Electric bed motor control method and electric bed

Abstract

The present application relates to the technical field of motor control, and particularly relates to a motor control method for an electric bed and the electric bed, which comprises the following steps: at the moment of starting the motor, sampling a current ripple signal, performing frequency spectrum analysis on the current ripple signal, extracting a load resonance frequency, generating an anti-phase harmonic current, and injecting the anti-phase harmonic current into a motor winding; at a motor pre-braking stage, injecting a damping harmonic into the motor winding, and at a motor main-braking stage, injecting a phase-synchronous reverse current into the motor winding. The present application realizes low-impact control of starting and stopping of the electric bed through an electromechanical energy hedging mechanism, and is compatible with the existing electromechanical architecture of the electric bed. At the moment of starting the motor, the anti-phase harmonic current is injected to neutralize high-frequency resonance exciting force. At the pre-braking stage, the damping harmonic is used to dissipate kinetic energy, and at the main-braking stage, the current is reversed to lock, so that the residual amplitude at the destination approaches zero. Based on real-time frequency spectrum analysis, control parameters are dynamically tuned to ensure that the starting and stopping time is relatively constant under varying loads, and the problems of overshoot under light load and delay under heavy load are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor control, and particularly relates to a motor control method for an electric bed and the electric bed. BACKGROUND

[0002] The electric bed realizes functions such as bed body lifting and back plate adjustment through motor driving, and is widely applied to postoperative rehabilitation, long-term nursing and the like. The traditional electric bed adopts a motor cooperating with a speed reduction mechanism, and is started and stopped through open-loop PWM control. However, the control mode has the following problems:

[0003] The trapezoidal acceleration-deceleration curve currently adopted causes discontinuous bed body movement and mechanical impact; fixed control parameters cannot adapt to dynamic load changes, causing adjustment delay or overshoot; and the coupling of the above defects causes mechanical impact to be transmitted to the human interface, especially for postoperative patients, and the discontinuity of movement may cause abnormal mechanical stress on the wound surface, affecting the rehabilitation process. SUMMARY

[0004] The present application provides a motor control method for an electric bed and the electric bed, which can effectively solve the problems in the background art.

[0005] In order to achieve the above purpose, the technical solution adopted by the present application is as follows:

[0006] A motor control method for an electric bed, comprising:

[0007] At the moment of starting the motor, a current ripple signal is sampled, the current ripple signal is subjected to frequency spectrum analysis, a load resonance frequency is extracted, an anti-phase harmonic current is generated based on the load resonance frequency, and is injected into the motor winding;

[0008] A damping harmonic is injected into the motor winding at the motor pre-braking stage, and a phase-synchronous reverse current is injected into the motor winding at the motor main braking stage.

[0009] Further, when it is detected that the extracted load resonance frequency exceeds a set threshold, an inertial weighted filter is adopted to update the load resonance frequency; the anti-phase harmonic current is generated based on the updated load resonance frequency, and the update adopts the following formula:

[0010] ;

[0011] In the formula,

[0012] f res is the updated load resonance frequency;

[0013] f last is a historically stored load resonance frequency;

[0014] f new is the currently extracted load resonance frequency;

[0015] α and β are the first and second coefficients, respectively, and α + β = 1.

[0016] Furthermore, the methods for determining the first coefficient α and the second coefficient β include:

[0017] The stiffness of the target electric bed transmission system is calibrated to obtain stiffness values;

[0018] A step load is applied within the range of no-load to full-load, and the response data of the load resonant frequency as a function of time are collected.

[0019] Based on the stiffness value and the response data, the value of the first coefficient α is calculated using an optimization function. This optimization function aims to balance response speed and stability, and is defined as follows:

[0020] ;

[0021] In the formula, σ f The standard deviation of frequency fluctuation is γ, which is positively correlated with the stiffness value.

[0022] The second coefficient β is calculated based on β = 1 - α.

[0023] Further, spectral analysis is performed on the current ripple signal to extract the load resonant frequency, including:

[0024] The current ripple signal is processed using a fast Fourier transform algorithm to obtain the spectral distribution in the 50~500Hz frequency band;

[0025] Within the frequency band, local amplitude maxima points with amplitudes exceeding the floor noise by more than 10 dB are identified to form an initial screening peak set.

[0026] For each peak in the initial screening peak set, calculate the bandwidth and identify candidate peaks with a bandwidth less than 10Hz;

[0027] The candidate peak frequency with the highest amplitude is selected as the load resonant frequency.

[0028] Furthermore, the anti-phase harmonic current is generated using the following formula:

[0029] ;

[0030] In the formula,

[0031] I c It is an anti-phase harmonic current;

[0032] I a To compensate for the current amplitude;

[0033] fres This is the updated load resonant frequency;

[0034] n is the sequence number of the odd-order harmonics;

[0035] t is the time base variable.

[0036] Furthermore, the anti-phase harmonic current is converted into a PWM duty cycle command through an H-bridge drive circuit, and the PWM duty cycle command is applied to the power input terminal of the motor winding to achieve current injection.

[0037] Furthermore, prior to the spectral analysis, the following is also included:

[0038] The sampling time window for the current ripple signal is set as an integer multiple of the power grid frequency cycle;

[0039] The reference spectrum distribution under no-load conditions of the motor is pre-stored, and the amplitude component corresponding to the reference spectrum distribution is deducted from the measured spectrum distribution point by point.

[0040] Furthermore, the sampling of the current ripple signal is completed within 3 to 10 ms after the motor starts.

[0041] Furthermore, the pre-braking phase is triggered when the motor current has a zero-crossing offset greater than 5µs for three consecutive power frequency cycles, and the main braking phase is triggered when the motor current has a zero-crossing offset less than 2µs for two consecutive power frequency cycles during the pre-braking phase.

[0042] An electric bed employs the electric bed motor control method described above.

[0043] The technical solution of this invention can achieve the following technical effects:

[0044] This invention achieves low-impact control of electric bed start-up and shutdown through an electromechanical energy counterbalancing mechanism, compatible with existing electric bed electromechanical architecture. It utilizes the real-time analysis of the load-transmission system resonant frequency using the transient current ripple spectrum during motor start-up, requiring no structural modifications. At the moment of motor start-up, injecting anti-phase harmonic current neutralizes the high-frequency resonant excitation force, eliminating acceleration step jumps. During the pre-braking phase, kinetic energy is dissipated through damped harmonics, and during the main braking phase, current is applied in reverse to lock the system, making the residual amplitude approach zero upon arrival. Based on real-time spectrum analysis and dynamic tuning of control parameters, it ensures constant start-up and shutdown times under varying loads, solving the problems of overshoot under light loads and hysteresis under heavy loads. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A flowchart of the electric bed motor control method;

[0047] Figure 2 A flowchart illustrating the method for determining the first coefficient α and the second coefficient β;

[0048] Figure 3 This is a flowchart of the spectrum analysis.

[0049] Figure 4 This is a flowchart of the electric bed motor control method at the moment of motor startup. Detailed Implementation

[0050] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0051] Example 1

[0052] like Figure 1 As shown, an electric bed motor control method includes:

[0053] At the moment of motor startup, the current ripple signal is sampled, the current ripple signal is subjected to spectrum analysis, the load resonant frequency is extracted, and an anti-phase harmonic current is generated based on the load resonant frequency and injected into the motor winding.

[0054] Damping harmonics are injected into the motor windings during the pre-braking phase, and a phase-synchronized reverse current is injected into the motor windings during the main braking phase.

[0055] This invention achieves low-impact control of electric bed start-up and shutdown through an electromechanical energy counterbalancing mechanism, compatible with existing electric bed electromechanical architectures. It utilizes the real-time analysis of the load-transmission system resonant frequency using the transient current ripple spectrum during motor startup, requiring no structural modifications. At the moment of motor startup, injecting anti-phase harmonic current neutralizes the high-frequency resonant excitation force, eliminating acceleration step jumps. During the pre-braking phase, kinetic energy is dissipated through damped harmonics. In this embodiment, the current amplitude of the damped harmonics is dynamically calculated based on the real-time kinetic energy at the pre-braking start point and the calibrated transmission damping coefficient. During the main braking phase, reverse current locking is applied, causing the residual amplitude at the braking position to approach zero. Based on real-time spectrum analysis and dynamic tuning of control parameters, the start-up and shutdown times are kept constant under varying loads, solving the problems of overshoot under light loads and hysteresis under heavy loads.

[0056] To address the issue of sudden load changes, as a preferred embodiment of the above approach, when the extracted load resonant frequency is detected to exceed a set threshold, an inertial weighted filter is used to update the load resonant frequency; an anti-phase harmonic current is generated based on the updated load resonant frequency, and the update is performed using the following formula:

[0057] ;

[0058] In the formula,

[0059] f res This is the updated load resonant frequency;

[0060] f last The load resonant frequency for historical storage;

[0061] f new This is the currently extracted load resonant frequency;

[0062] α and β are the first and second coefficients, respectively, and α + β = 1.

[0063] In scenarios involving sudden load changes, such as a patient suddenly standing up, when the detected change in the load resonant frequency exceeds a preset threshold, this preferred solution achieves rapid frequency value updates through inertial weighted filtering. This filtering mechanism integrates historically stored frequencies with the currently extracted frequency to generate an updated value, using f... last To preserve the long-term stable resonant state memory of the system and avoid control instability under sudden disturbances, f in this embodiment... last This refers to the load resonant frequency value that has been repeatedly verified and determined to be optimal under stable system operation, and is continuously refreshed during operation. The gradually changing f-frequency output through weighted filtering... res This ensures that the generated anti-phase harmonic current always maintains precise phase matching with the mechanical resonance, suppressing the rate of change of vibration acceleration under sudden load changes within a low range.

[0064] As a preferred option, a threshold of 2Hz can be selected. This value effectively isolates physiological interference while ensuring the capture of real mutations. Of course, this value is merely a preferred value; other values ​​that achieve the technical objectives of this invention are also within the scope of protection of this invention. For the first coefficient α and the second coefficient β, specific values ​​can be selected, such as α=0.7 and β=0.3. This method is a preferred way to determine the values. As another implementation method, such as... Figure 2 As shown, the method for determining the first coefficient α and the second coefficient β can be optimized by including:

[0065] A1: Perform stiffness calibration on the target electric bed transmission system and obtain stiffness values;

[0066] A2: Apply a step load within the range of no-load to full-load and collect response data of the load resonant frequency as a function of time;

[0067] A3: Based on the stiffness value and response data, the value of the first coefficient α is calculated using an optimization function. The optimization function aims to balance response speed and stability. The optimization function is as follows:

[0068] ;

[0069] In the formula, σ f The standard deviation of frequency fluctuation is γ, which is positively correlated with the stiffness value. The updated load resonant frequency f res The derivative with respect to time t, i.e., f res Rate of change over time t;

[0070] A4: Calculate the second coefficient β according to β=1-α.

[0071] In this preferred embodiment, the stiffness value can be obtained by first applying a known static load to the bed, measuring the deformation of the transmission shaft, and finally using the ratio of the known static load to the deformation of the transmission shaft as the calibrated stiffness value; σ f It can be calculated from the resonant frequency values ​​of the most recent N control cycles stored.

[0072] In step A3, α is used as an optimization variable, and the objective terms include speed and stability terms; in step A1, the mechanical characteristics of the bed are transformed into the basis for control parameter design by physically calibrating the stiffness of the transmission system, providing static structural parameters for step A3; step A2 provides dynamic behavior data for step A3, and the instantaneous rate of change of the load resonant frequency and the standard deviation of frequency fluctuation are both obtained based on the response data.

[0073] As a preferred embodiment of the above, such as Figure 3 As shown, spectral analysis is performed on the current ripple signal to extract the load resonant frequency, including:

[0074] B1: The current ripple signal is processed using the Fast Fourier Transform algorithm to obtain the spectral distribution in the 50~500Hz frequency band;

[0075] B2: Identify local amplitude maxima points within the frequency band that are 10dB higher than the floor noise, forming a preliminary set of peak values;

[0076] B3: Calculate the bandwidth for each peak in the initial peak set and identify candidate peaks with a bandwidth less than 10Hz;

[0077] B4: Select the candidate peak frequency with the highest amplitude as the load resonant frequency.

[0078] In this preferred scheme, setting a lower limit of 50Hz can eliminate power frequency interference and avoid the motor fundamental frequency noise affecting the accuracy of resonant frequency detection, while the upper limit of 500Hz can shield noise unrelated to load mechanical resonance; the fast Fourier transform algorithm meets the real-time requirements of embedded systems and can directly extract the frequency domain energy distribution from the time domain current ripple signal to accurately locate the resonance peak.

[0079] This preferred solution can accurately extract the load resonant frequency in a strong electromagnetic noise environment. Through dual screening of amplitude and bandwidth, it can effectively eliminate wideband interference such as motor cogging harmonics and PWM switching noise. Real-time narrowband resonant detection ensures that the anti-phase harmonic current accurately matches the mechanical characteristics of the load.

[0080] As a preferred embodiment of the above, the anti-phase harmonic current is generated using the following formula:

[0081] ;

[0082] In the formula,

[0083] I c It is an anti-phase harmonic current;

[0084] I a To compensate for the current amplitude, in this embodiment, the compensation current amplitude is a fixed preset value;

[0085] f res This is the updated load resonant frequency;

[0086] n is the odd-order harmonic number. The reason for setting this parameter is that the nonlinear vibration of the electric bed reduction mechanism excites odd-order harmonics.

[0087] t is a time base variable, representing the real-time time calculated from the start point of the current control cycle. It is reset to zero at the start point of each PWM control cycle and increases linearly within that cycle.

[0088] As a preferred embodiment of the above, such as Figure 4 As shown, the H-bridge driver circuit converts the anti-phase harmonic current into a PWM duty cycle command, which is then applied to the power input terminal of the motor winding to achieve current injection. In this optimized scheme, the power input terminal is the gate drive pin of the MOSFET in the H-bridge circuit, and the PWM duty cycle command value is converted into a high or low level signal by the gate driver chip.

[0089] As a preferred embodiment of the above, the method further includes the following step before spectrum analysis:

[0090] The sampling time window for the current ripple signal is set as an integer multiple of the power grid frequency cycle;

[0091] The reference spectrum distribution under no-load conditions of the motor is pre-stored, and the amplitude component corresponding to the reference spectrum distribution is deducted from the measured spectrum distribution point by point.

[0092] In this preferred scheme, the sampling window is strictly aligned with an integer multiple of the power grid cycle, so that the power frequency interference energy cancels itself out in the spectrum analysis; based on the pre-stored cogging harmonic fingerprint characteristics of the no-load spectrum, the reference component of the measured signal is subtracted point by point in the frequency domain to eliminate the masking effect of the motor's own electromagnetic noise on the load resonance characteristics.

[0093] As a preferred embodiment of the above, the sampling of the current ripple signal is completed within 3 to 10 ms after the motor starts. The above time covers the electromagnetic transient process during motor startup, including the establishment of stator current and initial rotor positioning. At this time, the load resonance characteristics have not yet been attenuated by mechanical damping, and the spectral energy is most significant. By executing this time, it can be done earlier than the mechanical vibration stabilization time, avoiding the signal pollution caused by the harmonic noise introduced by the rotor inertial motion.

[0094] As a preferred embodiment of the above, the pre-braking phase is triggered when the motor current has a zero-crossing offset of more than 5us for three consecutive power frequency cycles, and the main braking phase is triggered when the motor current has a zero-crossing offset of less than 2us for two consecutive power frequency cycles during the pre-braking phase.

[0095] For the pre-braking phase triggering, a zero-crossing offset greater than 5µs indicates distortion of the motor's back EMF waveform, while a sustained offset for three cycles can eliminate transient interference, such as PWM switching noise. For the main braking phase triggering, an offset less than 2µs indicates that the system has returned to stability, and maintaining this offset for two power frequency cycles ensures robustness of state switching and avoids the influence of random noise. This preferred scheme achieves precise dual-stage control using a single current zero-crossing signal.

[0096] Example 2

[0097] An electric bed is provided, employing the electric bed motor control method as described in Embodiment 1. The technical effects achieved in this embodiment are as described in Embodiment 1, and will not be repeated here.

[0098] Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to this invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method of controlling an electric motor of an electric bed, characterized in that, The method comprises the following steps: At the moment of starting the motor, the current ripple signal is sampled, the current ripple signal is subjected to frequency spectrum analysis, the load resonance frequency is extracted, the anti-phase harmonic current is generated based on the load resonance frequency, and the motor winding is injected with the anti-phase harmonic current; During the motor pre-braking stage, the damping harmonic is injected into the motor winding, and during the motor main braking stage, the phase-synchronous reverse current is injected into the motor winding; When it is detected that the extracted load resonance frequency exceeds the set threshold, the inertia weighted filter is used to update the load resonance frequency; the anti-phase harmonic current is generated based on the updated load resonance frequency, and the following formula is used for updating: ； In the formula, f res is the updated load resonance frequency; f last Load resonant frequency stored for history; f new fres the current extracted load resonance frequency; α and β are respectively the first coefficient and the second coefficient, and α+β=1; The anti-phase harmonic current is generated, and the following formula is used: ； In the formula, I c is the anti-phase harmonic current; I a to compensate for the current amplitude, the compensation current amplitude being a fixed preset value; f res is the updated load resonant frequency; n is an odd harmonic number; t is a time reference variable; The pre-braking stage is triggered when the motor current zero-crossing offset is greater than 5us for 3 consecutive power frequency periods, and the main braking stage is triggered when the motor current zero-crossing offset is less than 2us for 2 consecutive power frequency periods in the pre-braking stage.

2. The electric bed motor control method according to claim 1, wherein The determination method of the first coefficient α and the second coefficient β comprises: The stiffness of the target electric bed transmission system is calibrated to obtain the stiffness value; A step load is applied within the no-load to full-load range, and the response data of the load resonance frequency changing with time is collected; Based on the stiffness value and the response data, the value of the first coefficient α is calculated by an optimization function, the optimization function aims to balance the response speed and stability, and the optimization function is: ； where σ f is the standard deviation of the frequency fluctuations, and γ is positively correlated with the stiffness value. The second coefficient β is calculated according to β=1-α.

3. The power bed motor control method of claim 1, wherein, The frequency spectrum analysis of the current ripple signal to extract the load resonance frequency comprises: The fast Fourier transform algorithm is used to process the current ripple signal to obtain the frequency spectrum distribution within the frequency band of 50~500Hz; Local amplitude maximum points with an amplitude higher than the base noise by more than 10dB are identified within the frequency band to form a preliminary screening peak value set; The frequency band width of each peak value in the preliminary screening peak value set is calculated, and candidate peak values with a frequency band width less than 10Hz are identified; The candidate peak value with the highest amplitude is selected as the load resonance frequency.

4. The power bed motor control method of claim 1, wherein, The anti-phase harmonic current is converted into a PWM duty cycle instruction through an H-bridge drive circuit, and the PWM duty cycle instruction is applied to the power input end of the motor winding to realize current injection.

5. The power bed motor control method of claim 1, wherein, Before the frequency spectrum analysis, the following steps are further included: The sampling time window of the current ripple signal is set as an integer multiple of the power frequency period of the power grid; The reference frequency spectrum distribution under the no-load condition of the motor is pre-stored, and the amplitude component corresponding to the reference frequency spectrum distribution is subtracted from the measured frequency spectrum distribution point by point.

6. The power bed motor control method of claim 1, wherein, The sampling of the current ripple signal is completed within 3~10ms after the motor starts.

7. An electric bed, characterized in that The electric bed motor control method is used as claimed in any one of claims 1~6.

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