EP1574469B1 - Design method for the controller design of an active elevator car shock attenuation system - Google Patents

Description

The invention relates to a method for the design of a vibration damping controller on an elevator car, wherein the controller design is based on a model of the elevator car.

With the patent EP 0 731 051 B1 a device and a method for vibration damping in an elevator car has become known. Vibrations or accelerations occurring transversely to the direction of travel are reduced by rapid control so that they are no longer noticeable in the elevator car. To record the measured values inertial sensors are arranged on the cabin frame. In addition, a slow position controller automatically tracks the elevator car into a central position in the case of one-sided imbalance with respect to the guide rails, with position sensors supplying the measured values to the position controller.

The publication "Vibration Control for machining using H _∞ Techniques" by University of Kentucky, published on 26.03.95, describes a method for vibration damping.

To reduce the vibrations or the accelerations to the elevator car, a multi-size controller and another multi-size controller for maintaining the game on the guide rollers or the upright position of the elevator car are provided. The control signals of the two controllers are added together and control one actuator per roller guide and per horizontal direction.

The controller design is based on a model of the elevator car, which takes into account the essential structural resonances.

The disadvantage is that the overall model tends to great complexity, despite sophisticated methods for reducing the number of poles. As a result, the model-based controller also becomes complex.

The invention aims to remedy this situation. The invention, as characterized in claim 1 solves the problem of avoiding the disadvantages of the known method and to propose a simple method for the design of a regulator.

Advantageous developments of the invention are specified in the dependent claims.

Advantageously, in the method according to the invention, an overall model of the elevator car with a known structure is specified. It is a so-called multi-body system (MBS) model, which includes several rigid bodies. The MKS model describes the essential elastic structure of the elevator car with the guide rollers and the actuators as well as the force coupling with the guide rails. The model parameters are more or less well known or there are estimates, the parameters for the elevator car used to be identified or to be determined. The transfer functions or frequency responses of the model are compared with the measured transfer functions or frequency responses. Using an algorithm to optimize functions with several variables are used to modify the estimated model parameters to achieve the greatest possible match.

It is also advantageous that the active vibration damping system of the elevator car itself can be used as a measuring device for the transmission functions or frequency responses to be measured. With the actuators, the elevator car is excited and with the acceleration sensors or with the position sensors, the answers are measured.

This model-based design method of the controller ensures the best possible active vibration damping for the individual elevator cars with very different parameters.

The above-mentioned identification method ensures that the result is the simplest and most consistent model of the elevator car. Advantageously, the controller based on this model has a better quality or a better control quality. In addition, the process is systematically writable and can be largely automated and carried out in a much shorter time.

Based on the MKS model with identified parameters, a robust multi-size controller is designed to reduce the acceleration and a position controller to maintain the game on the guide rollers.

The acceleration controller has the behavior of a bandpass filter and the best effect in a medium Frequency range from about 1 Hz to 4 Hz. Below and above this frequency band, the gain and thus the efficiency of the acceleration controller is reduced.

In the low frequency range, the effect of the acceleration controller is limited by the available clearance on the guide rollers and the position controller to be interpreted thereon. The position controller causes the elevator car to track an average of the rail profiles while the acceleration controller is causing a linear movement. This target conflict is solved by the two controllers in different frequency ranges are effective. The gain of the positioner is large at low frequencies and then decreases. That is, it has the property of a low-pass filter. Conversely, the accelerator controller has a small gain at low frequencies.

In the high frequency range, the effect of the acceleration controller is limited by the elasticity of the elevator car. The first structural resonance can occur, for example, at 12 Hz, this value being highly dependent on the design of the elevator car and can be significantly lower. Above the first structural resonance, the controller can no longer reduce the acceleration on the cabin body. There is even a risk that structural resonances are stimulated or that instability can occur. With knowledge of the dynamic system model of the controlled system, the controller can be designed so that this can be avoided.

• Es zeigen:
  • Fig. 1
    ein Mehrkörpersystem (MKS) Modell einer Aufzugskabine,
  • Fig. 2 eine Führungsrolle mit Rollenkräften,
  • Fig. 3
    ein Stellglied mit Führungsrolle, Aktuator und Sensoren,
  • Fig. 4
    eine schematische Darstellung der geregelten Achsen,
  • Fig. 5
    die Verstärkung der gemessenen Beschleunigung und des identifizierten Modells,
  • Fig. 6 und Fig. 7
    einen optimierten Regler mit den identifizierten Parametern zur aktiven Schwingungsdämpfung,
  • Fig. 8
    Signalflussschema für den Entwurf eines H _∞-Reglers mit Regler und Regelstrecke,
  • Fig. 9
    den Verlauf der Singularwerte eines Positionsreglers in y-Richtung,
  • Fig. 10
    den Verlauf der Singularwerte eines Beschleunigungsreglers in y-Richtung und
  • Fig. 11 ein Kraftsignal zur Anregung der Aktuatoren.

The present invention will be explained in more detail with reference to the accompanying figures.

• Show it:
  • Fig. 1
    a multi-body system (MBS) model of an elevator car,
  • Fig. 2 a leadership role with role forces,
  • Fig. 3
    an actuator with guide roller, actuator and sensors,
  • Fig. 4
    a schematic representation of the controlled axes,
  • Fig. 5
    the gain of the measured acceleration and the identified model,
  • Fig. 6 and Fig. 7
    an optimized controller with the identified parameters for active vibration damping,
  • Fig. 8
    Signal flow diagram for the design of H _∞ knob with controller and control loop,
  • Fig. 9
    the course of the singular values of a position controller in the y-direction,
  • Fig. 10
    the course of the singular values of an acceleration controller in the y-direction and
  • Fig. 11 a force signal to excite the actuators.

The MKS model must reproduce the essential characteristics of the elevator car with regard to ride comfort. Since it is only possible to work with linear models when identifying the parameters, all nonlinear effects must be neglected. The first natural frequencies of the elastic elevator car are so deep that they can overlap with the so-called rigid body natural frequencies of the entire cabin.

As in Fig. 1 At least two rigid bodies are required for modeling the elastic elevator car 1, namely the cab body 2 and the cab frame 3. Cabin body 2 and cab frame 3 are connected by means of elastomer springs 4.1 to 4.6, the so-called cab insulation 4. This reduces the transmission of structure-borne noise from the frame to the frame Cab body. For modeling a rigid elevator car 1, it is sufficient to consider the cabin body and cabin frame as a whole as a whole.

The transverse stiffness of cab body 2 and cab frame 3 is substantially less than the rigidity in the vertical direction. This can be modeled with the division into at least two rigid bodies, namely cabin bodies 2.1 and 2.2 and cabin frames 3.1 and 3.2. The at least two sub-bodies are horizontally coupled by springs 5, 6.1 and 6.2 and can be considered vertically connected as rigid.

The guide rollers 7.1 to 7.8 with the proportional masses of levers and actuators can be modeled with at least 8 rigid bodies or even neglected. This depends on the associated natural frequencies of the guide rollers and on the upper limit of the frequency range considered. Since the natural frequency of the actuator-roller system in the controlled state can lead to instability, modeling with rigid bodies is preferred. These are only displaceable perpendicular to the footprint on the rail relative to the frame and coupled to the roller guide springs 8.1 to 8.8. In the other directions they are rigidly connected to the frame.

As in Fig. 2 shown is the leadership behavior or the power coupling between guide rollers and guide rails important. To model essentially only the two horizontal force components are necessary. The vertical force component resulting from the rolling resistance can be neglected. The normal force results from the elastic compression of the roller covering 9.1 to 9.8. The axial or transverse force results from the angle between the straight line perpendicular to the roller axis and parallel to the rail and the actual direction of movement of the roller center.

• F_RA : Rollenkraft in Achsrichtung in [N]
• α : Schräglaufwinkel in [rad]
• F_RN : Rollenkraft normal zur Aufstandsfläche [N]
• K : Konstante ohne Dimension, wird durch Messung bestimmt

Mathematically, the following relationships are relevant: $${\mathrm{F}}_{\mathrm{RA}}=-\tan \left(\alpha \right)*{\mathrm{F}}_{\mathrm{RN}}*\mathrm{K}$$

• F _RA : Rolling force in axial direction in [N]
• α: slip angle in [rad]
• F _RN : Roller force normal to footprint [N]
• K: constant without dimension, determined by measurement

The force law {1} becomes invalid at the latest when the limits of the static friction force are reached and at a large slip angle α. This becomes faster at low driving speed and is about 90 degrees at standstill. The force law {1} only applies to the moving cab.

$${\mathrm{F}}_{\mathrm{RA}}=-{\mathrm{v}}_{\mathrm{A}}*\left({\mathrm{F}}_{\mathrm{RN}}*\mathrm{K}/{\mathrm{v}}_{\mathrm{K}}\right)$$

• v_K : Vertikalgeschwindigkeit der Kabine [m/s]
• v_A : Geschwindigkeit der Kabine in Achsrichtung [m/s]

For the roller force in the axial direction when the car is moving then approximately: $${\mathrm{F}}_{\mathrm{RA}}=-{\mathrm{v}}_{\mathrm{A}}/{\mathrm{v}}_{\mathrm{K}}*{\mathrm{F}}_{\mathrm{RN}}*\mathrm{K}$$

$${\mathrm{F}}_{\mathrm{RA}}=-{\mathrm{v}}_{\mathrm{A}}*\left({\mathrm{F}}_{\mathrm{RN}}*\mathrm{K}/{\mathrm{v}}_{\mathrm{K}}\right)$$

• v _K: vertical speed of the car [m / s]
• v _A : speed of the car in the axial direction [m / s]

K is a constant, v _K and F _RN can be considered constant if the preload force is significantly greater than the dynamic part of the normal force. This means that the roller force in the axial direction is proportional and opposite to the speed in the axial direction and inversely proportional to the driving speed of the elevator car.

Transverse vibrations of the cabin are thus damped by the rollers as from a viscous damper, the effect is smaller with increasing speed.

As in Fig. 3 shown, the guide rollers 7 are connected to a about an axis 10 'rotatable lever 10 to the cabin frame 3, wherein the roller guide spring 8 generates a force between the lever and the cabin frame. An actuator 11 generates a force which acts parallel to the roller guide spring. A position sensor 12 measures the position of the lever 10 and the guide roller 7, respectively. An acceleration sensor 13 measures the acceleration of the car frame 3 perpendicular to the contact surface of the roller covering 9 on the guide rail 14. The reference number of the respective element is as in FIG Fig. 1 shown (for example, at the elevator car 1 bottom right: 7.1.8.1.9.1,10.1,11.1,12.1,13.1).

At the elevator car 1, four lower guide rollers 7.1 to 7.4 are provided with actuators and position sensors. In addition, four upper guide rollers 7.5 to 7.8 can be provided with actuators and position sensors. The number of required acceleration sensors 13 corresponds to the number of controlled axes, wherein at least three and at most six acceleration sensors are provided. As in Fig. 4 For the active vibration damping of the elevator car 1, the number of axles is reduced from eight to six, or from four to three axles, if only down active is controlled. For each axis An _i is a triple of signals Fn _i , Pn _i , _i for actuator force, position and acceleration. The index i is the consecutive numbering in the respective axis system and n stands for number of axes of the system.

• Das Kraftsignal F6₁ für die Aktuatoren 11.1 und 11.3 bzw. das Kraftsignal F6₄ für die Aktuatoren 11.5 und 11.7 wird in eine positive und eine negative Hälfte aufgeteilt. Jeder Aktuator wird nur von einer Hälfte angesteuert und kann nur Druckkraft im Rollenbelag erzeugen. Von den Signalen der Positionssensoren 12.1 und 12.3 wird ein Mittelwert gebildet und das gleiche gilt für die Positionssensoren 12.5 und 12.7. Von den Signalen der Beschleunigungssensoren 13.1 und 13.3, bzw. 13.5 und 13.7 wird ebenfalls ein Mittelwert gebildet. Da die Beschleunigungssensoren 13.1 und 13.3, bzw. 13.5 und 13.7 auf einer Achse liegen und durch den unteren oder oberen Kabinenrahmen starr verbunden sind, messen sie prinzipiell das Gleiche und es kann je ein Sensor des jeweiligen Paares weggelassen werden.

The signals of the lower and the upper roller pair between the guide rails 14.1 and 14.2 are summarized as follows:

• The force signal F6 ₁ for the actuators 11.1 and 11.3 and the force signal F6 ₄ for the actuators 11.5 and 11.7 is divided into a positive and a negative half. Each actuator is controlled by only one half and can only generate compressive force in the roller lining. Of the signals of the position sensors 12.1 and 12.3 an average value is formed and the same applies to the position sensors 12.5 and 12.7. Of the signals of the acceleration sensors 13.1 and 13.3, or 13.5 and 13.7, an average value is also formed. Since the acceleration sensors 13.1 and 13.3, or 13.5 and 13.7 lie on one axis and are rigidly connected by the lower or upper cabin frame, they measure in principle the same and it can ever be omitted a sensor of the respective pair.

During the measurement runs, one or more actuators with a force signal as in Fig. 11 shown driven and the elevator car 1 to vibrations transverse to the direction of travel excited so that clearly measurable signals in the position sensors 12 and in the acceleration sensors 13 arise. Thus, the correlation of the measurements with the Force signals can be reliably determined, usually only one actuator or actuator pair is driven. As shown in Table 1, at least as many test drives are necessary as active axles are provided. Table 1 Suggestion: one or more simultaneously Measurements: All at the same time F6 ₁ P6 ₁ a6 ₁ F6 ₂ P6 ₂ a6 ₂ F6 ₃ P6 ₃ a6 ₃ F6 ₄ P6 ₄ a6 ₄ F6 ₅ P6 ₅ a6 ₅ F6 ₆ P6 ₆ a6 ₆

$${G^a}_{i,j}\left(\omega \right)=\frac{a_i\left(\omega \right)}{F_j\left(\omega \right)}$$

$$G\left(\omega \right)=\left[\begin{array}{c}{G}^P\left(\omega \right)\\ G^a\left(\omega \right)\end{array}\right]$$

The frequency spectrum of the force signals and the measured position signals and acceleration signals are determined by Fourier transformation. The transfer functions in the frequency domain or frequency responses G _{i , j} (ω) with the angular frequency ω as an argument are determined by dividing the spectra of the measurements by the associated spectrum of the force signal. Where i is the index of the measurement and j is the index of the force. $${G^P}_{i.j}\left(\omega \right)=\frac{P_i\left(\omega \right)}{F_j\left(\omega \right)}$$

$${G^a}_{i.j}\left(\omega \right)=\frac{a_i\left(\omega \right)}{F_j\left(\omega \right)}$$

$$G\left(\omega \right)=\left[\begin{array}{c}{G}^P\left(\omega \right)\\ G^a\left(\omega \right)\end{array}\right]$$

G ^P _{i , j} (ω) are the individual frequency responses from force to position and G ^a _{i , j} (ω) are the individual frequency responses of Power to acceleration. The matrix G ^P (ω) contains all the frequency responses force to position and matrix G ^a (ω) all

Frequency responses force to acceleration. Matrix G (ω) arises from the vertical composition of G ^P (ω) and G ^a (ω).

For a 6-axis system, this results in 2 x 6 x 6 = 72 transfer functions and for a 3-axis system 2 x 3 x 3 = 18 transfer functions. In cabins whose center of gravity is on the axis between the guide rails 14.1 and 14.2, the couplings and the correlation between the two horizontal directions x and y are weak. Therefore, only about half of the transfer functions continue to be used, with the remainder eliminated because of insufficient correlation.

$$y=\mathit{Cx}+\mathit{Du}$$

x ist der Vektor der Zustände des Systems, welche im Allgemeinen von aussen nicht sichtbar sind. Zustände des Systems sind im vorliegenden Fall:

• Positionen und Geschwindigkeiten der Schwerpunkte im Starrkörpermodell, sowie Drehwinkel und
• Drehgeschwindigkeiten. Ableitungen der Zustände sind Geschwindigkeiten und Beschleunigungen. Die Geschwindigkeit ist damit Zustand und Ableitung zugleich.

The MKS model of the cabin is generally a linear system. If this contains nonlinear parts, a fully linearized model is generated in a suitable operating state by numerical differentiation. In the linear state space, the MKS model is described with the following equations: $$\dot{x}=\mathit{Ax}+\mathit{Bu}$$

$$y=\mathit{cx}+\mathit{You}$$

x is the vector of the states of the system, which are generally not visible from the outside. States of the system in the present case are:

• Positions and speeds of the centers of gravity in the rigid body model, as well as angles of rotation and
• Rotational speeds. Derivatives of the states are Speeds and accelerations. Speed is thus state and derivative at the same time.

The vector ẋ contains the derivatives of x after the time. y is a vector containing the measured quantities, ie positions and accelerations. The vector u contains the inputs (actuator forces) of the system. A, B, C and D are matrices which together form the so-called Jacobian matrix by which a linear system is completely described. The frequency response of the system is given by $$G^{\wedge }\left(\omega \right)=D+C{\left(\mathit{j\omega I}-A\right)}^{-1}B\mathrm{,}$$

G ^ (ω) is a matrix with the same number of rows as measurements in vector y and the same number of columns as inputs in vector u and contains all frequency responses of the MKS model of the cabin.

A Jacobian matrix contains all the partial derivatives of a system of equations. For a linear system of coupled 1st order differential equations, these are the constant coefficients of the A, B, C, and D matrices.

w(ω) ist eine von der Frequenz abhängige Gewichtung. Sie sorgt dafür, dass nur wichtige Teile der gemessenen Frequenzgänge im Modell nachgebildet werden.The model contains a number of well known parameters such as dimensions and mass and a number of poorly known parameters such as spring rates and damping constants. It is important to identify these poorly known parameters. The identification is performed by comparing the frequency responses of the model with the measured frequency responses. With an optimization algorithm, the poorly known model parameters are changed until the minimum of the sum e of all deviations of the frequency responses of the model from the measured frequency responses is found. $$\begin{array}{c}{e}_{i.j}\left(\omega \right)=\frac{\left|G_{i.j}^{\wedge }\left(\omega \right)\right|-\left|G_{i.j}\left(\omega \right)\right|}{\sqrt{\left|G_{i.j}\left(\omega \right)\right|}}\cdot w\left(\omega \right)\\ e={\displaystyle \underset{i}{\Sigma }}{\displaystyle \underset{j}{\Sigma }}{\displaystyle \underset{\omega }{\Sigma }}{\left[e_{i.\mathrm{<figure-callout\; id="2"\; label="j\; \omega "\; filenames="imgf0001.png,imgf0003.png"\; state="\{\{state\}\}">j\; </figure-callout>}}\left(\omega \right)\left(\right)\right]}^2\end{array}$$

w (ω) is a frequency-dependent weighting. It ensures that only important parts of the measured frequency responses are modeled in the model.

An optimization algorithm can be briefly rewritten as follows: Given is a function with multiple variables. We are looking for a minimum or maximum of this function. An optimization algorithm searches for these extremes. There are many different algorithms, e.g. the method of fastest descent seeks the greatest gradient with the help of partial derivatives and finds local minima quickly, but can overlook others. Optimization is applied mathematics in many disciplines and an important area of scientific research.

Fig. 5 shows the frequency-dependent gains of the acceleration measured and of the identified model. | G ^a _1,1 | means amount or amplitude of
Transfer function resp. The frequency response force to acceleration with output acceleration of axis 1 and with input force of axis 1. Dimension: 1 mg / N = 1 milli-g / N = 0.0981 m / s ^ 2 / N ~ 1 cm / s ^ 2 / N.

Fig. 11 shows the force signal for excitation of the actuators 11. The excitation takes place with a so-called random binary signal, which is generated by means of a random generator, wherein the Amplitude of the signal fixed, for example, can be set to ± 300 N and the spectrum is broad and evenly distributed.

The model with the identified parameters forms the basis for the design of an optimal controller for active vibration damping. Controller structure and parameters depend on the characteristics of the track to be controlled, in this case the elevator car. The elevator car has a static and dynamic behavior which is described by the model. Important parameters are: masses and moments of inertia, geometry such as height (s), width (s), depth (s), track mass, etc., spring rates and damping values. If the parameters change, this has an influence on the behavior of the elevator car and thus on the settings of the vibration damping controller. With a classic PID control (proportional, integral and differential control), three gains have to be set, which can be mastered manually. The controller for the present case has well over a hundred parameters, with a manual setting is practically no longer possible. The parameters must therefore be determined automatically. This is only possible with the aid of a model which describes the essential characteristics of the elevator car.

• Ein Positionsregler 15 und ein Beschleunigungsregler 16. Es sind auch andere Strukturen der Regelung möglich,
• insbesondere eine Kaskadenschaltung von Positions- und
• Beschleunigungsregler wie in Fig. 7 gezeigt. Die Regler sind linear, zeitinvariant, zeitdiskret und sie regeln mehrere Achsen gleichzeitig, daher kommt die Bezeichnung MIMO für Multi Input, Multi Output. n ist der fortlaufende Index des Zeitschrittes in einem zeitdiskreten oder "digitalen" Regler.

The scheme as in Fig. 6 shown is divided into two parallel regulators:

• A position regulator 15 and an acceleration regulator 16. Other structures of the control are also possible.
• in particular a cascade connection of position and
• Acceleration controller as in Fig. 7 shown. The controllers are linear, time-invariant, time-discrete and they control several axes simultaneously, hence the term MIMO for Multi Input, Multi Output. n is the continuous index of the time step in a time-discrete or "digital" controller.

The updated states x (n + 1) for the next time step are calculated to be available there.

A dynamic system is time-invariant if the descriptive parameters remain constant. A linear controller is time-invariant if system matrices A, B, C and D do not change. Controllers implemented on a digital computer are always time-discrete. That is, they make the inputs, calculations and outputs at fixed intervals.

In the controller design, the so-called H _∞ method is used. Fig. 8 shows the signal flow diagram of the H _∞ -Entwurfsverfahrens closed loop. The main advantage of the H _∞ design process is that it can be automated. Norm of the system to be controlled to minimize the closed loop - The H _∞ is. The H _∞ - norm of a matrix A mxn elements is given by: $${\Vert A\Vert }_{\infty }=\underset{i}{\mathrm{Max}}{\displaystyle \underset{j=1}{\overset{n}{\Sigma }}}\left|a_{i.j}\right|\left(\mathrm{maximum\; "row\; sum"}\right)$$

• w_v modelliert die Störungen im Frequenzbereich am Eingang des Systems
• w_r ist ein kleiner konstanter Wert
• w_u limitiert den Reglerausgang
• w_y hat den Wert eins

The system to be controlled is the identified model of the elevator car 1 with the designation P for Plant as in Fig. 8 shown. The desired behavior of the regulator K with reference numeral 17 is generated by means of additional weighting functions at the input and the output of the system.

• w _v models the disturbances in the frequency domain at the input of the system
• w _r is a small constant value
• w _u limits the controller output
• w _y has the value one

Fig. 8 is a scheme to design the controller using the H _∞ ^- method. w is the vector signal at the input and is composed of v and r. z is the vector signal at the output, where z = T * w. T consists of controller, control section and weighting functions. P6 or a6 constitute the closed-loop feedback, the position controller or the accelerometer separately designed. F6 is the output or the control signal of the controller.

The H _∞ norm is minimized by | | z | | _∞ / / | | w | | _∞ = | | T | | _∞ This again requires an optimization algorithm that changes the parameters of the controller until a minimum is found.

Fig. 9 shows the course of the singular values of a position controller in the y-direction. This one has predominantly an integrating behavior.

Fig. 10 shows the course of the singular values of an acceleration controller in the y-direction. This has a bandpass characteristic.

Singular values are a measure of the overall gain of a matrix. An n x n matrix has n singular values. Dimension: 1 N / mg = 1 N / milli-g = N / (0.0981 m / s ^ 2) ~ 1 N / (cm / s ^ 2).

Claims (9)

1. Design method for a controller for vibration damping on a lift cabin (1), the controller design being based on a model of the lift cabin (1), wherein an overall model of the lift cabin (1) having more or less well-known or estimated model parameters is used, wherein the parameters of the lift cabin used are identified by comparing the transfer functions or the frequency responses of the model with the measured transfer functions or the measured frequency responses, and the model parameters are changed in order to obtain the greatest possible agreement with the measured frequency responses, wherein the model having the identified parameters serves as the basis for the design of an optimal controller for the active vibration damping, and wherein the active vibration damping system of the lift cabin (1) itself is provided as a measuring device for the transfer functions or frequency responses to be measured, wherein the lift cabin (1) is excited by means of actuators (11) and the responses are measured by means of acceleration sensors (13) or by means of position sensors (12).
2. Method according to Claim 1,
   characterized in that
   the model parameters are changed by means of optimization algorithms until the minimum of the sum (e) of all the deviations of the frequency responses of the model from the measured frequency responses has been found.
3. Method according to Claim 2,
   characterized in that
   the deviations between the frequency responses of the model and the measured frequency responses are weighted with a frequency-dependent value w (ω) when calculating the sum (e).
4. Method according to one of the preceding claims,
   characterized in that
   the controller (17) is designed with the aid of the H _∞ method.
5. Method according to Claim 4,
   characterized
   in that the controller (17) has a position controller (15), which drives the actuators (11) as a function of the position of the lift cabin (1), the guide elements (7) assuming a predefined position, and
   in that the controller (17) has an acceleration controller (16), which drives the actuators (11) as a function of the acceleration of the lift cabin (1), vibrations occurring on the lift cabin (1) being suppressed.
6. Method according to Claim 5,
   characterized in that
   the position controller (15) and the acceleration controller (16) are connected in parallel, the actuating signals from the position controller (15) and from the acceleration controller (16) being added and fed to the actuators (11) as an aggregate signal.
7. Method according to Claim 5,
   characterized in that
   the position controller (15) and the acceleration controller (16) are connected in series, the actuating signal from the position controller (15) being fed to the acceleration controller (16) as an input signal.
8. Method according to one of Claims 5 to 7,
   characterized in that
   the position controller (15) and the acceleration controller (16) are active substantially in different frequency ranges.
9. Method according to one of the preceding claims,
   characterized in that
   the multi-body system (MBS) model for an elastic lift cabin comprises at least two bodies describing the cabin body (2) and the cabin frame (3) or, for a rigid lift cabin (1), comprises cabin body (2) and cabin frame (3) in their entirety as one body.

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