• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    The invention relates to a method for estimating a periodic or substantially periodic force present in a mechanical or electromechanical system, the method comprising: estimating, by a processing device, one or more harmonic frequencies of an acceleration signal representing an acceleration in the system, the substantially periodic force contributing to said acceleration; and estimating, by the processing device, the force based on a dynamic model of the system, the dynamic model being defined by said one or more estimated harmonic frequencies.
    公开号:FR3055440A1
    申请号:FR1657939
    申请日:2016-08-25
    公开日:2018-03-02
    发明作者:Molina John Jairo Martinez
    申请人:Institut Polytechnique de Grenoble；
    IPC主号:
    专利说明:

    Field of the invention
    The present description relates to the field of force estimation methods and devices, and in particular to methods and devices for estimating a force in a mechanical or electromechanical system.
    Presentation of the prior art
    In some areas it would be desirable to be able to estimate one or more variable forces that influence the system. For example, in the case of a motor-assisted bicycle, it may be desirable to be able to estimate the force exerted by the cyclist. Similarly, in the case of a wind turbine, it might be desirable to be able to estimate the force generated by the wind.
    In the case of motor-assisted bicycles, it has been proposed to use a torque or power detector in order to detect the force exerted by each foot of the cyclist. Such torque or power detectors are based on the detection of mechanical deformation, for example the torsion of the shaft in the pedal shaft. It has also been proposed to provide force detectors in the pedals of the bicycle. However, a disadvantage of such solutions is that their accuracy is in
    B13390 generally affected by temperature variations and by the aging of the materials used, which means that frequent calibration is usually necessary. In addition, such detectors add weight to the structure and are relatively expensive to install.
    There is therefore a need in the art for a method and a device for estimating forces which solve some or all of the abovementioned drawbacks.
    summary
    An object of embodiments of the present description is to respond at least partially to one or more needs of the prior art.
    According to one aspect, a method of estimating a periodic or substantially periodic force present in a mechanical or electromechanical system is provided, the method comprising: estimating, by a processing device, one or more harmonic frequencies of a signal of acceleration representing acceleration in the system, the substantially periodic force contributing to said acceleration; and estimating, by the processing device, the force on the basis of a dynamic model of the system, the dynamic model being defined by said one or more estimated harmonic frequencies.
    According to one embodiment, the dynamic model is updated on the basis of an error signal representing the difference between a captured speed signal and an estimated speed signal.
    According to one embodiment, the method further comprises the generation of an acceleration signal on the basis of a time differential calculation for two or more values of a speed signal representing an angular or linear speed in the system.
    According to one embodiment, the estimation of said one or more harmonic frequencies of the acceleration signal involves the calculation of an error signal equal to:
    B13390 where r (k) is a current acceleration value, and <p T 0 (k) is an estimate of the acceleration based on a vector θ® representing the harmonic frequencies and a vector φ representing one or more values of previous acceleration.
    According to one embodiment, the mechanical or electromechanical system is a bicycle assisted by a motor, and the periodic or substantially periodic force is the pedaling force generated by the cyclist, and the dynamic model comprises one or more of the estimates v (k) , F (k), F H (k) and z (fc), where each of these estimates is defined as follows:
    v (k) = v (k - 1) + · F m (fc) + ~ · F (k - 1) + · F H (k - 1)
    F (k) = F (k - 1)
    F w (k) = zÇk - 1) z (k) = -Ô 2 (k) · F (k - 1) - êfjt) z (k - 1) where Ts is the sampling period, M is the mass of the cyclist and the bicycle, Fjv [is the force generated by the motor, #i (k) and 0 2 (k) represent one of the harmonic frequencies, F „(k-1) is the estimate of the force exerted by the cyclist, and F (fc-l) is an estimate of other forces in the system.
    According to one embodiment, the mechanical or electromechanical system is a wind turbine, and the periodic or substantially periodic force is a periodic component of the force of the wind on the blades of the wind turbine, and the dynamic model comprises one or more of the estimates ώ Γ (4), F (k), F H (k) and z (k), where each of these estimates is defined as follows:
    ii) y (k) - (ü T (k 1) Tg enera to r (k ') F (Âwind F Twind)
    F (k) = F (fc -1)
    Ai (4) = z (k - 1)
    B13390 z (k) = -0 2 (fc) · F (k - 1) - ^ (fc) · z (k - 1) where Ts is the sampling period, 0 χ (/ ί) and @ 2 ( Jc) represent one of the harmonic frequencies is the estimate of the force exerted by the wind, and F (k - 1) is an estimate of other forces in the system, where ù) r is the speed of the turbine, and ώ τ (/ ί) is an estimate of the speed of the turbine, I is the inertia of the turbine, T generator is the torque applied by the generator, equal for example to Ke · I generator , where Ke is the constant of motor speed and Igenerator is the current generated by the generator, and î wind + î W £ nd represents the total torque generated by the wind, î w / nd representing a cyclic component of this torque.
    According to another aspect, there is provided a processing device arranged to estimate a periodic or substantially periodic force present in a mechanical or electromechanical system, the processing device being arranged to: estimate one or more harmonic frequencies of an acceleration signal representing acceleration in the system, the substantially periodic force contributing to the acceleration; and estimating the force based on a dynamic model of the system, the dynamic model being defined by said one or more estimated harmonic frequencies.
    Brief description of the drawings
    The aforementioned objects and advantages, and others, will become apparent with the following detailed description of embodiments, given by way of illustration and not by limitation, with reference to the accompanying drawings, in which:
    FIG. 1 illustrates a bicycle assisted by a motor according to an exemplary embodiment of the present description;
    FIG. 2 schematically represents a force estimation system according to an exemplary embodiment;
    FIG. 3 is a flowchart illustrating steps in a method of force estimation in a mechanical or electromechanical system according to an exemplary embodiment;
    B13390 FIG. 4 is a graph representing frequency components of an acceleration signal according to an exemplary embodiment;
    FIG. 5 schematically illustrates a method for estimating force in a mechanical or electromechanical system according to an exemplary embodiment;
    Figure 6 schematically illustrates part of the method of Figure 6 in more detail according to an exemplary embodiment;
    Figure 7 is a flowchart showing in more detail steps in the method of Figure 3 according to an exemplary embodiment;
    FIG. 8A is a graph illustrating an acceleration signal and a resultant force according to an exemplary embodiment;
    FIG. 8B is a graph illustrating an acceleration signal and a dynamic stress according to an exemplary embodiment;
    FIG. 9 schematically illustrates a computer device according to an exemplary embodiment; and Figure 10 illustrates a wind turbine according to an exemplary embodiment.
    detailed description
    The embodiments described here relate to a method and a device intended to estimate a periodic or substantially periodic force in a mechanical or electromechanical system. The term periodic force is used here to designate a force which is, at least within certain limits, of a cyclic nature. For example, the force exerted by a cyclist on the pedals of a bicycle is periodic since the force will generally result from the downward push applied by the cyclist's foot when each pedal reaches a certain range of angular position in its cycle. rotation. There are other mechanical or electromechanical systems in which a periodic or substantially periodic force is present and
    B13390 can be estimated by the techniques described here. For example, the present inventor has discovered that the force exerted by the wind on the blades of a wind turbine generally has a periodic or substantially periodic component which can be estimated, as will be described in more detail below. Those skilled in the art will know other applications in which a periodic or substantially periodic force could be estimated according to the techniques described here, such as for example the force exerted on an oar by a rower, or by waves on a boat.
    FIG. 1 illustrates a bicycle 100 assisted by a motor according to an exemplary embodiment. The bicycle 100 comprises for example an electric motor 102. In the example of FIG. 1, the motor 102 is for example a roller motor pressing on the tire mounted near the bottom bracket 103, so that the motor applies force directly to the rear wheel tire. In alternative embodiments, the motor 102 could apply a force to the pedal axle 103, or could be mounted elsewhere, such as on the hub of the front or rear wheel. The motor 102 is for example an electric motor powered by a battery 104, which is for example mounted on a luggage rack arranged above the rear wheel of the bicycle. In alternative embodiments, the battery 104 could be mounted elsewhere on the bicycle. The engine is for example controlled by a computer 106, which is for example mounted on the handlebars of the bicycle, although here again it could be mounted elsewhere. The computer 106 allows the cyclist, for example, to select the level of assistance provided by the engine.
    The computer 106 is for example adapted to estimate the useful force exerted by the cyclist. The useful force is for example the force which leads to an acceleration of the bicycle, and excludes for example certain components of the force which are lost due to friction, etc. The useful force will generally correspond to the torque applied to the crank axle 103 from the pedal cranks. In some embodiments,
    B13390 the computer 106 includes a display, and is adapted to display an indication of the estimated force.
    In addition or instead, the computer 106 is for example adapted to control the motor 102 on the basis of the estimated force. For example, the level of assistance provided by the motor 102 is controlled so that the overall acceleration of the bicycle remains relatively constant, for example in a range of 10% around the average value. In other words, the motor is for example controlled so as to provide greater assistance between the cyclist's pedal strokes. Alternatively, the computer 106 may be adapted to control the motor 102 in a different manner based on the estimated force, for example to provide a level of assistance which is proportional or inversely proportional to the estimated force.
    FIG. 2 schematically illustrates a force estimation system 200 according to an exemplary embodiment. The system 200 is for example partially implemented by the computer 106, and partially implemented by detectors and sensors described in more detail below.
    The system 200 comprises for example a motor force detector (MOTOR FORCE DETECTOR) 202, which provides for example a signal Fjq indicating the force applied by the motor at a given instant. The system 200 also includes for example a pedaling sensor (PEDALLING SENSOR) 204, which provides a signal ωρ indicating the angular velocity of the pedal cranks. This signal can for example be used to determine the top speed of the bicycle. In addition to or in place of the pedaling sensor, a wheel speed sensor (WHEEL SPEED SENSOR) 206 is for example provided and generates a signal (¾ indicating the speed of the bicycle. In variant embodiments, the top signal indicating the speed of the bicycle could be supplied directly by the motor, in which case the wheel speed sensor could be omitted. The signal ωρ is for example composed of digital samples and includes at least five samples for each complete rotation of a crank pedal, and
    B13390 preferably at least seven samples for each complete rotation of a pedal crank.
    The system of 200 further comprises an unknown force estimator (UNKNOWN FORCE ESTIMATOR) 208, implemented for example by the computer 106, and adapted to estimate an unknown force F in the system, this force corresponding to the useful force exerted by the cyclist. The estimator 208 receives for example the signal Fjyj and an indication of the mass (MASS) of the bicycle and the cyclist. In some embodiments, the mass is provided by a mass estimator (MASS ESTIMATOR) 210, which for example receives the force of the motor Fjy (from the motor force detector 202, and also the speed of the bicycle û) g, and estimates the mass based on these parameters while the cyclist is not pedaling. Alternatively, the mass can be obtained by an information input provided by the user.
    In some embodiments, the unknown force estimator also receives a pedaling speed signal ωρ from the pedaling sensor 204 and / or a bicycle speed signal Cùg from a wheel speed sensor. The estimator 208 also receives for example a constrained cyclist force Fq from a previous force calculation, which is used as the basis for the calculation of a next force value during a next iteration, as described in more detail below.
    The unknown force estimator 208 provides for example an estimate F of the useful force generated by the cyclist in the system, force which contributes to the acceleration (Qg of the bicycle.
    A kinematic constraint generator (KINEMATIC CONSTRAINTS) 212 receives for example the estimated forces F and the pedaling speed ωρ, and generates a constrained cyclist force Fq, which is supplied on a feedback path to the unknown force estimator 208. The constrained cyclist force signal F c is also for example supplied to a power estimator of
    B13390 cyclist (CYCLIST POWER ESTIMATOR) 214, which also receives for example the speed of the bicycle ωρ, and generates the estimated cyclist power Power Cyclist (k). In particular, it will be clear to a person skilled in the art that, while in the embodiments described a cyclist's force is estimated, in certain embodiments this force could be expressed in the form of a power value, equal to the force multiplied by the distance over time. For example, in some embodiments the wattage can be obtained by the following equation:
    Power Cyclist (k) = | F h · v (k) where v (k) is the speed of the bicycle, and P H is the estimated useful force of the cyclist.
    Alternatively, rather than being expressed as a linear force, the force exerted by the cyclist could be expressed as a couple. For example, the torque in Nm can be obtained by the following equation:
    Pedalling Torque CycUst (k) =
    PowerçycUstOC
    UpW
    In yet another example, the acceleration in m / s 2 generated by the cyclist could be obtained by the following equation:
    Acceleration Cyclist (k) = where M is the mass of the cyclist and the bicycle.
    Of course, in certain embodiments, the motor could be cut, so that the force of the motor Fj ^ remains equal to zero.
    Figure 3 is a flowchart illustrating steps in a method of determining the strength of a cyclist on a motor-assisted bicycle, such as the bicycle 100 of Figure 1. The method is for example implemented by the computer 106.
    B13390
    In a step 301, a reading of the speed v (k) of the bicycle is for example obtained. In some embodiments, this speed can be generated based on the rooster signal from a wheel speed sensor or other input. As a variant, the speed v (k) can be calculated on the basis of a reading of the speed of the OmOTOR 'motor in radiant per second, and on the basis of the radius of the motor Rm, with for example v (k) = OmotoR - ^ 111 ·
    In a step 302, an acceleration value a (k) is calculated on the basis of the speed value, for example by a differential calculation on the speed signal over time.
    In a step 303, the frequency of one or more harmonics of the acceleration signal a (t) is for example determined. The term harmonic is used to designate a fundamental frequency and / or the first, second, third, etc., harmonic frequencies. In an embodiment described in more detail below, the harmonic frequencies are determined on the basis of an iterative algorithm.
    Figure 4 is a graph illustrating an example of the amplitudes of frequency components of the acceleration signal. The frequency axis presents for example the normalized frequency in the form of fractions of the sampling frequency 1 / Ts, where Ts is the sampling period. In one embodiment, the sampling frequency is equal to 20 Hz, and thus the sampling period Ts is equal to 50 ms. More generally, the sampling frequency is for example between 10 and 50 Hz.
    In the example in FIG. 4, the harmonic frequencies correspond to peaks of the frequency distribution curve, referenced 402, 404 and 406, which are for example approximately 3, 5 and 9 Hz respectively. There are also by example a component of direct current, DC, of the acceleration signal represented by a peak 408 at 0 Hz. Step 303 of FIG. 3 implies for example the estimation of the frequency of one or more of the harmonics 402, 404 , 406.
    B13390
    Referring again to Figure 3, in a step 304, a model is generated for the forces present in the bicycle system, based on the harmonic frequencies. This model includes a force component F H (k).
    In a step 305, the force component Fh (k) is extracted from the model to obtain the estimate of the useful force exerted by the cyclist.
    In some embodiments, another step 306 involves determining an error value ERROR associated with the estimated force F H (k).
    Figure 5 is a block diagram showing steps 302 to 305 of Figure 3 in more detail.
    A block 502 in FIG. 5 represents the implementation of steps 302 and 303 of FIG. 3. Block 502 receives the speed value v (k), and calculates, on the basis of a model of a transfer function Η (Θ, Ζ), the harmonics 0 (k) updated on the basis of the speed value v (k).
    A block 504 in FIG. 5 represents the implementation of steps 304, 305 and 306 of FIG. 3. Block 504 receives the speed value v (k), the harmonics 0 (fc) generated by block 502, and the motor force Fjy [. The force of the motor can for example be determined on the basis of the speed constant of the motor Ke and a measurement of the current I supplied to the motor. In particular, the force of the motor is for example generated on the basis of the motor torque Tjyj, equal for example to Ke * I, where Ke is the speed constant of the motor and I is the current. The force of the motor Fpj is therefore for example equal to Tjvj / Rm, where Rm is the radius of the motor, in the case of a roller motor on a tire, like the motor 102 in FIG. 1. The block 504 generates for example an estimate F h (k) of the useful force exerted by the cyclist. In some embodiments, block 504 further generates an ERROR error value indicating a margin of error for the force estimate.
    Figure 6 illustrates the block 504 of Figure 5 in more detail according to an exemplary embodiment.
    B13390
    A dynamic model (DYNAMIC MODEL) 606 receives for example the force of the motor F M (k), the harmonic frequencies 0 (k), and an error value e 2 (k) obtained by subtracting, from the speed value v (k), an estimate v (k) of the speed. Block 606 provides for example an updated dynamic model Xe (k). A block 608 represents the extraction from the dynamic model Xe (k) of the force estimate F H (k).
    Another block 610 represents the determination of the error associated with the force estimate.
    Figure 7 is a flowchart showing the method of Figure 3 in more detail according to an exemplary embodiment.
    After step 301 in which the speed value v (k) is read, step 302 implies for example the calculation of the acceleration value a (k) in the form of a value cî) P (k) based on the pedal speed value a) P (k). For example, the pedal acceleration value ώ Ρ (k) is calculated by the following equation:
    . „ Λ (ω ρ (k) - ω ρ (k - 1)) where ü) p (k - 1) is a previous value of the pedal speed, and Ts is the sampling period, for example equal to the period of time between samples ü) p (k - 1) and ω ρ (/ ί) of the pedal speed.
    Of course, rather than being based on the pedal speed ω ρ (Κ), in alternative embodiments the acceleration value a (k) could be calculated on the basis of another speed signal.
    Steps 703 to 705 of Figure 7 implement step 303 of Figure 3, involving finding one or more harmonics of the acceleration signal.
    Step 703 implies for example the calculation of a vector φ in the form [r (k-1); r (k — 2)], where r (k-1) = ώ Ρ (& - 1) and r (k2} = b) P (k - 2).
    B13390
    Step 704 involves for example the calculation of an error value e] _ (k) and a parameter L (k).
    The error value e ^ (k) is for example based on the following formula:
    efjc) = r (k) - (p T § (k) where r (k) = ώ Ρ (/ ί), and § (Jt) is for example a vector representing an estimate of harmonic frequencies and which is initialized to zero , and <p T 0 (k) is an estimate of the acceleration.
    The parameter L (k) is for example based on the following formula:
    L (fc) = P (k) <p (<p r P (k) <p + FF) where FF is a forgetting factor, for example equal to 0.95, and P (k) is a matrix which is for example initialized to a certain value, for example to a value of:
    w = (7 wo) and is recalculated for each new iteration by the following formula:
    p ( k + 1) = (J) · (P (fc) - L (kWP (k ”.
    In a step 705, a harmonic vector Q (k +1) is for example generated on the basis of the following formula:
    6 (k + 1) = B (k) + L (k) · e ^ k).
    For example, in the case of a single harmonic, the harmonic vector 0 (k + 1) has for example the following form:
    Ôi (k + 1)
    Ô 2 (k + l) J where Q r represents the frequency of the harmonic, for example in the form (f = - 2cos (îo. T s ), where ω = 2πί, f being the frequency of the harmonic, and θ 2 represents the quality factor of the harmonic, which is for example close to 1.
    Step 304 involves, for example, sub-steps 706 and
    707. In these steps, a dynamic model Xe representing the bicycle system is for example modified on the basis of the
    B13390 last speed value v (k). For example, the dynamic model is based on the following equation for the driving force of the bicycle:
    . (v (/ e) - v (k - 1)
    Total Force = M · v (t) = M (- 1 = F M + F H + F where F h represents the force exerted by the cyclist and F represents the other forces contributing to the overall force on the bicycle, such as wind, terrain, etc. On the basis of this equation, the following dynamic model Xe can for example be defined as being a vector having the following components:
    /
    F (k)
    F H W>
    z (k) J where F H (k - ï), v (fc), F (k), F H (k) and z (fc) are defined as follows:
    F H (k - 1) = F c (k)
    Xe = v (k) = v (fc - 1) + ^ · F M (fc) + · F (fc - 1) + · F „(fc - 1)
    F (k) = F (k - 1)
    F „(k) = z (k - 1) z (fc) = -0 2 (fc) · Fn (k - 1) - Ô ^ k) · z (k - 1)
    In a step 706, a matrix Ad (k), and vectors Ld (k) and P 2 (k + 1) are for example calculated.
    The matrix Ad (k) is for example calculated on the basis of the following formula:
    Ad (k) = T / m 1 0 0 T / m 0 0
    1
    -0i (k) J where Ts is the sampling period, bicycle and cyclist.
    and M is the mass of the
    B13390
    The vector Ld (k) is for example calculated on the basis of the following formula:
    Ld (fc) = ((Cd · P 2 · Cd T + Vd} · Cd · P 2 · Ad T ) T where Cd is for example the vector [1 0 0 0], and Vd is a constant representing the expected covariance speed measurements.
    The vector P2 (k + 1) is for example calculated on the basis of the following formula:
    P 2 (fc + 1) = Ad · P 2 (k) · Ad T + Wd - Ad · P 2 (k) Cd T Ld T where Wd is a constant matrix representing the expected covariance of the process disturbances, that is ie the covariance of exogenous forces.
    In a step 707, the vector Xe (k + 1) is for example calculated on the basis of the following formula:
    Xe (k + 1) = (Ad - Ld · Cd) Xe (Jt) + Bd · F M (k} + Ld · e 2 (k} where e 2 (k) is an error value equal to v ( k} - v (k}, and v (/ c) is an estimate of the speed v (k).
    In a step 305, the force F H exerted by the cyclist is extracted, corresponding for example to the third element Xe (3) of the vector Xe. Additionally, the other forces F can be extracted, these corresponding for example to the second element Xe (2) of the vector Xe.
    Step 306 implies for example the determination of an error value ERROR_F associated with the estimated force, as represented by block 610 in FIG. 6. The error value is for example determined on the basis of the following equation for an error vector ERROR of the vector Xe:
    ERROR = f diagÇP 2 Çk}} · e 2 2 (k)
    The error value ERROR_F is then, for example, extracted as the third element ERROR (3) of the vector ERROR. In certain embodiments, the amplitude of the error value ERROR_F is then compared with an admissible level, to decide whether the force estimate is sufficiently precise to be useful. For example, the following operation is implemented:
    B13390
    If ERRORp <ε, Then Force estimation is admissible,
    Otherwise inadmissible ”where ε is the admissible level, for example equal to a value between 2 and 10 percent, and in certain embodiments between 2 and 5 percent.
    Optionally, in a step 708, the motor of the bicycle is controlled on the basis of the estimated force, if for example the estimated force is determined in step 306 as being admissible.
    FIG. 8A is a graph comprising a curve 802 representing an example of the estimated acceleration produced by the cyclist before considering the constraints. Dotted lines 804 and 806 respectively represent the maximum and minimum accelerations produced by gravity, assuming a road gradient of no more than 20 percent. Curves 808 and 810 represent other constraints, calculated for example by the module 212 of FIG. 2 in real time. In particular, curve 808 represents the maximum acceleration produced by the weight of the cyclist on the pedals, this signal falling for example to zero during periods in which the cyclist stops pedaling. The curve 810 represents for example the maximum acceleration in the presence of aerodynamic losses.
    FIG. 8B is another graph illustrating the stresses 804, 806, 808 and 810 in FIG. 8A, and further illustrating a curve 812 representing the constrained force Fq after taking the constraints into account. In particular, curves 804, 808 and 810 represent maximum stress values Cmax (t), while curve 806 represents a minimum stress value Cmin (t).
    The constrained force Fq is for example equal to the force of the estimated unconstrained cyclist F if none of the constraints is exceeded, for example since Cmin (t) <= F <= Cmax (t). As a variant, if F <Cmin (t), Fq is for example equal to Cmin (t), and if
    F> Cmax (t), Fq is for example equal to Cmax (t).
    B13390
    FIG. 9 schematically illustrates a computer 900 arranged to implement the method of FIG. 3 and / or of FIG. 7 to calculate a cyclist force, and which corresponds for example to the computer 106 of FIG. 1.
    The computer 900 comprises for example a processing device (P) 902 comprising one or more processors under the control of instructions stored in an instruction memory (INSTRUCTION MEMORY) 904. An input interface (I / O INTERFACE) 906 allows for example to introduce into the processing device 902 readings from different measuring devices, such as for example from the electric motor, and / or a separate speed sensor. A memory (MEMORY) 908 stores for example the various parameters, vectors and matrices described above for the implementation of the method.
    Rather than being used to calculate an estimate of the force exerted by a cyclist, in an alternative embodiment, the methods of FIGS. 3 and 7, and the device of FIG. 9, could be applied to estimate the force exerted by the cyclist. wind on the blades of a wind turbine, as will now be described in more detail with reference to FIG. 10.
    FIG. 10 illustrates an example of a wind turbine 1000, comprising two or more blades 1002 coupled to an axis 1004, which in turn is coupled to an electricity generator 1006.
    Among the equations 1 to 5 indicated above and representing the dynamic model of the forces on the bicycle, the equations 1, 2 and 5 remain for example unchanged, while the equations 2 and 3 are for example replaced by the following equations 1 ', 2 'and 3':
    generator ^ k) + ( ; ) (fwind b Î wind) '
    -2 '
    -3 '
    Q T (k) = Q T (k - 1) - (y) η = ^ wind îwindik) = z (k - 1) where ω τ is the speed of the turbine, and co T (k) is an estimate of the speed of the turbine, Ts is the sampling period,
    B13390
    I is the inertia of the turbine, T generator is the torque applied by the generator, equal for example to Ke · I generat: or , where Ke is the motor speed constant and Igenerator is the current generated by the generator, and ï wind + T wind represents the total torque generated by the wind, ï W i nd representing a cyclic component of this torque. The speed of the turbine ω τ is for example detected by a speed sensor located on the main shaft of the turbine and / or by using a generator speed measurement, based for example on rotation encoders.
    The speed of the generator depends on both the torque produced by the wind and the torque produced by the generator, which is proportional to the electric current Igenerator drawn from the generator. In some embodiments, estimating the wind torque produced by the wind can be used to control the speed of the generator by controlling the level of the Igenerator current. An advantage of controlling the generator torque in this way is that the 'Cyclic fluctuations in the speed of the blades 1002 can be avoided, thus avoiding or reducing the risk of damaging the turbine.
    An advantage of the embodiments described here is that a periodic or substantially periodic force in a mechanical or electromechanical system can be estimated practically in real time and with relatively high precision in a relatively simple manner.
    With the description thus made of at least one illustrative embodiment, various alterations, modifications and improvements will readily appear to those skilled in the art. For example, although a particular example of a dynamic model is given by equations 1 to 5 above, it will be clear to those skilled in the art that modifications could be made to these equations, for example to take into account additional forces present in the system, and that the equations could be adapted to other applications than the examples of bicycle and wind turbine described here.
    B13390
    权利要求:
    Claims (7)
    [1" id="c-fr-0001]
    1. A method for estimating a periodic or substantially periodic force present in a mechanical or electromechanical system, the method comprising:
    estimating, by a processing device, one or more harmonic frequencies of an acceleration signal representing an acceleration in the system, the substantially periodic force contributing to said acceleration; and estimating, by the processing device, the force on the basis of a dynamic model of the system, the dynamic model being defined by said one or more estimated harmonic frequencies.
    [2" id="c-fr-0002]
    2. Method according to claim 1, in which the dynamic model is updated on the basis of an error signal (e2 (k)) representing the difference between a captured speed signal (v (k)) and a estimated speed signal (v (k)).
    [3" id="c-fr-0003]
    3. Method according to claim 1 or 2, further comprising the generation of an acceleration signal on the basis of a time differential calculation for two or more values of a speed signal representing an angular speed or linear in the system.
    [4" id="c-fr-0004]
    4. Method according to any one of claims 1 to 3, in which the estimation of said one or more harmonic frequencies of the acceleration signal involves the calculation of an error signal equal to:
    6i (fc) rÇk) - <p r 0 (fc) where r (k) is a current acceleration value, and çp T 0 (k) is an estimate of acceleration based on a vector representing the harmonic frequencies and a vector φ representing one or more previous acceleration values.
    [5" id="c-fr-0005]
    5. Method according to any one of claims 1 to 4, in which the mechanical or electromechanical system is a bicycle assisted by a motor, and the periodic or substantially periodic force is the pedaling force generated by the
    B13390 cyclist, and wherein the dynamic model includes one or more of the estimates v (k), F (k), Fh (k) and z (k), where each of these estimates is defined as follows:
    v (fc) = v (k - 1) + · F M (k) + ^ · F (k - 1) + ^ · F H Çk - 1)
    F (k) = F (k - 1)
    F „(k) = z (k - 1) z (k) = -0 2 (k) · F (k - 1) - 0i (k) · z (k - 1) where Ts is the sampling period , M is the mass of the cyclist and the bicycle, Fjy [is the force generated by the motor, ^ (k) and 0 2 (k) represent one of the harmonic frequencies, F „(fc-i) is the estimate of the force exerted by the cyclist, and F (k - 1) is an estimate of other forces in the system.
    [6" id="c-fr-0006]
    6. Method according to any one of claims 1 to 4, in which the mechanical or electromechanical system is a wind turbine, and the periodic or substantially periodic force is a periodic component of the force of the wind on the blades of the wind turbine, and in which the dynamic model includes one or more of the estimates ùî r (k), F „(k), © (k) and z (k), or each of these estimates is defined as follows:
    ùJy (k) (Pj-Çk 1) (P) ^ generatorOÔ F (fwind + T-wind)
    F (k) = F (k ~ 1)
    F H (k) = z (k - 1) z (k) = —0 2 (k) · F (k - 1) - 0! (K) · z (k - 1) where Ts is the period of sampling, 0 x (k) and 0 2 (k) represent one of the harmonic frequencies, F „(k-1) is the estimate of the force exerted by the wind, and F (k - 1) is an estimate other forces in the system, where ω Γ is the speed of the turbine, and τ τ (/ ί) is an estimate of the speed of the turbine, I is the inertia of the turbine, T generator is the applied torque speak
    B13390 generator, equal for example to Ke · I generator , where Ke is the motor speed constant and Igenerator is the current generated by the generator, and T wind + f wind represents the total torque generated by the wind, ï w i nd representing a cyclic component of this couple.
    5
    [0007]
    7. Treatment device arranged to estimate a periodic or substantially periodic force present in a mechanical or electromechanical system, the treatment device being arranged for:
    estimating one or more harmonic frequencies of an acceleration signal representing an acceleration in the system, the substantially periodic force contributing to the acceleration; and estimate the force on the basis of a dynamic model of the system, the dynamic model being defined by said one or
    15 several estimated harmonic frequencies.
    B13390
    1/5
    200
    202 206 204 Fig 2
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    同族专利:
    公开号 | 公开日
    FR3055440B1|2018-09-28|
    WO2018037191A1|2018-03-01|
    JP2019534997A|2019-12-05|
    US20190188235A1|2019-06-20|
    EP3504633A1|2019-07-03|
    JP6978490B2|2021-12-08|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    EP1466824A2|2003-04-10|2004-10-13|Transceiving System Technology Corp.|Pedal force sensing apparatus for an electric bicycle|
    EP2143628A1|2007-03-28|2010-01-13|Sunstar Giken Kabushiki Kaisha|Electrically assisted bicycle and unit adapted for use in electrically assisted bicycle and capable of being attached to bicycle body frame|
    EP2658114A1|2010-12-22|2013-10-30|Microspace Corporation|Motor drive control device|CN112555097A|2020-12-08|2021-03-26|东方电气风电有限公司|Method for preventing wind turbine generator from polluting residents' residences by light and shadow|
    JP6730144B2|2016-09-09|2020-07-29|株式会社シマノ|Bicycle component and its communication part|
    CN108488036B|2018-05-04|2019-10-25|曲阜师范大学|Wind-powered electricity generation magnetic suspension yaw system suspension control method based on model mismatch compensator|
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    优先权:
    申请号 | 申请日 | 专利标题
    FR1657939|2016-08-25|
    FR1657939A|FR3055440B1|2016-08-25|2016-08-25|FORCE ESTIMATING METHOD AND DEVICE|FR1657939A| FR3055440B1|2016-08-25|2016-08-25|FORCE ESTIMATING METHOD AND DEVICE|
    EP17768178.0A| EP3504633A1|2016-08-25|2017-08-24|Method and device for estimating force|
    PCT/FR2017/052269| WO2018037191A1|2016-08-25|2017-08-24|Method and device for estimating force|
    US16/328,059| US20190188235A1|2016-08-25|2017-08-24|Method and device for estimating force|
    JP2019511534A| JP6978490B2|2016-08-25|2017-08-24|Force estimation method and equipment|
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