Magnetic suspension bearing system control parameter self-tuning method and system

Abstract

The invention discloses a magnetic suspension bearing system control parameter self-tuning method and system. The method comprises the following steps: acquiring a rotor displacement signal of a magnetic suspension bearing system; according to the rotor displacement signal, initial suspension of the rotor is achieved with the steady-state position and the response speed as indexes; and further optimizing the control parameters by taking the peak value of the sensitivity function as an optimization target, and realizing the self-tuning of the control parameters by adopting a breadth-first pruning search tree algorithm.

Description

Technical Field

[0001] The present invention belongs to the technical field of magnetic bearing self-tuning, and more specifically, relates to a magnetic bearing system control parameter self-tuning method and system. Background Art

[0002] The active magnetic bearing system suspends the rotor in the middle of the stator magnetic poles through controllable electromagnetic force, so it has the advantages of no friction, no need for lubrication, no pollution, high speed, long life, etc. At present, active magnetic bearings have been used as an advanced mechatronics product in the industrial field, such as gyroscopes, high-speed motors, bearingless motors, aircraft engines, artificial heart pumps and distributed power generation systems.

[0003] The existing magnetic bearing control system usually needs to go through complex processes such as model linearization, simulation analysis and on-site debugging when performing parameter tuning, which has a long design cycle and cumbersome steps. Therefore, it is necessary to use machine learning algorithms to perform online self-tuning of the control parameters of the magnetic bearing system. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a method for self-tuning control parameters of a magnetic bearing system, comprising:

[0005] Acquire a rotor displacement signal of a magnetic bearing system;

[0006] According to the rotor displacement signal, the initial suspension of the rotor is achieved with steady-state position and response speed as indicators;

[0007] Taking the peak value of the sensitivity function as the optimization target, the control parameters are further optimized, and the breadth-first pruning search tree algorithm is used to realize the self-tuning of the control parameters.

[0008] Further, including:

[0009] The plane formed by the combination of control parameters of the PID controller with a fixed Ki value is infinitely subdivided into grids, wherein the plane includes: a Kp-Kd plane, Kp is the proportional link coefficient of the PID controller, and Kd is the differential link coefficient;

[0010] Test and score the parameters represented by the grid nodes;

[0011] The pruning algorithm is used to select the node with the highest score, and the pruned node is used as the parent node to derive child nodes around it to conduct a more detailed exploration of the parameter plane.

[0012] Further, including:

[0013] Set the search range and step size of the PID controller control parameters;

[0014] The range of the control parameters of the PID controller is subdivided into multiple grid points according to the step size, and all grid nodes are added to the search queue;

[0015] Selecting a first control parameter combination from the search queue for testing, and determining whether the selected control parameter combination can enable the rotor to achieve a stable suspension state;

[0016] The control parameter combination that passes the stability test is recorded as a stable node, and the control parameter combination is removed from the search queue.

[0017] Further, including:

[0018] For each control parameter combination that passes the stability test of each degree of freedom, the geometric center of the closed space formed on the parameter plane of each degree of freedom is taken, and the control parameter combination corresponding to the geometric center is used as the control parameter combination of the root node of the search tree;

[0019] Perform a frequency sweep test of the sensitivity function on the control parameter combination of the root node, and record the score of the control parameter combination of the root node and the amplitude of the sensitivity function.

[0020] Further, including:

[0021] The PID controller adds excitation signals of different frequencies as rotor displacement instructions in the closed-loop control system and outputs a displacement error signal;

[0022] Based on the frequency of the frequency sweep and the corresponding sensitivity test results, the test results of each degree of freedom are plotted.

[0023] The present invention also provides a magnetic bearing system control parameter self-tuning system, comprising:

[0024] A signal acquisition module is used to acquire a rotor displacement signal of a magnetic bearing system;

[0025] A preliminary suspension module, used to realize preliminary suspension of the rotor according to the rotor displacement signal, taking the steady-state position and the response speed as indicators;

[0026] The self-tuning module is used to further optimize the control parameters with the peak value of the sensitivity function as the optimization target, and realize the self-tuning of the control parameters by using the breadth-first pruning search tree algorithm.

[0027] Further, including:

[0028] The plane formed by the combination of control parameters of the fixed Ki value PID controller is infinitely subdivided into grids, wherein the plane includes: a Kp-Kd plane, Kp is the proportional link coefficient of the PID controller, and Kd is the differential link coefficient;

[0029] Test and score the parameters represented by the grid nodes;

[0030] The pruning algorithm is used to select the node with the highest score, and the pruned node is used as the parent node to derive child nodes around it to conduct a more detailed exploration of the parameter plane.

[0031] Further, including:

[0032] Set the search range and step size of the PID controller control parameters;

[0033] The range of the control parameters of the PID controller is subdivided into multiple grid points according to the step size, and all grid nodes are added to the search queue;

[0034] Selecting a first control parameter combination from the search queue for testing, and determining whether the selected control parameter combination can enable the rotor to achieve a stable suspension state;

[0035] The control parameter combination that passes the stability test is recorded as a stable node, and the control parameter combination is removed from the search queue.

[0036] Further, including:

[0037] For each control parameter combination that passes the stability test of each degree of freedom, the geometric center of the closed space formed on the plane of each degree of freedom is taken, and the control parameter combination corresponding to the geometric center is used as the control parameter combination of the root node of the search tree;

[0038] Perform a frequency sweep test of the sensitivity function on the control parameter combination of the root node, and record the score of the control parameter combination of the root node and the amplitude of the sensitivity function.

[0039] Further, including:

[0040] The PID controller adds excitation signals of different frequencies as rotor displacement instructions in the closed-loop control system and outputs a displacement error signal;

[0041] Based on the frequency of the frequency sweep and the corresponding sensitivity test results, the test results of each degree of freedom are plotted.

[0042] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0043] Through the above technical solution, the present invention can perform accurate and efficient self-tuning operations on the control parameters of the magnetic suspension bearing system based on the sensitivity function. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is the first stage flow chart of the PID parameter optimization algorithm;

[0045] Figure 2 It is a simplified control block diagram of a rigid magnetically suspended rotor closed-loop system;

[0046] Figure 3 This is the principle block diagram of the frequency sweep test of the magnetically suspended rotor (single degree of freedom) system;

[0047] Figure 4 This is an example diagram of the sensitivity function scanning results;

[0048] Figure 5 This is the flow chart of the third stage of the PID parameter optimization algorithm;

[0049] Figure 6 This is a system structure diagram of Example 2. DETAILED DESCRIPTION

[0050] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0051] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0052] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0053] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0054] The display screen is used to display the user interface of each application.

[0055] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0056] Example 1

[0057] The core mechanism of the present invention is to use a search tree to combine the control parameters of the PID controller to form a plane (K p -K d Plane, K p K is the proportional coefficient of the PID controller. d The network (where the differential link coefficient is) is infinitely subdivided into small grids, and the parameters represented by the grid nodes are tested and scored. The nodes with the best scores are selected using the pruning algorithm, and the pruned nodes are used as parent nodes to derive child nodes around them for a more detailed exploration of the parameter plane.

[0058] The first stage: initial parameter (root node) search. The flowchart of this stage is as follows: Figure 1 As shown:

[0059] In the initial parameter search phase (Phase I), our goal is to explore the initial stable parameter combination within the predetermined PID controller parameter range through a breadth-first search algorithm. The entire process is divided into several key steps, each of which revolves around how to effectively cover the parameter space and find stable parameters.

[0060] Based on the physical characteristics of the rotor of the magnetic bearing, we share the control parameters and test results of the four radial degrees of freedom (X and Y degrees of freedom at the DE and NDE ends of the rotor) and the two degrees of freedom (X and Y degrees of freedom) contained in each degree of freedom combination at both ends in this stage.

[0061] Each degree of freedom combination requires alternating parameter searches according to the following steps.

[0062] 1) First, we need to set the search range and step size of the PID parameters. These ranges and step sizes determine the starting point of our search and the amplitude of each parameter adjustment to ensure that the entire parameter space can be quickly and effectively covered and can be adjusted according to actual conditions.

[0063] 2) Next, the parameter range is subdivided into multiple grid points according to the step size, and all these grid points are added to the search queue. This queue will be used for subsequent breadth-first search.

[0064] 3) Before entering the formal search, we need to check whether there are any untested parameter combinations in the search queue. If the search queue is empty, it means that all possible parameter combinations under the current step size have been tested. At this time, it is necessary to reduce the step size and regenerate the search queue (i.e. repeat step 2) to continue a more detailed search.

[0065] 4) When there are still untested parameter combinations in the search queue, we will select the first parameter combination from the queue for testing. At this point, we need to check whether the selected parameter combination has been tested. If the parameter combination has been tested, it will be removed from the search queue and step 4 will be restarted to select the next parameter combination for testing.

[0066] 5) For parameter combinations that have not been tested, we will perform stability tests to determine whether the parameter combination can make the rotor reach a stable suspension state (the judgment indicators are described below). If the test results show that the parameter combination can stabilize the system, we will record it as a stable node and remove the parameter combination from the search queue. If it is unstable, it will be recorded in the search results, and its parent node information will be recorded as K p =0, K d =0, the score is infinite, the score reduction rate is infinitesimal, and the frequency response results of the sensitivity function are all recorded as infinite. Delete this set of parameters in the search queue and return to step 4.

[0067] 6) After recording the stable nodes, check again whether there are any untested parameter combinations in the search queue. If the queue is empty, return to step 3 to continue to reduce the step size and regenerate the search queue. When there are still untested parameter combinations in the search queue, we need to find the geometric center of the closed space formed by all the stable nodes of the current degree of freedom on the parameter plane. This center point will ensure that the control system will be relatively stable and not close to collapse, so as to help us reduce interference with the floating test of other degrees of freedom.

[0068] 7) Next, check whether the number of stable nodes found meets the predetermined requirement. If the number is sufficient, we can end the first stage of search. If the number is insufficient, continue the search in step 3 until enough stable nodes are found.

[0069] 8) After finding enough stable nodes, the first phase of the search task is completed.

[0070] Static suspension judgment index

[0071] Stability is the primary consideration for algorithm scoring. The system needs to remain stable under different PID parameter combinations to ensure its reliability in practical applications.

[0072] Let's take an active magnetic bearing with a central active space radius of 150 units as an example.

[0073] For the rotor floating time domain position feedback signal with a duration of 2 seconds (1KHz sampling frequency) returned by the lower computer, we can use the following points to determine whether it can be stably suspended:

[0074] a) The highest point of the floating waveform (overshoot) is between 140 and 170;

[0075] b) The average value (steady-state error) of the final 50 sampling points of the floating waveform is between 140 and 160;

[0076] c) The peak-to-peak value (oscillation) of the final 50 sampling points of the floating waveform is within 10.

[0077] If the system exhibits unstable behavior, such as continuous oscillation or divergence, under a certain set of PID parameters, the set of parameters will be directly judged as unstable, and the frequency sweep result and parameter score will be recorded as infinity (Inf).

[0078] Phase 2: Root node stability and performance index calculation

[0079] For a set of stable parameters of each degree of freedom obtained in the first stage, we take the geometric center of the closed space formed by them on the parameter plane of their respective degrees of freedom, and use the parameters corresponding to this geometric center as the root node parameters of the search tree.

[0080] It should be noted that from this stage on, we will no longer explore in the form of combinations of degrees of freedom, that is, the parameters of each degree of freedom are explored, tested, scored and the results stored separately.

[0081] For the root node of the search tree for each degree of freedom, we need to perform a frequency sweep test (method as follows) of the sensitivity function (defined below) and record the root node parameters, score (score evaluation index as follows) and the sensitivity function amplitude. Since the root node has no parent node, its parent node K is recorded. p =0, K d =0, the score is infinite, and the rate of score decrease is infinitesimal.

[0082] like Figure 2 As shown in the figure, in this closed-loop architecture, the controlled magnetically suspended rotor G includes a magnetic bearing-rigid rotor, a power amplifier, and a displacement sensor, and considers the effects of three independent inputs: the position reference command r, the control object input disturbance d, and the control object output disturbance (or noise) n. The output characteristics of the system are characterized by the closed-loop transfer function T0(s), and the forward channel parameter changes are characterized by the controlled object transfer function G(s). When only the position reference command r input is considered, the closed-loop transfer function T0(s) is expressed as:

[0083] T o (s) = G(s)K(s)(I+G(s)K(s)) -1

[0084] T o (s) output sensitivity S to G(s) o (s) can be written as:

[0085]

[0086] When the sensitivity |S o The smaller (jω)| is, the better the magnetic bearing system has the following control characteristics:

[0087] (1) The system is highly robust to perturbations of magnetic bearing model parameters;

[0088] (2) The tracking error is small and the control accuracy of the rotor position is high;

[0089] (3) The system has good ability to suppress output noise or disturbance.

[0090] Overall, S. o It can be used as a key performance indicator to measure the closed-loop system and directly reflects the response quality of the system. On the other hand, the correlation between the sensitivity peak and the phase margin and gain margin of the closed-loop system also shows that the smaller the peak value of the sensitivity function, the higher the stability margin of the closed-loop system.

[0091] Figure 3 In the figure, the variable superscript k = 1, 2; 3, 4. They represent the relevant signals or transfer functions on the X degree of freedom and Y degree of freedom of the radial magnetic bearings (i.e. bearings A and B) at the DE and NDE ends of the rotor, respectively. x represents a series of variables related to the rotor displacement. The controller is Figure 3 The closed-loop control system shown in the figure adds an excitation signal with an amplitude of A at different frequencies as the rotor displacement command. And output displacement error signal Frequency f h The test range is 5~300Hz. Based on the frequency of the frequency sweep and the corresponding sensitivity test results, the test results of each degree of freedom are plotted. Figure 4 An example of a sensitivity function scan result.

[0092] In order to make the sensitivity function more accurate, the present invention optimizes the sensitivity function as follows:

[0093]

[0094] Among them, G ij is the jth input variable u j The transfer function to the i-th output variable, s is the complex frequency variable in the Laplace transform, u is the input variable of the magnetic bearing system, a ijn is the jth input variable u j The nth coefficient of the numerator polynomial of the transfer function to the ith output variable, b ijm is the jth input variable u jThe mth coefficient of the denominator polynomial of the transfer function to the ith output variable, u i is the i-th input variable, and t is the time.

[0095] Introducing random disturbances and uncertainties in system parameters, they are described by random processes and probabilistic models:

[0096] H=H0+σH1

[0097]

[0098] Among them, H is a random process and probability model, H0 is the baseline value of the magnetic bearing system, σ is, H1 is the disturbance term of the magnetic bearing system, σ is the standard deviation, which indicates the amplitude of the disturbance and obeys the normal distribution

[0099]

[0100] Among them, u(t) is the input variable of the magnetic bearing system at time t, K is the gain matrix, r(t) is the reference signal vector at time t, y(t) is the output signal vector at time t, is the jth first nonlinear function of the i-th reference signal vector, β ij is the second weight of the jth nonlinear function of the ith reference signal vector, ψ ij is the jth second nonlinear function of the i-th reference signal vector, r i (τ) is the i-th reference signal vector at the time variable τ of the integration, y i (τ) is the i-th output signal vector at the time variable τ of the integration, n is the number of reference signal vectors, m is the number of nonlinear functions, α ij is the first weight of the j-th nonlinear function of the ith reference signal vector.

[0101] The first nonlinear function and the second nonlinear function are polynomial function, exponential function, logarithmic function, sine function and cosine function.

[0102] System overall scoring criteria:

[0103] In order to comprehensively consider the performance of parameters, we designed a heuristic function for multiple optimization objectives to evaluate and score the performance of parameters:

[0104] For the frequency response function amplitude or maximum amplitude A at a certain frequency or frequency band, we hope that the optimized amplitude is T (the optimization target T should be a realistic and feasible target or a value very close to the feasible range to prevent the score from being too large after scoring and thus ignoring several other optimization targets), and its weight is W (the weights of multiple targets only need to be proportional). In this case, we can use the following formula to evaluate the system:

[0105] Parameter score = ∑W × |TA|

[0106] In actual testing, we have two optimization goals:

[0107] (1) The frequency response function amplitude of the low frequency band (5Hz) is -5dB and the weight is 2;

[0108] (2) The maximum amplitude of the frequency response function in the mid-frequency band (5-300 Hz) is 0 dB, and the weight is 8.

[0109] From this we can get the heuristic function for the above two optimization objectives:

[0110] Parameter score = 2×|(-5)-A 5Hz |+8×|0-A 5～300Hz |

[0111] Phase 3: Pruning the Breadth-First Search Tree

[0112] The main task of the third stage is to efficiently explore the parameter space through the root node data recorded in the second stage to further optimize the performance of the PID controller. In this stage, we must not only ensure the stability of the system in each degree of freedom, but also comprehensively consider the performance of the sensitivity function of each degree of freedom. The following are the detailed steps of the third stage:

[0113] 1) Initialize the search: Before the third phase begins, we first reset the step size of the parameter search. As the optimization progresses, the step size is gradually reduced to continuously improve the search accuracy.

[0114] 2) Prepare the parent node queue: sort all parameter combinations that currently have scoring result records from best to worst according to the expected scores calculated by the score reduction rate, select several groups of parameter combinations that are expected to be the best, and add them to the parent node queue.

[0115] 3) Improve the parent node queue: Determine whether the number of elements in the parent node queue is sufficient. If not, select the parameter group with the smallest score to join the parent node queue. The length of the parent node queue should always be larger than the number of parameter combinations selected by the expected score to ensure that there will be a certain number of low-scoring high-quality parameter combinations that can serve as parent nodes. The score reduction rate is calculated as shown in formula [3]:

[0116]

[0117] 4) Prepare the search queue: For each parameter group in the parent node queue, take it as the center, generate new parameter combinations in all directions on the parameter plane, and add these new parameter combinations to the search queue.

[0118] 5) Select parameter combination: Select the first set of parameters from the search queue to prepare for stability testing and performance evaluation. If the parameter combination has been tested and the results have been recorded, skip it directly to avoid repeated testing.

[0119] 6) Stability test: Perform a stability test on the new parameter combination to determine whether the system can maintain stable operation under the new parameter combination without oscillation or divergence. If the test results show that the system runs stably under the new parameter combination, the parameter combination is recorded as a new stable node and the test results are saved. Otherwise, the parameter combination is removed from the search queue.

[0120] 7) Sensitivity function frequency sweep test: For the parameter combinations that pass the stability test, perform a frequency response test and record the system's frequency response and performance score for subsequent analysis and optimization.

[0121] 8) Determine whether there are any elements in the search queue: Determine whether there are any untested parameter combinations in the search queue. If yes, continue to select new parameter combinations from the search queue for testing and verification, i.e. repeat steps 5 to 7.

[0122] 9) Switch degrees of freedom: If all parameter combinations have been tested, switch to the next degree of freedom and repeat steps 2 to 8 until the parameters of all degrees of freedom have been optimized and verified, and start building the next level of search tree.

[0123] 10) Determine whether the maximum number of search layers has been reached: Determine whether the number of layers currently searched has reached the preset maximum number of search layers. If it has not reached the maximum number of layers, continue to search and verify the parameters; if it has reached the maximum number of layers, end the third stage.

[0124] 11) End of the third stage: When all parameter combinations of all degrees of freedom have been tested and the number of search layers has reached the preset maximum number of layers, the third stage ends. At this time, all test data scores are arranged from small to large to determine the optimal parameter combination of the system in each degree of freedom.

[0125] Through the third stage of pruning the breadth-first search tree, we can not only further optimize the performance of the PID controller, but also ensure the stability and reliability of the system under different working conditions. Flowchart of this stage Figure 5 As shown:

[0126] Example 2

[0127] like Figure 6 As shown, an embodiment of the present invention further provides a magnetic bearing system control parameter self-tuning system, comprising:

[0128] A signal acquisition module is used to acquire a rotor displacement signal of a magnetic bearing system;

[0129] A preliminary suspension module, used to realize preliminary suspension of the rotor according to the rotor displacement signal, taking the steady-state position and the response speed as indicators;

[0130] The self-tuning module is used to further optimize the control parameters with the peak value of the sensitivity function as the optimization target, and realize the self-tuning of the control parameters by using the breadth-first pruning search tree algorithm.

[0131] Specifically, they include:

[0132] The plane formed by the combination of control parameters of the PID controller is infinitely subdivided into grids, wherein the plane includes: a Kp-Kd plane, Kp is the proportional link coefficient of the PID controller, and Kd is the differential link coefficient;

[0133] Test and score the parameters represented by the grid nodes;

[0134] The pruning algorithm is used to select the node with the highest score, and the pruned node is used as the parent node to derive child nodes around it to conduct a more detailed exploration of the parameter plane.

[0135] Specifically, they include:

[0136] Set the search range and step size of the PID controller control parameters;

[0137] The range of the control parameters of the PID controller is subdivided into multiple grid points according to the step size, and all grid nodes are added to the search queue;

[0138] Selecting a first control parameter combination from the search queue for testing, and determining whether the selected control parameter combination can enable the rotor to achieve a stable suspension state;

[0139] The control parameter combination that passes the stability test is recorded as a stable node, and the control parameter combination is removed from the search queue.

[0140] Specifically, they include:

[0141] For each control parameter combination that passes the stability test of each degree of freedom, the geometric center of the closed space formed on the plane of each degree of freedom is taken, and the control parameter combination corresponding to the geometric center is used as the control parameter combination of the root node of the search tree;

[0142] Perform a frequency sweep test of the sensitivity function on the control parameter combination of the root node, and record the score of the control parameter combination of the root node and the amplitude of the sensitivity function.

[0143] Specifically, they include:

[0144] The PID controller adds excitation signals of different frequencies as rotor displacement instructions in the closed-loop control system and outputs a displacement error signal;

[0145] Based on the frequency of the frequency sweep and the corresponding sensitivity test results, the test results of each degree of freedom are plotted.

[0146] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0147] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0148] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0149] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0150] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0151] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0152] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for self-tuning control parameters of a magnetic bearing system. It is characterized in that include: Acquire a rotor displacement signal of a magnetic bearing system; According to the rotor displacement signal, the initial suspension of the rotor is achieved with steady-state position and response speed as indicators; Taking the peak value of the sensitivity function as the optimization target, the control parameters are further optimized, and the breadth-first pruning search tree algorithm is used to realize the self-tuning of the control parameters.

2. A method for self-tuning control parameters of a magnetic bearing system according to claim 1, It is characterized in that include: The plane formed by the combination of control parameters of the PID controller with a fixed Ki value is infinitely subdivided into grids, wherein the plane includes: a Kp-Kd plane, Kp is the proportional link coefficient of the PID controller, and Kd is the differential link coefficient; Test and score the parameters represented by the grid nodes; The pruning algorithm is used to select the node with the highest score, and the pruned node is used as the parent node to derive child nodes around it to conduct a more detailed exploration of the parameter plane.

3. A method for self-tuning control parameters of a magnetic bearing system as claimed in claim 2, It is characterized in that include: Set the search range and step size of the PID controller control parameters; The range of the control parameters of the PID controller is subdivided into multiple grid points according to the step size, and all grid nodes are added to the search queue; Selecting a first control parameter combination from the search queue for testing, and determining whether the selected control parameter combination can enable the rotor to achieve a stable suspension state; The control parameter combination that passes the stability test is recorded as a stable node, and the control parameter combination is removed from the search queue.

4. A method for self-tuning control parameters of a magnetic bearing system as claimed in claim 3, It is characterized in that include: For each control parameter combination that passes the stability test of each degree of freedom, the geometric center of the closed space formed on the plane of each degree of freedom is taken, and the control parameter combination corresponding to the geometric center is used as the control parameter combination of the root node of the search tree; Perform a frequency sweep test of the sensitivity function on the control parameter combination of the root node, and record the score of the control parameter combination of the root node and the amplitude of the sensitivity function.

5. A method for self-tuning control parameters of a magnetic bearing system as claimed in claim 4, It is characterized in that include: The PID controller adds excitation signals of different frequencies as rotor displacement instructions in the closed-loop control system and outputs a displacement error signal; Based on the frequency of the frequency sweep and the corresponding sensitivity test results, the test results of each degree of freedom are plotted.

6. A magnetic bearing system control parameter self-tuning system, It is characterized in that include: A signal acquisition module is used to acquire a rotor displacement signal of a magnetic bearing system; A preliminary suspension module, used to realize preliminary suspension of the rotor according to the rotor displacement signal, taking the steady-state position and the response speed as indicators; The self-tuning module is used to further optimize the control parameters with the peak value of the sensitivity function as the optimization target, and realize the self-tuning of the control parameters by using the breadth-first pruning search tree algorithm.

7. A magnetic bearing system control parameter self-tuning system as claimed in claim 6, It is characterized in that include: The plane formed by the combination of control parameters of the fixed Ki value PID controller is infinitely subdivided into grids, wherein the plane includes: a Kp-Kd plane, Kp is the proportional link coefficient of the PID controller, and Kd is the differential link coefficient; Test and score the parameters represented by the grid nodes; The pruning algorithm is used to select the node with the highest score, and the pruned node is used as the parent node to derive child nodes around it to conduct a more detailed exploration of the parameter plane.

8. A magnetic bearing system control parameter self-tuning system as claimed in claim 7, It is characterized in that include: Set the search range and step size of the PID controller control parameters; The range of the control parameters of the PID controller is subdivided into multiple grid points according to the step size, and all grid nodes are added to the search queue; Selecting a first control parameter combination from the search queue for testing, and determining whether the selected control parameter combination can enable the rotor to achieve a stable suspension state; The control parameter combination that passes the stability test is recorded as a stable node, and the control parameter combination is removed from the search queue.

9. A magnetic bearing system control parameter self-tuning system as claimed in claim 8, It is characterized in that include: For each control parameter combination that passes the stability test of each degree of freedom, the geometric center of the closed space formed on the plane of each degree of freedom is taken, and the control parameter combination corresponding to the geometric center is used as the control parameter combination of the root node of the search tree; Perform a frequency sweep test of the sensitivity function on the control parameter combination of the root node, and record the score of the control parameter combination of the root node and the amplitude of the sensitivity function.

10. A magnetic bearing system control parameter self-tuning system as claimed in claim 9, It is characterized in that include: The PID controller adds excitation signals of different frequencies as rotor displacement instructions in the closed-loop control system and outputs a displacement error signal; Based on the frequency of the frequency sweep and the corresponding sensitivity test results, the test results of each degree of freedom are plotted.

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