A bearingless compound rotor cage asynchronous motor vibration real-time compensation control method

Abstract

The application relates to the technical field of motor control and discloses a bearing-free composite rotor cage-type asynchronous motor vibration real-time compensation control method, which comprises the following steps: collecting a rotor displacement signal and pre-processing the same to obtain a filtered displacement signal; processing the filtered displacement signal based on a second-order generalized integrator to extract a fundamental harmonic component synchronized with a rotor mechanical angular velocity; dynamically optimizing the amplitude weight and the phase weight of a compensation force by adopting a simplified LMS algorithm, and generating a feedforward compensation force according to the optimized weight; constructing a double-channel control framework to synthesize a total control force; and generating corresponding radial suspension forces by driving a suspension force winding through an inverter, so that the unbalanced vibration of the rotor is counteracted; the second-order generalized integrator harmonic observer is adopted to realize high-precision extraction of the fundamental frequency component, and a frequency self-adaptive adjustment mechanism is matched, so that the rotor mechanical angular velocity change can be tracked in real time.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, and more specifically, to a real-time vibration compensation control method for a bearingless composite rotor squirrel-cage asynchronous motor. Background Technology

[0002] Currently, the main technical solutions for vibration suppression control of bearingless composite rotor squirrel-cage induction motors are as follows:

[0003] Feedback control of motor vibration displacement signals using a proportional-integral-derivative (PI-DE) controller, adjusting the compensation force in real time based on displacement error, is simple in principle and easy to implement. However, parameter tuning relies on experience, making it difficult to adapt to changes in motor speed and load disturbances, and its suppression effect on fundamental frequency vibration is limited. Transforming the vibration signal to the frequency domain using the FFT algorithm, extracting the main frequency components, and then generating corresponding compensation forces for specific frequencies can effectively identify the frequency characteristics of vibration. However, FFT computation is complex, has poor real-time performance, and suffers from windowing effects and spectral leakage, affecting the accuracy of vibration suppression. Establishing a mathematical model of rotor unbalanced vibration, identifying model parameters online, and generating a compensation force equal in magnitude and opposite in direction to the vibration force are all possible. This method does not rely on feedback signals from displacement sensors, reducing system delay effects. However, it requires high model accuracy, has a complex parameter identification process, and insufficient anti-disturbance capability. Combining bandpass and notch filters to extract vibration characteristics, and employing a strategy combining feedforward and feedback control, this method performs well under specific operating conditions. However, fixed filter parameters make it difficult to adapt to environments with changing speeds, and the control algorithm is highly complex.

[0004] The existing technical solutions have the following problems:

[0005] The extraction of vibration harmonic components mainly relies on methods such as FFT or bandpass filters. FFT algorithms require long data windows, resulting in poor real-time performance; bandpass filters have a fixed center frequency, making it difficult to adapt to changes in motor speed. These methods cannot accurately extract the fundamental frequency vibration component synchronized with the rotational speed when the rotor's mechanical angular velocity changes dynamically, reducing the vibration suppression effect. The root cause of this problem is that traditional harmonic extraction algorithms lack a frequency adaptive mechanism and cannot track speed changes in real time. In traditional PID control and feedforward compensation control, parameter tuning mainly relies on experience or offline optimization, making it difficult to adapt to changes in system state. While complex least mean square algorithms have adaptive capabilities, the real-time calculation of multi-dimensional weight vectors leads to high computational complexity, which is not conducive to embedded system implementation. This problem stems from the lack of an efficient parameter adaptive mechanism in the control algorithm, making it unable to adapt to changes in the rotational speed. Achieving high-precision parameter optimization under limited computing resources is challenging; vibration suppression and dynamic response are difficult to balance. Existing control methods often focus on either static vibration suppression or dynamic response performance optimization, making it difficult to achieve an effective balance between the two. While simple feedforward compensation methods can effectively suppress fundamental frequency vibration, they have poor adaptability to changes in system parameters and load disturbances. Pure feedback control, on the other hand, is limited by system delay and cannot respond quickly to high-frequency vibrations. The root cause of this contradiction lies in the failure of the control architecture design to effectively coordinate the cooperation mechanism between feedforward compensation and feedback correction. Existing technologies show a significant decrease in vibration suppression effectiveness when facing changes in operating conditions such as speed fluctuations and sudden load changes. Especially under high-speed rotation conditions, centrifugal force increases with the square of the speed, making the system more sensitive to parameter changes. The lack of effective adaptive mechanisms and disturbance rejection designs has become a key factor limiting the robustness of the system.

[0006] In summary, the key challenge is to design a computationally efficient, highly accurate, and real-time vibration suppression and control method that can accurately identify and compensate for fundamental frequency vibrations under various speed and load conditions, while also considering the system's dynamic response performance and steady-state accuracy, and improving the system's robustness to external disturbances and parameter changes. Summary of the Invention

[0007] This invention provides a real-time vibration compensation control method for a bearingless composite rotor squirrel-cage asynchronous motor, which solves the technical problems of insufficient fundamental frequency vibration suppression accuracy, difficulty in optimizing control parameters, difficulty in balancing vibration suppression and dynamic response, and insufficient system robustness during the operation of the bearingless composite rotor squirrel-cage asynchronous motor.

[0008] This invention provides a real-time vibration compensation control method for a bearingless composite rotor squirrel-cage induction motor, comprising:

[0009] The rotor displacement signal is acquired and preprocessed to obtain the filtered displacement signal;

[0010] The filtered displacement signal is processed using a second-order generalized integrator to extract the fundamental frequency harmonic component that is synchronized with the rotor's mechanical angular velocity.

[0011] Based on the extracted fundamental harmonic components, a simplified LMS algorithm is used to dynamically optimize the amplitude weight and phase weight of the compensation force, and the feedforward compensation force is generated according to the optimized weights.

[0012] A dual-channel control architecture is constructed, which combines the feedforward compensation force generated based on the fundamental frequency harmonic component with the PID correction force output from the feedback correction channel to synthesize the total control force;

[0013] Based on the total control force, the inverter drives the levitation winding to generate a corresponding radial levitation force, thereby canceling out the rotor's unbalanced vibration.

[0014] Furthermore, the preprocessing step includes: acquiring the displacement signals of the rotor in the x-axis and y-axis directions through an eddy current sensor, and using a second-order Butterworth low-pass filter to filter high-frequency noise and sensor interference, with the cutoff frequency of the filter set to 1.2 times the rotor mechanical angular velocity.

[0015] Furthermore, the difference equation for the second-order Butterworth low-pass filter is:

[0016] v f (k1)=a1v f (k1-1))+a2v f (k1-2)+b0v(k1)+b1v(k1-1)+b2v(k1-2)

[0017] Where v f (k1) represents the filtered signal at the current time; v f (k1-1) represents the filtered signal from the previous moment; v f (k1-2) represents the filtered signal from the previous two time points; v(k1) represents the original signal at the current time point; v(k1-1) represents the original signal from the previous time point; v(k1-2) represents the original signal from the previous two time points; a1 is the first autoregressive coefficient of the filter, representing the influence weight of the filtered signal from the previous time point on the current output; a2 is the second autoregressive coefficient of the filter, representing the influence weight of the filtered signal from the previous two time points on the current output; b0 is the current input coefficient, representing the influence weight of the original signal from the current time point on the output; b1 is the previous input coefficient, representing the influence weight of the original signal from the previous time point on the output; b2 is the first two input coefficients, representing the influence weight of the original signal from the previous two time points on the output.

[0018] Furthermore, the transfer function model of the second-order generalized integrator includes an in-phase component transfer function and an orthogonal component transfer function, with a damping coefficient set to 0.8 to ensure a balance between system stability and response speed.

[0019] Furthermore, the second-order generalized integrator has a frequency adaptive adjustment mechanism, which updates the center frequency of SOGI in real time according to the rotational speed change, as expressed as:

[0020] Center frequency:

[0021]

[0022] Where ω m π is the rotor's mechanical angular velocity; n is the circumference of a circle; f is the rotor's rotational speed. m This is the rotor mechanical frequency.

[0023] Furthermore, the simplified LMS algorithm only retains the magnitude weight k. p and phase weight k i Two parameters, k is set during algorithm initialization. p (0) = 0.5, k i (0) = 0.1.

[0024] Furthermore, the parameter update law of the simplified LMS algorithm is:

[0025]

[0026] Where k p (k3+1) represents the amplitude weight at the next time step; k p (k3) represents the amplitude weight at the current time; k i (k3+1) represents the phase weight at the next time step; k i (k3) represents the phase weight at the current time; μ p μ is the amplitude step size factor. i e is the phase step size factor; e(k3) is the displacement error signal; v h (k3) represents the fundamental frequency harmonic component.

[0027] Furthermore, the feedforward compensation force is generated in the following way:

[0028]

[0029] Where F comp (k3) represents the feedforward compensation force; v p (k3) represents the magnitude weight; k i (k3) represents the phase weight; v h (k3) represents the fundamental frequency harmonic component.

[0030] Furthermore, the feedback correction channel in the dual-channel control architecture uses an adaptive PID controller to handle the residual error after feedforward compensation.

[0031] The present invention provides a computer storage medium, including a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to execute the above-described method for real-time vibration compensation control of a bearingless composite rotor squirrel-cage induction motor.

[0032] The beneficial effects of this invention are as follows: a second-order generalized integrator is used to achieve high-precision extraction of the fundamental frequency component. Combined with a frequency adaptive adjustment mechanism, it can track the changes in rotor mechanical angular velocity in real time. The SOGI observer avoids the high computational complexity and window effect problems of the traditional FFT algorithm, and effectively solves the technical problem of insufficient accuracy in harmonic component extraction.

[0033] By simplifying the least mean square algorithm, the multidimensional weight vector is reduced to two parameters: magnitude weight and phase weight, which reduces the computational burden. At the same time, a dynamically updated Butterworth low-pass filter is used for signal preprocessing, ensuring the efficient implementation of the algorithm in embedded systems.

[0034] This invention proposes a dual-channel control architecture that organically combines feedforward compensation and feedback correction. The feedforward channel accurately cancels the fundamental frequency vibration synchronized with the mechanical angular velocity through SOGI harmonic observation and a simplified LMS algorithm. The feedback channel processes residual errors through an adaptive PID controller, further improving the steady-state accuracy of the system.

[0035] The frequency adaptive adjustment mechanism of this invention can update the center frequency of SOGI in real time according to the speed change. The adaptive PID controller responds to the changes in system parameters through online parameter optimization, so that the control system maintains a stable vibration suppression effect under different speed and load conditions. Attached Figure Description

[0036] Figure 1 This is a flowchart of a real-time vibration compensation control method for a bearingless composite rotor squirrel-cage asynchronous motor according to the present invention;

[0037] Figure 2 This is a schematic diagram of CC-BIM levitation force generation;

[0038] Figure 3 This is a schematic diagram of the CCR-BIM rotor eccentricity dynamic coordinate system;

[0039] Figure 4 This is a flowchart of harmonic component extraction and displacement error signal generation;

[0040] Figure 5 It is a flowchart of the suspension force collaborative adaptive compensation process with parameter tuning and closed-loop feedback;

[0041] Figure 6 This is the control block diagram of the CCR-BIM system;

[0042] Figure 7 This is a schematic diagram of the initial parameters of the control algorithm in an embodiment of the present invention;

[0043] Figure 8 This is a schematic diagram of the parameters of a bearingless composite rotor squirrel-cage asynchronous motor in an embodiment of the present invention;

[0044] Figure 9 This is a waveform diagram of the load speed under sudden load application.

[0045] Figure 10 This is a comparison diagram of x-axis vibration displacement in an embodiment of the present invention;

[0046] Figure 11 This is a comparison diagram of y-axis vibration displacement in an embodiment of the present invention;

[0047] Figure 12 These are experimental diagrams of the rotor shaft end trajectories under four compensation control methods. Detailed Implementation

[0048] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0049] At least one embodiment of the present invention discloses a real-time vibration compensation control method for a bearingless composite rotor squirrel-cage asynchronous motor, such as... Figure 1 As shown, it includes:

[0050] Step 1: Acquire rotor displacement signal and preprocess it to obtain filtered displacement signal;

[0051] The bearingless composite cage induction motor (CCR-BIM) achieves rotor rotation and levitation through the synergistic action of torque windings and levitation windings. For example... Figure 2 As shown, CCR-BIM utilizes the similarity between magnetic bearings and the stator structure of an asynchronous motor to simultaneously embed a set of three-phase suspension windings and torque windings. The number of pole pairs of the torque winding is p1 = 1, and the number of pole pairs of the suspension winding is p2 = 1. The two sets of windings satisfy the conditions p1 = p2 ± 1 and ω1 = ω2, thereby generating a controllable radial suspension force.

[0052] During the CCR-BIM process, such as Figure 3As shown, manufacturing tolerances or assembly errors in the rotor cause a deviation Δr between the rotor's center of mass and its geometric center, leading to unbalanced vibration. The unbalanced vibration force F of the rotor... c The dynamic characteristics are described by the centrifugal force equation:

[0053]

[0054] Where m is the rotor mass; ω m For ω is the rotor mechanical angular velocity; |Δr| is the rotor mass eccentricity; F c This is an unbalanced vibration force.

[0055] In the fixed coordinate system (x, y), the orthogonal components of the rotor unbalanced vibration force are:

[0056]

[0057] Where F cx F represents the vibration force component in the x-direction. cy F represents the y-direction vibration force component. c For unbalanced vibration force; ω m η is the rotor's mechanical angular velocity; t is time; η is the angle between the line connecting the geometric center to the center of mass and the d-axis; cos is the cosine function; sin is the sine function.

[0058] The corresponding vibration displacement components are:

[0059]

[0060] Where x c The vibration displacement is in the x-direction; y c ω represents the vibration displacement in the y-direction; A represents the vibration amplitude; ω m ω is the rotor mechanical angular velocity; t is time; η is the angle between the line connecting the geometric center to the center of mass and the d-axis; γ is the phase lag angle between the vibration displacement and the vibration force; cos is the cosine function; sin is the sine function.

[0061] Based on the above vibration mechanism analysis, an eddy current sensor is used to collect the rotor displacement signals v(k1) in the x and y axes. Since the original signal contains high-frequency noise and sensor interference, it needs to be preprocessed using a second-order Butterworth low-pass filter.

[0062] The transfer function of a second-order Butterworth low-pass filter is:

[0063]

[0064] Where H LPF (s) is the transfer function of the low-pass filter; ω m ω is the rotor mechanical angular velocity; s is the complex variable of the Laplace transform.

[0065] The filter cutoff frequency is set to 1.2ω. m It can suppress high-frequency noise while retaining ω. m The integrity of the fundamental frequency component. Discretize the transfer function into a difference equation:

[0066] v f (k1)=a1v f (k1-1)+a2v f (k1-2)+b0v(k1)+b1v(k1-1)+b2v(k1-2)

[0067] Where v f (k1) represents the filtered signal at the current time; v f (k1-1) represents the filtered signal from the previous moment; v f (k1-2) represents the filtered signal from the previous two time points; v(k1) represents the original signal at the current time point; v(k1-1) represents the original signal from the previous time point; v(k1-2) represents the original signal from the previous two time points; a1 is the first autoregressive coefficient of the filter, representing the influence weight of the filtered signal from the previous time point on the current output; a2 is the second autoregressive coefficient of the filter, representing the influence weight of the filtered signal from the previous two time points on the current output; b0 is the current input coefficient, representing the influence weight of the original signal from the current time point on the output; b1 is the previous input coefficient, representing the influence weight of the original signal from the previous time point on the output; b2 is the first two input coefficients, representing the influence weight of the original signal from the previous two time points on the output.

[0068] The filter coefficients are based on the rotor mechanical angular velocity ω m Dynamically updated:

[0069]

[0070] Where K is an intermediate variable; tan is the tangent function; ω m ω is the rotor mechanical angular velocity; T is the sampling period; b0 and b2 are the current input coefficient and the previous two input coefficients, respectively, and their values ​​are equal; b1 is the previous input coefficient, and its value is twice that of b0; a1 and a2 are the first and second autoregressive coefficients of the filter, respectively, used to construct the pole characteristics of the filter.

[0071] Where T is the sampling period, set to T = 0.0001s.

[0072] Step 2: Process the filtered displacement signal based on the second-order generalized integrator to extract the fundamental frequency harmonic component that is synchronized with the rotor's mechanical angular velocity.

[0073] A second-order generalized integrator (SOGI) is a harmonic extractor based on orthogonal signal generation, capable of accurately separating harmonic components of specific frequencies from noisy displacement signals. The transfer function model of SOGI is:

[0074]

[0075] Where H α (s) is the in-phase component transfer function; H β (s) is the transfer function of the orthogonal components; v α (s) is the in-phase output signal; v β (s) is the quadrature output signal; v f (s) represents the input displacement signal; ξ is the damping coefficient; ω m ω is the rotor mechanical angular velocity; s is the complex variable of the Laplace transform.

[0076] The preprocessed signal v f (k2) Input the SOGI harmonic observer to extract the mechanical angular velocity ω. m Synchronous fundamental frequency component. The discretization implementation of SOGI is as follows:

[0077]

[0078] Where v α (k2) represents the in-phase output at the current time; v α (k2-1) represents the in-phase output from the previous time step; v β (k2) represents the quadrature output at the current time; v β (k2-1) represents the quadrature output from the previous time step; v f (k2) represents the filtered signal at the current time; v f (k2-1) represents the filtered signal from the previous moment; T is the sampling period; ω m ξ is the rotor mechanical angular velocity; ξ is the damping coefficient.

[0079] The damping coefficient ξ = 0.8 ensures a balance between system stability and response speed.

[0080] The amplitude and phase of the fundamental harmonic component are calculated using the quadrature components:

[0081]

[0082] Where v h (k2) represents the fundamental frequency harmonic component; v α (k2) is the in-phase output; v β (k2) represents orthogonal output; For square root operation; cos is the cosine function; ωm φ is the rotor mechanical angular velocity; T is the sampling period; φ is the phase angle.

[0083] Wherein the phase angle is:

[0084]

[0085] Where φ is the phase angle; arctan is the arctangent function; v β (k2) represents orthogonal output; v α (k2) is the in-phase output.

[0086] The frequency adaptive adjustment mechanism updates the SOGI's center frequency in real time based on changes in rotational speed.

[0087]

[0088] Where ω m π is the rotor's mechanical angular velocity; n is the circumference of a circle; f is the rotor's rotational speed. m This is the rotor mechanical frequency.

[0089] Displacement error signal generation:

[0090] e(k2)=v ref (k2)-v h (k2);

[0091] Where e(k2) is the displacement error signal; v ref (k2) represents the desired displacement value; v h (k2) represents the fundamental frequency harmonic component.

[0092] Where v ref (k2) is the desired displacement value, which is set to 0.

[0093] like Figure 4 As shown, the harmonic component extraction process includes four stages: signal preprocessing, SOGI processing, harmonic reconstruction, and error generation, which realizes the accurate extraction from the original displacement signal to the fundamental frequency component.

[0094] Step 3: Based on the extracted fundamental frequency harmonic components, the simplified LMS algorithm is used to dynamically optimize the amplitude weight and phase weight of the compensation force, and the feedforward compensation force is generated according to the optimized weights.

[0095] Traditional LMS algorithms require adjusting multi-dimensional weight vectors, resulting in high computational complexity. This invention reduces the dimensionality, retaining only the magnitude weight k. p and phase weight k i Two parameters reduce computational burden. The simplified objective function of the LMS algorithm is:

[0096] J(k p ki )=e[(k3)-k p v α (k3)-k i v β (k3)) 2 ];

[0097] Where J(k) p k i ) represents the objective function; E[·] represents the desired operation; e(k3) represents the displacement error signal; k p For amplitude weights; k i For phase weights; v α (k3) is the in-phase output; v β (k3) represents orthogonal output.

[0098] The parameter update law is:

[0099]

[0100] Where k p (k3+1) represents the amplitude weight at the next time step; k p (k3) represents the amplitude weight at the current time; k i (k3+1) represents the phase weight at the next time step; k i (k3) represents the phase weight at the current time; μ p μ is the amplitude step size factor. i e is the phase step size factor; e(k3) is the displacement error signal; v h (k3) represents the fundamental frequency harmonic component.

[0101] The magnitude weight k of the compensation force is dynamically adjusted based on a simplified LMS algorithm. p and phase weight k i .

[0102] When simplifying the initialization of the LMS algorithm, set k p (0) = 0.5, k i (0) = 0.1, step size factor μ p =0.02, μ i =0.01.

[0103] Parameter update process:

[0104] Calculate the displacement error e(k3) and the fundamental frequency component v at the current moment. h (k3);

[0105] Update amplitude weight k p (k3+1) and phase weight k i (k3+1);

[0106] Check the convergence conditions to ensure the step size factor is satisfied:

[0107]

[0108] Where μ p μ is the amplitude step size factor. i λ is the phase step size factor; max For the operation of the largest eigenvalue; R p R is the autocorrelation matrix of the input signal with amplitude. i This is the autocorrelation matrix of the phase input signal.

[0109] Generation of feedforward compensation force:

[0110]

[0111] Where F comp (k3) represents the feedforward compensation force; k p (k3) represents the magnitude weight; k i (k3) represents the phase weight; v h (k3) represents the fundamental frequency harmonic component.

[0112] The feedforward compensation force directly cancels out ω m The synchronous fundamental frequency vibration component, whose amplitude and phase are determined by k p and k i Dynamic adjustment.

[0113] The feedforward compensation force directly cancels out ω m The synchronous fundamental frequency vibration component, whose amplitude and phase are determined by k p and k i Dynamic adjustment.

[0114] like Figure 5 As shown, the adaptive compensation process is implemented collaboratively through three stages:

[0115] During the initialization phase, the compensation force parameter k is set. p and k i The initial value is set, and the pre-trained step size factor μ is loaded. p and μ i During the real-time update phase, k is optimized online by simplifying the LMS algorithm. p and k i Dynamically adjust the compensation force F comp Amplitude and phase;

[0116] In the closed-loop feedback stage, the residual error after feedforward compensation is input into the adaptive PID controller to further correct the control force F. PID And ultimately gain total control.

[0117] Step 4: Construct a dual-channel control architecture, which combines the feedforward compensation force generated based on the fundamental frequency harmonic component with the PID correction force output from the feedback correction channel to synthesize the total control force.

[0118] The dual-channel control architecture includes a feedforward compensation channel and a feedback correction channel. The feedforward channel has already generated the compensation force A in step 3 using a simplified LMS algorithm. The feedback channel employs an adaptive PID controller to handle the residual error e after feedforward compensation. PID (k4), generating correction force F PID (k4):

[0119]

[0120] Where F PID (k4) represents the PID correction force; K p K is the proportionality coefficient. i K is the integral coefficient; d e is the differential coefficient; PID (k4) represents the residual error; ∫ represents the integral operation.

[0121] The parameter update law of the adaptive PID controller is:

[0122]

[0123] Where K p (k4+1) is the scaling factor for the next time step; K p (k4) is the scaling factor at the current time; K i (k4+1) represents the integral coefficient at the next time step; K i (k4) represents the integral coefficient at the current time; K d (k4+1) represents the differential coefficient at the next time step; K d (k4) represents the differential coefficient at the current time; α p The proportional learning rate; α i The integral learning rate; α d e is the differential learning rate; PID (k4) represents the residual error; v h (k4) represents the fundamental frequency harmonic component; ∑ represents the summation operation.

[0124] The total control force is the sum of the outputs from the two channels:

[0125] F total (k4)=F comp (k4)+F PID (k4);

[0126] Where F total (k4) represents the total control force; F comp (k4) represents the feedforward compensation force; FPID (k4) represents the PID correction force.

[0127] The advantage of this dual-channel architecture design is that the feedforward channel accurately cancels the fundamental frequency vibration, while the feedback channel suppresses residual errors through adaptive PID, achieving synergistic optimization of dynamic response speed and steady-state accuracy.

[0128] like Figure 6 As shown, the entire CCR-BIM system control block diagram includes two subsystems: suspension force vibration compensation control and speed control. Through their collaborative work, they achieve stable suspension and precise control of the rotor.

[0129] Step 5: Based on the synthesized total control force, the inverter drives the levitation winding to generate a corresponding radial levitation force, thereby canceling the rotor unbalanced vibration.

[0130] Total control force F total (k5) drives the levitation winding through the three-phase full-bridge inverter to generate a corresponding radial levitation force to counteract rotor unbalance vibration.

[0131] System stability was verified using the Lyapunov energy function:

[0132]

[0133] Where V(k5) is the Lyapunov energy function; e(k5) is the displacement error signal; This refers to the proportional parameter error; This refers to the error in the integral parameter. This is the error of the differential parameter.

[0134]

[0135] in For proportional parameter error; K p This is the current scaling factor; This is the ideal value of the proportionality coefficient.

[0136]

[0137] in For the integral parameter error; K i This is the current integral coefficient; This represents the ideal value of the integral coefficient.

[0138]

[0139] in For differential parameter error; K d These are the current differential coefficients; These are the ideal values ​​for the differential coefficients.

[0140] When the step size factor satisfies the convergence condition, the energy difference ΔV(k5)=V(k5+1)-V(k5)<0, and the system approaches global stability.

[0141] A computer storage medium includes a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to perform the above-described real-time vibration compensation control method for a bearingless composite rotor squirrel-cage induction motor.

[0142] Here, the present invention provides an implementation example:

[0143] To address the unbalanced vibration problem generated by a bearingless composite rotor squirrel-cage induction motor during high-speed rotation, a real-time compensation control method based on SOGI harmonic observation and dual-channel adaptive PID is applied. The experimental platform adopts... Figure 7 The key parameter settings are shown below. Detailed parameters for CCR-BIM are as follows: Figure 8 As shown.

[0144] This application scenario focuses on vibration suppression control of a CCR-BIM system operating within a speed range of 3000-6000 r / min. The rotor weighs 2.97 kg and has an eccentricity of 0.05 mm, exhibiting significant unbalanced vibration during high-speed rotation. Four control methods were compared: uncompensated control, traditional PID compensated control, feedforward compensated control, and the proposed real-time vibration compensation control method based on SOGI harmonic observation and dual-channel adaptive PID.

[0145] The experimental conditions included both steady-state and dynamic conditions: under steady-state conditions, the vibration suppression effect at different speeds was tested; under dynamic conditions, the dynamic response performance of the system under sudden load conditions was tested. The experiment collected displacement signals of the rotor in the x and y axes, and recorded the vibration waveforms before and after control, the rotor shaft trajectory, and the speed fluctuations under load disturbance using an oscilloscope.

[0146] First, the unbalanced vibration mechanism of the CCR-BIM rotor is analyzed. The CCR-BIM utilizes the synergistic effect of the torque winding and the levitation winding to achieve the rotor's rotation and levitation functions. On the experimental platform, it was observed that rotor manufacturing tolerances and assembly errors caused a deviation of approximately 0.05 mm between the rotor's center of mass and its geometric center, leading to unbalanced vibration.

[0147] Displacement signals of the rotor in the x and y axes are acquired using an eddy current sensor, with a sampling period of 0.0001 s. Since the raw signal contains high-frequency noise and sensor interference, a second-order Butterworth low-pass filter is used for preprocessing, with the cutoff frequency set to 1.2 times the rotor's mechanical angular velocity. This suppresses high-frequency noise while preserving the integrity of the fundamental frequency component.

[0148] At 3000 r / min, the uncompensated raw vibration signal reaches a peak value of 5.4 μm in the x-axis direction and 5.9 μm in the y-axis direction. These vibrations can affect the operational accuracy and service life of CCR-BIM. The filtered signal retains the fundamental frequency vibration characteristics, providing a basis for subsequent harmonic component extraction.

[0149] The preprocessed signal is input into the SOGI harmonic observer to extract the fundamental frequency component synchronized with the mechanical angular velocity. The damping coefficient of the SOGI is set to 0.8 to ensure a balance between system stability and response speed.

[0150] At a speed of 3000 r / min, the SOGI harmonic observer successfully separated the fundamental frequency vibration component synchronized with the 50 Hz mechanical angular velocity from a noisy displacement signal. Compared to traditional bandpass filters, the SOGI observer exhibited higher extraction accuracy and better phase tracking capability, with the extraction error controlled within 3%.

[0151] When the rotational speed increases to 6000 r / min, the frequency adaptive adjustment mechanism automatically updates the center frequency of SOGI to 100 Hz, maintaining high harmonic extraction accuracy. Experiments show that throughout the entire rotational speed range of 3000-6000 r / min, the SOGI observer can accurately track the amplitude and phase changes of the fundamental frequency component, providing accurate vibration characteristic information for subsequent compensation force generation.

[0152] The simplified LMS algorithm is used to dynamically adjust the magnitude and phase weights of the compensation force. During algorithm initialization, the initial value of the magnitude weight is set to 0.5, the initial value of the phase weight is set to 0.1, and the step size factors are set to 0.02 and 0.01, respectively.

[0153] Under 3000 r / min conditions, the simplified LMS algorithm, after an adaptive adjustment process of approximately 0.2 seconds, resulted in the amplitude weight converging to 0.83 and the phase weight converging to 0.27, generating a compensation force that matched the vibration characteristics. Experiments showed that, using only the feedforward compensation channel, the x-axis vibration amplitude decreased from 5.4 μm to 2.9 μm, and the y-axis vibration amplitude decreased from 5.9 μm to 3.2 μm, achieving vibration suppression effects of 46.3% and 45.8%, respectively.

[0154] When the rotational speed increases to 6000 r / min, the algorithm automatically adjusts the parameters to adapt to the new vibration characteristics, converging to the new optimal value within approximately 0.3 seconds, maintaining a good feedforward compensation effect. This indicates that the simplified LMS algorithm has good adaptive capability and can effectively cope with changes in vibration characteristics caused by changes in rotational speed.

[0155] The residual error after feedforward compensation is input into the adaptive PID controller for further processing. The initial PID parameters are set as follows: proportional coefficient 0.5, integral coefficient 0.1, derivative coefficient 0.01, and learning rates of 0.01, 0.005, and 0.001, respectively.

[0156] Under 3000 r / min operating conditions, the dual-channel control architecture works collaboratively. The feedforward channel accurately cancels the fundamental frequency vibration, while the feedback channel suppresses residual errors through adaptive PID control. Experimental results show that after adopting dual-channel control, the x-axis vibration amplitude is further reduced to 1.6 μm, and the y-axis vibration amplitude is reduced to 1.7 μm. Compared with using only feedforward compensation, the vibration suppression effect is improved by 44.8% and 46.9%, respectively; compared with traditional PID compensation control, the vibration suppression effect is improved by 66.0% and 66.7%, respectively.

[0157] When the speed is increased to 6000 r / min, the dual-channel control architecture still maintains good synergy, and the rotor vibration amplitude is controlled within an acceptable range, indicating that the architecture design has high control accuracy and stability under different working conditions.

[0158] In practical applications, the total control force drives the levitation winding through a three-phase full-bridge inverter to generate a corresponding radial levitation force, which counteracts rotor imbalance vibration. Multiple sets of experiments verified the system's stability and robustness.

[0159] like Figure 9 As shown, under sudden load conditions, the speed fluctuation of the proposed method is significantly smaller than that of the other three methods, indicating that it has better disturbance rejection capability. Under a sudden 10N load disturbance, at 3000 r / min, the speed recovery time is shortened from 27.1 ms without compensation to 9.9 ms, a reduction of 63.5%; at 6000 r / min, the speed recovery time is shortened from 60.2 ms without compensation to 29.3 ms, a reduction of 51.3%. Compared with feedforward compensation control, the recovery time is reduced by 27.7% and 17.9%, respectively.

[0160] like Figure 10 and Figure 11 As shown in the comparative experimental results of x-axis and y-axis vibration displacement, the proposed method exhibits excellent vibration suppression effects under different speeds and load conditions. The maximum value of the rotor shaft center trajectory also decreased: at 3000 r / min, it decreased from 81.6 μm without compensation to 19.2 μm, a reduction of 76.5%; at 6000 r / min, it decreased from 251.1 μm without compensation to 84.1 μm, a reduction of 66.5%. Compared with feedforward compensation control, the trajectory stability was improved by 54.7% and 29.7%, respectively.

[0161] In practical applications, in addition to the stability verification mentioned above, a comprehensive evaluation of the system's vibration suppression effect at different rotational speeds was also conducted. For example... Figure 12 As shown, the vibration suppression effects of uncompensated control, traditional PID compensation control, feedforward compensation control, and the vibration real-time compensation control method based on SOGI harmonic observation and dual-channel adaptive PID proposed in this invention are compared in the speed range of 3000-6000 r / min.

[0162] Experimental results show that the vibration suppression effect of all methods decreases with increasing rotational speed, but the method proposed in this invention maintains the best vibration suppression effect throughout the entire rotational speed range. Especially in the high-speed range (5000-6000 r / min), the advantages of this method compared with other control methods are more obvious, with the vibration suppression rate remaining above 65%, while the vibration suppression rates of traditional PID control and feedforward compensation control drop to about 30% and 45%, respectively.

[0163] This result further verifies the adaptability and robustness of the proposed method under high-speed rotating conditions, and proves that the vibration real-time compensation control method based on SOGI harmonic observation and dual-channel adaptive PID can effectively suppress the unbalanced vibration of CCR-BIM over a wide range of speeds, providing a reliable vibration control solution for high-speed precision rotating machinery.

[0164] During long-term operation testing, the system remained stable without any divergence or instability, verifying the correctness of the Lyapunov stability analysis. Furthermore, when the rotational speed varied within the range of 3000-6000 r / min, the algorithm quickly adapted to the speed changes, maintaining a stable vibration suppression effect. The adaptive parameter adjustment mechanism effectively responded to changes in system parameters, and the dual-channel collaborative control ensured a balance between dynamic response speed and steady-state accuracy.

[0165] In summary, this application example demonstrates that the real-time vibration compensation control method for a bearingless composite rotor squirrel-cage induction motor based on SOGI harmonic observation and dual-channel adaptive PID control outperforms traditional control methods in terms of vibration suppression, dynamic response performance, and trajectory stability, providing an efficient and reliable solution for vibration control of high-speed rotating machinery. This method boasts advantages such as low algorithm complexity, good real-time performance, and strong robustness, making it suitable for various vibration control applications in high-speed rotating machinery. It possesses significant engineering practical value and broad application prospects.

[0166] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for real-time vibration compensation control of a bearingless composite rotor squirrel-cage induction motor, characterized in that, include: The rotor displacement signal is acquired and preprocessed to obtain the filtered displacement signal; The filtered displacement signal is processed using a second-order generalized integrator to extract the fundamental frequency harmonic component that is synchronized with the rotor's mechanical angular velocity. Based on the extracted fundamental harmonic components, a simplified LMS algorithm is used to dynamically optimize the amplitude weight and phase weight of the compensation force, and the feedforward compensation force is generated according to the optimized weights. A dual-channel control architecture is constructed, which combines the feedforward compensation force generated based on the fundamental frequency harmonic component with the PID correction force output from the feedback correction channel to synthesize the total control force; Based on the total control force, the inverter drives the levitation winding to generate a corresponding radial levitation force, thereby canceling out the rotor's unbalanced vibration.

2. The method for real-time vibration compensation control of a bearingless composite rotor squirrel-cage asynchronous motor according to claim 1, characterized in that, The preprocessing steps include: acquiring the displacement signals of the rotor in the x-axis and y-axis directions through an eddy current sensor, and using a second-order Butterworth low-pass filter to filter high-frequency noise and sensor interference. The cutoff frequency of the filter is set to 1.2 times the rotor mechanical angular velocity.

3. The real-time vibration compensation control method for a bearingless composite rotor squirrel-cage asynchronous motor according to claim 2, characterized in that, The difference equation for the second-order Butterworth low-pass filter is: ； in This is the filtered signal at the current moment; This is the filtered signal from the previous moment; These are the filtered signals from the first two time points; This is the original signal at the current moment; This is the original signal from the previous moment; These are the original signals from the previous two time points; The filter autoregressive coefficient is 1, which represents the weight of the influence of the filtered signal from the previous time step on the current output. The filter autoregressive coefficient 2 represents the weight of the influence of the filtered signal on the current output in the previous two time steps. The input coefficient represents the weight of the influence of the original signal on the output at the current moment; The previous input coefficient represents the weight of the influence of the original signal on the output at the previous moment; The first two input coefficients represent the weights of the influence of the original signal on the output at the first two time points.

4. The real-time vibration compensation control method for a bearingless composite rotor squirrel-cage induction motor according to claim 1, characterized in that, The transfer function model of the second-order generalized integrator includes in-phase component transfer function and quadrature component transfer function, with a damping coefficient set to 0.8 to ensure a balance between system stability and response speed.

5. The real-time vibration compensation control method for a bearingless composite rotor squirrel-cage asynchronous motor according to claim 4, characterized in that, The second-order generalized integrator has a frequency adaptive adjustment mechanism, which updates the center frequency of SOGI in real time according to the speed change, as expressed in: Center frequency: ； in This refers to the rotor's mechanical angular velocity; Pi; This refers to the rotor speed; This is the rotor mechanical frequency.

6. The real-time vibration compensation control method for a bearingless composite rotor squirrel-cage asynchronous motor according to claim 1, characterized in that, The simplified LMS algorithm retains only the magnitude weight. and phase weight Two parameters are set during algorithm initialization. , .

7. The real-time vibration compensation control method for a bearingless composite rotor squirrel-cage asynchronous motor according to claim 6, characterized in that, The parameter update law of the simplified LMS algorithm is: ； in As the amplitude weight for the next time step; The current moment's amplitude weight; The phase weight for the next time step; The phase weight at the current moment; This is the amplitude step size factor; This is the phase step size factor; This is the displacement error signal; This refers to the fundamental frequency harmonic component.

8. The real-time vibration compensation control method for a bearingless composite rotor squirrel-cage asynchronous motor according to claim 1, characterized in that, The feedforward compensation force is generated in the following way: ； in For feedforward compensation force; For magnitude weighting; For phase weights; This refers to the fundamental frequency harmonic component.

9. The real-time vibration compensation control method for a bearingless composite rotor squirrel-cage asynchronous motor according to claim 1, characterized in that, The feedback correction channel in the dual-channel control architecture uses an adaptive PID controller to handle the residual error after feedforward compensation.

10. A computer storage medium, characterized in that, The device includes a memory and one or more processors, wherein the memory stores executable code, and the one or more processors execute the executable code to perform a real-time vibration compensation control method for a bearingless composite rotor squirrel-cage induction motor as described in any one of claims 1-9.

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