steady state, that is to say, outside the transient phases of acceleration or braking of the cable20. Recorded in each cycle of duration DT are the value of the average mass m carried by the vehicles2or present on the cable20, the average tension value T imposed on the cable20, the value of the average speed v and the distance d between two successive vehicles2on the cable.

In other words, the learning and correction device4carries out measurement cycles of the parameters of the transport installation100over a duration DT when the transport installation100has reached a steady operating state. In steady state condition, it is considered that the control device5sends a constant speed instruction to the cable drive device20, while the actual speed of the mobile cable20changes around this constant speed instruction.

The value of the torque variations AC is evaluated at the driving pulley17during the cycles of duration DT by the measurement sensor12arranged on the electric motor16. The value of these torque variations AC is read by following the changes in the value of the current absorbed by the electric motor16.

The value of the instantaneous current absorbed by the electric motor16at the instant t, noted IC(t), then gives a direct image of the instantaneous engine torque CM(t) according to the relationship:
CM(t)=K*IC(t)
where K is a constant of proportionality depending on the type of electric motor16and the electromagnetic characteristics of the electric motor16.

To access the value of the torque variations AC, a first calculation of the average of the current signal IC absorbed by the motor over a cycle duration DT, noted MC, is carried out by the learning and correction device4. A calculation of the deviation ΔMC from the average MC of each current value IC absorbed by the electric motor16measured with a sampling period dT is then carried out:
ΔMC(dT)=|IC(dT)−MC|.

During a cycle, a number DT/dT of sampled values IC(dT) is saved. By averaging the DT/dT deviations ΔMC thus obtained over a cycle, i.e., the average of the variations around the average, an approximate average value of the value of the torque variations AC is obtained:

A⁢C=d⁢TD⁢T⁢∑d⁢TΔ⁢M⁢C⁡(d⁢T)

In another embodiment which is not shown, the value of the torque variations AC can be measured by performing a force measurement directly, for example, with a gear motor assembly mounted on a cradle free to rotate along the axis of the shaft connected to the driving pulley17and equipped with force sensors.

Instead of reading the value of the current absorbed from the motor16, other means can be envisaged for quantifying at least one oscillation of the cable20and/or of at least one of the vehicles2. For example, from a signal from an accelerometer placed in the vehicles2, and/or owing to the measurement sensors7on the pylons1in direct contact with the cable20and reacting to its movements, and/or more broadly by using any sensor that gives information on the movements of the cable20and vehicles2.

The value of the average tension T of the cable20, imposed by the tensioning system18, over the duration DT, is read from the signal from the measurement sensor13. In the case of a tension by counterweight, the mass of the tensioning system is indicated and considered to be fixed. In the case of a fixed anchoring of the cable, the tension is read from a force sensor located in the anchoring or any other sensor assembly performing a similar function.

The value of the average speed v of the mobile cable20over the duration DT is evaluated from the measurement sensor11in contact with the cable in one of the two stations3a,3b. The measurement sensor11is for example a tachometric pulley. In another embodiment, the evaluation of the average speed v of the cable20can be made from the measurement sensor7placed on the pylon1.

To calculate the distance d between two successive vehicles2, it is necessary to quantify the number nvecof vehicles2traveling along the cable20as well as the total length L of the cable loop20.

The number nvecof vehicles2traveling along the cable20is either known beforehand, since it is entered manually by the operator14into the control device5, or evaluated over the duration of the cycle with duration DT by an individual count of each vehicle2identified when passing through a station3a,3b. In this second case, the counting is performed by a sensor which may be of the inductive type or which performs a similar function.

The total length L of the cable loop20is measured by an angular measuring device, or by a device performing a similar function in direct or indirect contact with the cable20.

It is thus possible to calculate the distance d which separates two consecutive vehicles2considered to be uniformly distributed along the cable20according to the following formula:

d=Lnv⁢e⁢c

The average mass m carried by the vehicles2moving along the cable20over the duration DT of the cycle is estimated by the measurement sensors9,10positioned in the stations3a,3b. In the case of use for the transport of passengers, the average mass m is calculated from the number of passages made at the entrances and possibly exits of the transport installation100for each side operated. According to one embodiment, the transport installation100is integrated into a network in an urban area which requires the presence of control gates at each crossing point; the counting is then carried out both at the entrance and at the exit. According to another embodiment, the transport installation100is located in ski areas; a single count of passengers can then be carried out. In this case, only the entry passages are used to estimate the average mass m.

It is possible to operate the transport installation100only in one direction or else in both directions of travel of the cable20, which requires an estimate of the average mass m of only one side or of both sides of the cable loop20.

To refine the knowledge of the average mass m carried by the cable20, the passages at the entrances give an estimate of the mass of the first half of a side of the line or of its entirety if there is no counting at the exit, while the number of passages at the exit provides information on the mass of the second half. An estimator of the average mass m, internal to the learning and correction device4, thus uses this information to return an average mass value m over the duration DT.

According to another embodiment, the monitoring of the change in the displacement of the tensioning system18of the cable20or of the engine torque16is a means of accessing an estimate of the value of the average mass m carried by the vehicles2. According to another embodiment, it is also possible to use sensors directly inside the vehicles2.

These measurements define a table of parameters TP grouping together the four measured and/or calculated values of the four parameters of the transport installation100:
TP=[mTvd].

For each table of parameters read during the cycles of duration DT, the corresponding value of the torque variations AC is then associated. A database is thus constituted by all the torques (TP, AC) encountered during the operation of the transport installation100.

The first sampling sub-step202.1can form part of the learning cycle generally initiated before the commercial commissioning of the transport installation100in order to rapidly generate the database. The database can continue to be fed over the course of the successive learning cycles carried out during the operation of the transport installation100.

In order to enrich the database of torques (TP, AC) as quickly and efficiently as possible during the learning cycle, it may be necessary to define a learning cycle protocol recording the measurement of torque oscillations AC for average speeds v of the cable20, average tension values T of the cable20, numbers nvecof vehicles2attached to the cable20, average mass m carried by the cable20, these values being precisely defined by the protocol.

To vary the mass m carried by the cable20, it is possible to use controlled mass weights positioned on the vehicles2. It is also possible that the vehicles2are empty for the implementation of the learning cycle protocol.

This protocol therefore makes it possible to scan a wide range of torques (TP, AC) in order to obtain a rough map of the dynamic behavior of the transport installation100.

If the operator14wishes to impose a setpoint on the value of one of the parameters, it may be useful to complete the rough map by measuring the value of the torque variations AC for several values of the parameter on which the operator14wishes to impose a setpoint, the value of the other parameters being fixed.

Thus, if the operator wishes to impose a setpoint on the value of the average speed v of the cable20, it may be useful to complete the rough map by measuring the value of the torque variations AC for several different average speeds v, the average mass m, average tension T and distance d parameters being fixed. In other words, the average speed v varies while the other parameters are constant and known.

This results in curves C representing the torque variation AC depending on the average speed v, each curve C therefore being associated with a triplet of the fixed parameters [m T d] that are the average mass m, the average tension T and the distance d. In other words, the torque variation AC is a function g of the average speed v for a triplet of fixed parameters [m T d]:
AC=g[m,T,d](v)

The first sampling sub-step (202.1) can also comprise enriching the database during the operation of the transport installation100by recording an instantaneous torque (TP, AC) of the transport installation100carried out by the learning and correction device4over a duration DT. The instantaneous torque (TP, AC) is a priori different from the other torques already recorded in the database. In fact, the mass carried by the transport installation100changes continuously and varies according to the number of people. Likewise, the setpoint for the speed of the cable20varies according to the operating conditions; for example, the loading conditions of the vehicles2vary according to the crowds, the wind, or the presence of beginner skiers requiring the speed to be reduced in order to facilitate embarkation. There are therefore theoretically infinite possible tables of parameters TP.

During operation, if the instantaneous table of parameters TP encountered by the learning and correction device4is already present in the database, then the database is updated with the value of the instantaneous torque variation AC corresponding to the instantaneous table of parameters TP in order to take account of the change of the vibratory phenomena of the transport installation100according to the operating conditions such as, for example, wear, elongation of the cable or even temperature.

The transport installation100is considered to be operating and to have reached the steady state for a speed setpoint vrefgiven by the operator14. All the sensors allow the learning and correction device4to know, in real time, the value of the parameters that are the average mass m carried by the vehicles2, the average tension T of the cable20, the distance d between two successive vehicles evenly distributed over the cable20, i.e. the real triplet [m T d]realand the value of the torque variations ACreal. These measurements are therefore carried out in real time.

The step of generating the database202comprises a second sub-step of extrapolating202.2the data obtained during the first sub-step to complete the database202.1, in particular when the triplet [m T d]realis not in the database and therefore there is no curve C. Interpolation thus provides access to knowledge of the value of the torque variations AC of the cable20according to the average speed v of the cable for any given triplet encountered during the operation of the transport installation100which was not part of the learning cycles of the first sub-step202.1. In this way, it allows the learning and correction device4to correct the average speed v for any triplet of parameters of the transport installation100in order to avoid an inappropriate dynamic behavior of the transport installation100, i.e., at least one inappropriate oscillation of the cable20and/or of at least one of the vehicles2.

In order to perform the interpolation, it is assumed that the torque variation AC depending on the average cable speed v with respect to each parameter of a considered triplet is a linear function in the neighborhood of the considered triplet. It is thus possible to calculate the gradient of the torque variations AC with respect to each parameter of the considered triplet, that is to say, with respect to the average mass m, the average tension T and the distance d:

∇→A⁢C=(∂A⁢C∂m⁢(v)∂A⁢C∂T⁢(v)∂A⁢C∂d⁢(v))v⁢fixed

To perform this gradient calculation, we classify the curves C of torque variation AC by average speed families v. Each curve C being associated with a triplet of parameters [m T d], we then classify them into groups such that only one of the three parameters of the triplets [m T d] changes while the other two keep fixed values.

From these families and these groups thus created, the evaluation of the partial derivative

∂A⁢C∂.⁢(v)
of the torque variation AC is reached with respect to each parameter m, T and d of the triplets, and for each of the average speeds v present in the database.

Once the gradients are known, the interpolation is performed by looking in the database for the curve C of torque variations AC as a function of the average speed v whose triplet [m T d] corresponds notably to the triplet in real time [m T d]realof the transport installation100. Here, as well as in what follows, “notably” means that the distance between the triplet [m T d] and the real triplet [m T d]realis minimal.

The preceding calculation of the gradient is then applied to the variations between each of the parameters of the two targeted triplets, which makes it possible to obtain the curve I of the variations in torque AC interpolated according to the average speed v for the triplet [m T d]real.

Referring toFIG.3, a curve C, obtained during learning and representing the torque variation AC as a function of the average speed v for the parameter triplet [m T d]. From this curve C known by learning, an interpolation is carried out in order to access the knowledge of the curve I according to the interpolation method which has just been described. The curve I therefore represents the variation in torque, and therefore the amplitude of the oscillation of the cable20and/or of at least one of the vehicles2, as a function of the average speed v for a current triplet [m T d]real, which is not the result of learning.

The control method200comprises a step of comparison204between the oscillation value, namely the value of the variation in cut ACrealand a threshold value VS above which oscillation is prohibited and below which oscillation is authorized.

The plot of curves C and I representing the torque variation AC depending on the average speed v for a given triplet shows average speed threshold values above which the corresponding torque variation AC is unacceptable because it corresponds to an inadequate amplitude of the oscillation. In other words, the threshold values define a border in average speed v not to be crossed for each triplet of the database [m T d] and therefore the real triplet [m T d]real.

FIG.4shows a graph which shows the curve C ofFIG.3, that is to say, the torque variation AC as a function of the average speed v for a triplet [m T d]. The threshold values VS not to be exceeded define zones of instability ZI in which the threshold values not to be crossed are found.

The learning and correction device4chooses, from the database, the curve C whose triplet [m T d] matches the triplet [m T d]realor proceeds to the interpolation in order to obtain the curve I of the triplet [m T d]real. The learning and correction device4then determines the threshold values. Before proceeding to the oscillation reduction step203, the learning and correction device4can compare the value of the torque variation ACrealto the threshold value VS.

If the value of the torque variation ACrealis greater than or equal to the threshold value VS for the speed setpoint vrefand is therefore in the instability zone ZI for the speed setpoint vref, then the learning and correction device4proceeds to a sub-step203.1for improving the oscillation reduction step203, that is to say, to a reduction of the value of the torque variation AC by varying at least one of the parameters by choosing a value of said parameter in the database. As can be seen in the example illustrated inFIG.4, the value of the torque variation ACrealfor the triplet [m T d]realwhich corresponds to the triplet of [m T d] already registered in the database falls into a prohibited zone ZI.

For this step of reducing the oscillation203, the learning and correction device4will change the value of at least one of the parameters, for example on the average speed v and therefore on the speed setpoint vrefchosen by the operator14and/or on the average tension T of the cable20.

The sub-step203.1for improving the reduction step203of the oscillation, and therefore of the value of the torque variation AC, relates, in the example, to an average speed v in order to be placed below the threshold value VS by using the curves C or I, while guaranteeing an average speed v of the cable20as close as possible to the speed setpoint vref. In other words, the learning and correction device4will define a new speed setpoint vref,nand therefore a range of variation of the average speed v of the cable20around vref,nwhich is close to the speed setpoint vrefset by the operator14, and while remaining outside the zone of instability ZI using the database.FIG.4shows that the new speed setpoint vref,n, to which the torque variation ACref,ncorresponds, is outside the zones of instability.

Step203of reducing the value of the torque variation AC may comprise a first sub-step203.2for verifying the possibility of reaching the new speed setpoint vref,nclose to the speed setpoint vrefin order to stay outside the zone of instability ZI and to achieve either an acceptable oscillation or the absence of oscillation. In the event that the new speed setpoint vref,nis too restrictive, for example, because there is too much deviation from the speed setpoint vref, but is nevertheless necessary in order to deviate from an instability and to achieve the improvement under good conditions, the learning and correction device4makes the adjustment by making the correction on an alternative or complementary parameter of the speed, the alternative or complementary parameter being easily controllable, such as through the cable tension T for example. This change of parameter corrected by the learning and correction device4takes place until the correction can be resumed on the average speed, for example following a modification of the mass m on the line. The learning and correction device4then operates again as detailed previously, and reapplies a nominal tension to the cable20identical to that before modification.

The step of reducing the oscillation203, that is to say, the value of the torque variation AC, may comprise a speed optimization sub-step203.3. The goal is to very slightly modify the new speed setpoint vref,nfrom the database in order to obtain an optimized speed setpoint voptimin terms of minimum torque variations AC, knowing that the instability zones have already been escaped, that is to say, one is located at a minimum point of the oscillations according to the data obtained during the improvement sub-step203.1. Indeed, this is not the real minimum taking into account the sampling on the average speeds in the database and the fact that the new speed setpoint vref,nmust be closest to the speed setpoint vrefof the operator14, which remains unchanged. Indeed, the operator14imposes the speed setpoint vrefand the correction acts around it.

During this optimization sub-step203.3of the speed setpoint vref,n, a variation phase203.4of the new speed setpoint vref,nis carried out. The variation phase203.4consists in changing the new speed setpoint vref,nvery slightly. The speed setpoint is changed from vref,nto vref,n,var. A reading phase203.5of the torque variations AC by the learning and correction device4using all the sensors is performed before and after the variation phase203.4. Thus, the learning and correction device4has the value of the torque variation ACref,ncorresponding to the new speed setpoint vref,nand the value of the torque variation ACref,n,varfor the variation of the new speed setpoint vref,n,var.

The variation phase203.4and the reading phase203.5are followed by a comparison phase203.6between the values of the torque variations obtained in the reading phase203.4, that is to say between ACref,nand ACref,n,var.

The difference between the two values ACref,nand ACref,n,varobtained during the comparison phase203.6is then sent to a corrector internal to the learning and correction device4in order to confirm that the oscillation has actually been reduced; this is the confirmation phase203.7. If it is confirmed that the oscillation has been decreased, then steps203.4to203.7are repeated until a difference is obtained which confirms that there has been no decrease in the oscillation.

If it is confirmed that the oscillation has not decreased, then an optimized speed voptimfor a local minimum of oscillations has been found.

In addition, the constraint of remaining close to the speed setpoint vrefrequires stopping the optimization sub-step203.3if there is too much deviation from the setpoint vrefduring phases203.4to203.7.

When the learning and correction device4has obtained the speed setpoint voptim, which is therefore an optimization of the speed setpoint vref,n, the learning and correction device4carries out an adjustment sub-step203.8by sending the value of voptimto the control device5in the form of an instruction15to vary the drive of the motor and thus to adjust the average speed of the cable to voptimfrom the value of the enhanced parameter. InFIG.4, we see that voptimis, in terms of minima of torque variations AC, is an optimization of vref,n.

The step of reducing the value of the torque variation AC203may optionally comprise a second verification sub-step203.9of the possibility of reaching voptim, optimization of the speed setpoint vref,n, close to the speed setpoint vref. It is thus always ensured that there is either an acceptable oscillation or an absence of oscillation. In the case where voptimis too restrictive, for example, because there is too much deviation from the speed setpoint vref, the learning and correction device4then acts by adjusting an alternative or complementary parameter of the speed such as via the cable tension T for example. This parameter change corrected by the learning and correction device4takes place until the correction can be resumed on the average speed v and therefore on the speed setpoint, for example following a modification of the mass m on the line. The learning and correction device4then operates again as detailed previously, and reapplies a nominal tension to the cable20identical to that before modification.

If the value of the torque variation ACrealis less than the threshold value VS, then the oscillation is an acceptable oscillation. However, even if the oscillation is acceptable, it may be useful to try to decrease the oscillation, for example for passenger comfort. Moreover, the real minimum of the oscillations has not necessarily been reached.

To reduce the amplitude of the acceptable oscillation, the learning and correction device4proceeds directly to the optimization sub-step203.3. In this case, the principle of the optimization sub-step203.3described above is applied to the speed setpoint vrefgiven by the operator14.

The goal is to modify the speed setpoint vrefvery slightly in order to obtain an optimized speed setpoint voptimin terms of minimum torque variations AC, knowing that one is already outside areas of instability.

During this optimization sub-step203.3of the speed setpoint vref, a phase203.4for varying the speed setpoint vrefis carried out. The variation phase203.4consists in changing the speed setpoint vrefvery slightly. The speed setpoint is changed from vrefto vref,var. A reading phase203.5of the torque variations AC by the learning and correction device4using all the sensors is performed before and after the variation phase203.4. Thus, the learning and correction device4has the value of the torque variation ACrefcorresponding to the speed setpoint vrefand the value of the torque variation ACref,varfor the variation of the speed setpoint vref,var.

The variation phase203.4and the reading phase203.5are followed by a comparison phase203.6between the values of the torque variations obtained in the reading phase203.4, that is to say, between ACrefand ACref,var.

The difference between the two values ACrefand ACref,varobtained during the comparison phase203.6is then sent to the internal corrector of the learning and correction device4to confirm that the oscillation has indeed been reduced; this is the confirmation phase203.7. If it is confirmed that the oscillation has been reduced, then steps203.4to203.7are repeated until a difference is obtained which confirms that there is no further reduction in the oscillation.

If it is confirmed that the oscillation has not decreased or is no longer decreasing, then an optimized speed voptimfor a local minimum of oscillations has been found.

In addition, the constraint of remaining close to the speed setpoint vrefrequires stopping the optimization sub-step203.3if there is too much deviation from the setpoint vrefduring phases203.4to203.7.

Then the learning and correction device4continues with the adjustment sub-step203.8and possibly the second verification sub-step203.9described above.

The measurements of the average values of the parameters m, T, v, d and of the torque variations AC during the step of generating the database202are carried out over a duration DT. The correction phase203is carried out over a period of action DA. The durations DT and DA are chosen according to the frequency at which it is desired for the learning and correction device4to come into action.

The described method can also comprise a step of recording the optimized speeds voptim. Returning to the previous example, once voptimhas been reached, the learning and correction device4records the values of the parameters of the transport installation100and associates them with the optimum speed voptimcalculated for the speed setpoint vrefgiven by the operator14. The recording takes place in a data library. In this way, a set of speeds voptimis established from each optimization improvement, and associated with a triplet of parameters [m, T, d] and a given setpoint speed vref. In other words, the speed voptimis a function ƒ of the average speed vreffor a triplet [m T d]:
voptim=ƒ[m,T,d](vref)

This results in curves D representing the functions ƒ for a triplet of parameters [m T d]. Under these conditions, it suffices for the learning and correction device4to read the setpoint speed vrefgiven by the operator14as well as the instantaneous triplet [m T d] owing to all the sensors placed on the transport installation100, then to go and search the data library for the improvement to the corresponding speed optimization voptim.

An interpolation method, like the one carried out during the interpolation sub-step, can be carried out if the curve D is not found in the database.

With reference toFIG.5, a graph shows a curve D1illustrating the function ƒ[m1,T1,d1]and the curve D2illustrating the function ƒ[m2,T2,d2]. If the optimum speed is not known, the learning and correction device4can perform an interpolation similar to that described for the second data extrapolation sub-step. An interpolation is carried out from the function whose triplet is closest to the real triplet, therefore either from the function ƒ[m1,T1,d1]or from the function ƒ[m2,T2,d2], which are already known. It is thus possible to access ƒ[m3,T3,d3],interpol, giving the voptimfor any triplet [m3, T3, d3] and illustrated by curve D3. Then the control device can directly use voptimcorresponding to the speed setpoint vreffor the [m3, T3, d3].

This data library can then be used independently by the control device5, without it being necessary to carry out a succession of learning and correction phases, once a sufficient number of learning and optimization cycles have been carried out.

The description of the control method is developed for a transport system using a continuously moving cable with several vehicles evenly distributed along a single mobile cable loop to which they are attached. The principle detailed in this description nevertheless remains valid for reciprocating and continuous movement devices, provided with one or more vehicles on each track, with one or more mobile and/or fixed cables.

The disclosed method200is more widely applicable to any process for optimizing a characteristic quantity of a cable transport installation100as a function of a setpoint quantity. The optimization is further carried out by the device4, which in turn is integrated into the transport installation100, and using measurement points distributed over the transport installation100. The measurements are carried out in real time or else collected and saved for later use. A characteristic quantity is a quantity which relates to the operation of the transport installation100, to its movement and to its characteristics. For example, a characteristic quantity can be an amplitude of the oscillations of at least one cable and/or of at least one of the vehicles carried by the cable as described above, an electric consumption of the drive motors, any parameter of adjustment of the automatisms which generate the setpoints, any mechanical quantity which defines the movement of the device or even any quantity which characterizes the state of the system at a given instant.

The mathematical tools of the method according to the invention are only an illustration. The method according to the invention could use a stochastic probabilistic approach or machine learning algorithms instead of the described mathematical tools to achieve the optimization of the characteristic quantity as a function of the setpoint.

Of course, the invention is not limited to the examples which have just been described, and numerous modifications can be made to these examples without departing from the scope of the invention.