Magnetic suspension bearing dynamic adjustment algorithm based on double feedback of pressure and displacement sensor

Abstract

The invention relates to a magnetic suspension bearing dynamic adjustment algorithm based on pressure and displacement sensor double feedback, an eddy current sensor and a piezoelectric sensor are responsible for collecting position and vibration data of a magnetic suspension bearing, and a fuzzy PID controller is used for adjusting the position of the magnetic suspension bearing; position, vibration and rotating speed data of the magnetic suspension bearing are collected by monitoring feedback conditions of a sensor in real time and stored in a computer end, and a grey wolf algorithm is used for analysis and processing, so that performance evaluation and optimization results of a magnetic suspension bearing control algorithm are obtained. Determining the large parameter fluctuation range as an accurate parameter fluctuation range through a grey wolf algorithm; and inputting an approximate parameter range obtained through control of the fuzzy PID algorithm into a grey wolf algorithm, carrying out iterative calculation by using the grey wolf algorithm, and inputting an obtained accurate control parameter range into a cuckoo search algorithm for searching to obtain accurate parameters.

Description

Technical Field

[0001] The invention relates to the field of magnetic suspension bearings, and in particular to a dynamic adjustment algorithm for magnetic suspension bearings based on dual feedback of pressure and displacement sensors. Background Art

[0002] Magnetic bearings are devices that rely on the magnetic force generated by electromagnetic or permanent magnets to suspend the object being carried. They realize a non-contact suspension rotor support technology. This type of bearing has the characteristics of no friction, no wear, low noise and high precision, and has great application potential in various fields that require support technology. The magnetic bearing test platform is one of the important equipment for evaluating the performance of magnetic bearings.

[0003] In the field of magnetic bearing test platform research, the choice of control strategy is crucial to the optimization of system performance. At present, the main control methods include traditional PID control and advanced fuzzy PID control technology. Traditional PID control is famous for its high precision and fast response, but its performance is often limited when facing nonlinear and time-varying systems. Fuzzy PID control, as a highly adaptable control strategy, can effectively handle such complex systems. For magnetic bearing test platforms, although traditional PID control has been widely used, the existing system has shortcomings in control accuracy, response speed and parameter adjustment. In order to overcome these limitations, the present invention realizes a new test platform, which can flexibly adjust a variety of control parameters and methods through the host computer control system, and monitor sensor feedback in real time to evaluate the performance, accuracy and reliability of the control program. The gray wolf algorithm adjusts the fuzzy PID control parameters to adapt to system changes and further enhance the nonlinear processing capability of the system. Summary of the invention

[0004] The present invention introduces a magnetic bearing test system that integrates fuzzy PID algorithm and gray wolf algorithm. The system can monitor the position and vibration state of the rotating shaft in real time during operation, and then evaluate the performance of the host computer control system. The innovation of the present invention lies in that it combines fuzzy PID control technology, the monitoring functions of eddy current sensors and piezoelectric sensors, and the auxiliary role of axial tooling to accurately test the eight-pole magnetic bearing, thereby improving the accuracy and stability of the test. The magnetic bearing test platform has the advantages of high-precision testing, reliable control performance and fast response. It is suitable for the research and development of magnetic bearings, can effectively improve the test efficiency and effect of magnetic bearings, and provides strong support for the further development of magnetic bearing technology.

[0005] The present invention provides a dynamic adjustment algorithm for magnetic bearings based on dual feedback of pressure and displacement sensors. Eddy current sensors and piezoelectric sensors are responsible for collecting the position and vibration data of magnetic bearings, and a fuzzy PID controller is used to adjust the position of magnetic bearings.

[0006] The fuzzy PID transfer function is:

[0007]

[0008] The fuzzy PID control program formula is:

[0009]

[0010] Among them, u(k) is the control quantity at time k; K p , k i , k d are proportional, integral, and differential gains respectively; e(k) is the error at time K; is the integral term of the error; is the rate of change of the error, i.e. the differential term; Δt is the sampling time.

[0011] The further optimized solution is:

[0012] The position, vibration and speed data of the magnetic bearing are collected by real-time monitoring of sensor feedback, stored on the computer and analyzed and processed using the Grey Wolf algorithm, thereby obtaining performance evaluation and optimization results of the magnetic bearing control algorithm.

[0013] The further optimized solution is:

[0014] Then the Grey Wolf Algorithm is used to determine the approximate parameter fluctuation range as the accurate parameter fluctuation range.

[0015] Its mathematical model is as follows:

[0016]

[0017]

[0018] d=2a·r1-a (3)

[0019] c i =2r2 (4)

[0020] In the formula, represents the current location of the prey obtained by the gray wolf after the tth iteration; x (t) represents the current location of the gray wolf individual after the tth iteration, d represents the step length parameter, c irepresents a random vector used to increase the randomness of the algorithm. The interval of a is [0, 2] and decreases with the increase of the number of iterations. r1 and r2 are random numbers in the interval [0, 1]. |·| represents the modulus (Euclidean distance) of the two-dimensional vector.

[0021] The further optimized solution is:

[0022] The model for roundup is as follows:

[0023]

[0024]

[0025] in, Indicates the current location of the gray wolves α, β, and γ after the tth iteration.

[0026] The further optimized solution is:

[0027] The Grey Wolf Algorithm obtains the accurate range of fluctuation parameters, and then the Cuckoo Search Algorithm obtains the accurate parameters; the key formula is as follows:

[0028]

[0029] in, represents the position of the i-th bird's nest in the t-th generation; α represents the step length control value, which is used to control the step size. Usually, α=1; Levy(λ) is the Levy random search path, which belongs to random walk and adopts the Levy flight mechanism. The step length of its walk satisfies a heavy-tailed stable distribution.

[0030] Levy:u=t -λ ,1≤λ≤3

[0031] Among them, λ controls the step size distribution of Levy flight, affecting the globality and locality of the search; it should be noted that the probability of the parasitic nest owner discovering foreign bird eggs is p a , where 0≤p a ≤1; if the randomly generated value R that follows a uniform distribution from 0 to 1 is greater than p a , then Make random changes, otherwise remain unchanged.

[0032] The further optimized solution is:

[0033] The approximate parameter range obtained by the fuzzy PID algorithm control is input into the Grey Wolf Algorithm, and the Grey Wolf Algorithm is used for iterative calculation. The obtained accurate control parameter range is input into the Cuckoo Search Algorithm for search to obtain the accurate parameters.

[0034] Compared with the prior art, the advantages of the present invention are:

[0035] The magnetic bearing test platform has the advantages of high test accuracy, reliable control, fast response speed, etc., and can accurately monitor the working condition of the shaft in real time through eddy current sensors and piezoelectric sensors, and can be widely used in the research and development field of magnetic bearings.

[0036] This magnetic bearing test platform constructs an octapole magnetic field composed of eight permanent magnets, which enables the rotor to be suspended with the help of magnetic force. In terms of the control system, the platform uses the STM32 microcontroller as the core control unit, and the actuator is controlled by a digital signal processor (DSP). This configuration gives the platform efficient and precise control performance, and can finely regulate and monitor the magnetic bearing. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Picture 1 The workflow diagram of the real-time regulation algorithm combining fuzzy PID, pressure feedback and displacement feedback of the present invention;

[0038] Picture 2 A partial cross-sectional view of a magnetic bearing test platform provided by an embodiment of the present invention;

[0039] Icons: 1-rotating shaft; 2-two radial magnetic bearings; 3-two axial magnetic bearings; 4-permanent magnet high-speed synchronous motor; 5-ten piezoelectric sensors; 6-axial tooling; 7-eddy current sensor; 8-support. DETAILED DESCRIPTION

[0040] In order to explain in more depth how the present invention achieves its objectives and the specific effects of its technical solutions, the magnetic bearing test platform proposed in the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0041] The above and other technical contents, features and effects of the present invention are clearly presented in the following detailed description of the specific implementation methods in conjunction with the accompanying drawings. Through the description of the specific implementation methods, the technical means and effects adopted by this experiment to achieve the predetermined purpose can be more deeply and specifically understood. However, the attached drawings are only for reference and explanation purposes, and are not used to limit the technical solutions of the present invention.

[0042] The technical aspects of the present invention are further explained below in conjunction with the accompanying drawings. It needs to be stated here that the structure of the present invention is not the focus that needs to be protected. The focus is on the dynamic adjustment algorithm of the magnetic levitation bearing based on dual feedback of pressure and displacement sensors based on the structure. The structure is given as an example only to better illustrate the control method.

[0043] like Picture 2The figure shows the overall structure diagram of a magnetic suspension bearing test platform provided by an embodiment of the present invention, which includes a rotating shaft 1, two radial magnetic suspension bearings 2, two axial magnetic suspension bearings 3, a permanent magnet high-speed synchronous motor 4, ten piezoelectric sensors 5, an axial tooling 6, an eddy current sensor 7 and a support 8. The permanent magnet high-speed synchronous motor 4, the radial magnetic suspension bearings 2 and the axial magnetic suspension bearings 3 are all installed on the support 8.

[0044] The rotating shaft 1 passes through the permanent magnet high-speed synchronous motor 4 and is fixedly connected to the rotor end of the motor. The radial magnetic bearing 2 and the axial magnetic bearing 3 are arranged on both sides of the motor 4 in a symmetrical manner and are both sleeved on the rotating shaft 1. The radial magnetic bearing 2 and the axial magnetic bearing 3 are in close contact with each other. The rotating shaft 1 is a stepped shaft. The structure of the magnetic bearing test platform is symmetrical about the axial center plane of the rotating shaft 1. The magnetic bearing includes two pairs of radial magnetic bearings 2 and axial magnetic bearings 3, and the radial magnetic bearings 2 and the axial magnetic bearings 3 are connected by bolts. The radial magnetic bearing 2, the axial magnetic bearing 3, and the permanent magnet high-speed synchronous motor 4 are fixedly connected to the support 8. The radial magnetic bearing 2 includes a radial magnetic bearing stator and an electromagnetic coil (not shown in the figure).

[0045] The piezoelectric sensor 5 is installed at the bottom of the ten bolts. Through the arrangement of the bolts in the horizontal and vertical directions, four piezoelectric sensors are installed in each of the four quadrants of the shaft 1 in the vertical and horizontal directions, and two piezoelectric sensors are installed on each of the two axial fixtures. The system monitors the pressure changes detected by the piezoelectric sensor 5 through a data acquisition card, thereby obtaining the force and vibration information of the shaft 1 in different directions.

[0046] The eddy current sensors 7 are arranged in the transverse and longitudinal directions of the radial magnetic bearing. One radial magnetic bearing is equipped with four eddy current sensors. The system monitors the radial and axial displacements on the magnetic bearing through the eddy current sensors 7, thereby obtaining the radial and axial position changes of the rotating shaft 1.

[0047] The control system uses an STM32 microcontroller as the controller, and the eddy current sensor and piezoelectric sensor are responsible for collecting the position and vibration data of the magnetic bearing. These data are then processed by the signal conditioning circuit, converted into digital format, and then transmitted to the microprocessor. After receiving the digital signal, the microprocessor uses a fuzzy PID controller to accurately adjust the position of the magnetic bearing, thereby achieving precise control of the position of the magnetic bearing.

[0048] Furthermore, the fuzzy PID algorithm is used for control, and the fuzzy PID transfer function is:

[0049]

[0050] i＝1 i＝2 i＝3 i＝4 i＝5 α(i) 4.0985e+03 -362.1369 -3.5701e+03 -1.6695e+04 -368.0397 β(i) 0 6.0006e+03 6.5313e+03 0 2.1228e+04 Cr(i) -8.1970e+03 724.2739 7.1402e+03 3.3390e+04 736.0793 Ur(i) -3.9352e-03 -8.3151e-06 -8.3046e-07 -2.6854e-08 -2.1795e-08 Vr(i) 0 9.8248e-07 -5.9443e-05 0 -3.5053e-08 Kr(i) 1.6798e+07 3.6138e+07 5.5404e+07 2.7872e+08 4.5075e+08

[0051] Furthermore, the fuzzy PID control program formula is:

[0052]

[0053] Among them, u(k) is the control quantity at time k; K p , k i , k d are proportional, integral, and differential gains respectively; e(k) is the error at time K; is the integral term of the error; is the rate of change of the error, i.e. the differential term; Δt is the sampling time.

[0054] Optionally, the control program can be changed through the CCS6 software on the host control system computer, and the sensor test system status can be monitored in real time to test the actual control effect of other control programs.

[0055] Furthermore, the position, vibration, speed and other data of the magnetic bearing are collected by real-time monitoring of sensor feedback, stored in a computer and analyzed and processed using the Grey Wolf algorithm, thereby obtaining performance evaluation and optimization results of the magnetic bearing control algorithm.

[0056] Furthermore, the fuzzy PID algorithm control may not be able to obtain the accurate parameters we need. In this case, the Grey Wolf algorithm is used to determine the approximate parameter fluctuation range as the accurate parameter fluctuation range. Its mathematical model is as follows:

[0057]

[0058]

[0059] d=2a·r1-a (3)

[0060] c i =2r2 (4)

[0061] In the formula, represents the current location of the prey obtained by the gray wolf after the tth iteration; x (t) represents the current location of the gray wolf individual after the tth iteration, d represents the step length parameter, c i represents a random vector, which is used to increase the randomness of the algorithm. The interval of a is [0, 2] and decreases with the increase of the number of iterations. r1 and r2 are random numbers in the interval [0, 1]. |·| represents the modulus (Euclidean distance) of the two-dimensional vector. The specific roundup model is as shown in Formula 5:

[0062]

[0063]

[0064] in, Indicates the current location of the gray wolves α, β, and γ after the tth iteration.

[0065] Furthermore, the Grey Wolf Algorithm obtains the accurate range of fluctuation parameters, and then the Cuckoo Search Algorithm obtains the accurate parameters. The key formula is as follows:

[0066]

[0067] in, represents the position of the i-th bird's nest in the t-th generation; α represents the step size control value, which is used to control the step size. Usually, α=1; Levy(λ) is the Levy random search path, which belongs to random walk and adopts the Levy flight mechanism. Its walking step size satisfies a heavy-tailed stable distribution.

[0068] Levy:u=t -λ ,1≤λ≤3 (7)

[0069] Among them, λ controls the step size distribution of Levy flight, affecting the globality and locality of the search; it should be noted that the probability of the parasitic nest owner discovering foreign bird eggs is p a , where 0≤p a ≤1. If the randomly generated value R that follows a uniform distribution from 0 to 1 is greater than p a , then Make random changes, otherwise remain unchanged.

[0070] Furthermore, the approximate parameter range obtained by the fuzzy PID algorithm control is input into the Grey Wolf Algorithm, and the Grey Wolf Algorithm is used for iterative calculation. The obtained accurate control parameter range is input into the Cuckoo Search Algorithm for search to obtain accurate parameters.

[0071] The magnetic bearing system of the embodiment of the present invention is a completely symmetrical system structure, and a complete balance of static axial force is achieved by balancing the motor rotor and the shaft by adding or removing weight.

[0072] The workflow of the real-time regulation algorithm combining fuzzy PID, pressure feedback and displacement feedback is as follows:

[0073] (1) Real-time data acquisition: Obtain rotor displacement data and pressure data from displacement sensors and pressure sensors.

[0074] (2) Error calculation: Calculate the displacement error (e(t)) and pressure error (p(t)). These error values ​​reflect the deviation of the current system and directly affect the output of the controller.

[0075] (3) Fuzzy processing: The displacement error and pressure error are fuzzified through the membership function to obtain the fuzzy input signal.

[0076] (4) Fuzzy reasoning: Reasoning the fuzzy input signal based on the fuzzy rule base to determine the parameter adjustment direction of the PID controller

[0077] (5) PID controller adjustment: According to the results of fuzzy reasoning, the proportional, integral and differential gain parameters of the PID controller are adjusted in real time.

[0078] (6) Output control signal: Based on the adjusted PID gain, the control signal is calculated and the current of the electromagnet is adjusted to control the position and suspension force of the rotor.

[0079] (7) Feedback loop: The real-time adjusted control signal is fed back to the system to continue displacement and pressure monitoring to form a closed-loop control.

[0080] Implementation Case 1, Case Background: High-speed magnetic levitation motor system;

[0081] High-speed magnetic levitation motors are used in aviation, aerospace or high-speed trains. Magnetic bearings play a vital role in these applications, requiring the system to not only achieve stable suspension of the rotor, but also maintain high precision and high responsiveness under extremely high speeds and dynamic loads.

[0082] The system uses magnetic bearings to suspend the rotor, which does not contact the stator, significantly reducing friction, improving efficiency and supporting extremely high speeds. In this application, the rotor's suspension stability, position accuracy and fast response are extremely important, so advanced control algorithms need to be introduced to ensure its reliability in complex environments.

[0083] 1. System architecture

[0084] In this system, pressure sensors and displacement sensors provide real-time information about the relationship between the rotor and the stator. The fuzzy PID control algorithm adjusts the magnetic force based on these feedback signals to achieve precise suspension of the rotor.

[0085] Pressure sensor: used to measure the magnetic force between the rotor and the stator, especially the axial and radial force values. Pressure feedback is mainly used to avoid collision or overload between the rotor and the stator, and ensure that the suspension force is within a safe range.

[0086] Displacement sensor: used to monitor the displacement of the rotor in real time, mainly detecting the axial and radial offset of the rotor to ensure that the rotor remains in the ideal suspension position.

[0087] Fuzzy PID controller: adjusts the current intensity of the electromagnet according to the feedback signals of pressure and displacement to achieve precise control of the magnetic force.

[0088] 2. Implementation steps

[0089] 2.1 Data Collection and Sensor Feedback

[0090] The displacement sensor and pressure sensor collect the displacement error and pressure error between the rotor and the stator in real time. The displacement error refers to the degree of deviation of the rotor relative to the stator, while the pressure error reflects whether the force between the rotor and the stator is too large or too small.

[0091] Displacement sensors usually use laser displacement sensors or inductive displacement sensors to obtain high-precision data of the rotor position, achieving sub-micron accuracy.

[0092] Pressure sensors are used to detect changes in suspension force. Hall effect sensors or electromagnetic force sensors are usually used. They can provide real-time feedback on the magnitude of the force to prevent excessive force from causing contact between the rotor and the stator.

[0093] 2.2 Fuzzy processing and PID control

[0094] Displacement error fuzzification: The displacement error e(t) = xdesired - xactual will be fuzzified and usually divided into fuzzy categories such as "large offset", "small offset" and "zero offset".

[0095] Pressure error fuzzification: The pressure error p(t) = Fdesired-Factual will also be fuzzified, usually divided into several situations: "excessive pressure", "appropriate pressure" and "too low pressure".

[0096] Fuzzy rule base: Set fuzzy rules according to system requirements. For example, "if the displacement error is large and the pressure error is small, increase the magnetic field"; "if the displacement error is zero and the pressure error is appropriate, keep the magnetic field unchanged."

[0097] Fuzzy reasoning: The fuzzy controller determines the gain parameter adjustment of the PID controller through fuzzy reasoning based on the input fuzzy error (displacement error and pressure error).

[0098] PID controller adjustment: The control signal after fuzzy reasoning will adjust the proportional (Kp), integral (Ki) and differential (Kd) parameters of the PID controller. The PID controller accurately adjusts the current of the electromagnet based on these gain parameters, thereby changing the size of the magnetic field and keeping the rotor suspended in the predetermined position.

[0099] 2.3 Real-time Adjustment and Feedback Loop

[0100] The control system continuously obtains real-time data of pressure and displacement and sends these data to the fuzzy PID controller for processing.

[0101] The controller adjusts the PID parameters according to the real-time error signal and sends out a control signal to adjust the electromagnet current.

[0102] After the electromagnet is adjusted, the position and suspension force of the rotor will change, and the displacement and pressure sensors will measure the state between the rotor and the stator again and feed the data back to the control system to form a closed loop.

[0103] This process is continuously iterated, and through continuous feedback and adjustment, the system is able to maintain stable suspension of the rotor in complex environments.

[0104] 3. Specific application case: high-speed magnetic levitation motor

[0105] Taking a high-speed magnetic levitation motor in practical application as an example, the magnetic levitation bearing needs to work stably at an extremely high speed (eg 100,000 rpm).

[0106] During high-speed operation: The rotor may undergo slight radial or axial displacement due to centrifugal force, temperature changes, external disturbances, etc. If the system relies solely on displacement feedback, it may not be able to respond to external disturbances or system parameter changes in a timely manner, resulting in the rotor being unable to stably suspend.

[0107] Introducing pressure feedback: By measuring the pressure between the rotor and the stator in real time, the system can quickly determine whether there is excessive magnetic force, avoiding overload or contact risks caused by excessive magnetic force.

[0108] Fuzzy PID control: Based on displacement and pressure feedback, fuzzy PID control can dynamically adjust the parameters of the PID controller to cope with complex situations under high-speed operation.

[0109] Through this dual feedback control system that combines fuzzy control, PID control, pressure feedback and displacement feedback, the high-speed magnetic levitation motor can ensure that the rotor remains stably suspended under conditions of high-speed rotation, load changes and external disturbances.

[0110] The magnetic bearing test platform of the embodiment of the present invention, based on the fully symmetrical structure of the magnetic bearing system, effectively compensates for the dynamic axial force caused by external factors such as processing errors by precisely controlling the axial force generated by the axial magnetic bearing, thereby greatly enhancing the reliability of the shaft operation. In addition, due to the use of radial magnetic bearings, the shaft will not generate friction when rotating after radial suspension, and no lubrication is required. The rotor of the motor is directly connected to the shaft, reducing energy loss during the transmission process and improving efficiency. This design also brings many advantages such as simplified structure, reduced cost, and reduced friction noise.

[0111] The implementation mode of the present invention controls the magnetic bearing and collects data through the host computer control system, thereby realizing the evaluation and optimization of the performance of the magnetic bearing, has high accuracy and stability, and is suitable for the performance test and optimization research of the magnetic bearing.

Claims

1. A dynamic adjustment algorithm for magnetic bearings based on dual feedback of pressure and displacement sensors, characterized in that: The eddy current sensor and piezoelectric sensor are responsible for collecting the position and vibration data of the magnetic bearing, and the fuzzy PID controller is used to adjust the position of the magnetic bearing. The fuzzy PID transfer function is: The fuzzy PID control program formula is: Among them, u(k) is the control quantity at time k; K p , k i , k d are proportional, integral, and differential gains respectively; e(k) is the error at time K; is the integral term of the error; is the rate of change of the error, i.e. the differential term; Δt is the sampling time.

2. The dynamic adjustment algorithm of magnetic bearing based on dual feedback of pressure and displacement sensor according to claim 1 is characterized in that: The position, vibration and speed data of the magnetic bearing are collected by real-time monitoring of sensor feedback, stored on the computer and analyzed and processed using the Grey Wolf algorithm, thereby obtaining performance evaluation and optimization results of the magnetic bearing control algorithm.

3. The dynamic adjustment algorithm of magnetic bearing based on dual feedback of pressure and displacement sensor according to claim 2 is characterized in that: Then the Grey Wolf Algorithm is used to determine the approximate parameter fluctuation range as the accurate parameter fluctuation range. Its mathematical model is as follows: d=2a·r1-a(3) c i ＝2r2(4) In the formula, represents the current location of the prey obtained by the gray wolf after the tth iteration; x( t ) represents the current location of the gray wolf individual after the tth iteration, d represents the step length parameter, c i represents a random vector used to increase the randomness of the algorithm. The interval of a is [0, 2] and decreases with the increase of the number of iterations. r1 and r2 are random numbers in the interval [0, 1]. |·| represents the modulus (Euclidean distance) of the two-dimensional vector.

4. The dynamic adjustment algorithm of magnetic bearing based on dual feedback of pressure and displacement sensors according to claim 3 is characterized in that: The model for roundup is as follows: in, Indicates the current location of the gray wolves α, β, and γ after the tth iteration.

5. The dynamic adjustment algorithm of magnetic bearing based on dual feedback of pressure and displacement sensor according to claim 4 is characterized in that: The Grey Wolf Algorithm obtains the accurate range of fluctuation parameters, and then the Cuckoo Search Algorithm obtains the accurate parameters; the key formula is as follows: in, represents the position of the i-th bird's nest in the t-th generation; α represents the step length control value, which is used to control the step size. Usually, α=1; Levy(λ) is the Levy random search path, which belongs to random walk and adopts the Levy flight mechanism. The step length of its walk satisfies a heavy-tailed stable distribution. Levy:u=t -λ ,1≤λ≤3 Among them, λ controls the step size distribution of Levy flight, affecting the globality and locality of the search; it should be noted that the probability of the parasitic nest owner discovering foreign bird eggs is p a , where 0≤p a ≤1; if the randomly generated value R that follows a uniform distribution from 0 to 1 is greater than p a , then Make random changes, otherwise remain unchanged.

6. The dynamic adjustment algorithm of magnetic bearing based on dual feedback of pressure and displacement sensor according to claim 5 is characterized in that: The approximate parameter range obtained by the fuzzy PID algorithm control is input into the Grey Wolf Algorithm, and the Grey Wolf Algorithm is used for iterative calculation. The obtained accurate control parameter range is input into the Cuckoo Search Algorithm for search to obtain the accurate parameters.

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