Control method and equipment for high-speed motor and electromagnetic bearing system

Abstract

The invention belongs to the technical field of electromagnetic bearings, and discloses a control method and device for a high-speed motor and electromagnetic bearing system, and the method comprises the steps: updating an individual optimal position pbest and a group historical optimal position gbest through calculating an adaptive value fSwarm of each particle, and calculating a current global optimal adaptive value fgbest and a corresponding PID parameter; and judging whether the optimized particle meets a termination condition or not, and taking an output result as an initial control signal of a control system until the evaluation value of the optimal particle meets a design requirement. According to the invention, the improved particle swarm optimization algorithm maintains a strong search capability in the optimization process, and significantly improves the precision and robustness of PID controller parameter setting, thereby suppressing the same-frequency vibration caused by residual imbalance.

Description

Technical Field

[0001] The present invention belongs to the technical field of electromagnetic bearings, and particularly relates to a control method and device for a high-speed motor and an electromagnetic bearing system. Background Art

[0002] The combination of a high-speed motor and active magnetic bearings (AMB) has become an important technical solution, especially in those occasions with extremely high requirements for precision, speed, and reliability. However, the synchronous vibration caused by residual unbalance and the insufficient anti-interference ability under low stiffness characteristics are still the core challenges faced by the practical application of the AMB system. Therefore, the development of an efficient vibration control strategy has become the research focus in the field of AMB.

[0003] The Chinese patent publication number is CN113009834B, and the name of the patent application is a method for controlling the composite vibration of an active magnetic bearing rotor. The method includes: establishing a four-degree-of-freedom rigid rotor dynamics model to explore the synchronous vibration caused by rotor eccentricity; establishing a cascaded system of AMB-rotor and a notch filter to explore the nonlinear influence of notch filter parameters on the phase margin of the system; designing a fusion improved particle swarm optimization algorithm (IPSO), proposing a strategy combining parameter adaptive adjustment and fitness and displacement feedback mechanisms, and optimizing the parameters Kp, Ki, and Kd of the PID controller based on the IPSO algorithm. This patent application cannot adapt to the high-speed motor and electromagnetic bearing system during application. Summary of the Invention

[0004] In order to overcome the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a control method and device for a high-speed motor and an electromagnetic bearing system. The improved particle swarm optimization algorithm maintains a strong search ability during the optimization process, significantly improving the accuracy and robustness of the PID controller parameter tuning, thereby suppressing the synchronous vibration caused by residual unbalance.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a control method for a high-speed motor and an electromagnetic bearing system, including the following steps: S1: Collect displacement errors; S2: Construct a model of the particle swarm algorithm, and input the error value E into the improved particle swarm algorithm model; S3: Set the initial parameters of the particle swarm optimization algorithm, and initialize the particle swarm and the initial optimal value; S4: Adjust the inertia weight ω according to the iteration progress and the particle displacement; S5: Set the update speed and the update position range; S6: Calculate the fitness value fSwarm of each particle, update the individual best position pbest and the global historical best position gbest of the swarm. Meanwhile, calculate the current global best fitness value fgbest and the corresponding PID parameters. S7: Determine whether the optimized particle meets the termination condition. If not, re-enter step S2 for calculation until the evaluation value of the best particle meets the design requirements, and then use the output result as the initial control signal of the control system.

[0006] Optionally, the algorithm parameters include: the range of the improved adaptive inertia weight, the range of the learning factor, the population size, the maximum number of iterations, the boundary of the solution space, the boundary of the velocity, the velocity and position of the initial particle, the individual historical best position and fitness value, and the global historical best position and fitness value of the swarm.

[0007] Optionally, while adjusting the weight, use the Sigmoid function to dynamically adjust the individual learning factor c1 and the global learning factor c2, and adjust the dynamic factor a according to the fitness value.

[0008] Optionally, the calculation formula for adjusting the inertia weight by the particle displacement is: ; where ω is the inertia weight adjusted by the particle displacement; ωmax is the initial inertia weight; ωmin is the inertia weight at the end of the algorithm; a is the dynamic adjustment factor; iter is the current number of iterations; ger is the maximum number of iterations; pavg is the average value of the position change of each particle.

[0009] Optionally, in the Sigmoid function, adjust the dynamic adjustment factor a according to the fitness change rate Δf. When Δf < 0.01, ; when Δf ≥ 0.01, where a is the dynamic adjustment factor; Δf is the fitness change rate.

[0010] Optionally, the calculation formulas for the individual learning factor c1 and the global learning factor c2 are: ; where c1 is the individual learning factor; c2 is the global learning factor; c1max is the initial individual learning factor, c1min is the individual learning factor at the end of the algorithm; c2min is the initial global learning factor, c2max is the global learning factor at the end of the algorithm; a is the dynamic adjustment factor; iter is the current number of iterations; ger is the maximum number of iterations.

[0011] In a second aspect, the present invention provides a control system for a high-speed motor and an electromagnetic bearing system, including: A data acquisition module for acquiring displacement errors; A model establishment module, which is used to build a model of the particle swarm optimization algorithm and input the error value E into the improved particle swarm optimization algorithm model; A parameter setting module, which is used to set the initial parameters of the particle swarm optimization algorithm, and initialize the particle swarm and the initial optimal value; A weight adjustment module, which is used to adjust the inertia weight ω according to the iteration progress and the particle displacement; A range setting module, which is used to set the update speed and the update position range; A calculation module, which is used to calculate the fitness value fSwarm of each particle, update the individual optimal position pbest and the global historical optimal position gbest of the group, and at the same time calculate the current global optimal fitness value fgbest and the corresponding PID parameters; A judgment module, which is used to judge whether the optimized particle meets the termination condition. If not, it will re-enter the model establishment module for calculation until the evaluation value of the optimal particle reaches the design requirement, and then use the output result as the initial control signal of the control system.

[0012] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the control method of the high-speed motor and the electromagnetic bearing system is implemented.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the control method of the high-speed motor and the electromagnetic bearing system is implemented.

[0014] In a fifth aspect, the present invention provides a computer program product including a computer-readable medium. On the computer-readable medium, computer-readable program code is included, and the program code executes the control method of the high-speed motor and the electromagnetic bearing system.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention adopts an intelligent control algorithm to suppress the synchronous vibration caused by the residual unbalance of the rotor. The improved particle swarm optimization algorithm maintains a strong search ability during the optimization process, significantly improving the accuracy and robustness of the PID controller parameter tuning. The control method proposed by the present invention shows good control effects in the actual high-speed motor and electromagnetic bearing system. In the wide speed range of 0 - 24000 rpm, the PID controller controlled by the improved particle swarm optimization algorithm can reduce the radial displacement amplitude of the rotor, decrease the peak value of the controller current, and shorten the response time under the acceleration condition, improving the dynamic stability of the system control. Description of the Drawings

[0016] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure of the present invention in any way.

[0017] In the accompanying drawings: Figure 1 is a diagram of a radial four-degree-of-freedom AMB-rigid rotor system; Figure 2 is a diagram of the AMB-rigid rotor and notch filter system of the present invention; Figure 3 is a diagram of the influence of the gain coefficient ε of the present invention on the frequency characteristics of the notch filter; Figure 4 is a flowchart of the IPSO algorithm of the present invention; Figure 5 is a diagram of the rotor displacement, electromagnetic force, and controller current at AMB-A under constant speed of the present invention; Figure 6 is a diagram of the electromagnetic force, controller current, power amplifier current, and rotor displacement at AMB-A under variable speed of the present invention. Detailed implementation manners

[0018] In order to enable those skilled in the art of the present technology to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0019] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used in the present invention includes any and all combinations of one or more of the related listed items.

[0020] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present application can be implemented by means of hardware including several different components and by means of a properly programmed computer. In the unit claims listing several devices, several of these devices can be embodied by the same hardware item. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names. In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of this application, "a plurality of" means two or more, unless otherwise specifically defined. The present invention will be described in detail below with reference to the accompanying drawings.

[0021] A control method for a high-speed motor and an electromagnetic bearing system of the present invention includes the following steps: S1: Collect displacement errors; S2: Construct a model of the particle swarm algorithm and input the error value E into the improved particle swarm algorithm model; S3: Set the initial parameters of the particle swarm optimization algorithm, and initialize the particle swarm and the initial optimal value; S4: Adjust the inertia weight ω according to the iteration progress and the particle displacement; S5: Set the update speed and the update position range; S6: Calculate the fitness value fSwarm of each particle, update the individual optimal position pbest and the global historical optimal position gbest of the group, and at the same time calculate the current global optimal fitness value fgbest and the corresponding PID parameters; S7: Determine whether the optimized particles meet the termination condition. If not, re-enter step S2 for calculation until the evaluation value of the optimal particle reaches the design requirement, and then use the output result as the initial control signal of the control system.

[0022] The present invention adopts an intelligent control algorithm to suppress the synchronous vibration caused by the residual unbalance of the rotor. The improved particle swarm optimization algorithm maintains a strong search ability during the optimization process, significantly improving the accuracy and robustness of the PID controller parameter tuning. The control method proposed by the present invention shows good control effects in the actual high-speed motor and electromagnetic bearing system. In the wide speed range of 0 - 24000 rpm, the PID controller controlled by the improved particle swarm optimization algorithm can reduce the radial displacement amplitude of the rotor, decrease the peak value of the controller current, and shorten the response time under the acceleration condition, improving the dynamic stability of the system control.

[0023] Example 1 Proportional Integral Derivative (PID) control, as a classic control method, is widely used in various control systems because of its simple structure, easy implementation, and convenient parameter debugging. PID obtains precise control actions through accurate proportional-integral-derivative operations, acts on the magnetic levitation bearing, achieves the effect of stable levitation, and thus realizes the optimization of system performance. PID is usually adopted as the main controller in the active electromagnetic bearing-rigid rotor system to ensure system stability.

[0024] The Particle Swarm Optimization (PSO) algorithm simulates the foraging behavior of bird flocks and searches for the optimal solution in a group collaboration manner. In its standard form, each particle updates its velocity and position according to its individual historical best and the group historical best. Due to its advantages such as simple structure and fast convergence, this algorithm is widely used in fields such as controller parameter tuning and mechanical structure optimization.

[0025] When there are uncertain factors or large changes in model parameters in the controlled object, the control effect of PID has certain limitations, and the parameter tuning process and the principles on which it is based are not clear. In many cases, it relies on empirical debugging. Simple PID control can no longer meet the increasingly complex application requirements. Adding the PSO intelligent algorithm to the traditional PID control can be used to achieve the active control of the active electromagnetic bearing-rigid rotor system.

[0026] Analyze the composition of the active electromagnetic bearing-rigid rotor system and establish a model for the four-degree-of-freedom active electromagnetic bearing-rigid rotor system.

[0027] Figure 1 It is a typical radial four-degree-of-freedom active electromagnetic bearing-rigid rotor system. When there is a deviation between the real-time position of the rotor and the target position, the controller will receive the real-time position signal of the rotor transmitted back by the displacement sensor and send out a control signal according to the set algorithm. After being amplified by the power amplifier, it drives the electromagnetic bearing to adjust the magnitude of the electromagnetic force, thereby correcting the rotor position in real time so that it can levitate stably at the ideal position.

[0028] In Figure 1The relevant planes and coordinate systems are defined. The AMB-rotor system is a four-degree-of-freedom system with translational degrees of freedom along the x-axis and y-axis and rotational degrees of freedom about the x-axis and y-axis. In the figure, la and lb respectively represent the distances from the magnetic bearings A and B to the rotor's center of mass, and l = la + lb. θx and θy are respectively the rotational angles of the rotor about the x-axis and y-axis. fax, fbx, fay, and fby are respectively the electromagnetic forces at the left and right ends in the x and y directions, and θy and θx respectively represent the counterclockwise rotational angles of the rotor about the y-axis and x-axis. Since the parameters and structures of the left and right magnetic bearings in the present invention are the same, the current stiffness and displacement stiffness coefficients of the four degrees of freedom are the same.

[0029] The electromagnetic forces fax, fbx, fay, and fby at the left and right ends in the x and y directions can be expressed as: Equation 1 In Equation 1, fax, fbx, fay, and fby are respectively the electromagnetic forces at the left and right ends in the x and y directions; ks and ki are respectively the displacement stiffness coefficient and the current stiffness coefficient; la and lb respectively represent the distances from the magnetic bearings A and B to the rotor's center of mass, and l = la + lb; xa, ya, xb, and yb are the radial displacements of the rotor at the two magnetic bearings A and B.

[0030] If the radial displacements of the rotor at the two magnetic bearings A and B are respectively xa, ya, xb, and yb, then the displacements x and y of the geometric center c of the rotor, and the rotational angles θx and θy of the rotor about the axis are given by Equation 2.

[0031] Equation 2 In Equation 2, x and y are the displacements of the geometric center c of the rotor; θx and θy are the rotational angles of the rotor about the axis; la and lb respectively represent the distances from the magnetic bearings A and B to the rotor's center of mass, and l = la + lb; xa, ya, xb, and yb are the radial displacements of the rotor at the two magnetic bearings A and B.

[0032] When the rotor is axisymmetric, the x-axis and y-axis have the same moment of inertia. The two rotational motion equations of the rotor are coupled through the gyroscopic torque term ωJz, indicating that there is cross-coupling between the forces and displacements of the rotor in the radial x and y directions. This phenomenon is called gyroscopic coupling. According to Newton's second law and rotor dynamics theory, under the condition of constant rotational speed, the differential equations of motion of the four-degree-of-freedom rotor system of the magnetic bearings can be derived as follows: Equation 3 In Equation 3, m is the rotor mass; ωc is the rotor angular frequency; J is the moment of inertia of the rotor about the x (or y) axis; Jz is the moment of inertia of the rotor about the z axis. θx and θy are the rotation angles of the rotor about the axes; la and lb represent the distances from the magnetic bearings A and B to the rotor's center of mass respectively, and l = la + lb; xa, ya, xb, and yb are the radial displacements of the rotor at the two magnetic bearings A and B.

[0033] Analyze the non-linear influence of the notch filter parameters on the system phase margin, analyze the working principle of the notch filter in the active AMB-rotor system, and design the cascade control of the AMB-rotor system and the notch filter.

[0034] For an ideal notch filter, assuming the notch filter bandwidth is Δω and the center angular frequency is ω0, its amplitude-frequency characteristic is: Equation 4 In Equation 4, Δω is the notch filter bandwidth; ω0 is the center angular frequency.

[0035] When the center angular frequency of the notch filter is set equal to the angular frequency of the rotor, the notch filter can completely filter out the rotational speed synchronous component in the displacement error signal input to the controller. In applications, the notch filter cannot achieve ideal characteristics, and its actual characteristics depend on the structure and order of the notch filter.

[0036] In the automatic balance control of the AMB-rotor system, the basic structure is as Figure 2 shown. G(s) is the transfer function of the AMB rotor system, GC(s), GP(s), and GS(s) are the transfer functions of the PID controller, power amplifier, and displacement sensor respectively. Nf(s) is the feedback link of the notch filter, ε is the adjustable parameter of the notch filter, ω0 is the angular frequency. d(t) is the input of the feedback link Nf(s), and e(t) is the output of the feedback link Nf(s). In the AMB system, the notch filter is usually deployed in the feed-forward channel of the control loop, and its design needs to meet dynamic frequency tracking, phase compensation, and anti-aliasing capabilities. Before the notch filter is embedded in the closed-loop controller, the transfer function of the notch filter can be expressed as: Equation 5 For the second-order notch filter shown in Equation (5), the gain coefficient ε directly determines its frequency characteristics. The frequency characteristic curves of the notch filter for different ε values are plotted as Figure 3 shown.

[0037] Figure 3 (a) shows the relationship curve between the notch depth and ε. It can be seen that the larger the ε value, the deeper the notch depth, but it may also cause system instability. Figure 3(b) shows the relationship curve between the quality factor (Q value) and ε. As ε increases, the Q value gradually decreases, indicating that the bandwidth of the notch filter increases and the selectivity decreases. In the automatic balance control of the AMB-rotor system, it is desired that the notch filter can quickly and effectively filter out the synchronous frequency components in the signal and allow a certain frequency mismatch error, while minimizing the impact on the phase-frequency characteristics of the original system. Therefore, a smaller ε value should be selected while ensuring that the algorithm can stably track the vibration signal.

[0038] Design and fuse the improved particle swarm optimization algorithm IPSO to optimize the parameters of the PID controller; Design steps of the IPSO algorithm: 1) Improve the inertia weight ω In the initial stage of algorithm iteration, a higher inertia weight ω is beneficial to enhancing the global search ability to achieve extensive exploration; while in the later stage of algorithm convergence, a smaller ω value helps to improve the local fine search efficiency. For this reason, the present invention proposes an adaptive adjustment strategy of inertia weight ω combining the Sigmoid function and displacement-weighted enhanced exploration, that is: Equation 6 In Equation 6, ωmax is the initial inertia weight; ωmin is the algorithm termination inertia weight; a is the dynamic adjustment factor; iter is the current iteration number; ger is the maximum iteration number; pavg is the average value of the position change of each particle.

[0039] The Sigmoid function makes ω decay slowly in the early stage to maintain global search, and decay quickly in the later stage to enhance local development; ω is further adjusted according to the average displacement of the particles. If the overall displacement of the particles is large, then ω is increased to strengthen global exploration; otherwise, ω is decreased to accelerate convergence.

[0040] 2) Add fitness change feedback.

[0041] Adjust the dynamic adjustment factor a according to the fitness change rate Δf, that is: When Δf < 0.01, ; When Δf ≥ 0.01, Equation 7 In Equation 7, a is the dynamic adjustment factor; Δf is the fitness change rate.

[0042] If the fitness changes slowly (Δf < 0.01), then a increases, making the Sigmoid curve flatter, delaying the decay speed of the inertia weight ω adjusted by the particle displacement and the individual learning factor c1, and extending the global search stage; if the fitness changes quickly, then a decreases, making the Sigmoid curve steeper, accelerating the decay speed of the inertia weight ω adjusted by the particle displacement and the individual learning factor c1, and advancing to the local development stage in advance.

[0043] 3) Improve the individual learning factor c1 and the swarm learning factor c2.

[0044] Similar to the treatment of the inertia weight ω, the individual learning factor c1 and the swarm learning factor c2 of the learning factors also increase and decrease non-linearly with the number of iterations, that is: Equation 8 In Equation 8, c1 is the individual learning factor; c2 is the swarm learning factor; c1max is the initial individual learning factor, c1min is the individual learning factor at the end of the algorithm; c2min is the initial swarm learning factor, c2max is the swarm learning factor at the end of the algorithm; a is the dynamic adjustment factor; iter is the current number of iterations; ger is the maximum number of iterations.

[0045] 4) Design the algorithm flow Combined with the improvement strategy, design the IPSO algorithm flow, as Figure 4 shown. The specific steps of the IPSO algorithm are as follows: Step 1: Set the algorithm parameters, the initial particle swarm and the initial optimal value, including: the improved adaptive inertia weight range (ωmin, ωmax), the learning factor range (individual learning factor c1min, individual learning factor c1max), (swarm learning factor c2min, swarm learning factor c2max), the population size N, the maximum number of iterations M, the solution space boundary (Lb, Ub), the velocity boundary (Vmin, Vmax), the velocity VStep of the initial particle and the position Swarm, the individual historical optimal position pbest and the fitness value fpbest, the swarm historical optimal position gbest and the fitness value fgbest.

[0046] Step 2: Dynamic parameter adjustment, including: adjusting the inertia weight ω according to the iteration progress and the particle displacement, using the Sigmoid function to dynamically adjust the individual learning factor c1 and the swarm learning factor c2, and adjusting the factor a according to the fitness value.

[0047] Step 3: Update the particle state, including: updating the velocity Vstep, and the velocity needs to be limited within the range of (Vmin, Vmax), and updating the position Swarm, and the position needs to be limited within the range of (Lb, Ub).

[0048] Step 4: Fitness evaluation and optimal update, including: calculating the fitness value fSwarm of each particle, and updating the individual optimal position pbest and the swarm historical optimal position gbest.

[0049] Step 5: Record the iteration results, including: saving the current global optimal fitness value fgbest and the corresponding PID parameters.

[0050] Step 6: Perform boundary processing, including: if a particle exceeds the boundary, perform boundary absorption processing.

[0051] Step 7: Determine whether the superior particle meets the termination condition. Otherwise, return to Step 2 until the evaluation value of the optimal particle reaches the design requirement, and then output the optimization result.

[0052] Reach the maximum number of iterations; when the number of iterations iter exceeds the preset MaxIter, the loop terminates.

[0053] Find a solution that meets the accuracy requirement: if the global best fitness fzbest is less than or equal to the set minimum fitness value MinFit, terminate the optimization prematurely.

[0054] Figure 2 In this embodiment, the AMB-rigid rotor and notch filter system, G(s) is the transfer function of the AMB rotor system, GC(s), GP(s), and GS(s) are the transfer functions of the PID controller, power amplifier, and displacement sensor respectively. Nf(s) is the feedback link of the notch filter, ε is the adjustable parameter of the notch filter, and ω0 is the angular frequency. d(t) is the input of the feedback link Nf(s), and e(t) is the output of the feedback link Nf(s). Among them 。

[0055] The notch feedback link Nf(s) is used to suppress specific frequency noise by inputting the interference signal d(t) of the feedback link, combining the adjustable parameter ε and the angular frequency ω0, and outputting the corrected error signal e(t) as the input of the PID controller.

[0056] The notch filter N(s) is used to output the error signal Δe(t) by inputting the corrected error signal e(t) and the displacement measurement signal of the displacement sensor, as the input of the comparator.

[0057] The PID controller Gc(s) is used to calculate the initial control signal of the control system by inputting the displacement error signal E (generated by subtracting the actual displacement from the reference displacement at the comparator). The power amplifier Gp(s) is used to amplify the power of the control signal output by the PID controller. The input is the output signal of the PID controller, and the output is the amplified control signal for driving the actuator of the AMB rotor system. The displacement sensor Gs(s) is used to output the corresponding displacement measurement signal by inputting the actual displacement signal x(t) of the AMB rotor system, and this signal is fed back to the comparator for error calculation with the reference displacement. The first sine multiplier is used to input the sine signal sin(ωt) and the interference signal d(t) of the feedback link, and output the result after integrating the input signals. A first cosine multiplier, which is used to input a cosine signal cos(ωt) and an interference signal d(t) of a feedback loop, and output the result after integrating the input signal; A first integrator, which is used to input the output signal from the first sine multiplier (i.e., the product of sin(ωt).d(t)), and output the result after integrating the input signal; A second integrator, which is used to input the output signal from the first cosine multiplier (i.e., the product of cos(ωt).d(t)), and output the result after integrating the input signal; A second sine multiplier, which is used to input a sine signal sin(ωt) and the output signal J1 of integrator 1, and output the product of the composite signal sin(ωt).J1; A second cosine multiplier, which is used to input a cosine signal cos(ωt) and the output signal J2 of integrator 1, and output the product of the composite signal cos(ωt).J2; An adder, which is used to input the signal product sin(ωt).J1 and the composite signal product cos(ωt).J2, and output the corrected error signal e(t); A comparator, which is used to input a given position and the error signal Δe(t), and output the final error signal E.

[0058] Embodiment 2 The simulation verifies the characteristics of the rotor system under IPSO-PID control, and explores the influence on the stability of the AMB-high speed motor and the electromagnetic bearing system.

[0059] The MATLAB / Simulink function is used to perform dynamic simulation on the system, and the influence of the IPSO-PID controller on the translational and conical motion modes of the AMB-rotor system under constant speed and variable speed is studied to verify the feasibility of the algorithm; An IPSO-PID controller is built in the simulink environment. The rotor vibration information is used as the input signal. Based on the IPSO algorithm, the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd of the PID controller are optimized, and then the active control of the AMB-high speed motor and the electromagnetic bearing system is realized to verify the optimization effect of the control algorithm. The position vector of each particle (the row of the Swarm matrix) corresponds to a PID parameter combination: Swarm(j, :) = [Kp, Ki, Kd]; the fitness function receives [Kp, Ki, Kd] as the input, calculates the performance index (integral time absolute error) of the control system, and returns the fitness value. The smaller the fitness value, the better the control effect of this group of parameters; the particle velocity is adjusted according to the individual historical optimum (gbest) and the group optimum (zbest), and the values of Kp, Ki, and Kd are dynamically adjusted with the particle position, gradually approaching the optimal solution.

[0060] The control response of the AMB-rotor system of IPSO at a constant speed in this embodiment is as follows Figure 5 as shown. It can be seen from Figure 5 (a) and (b) that when the AMB-rotor system operates at 3000 rpm and IPSO control is adopted, the overshoot of the rotor vibration at the A end of the AMB is reduced from 6.37×10-7m to 2.28×10-7m, the overshoot of the electromagnetic force is reduced from 1.67 N to 1.45 N, and the overshoot of the controller current is reduced from 0.46×10-2 A to 0.37×10-2 A; when the AMB-rotor system operates at 7200 rpm and IPSO control is adopted, the overshoot of the rotor vibration at the A end of the AMB is reduced from 2.07×10-6m to 1.74×10-6m, the electromagnetic force is reduced from 5.55 N to 3.70 N, and the controller current is reduced from 1.95×10-2 A to 1.46×10-2 A. The test results show that: under the action of IPSO, the overshoots of the rotor vibration displacement, the controller current, and the electromagnetic force are all reduced to a certain extent, effectively suppressing the unbalanced vibration of the rotor, and proving the effectiveness of the control algorithm of the present invention in online unbalance compensation at a constant rotor speed.

[0061] The control response of the AMB-rotor system of IPSO under variable speed in this embodiment is as follows Figure 6 as shown. During the rotor acceleration process, the IPSO-PID controller reduces the vibration displacement of the rotor at the A end of the AMB from 6.0×10-6m to 3.5×10-6m, a decrease of 41.7%; the electromagnetic force is reduced from 62.1 N to 34.2 N, a decrease of 45.0%; the controller current is reduced from 0.14 A to 0.08 A, a decrease of 42.9%; the power amplifier current is reduced from 0.14 A to 0.06 A, a decrease of 57.1%. It should be noted that the system effectively filters out the synchronous frequency component of the rotational speed within a short time and has no negative impact on the system stability, indicating that the proposed control algorithm still has good synchronous vibration suppression ability under the acceleration condition.

[0062] Embodiment 3 Based on the control method of the high-speed motor and the electromagnetic bearing system in Embodiment 1, a control system of the high-speed motor and the electromagnetic bearing system is disclosed, including: a data acquisition module for acquiring displacement errors; a model establishment module for constructing a model of the particle swarm algorithm and inputting the error value E into the improved particle swarm algorithm model; a parameter setting module for setting the initial parameters of the particle swarm optimization algorithm, initializing the particle swarm and the initial optimal value; a weight adjustment module for adjusting the inertia weight ω according to the iteration progress and the particle displacement; A range setting module for setting the update speed and the update position range; A calculation module for calculating the fitness value fSwarm of each particle, updating the individual optimal position pbest and the global historical optimal position gbest of the swarm, and simultaneously calculating the current global optimal fitness value fgbest and the corresponding PID parameters; A judgment module for judging whether the optimized particles meet the termination condition. If not, it re-enters the model establishment module for calculation until the evaluation value of the optimal particle reaches the design requirement, and then takes the output result as the initial control signal of the control system.

[0063] Embodiment 4 The purpose of this embodiment is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the control method of the high-speed motor and the electromagnetic bearing system.

[0064] Embodiment 5 The purpose of this embodiment is to provide a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the control method of the high-speed motor and the electromagnetic bearing system.

[0065] Embodiment 6 The purpose of this embodiment is to provide a computer program product including a computer-readable medium, on which computer-readable program code is included, and the program code implements the control method of the high-speed motor and the electromagnetic bearing system.

[0066] The steps involved in the devices of the above Embodiments 3, 4, 5, and 6 correspond to those of Method Embodiment 1, and the specific implementation manners can be referred to the relevant description part of Embodiment 1.

[0067] Those skilled in the art in this technical field should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction means that implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks. These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0068] In the above embodiments, the working modes or control modes involved, unless otherwise specified, are all conventional working modes or control modes in the art.

[0069] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Any other modifications or equivalent replacements made by those of ordinary skill in the art to the technical solutions of the present invention should be covered within the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solutions of the present invention.

Claims

1. A control method for a high-speed motor and an electromagnetic bearing system, characterized in that, It includes the following steps: S1: Collect displacement errors; S2: Build a model of the particle swarm optimization algorithm and input the error value E into the improved particle swarm optimization algorithm model; S3: Set the initial parameters of the particle swarm optimization algorithm, and initialize the particle swarm and the initial optimal value; S4: Adjust the inertia weight ω according to the iteration progress and the particle displacement; S5: Set the update speed and the update position range; S6: Calculate the fitness value fSwarm of each particle, update the individual best position pbest and the global historical best position gbest of the swarm, and at the same time calculate the current global best fitness value fgbest and the corresponding PID parameters; S7: Judge whether the optimized particle meets the termination condition. If not, re-enter step S2 for calculation until the evaluation value of the optimal particle reaches the design requirement, and then use the output result as the initial control signal of the control system.

2. The control method of a high-speed motor and electromagnetic bearing system according to claim 1, characterized in that, The algorithm parameters include: the range of the improved adaptive inertia weight, the range of the learning factor, the population size, the maximum number of iterations, the boundary of the solution space, the speed boundary, the speed and position of the initial particle, the individual historical best position and fitness value, and the global historical best position and fitness value of the swarm.

3. A control method for a high-speed motor and an electromagnetic bearing system according to claim 1, characterized in that, While adjusting the weight, use the Sigmoid function to dynamically adjust the individual learning factor c1 and the global learning factor c2 of the swarm, and adjust the dynamic factor a according to the fitness value.

4. A control method for a high-speed motor and an electromagnetic bearing system according to claim 3, characterized in that, The calculation formula for adjusting the inertia weight by particle displacement is as follows: ; ; where ω is the inertia weight adjusted by particle displacement, ωmax is the initial inertia weight, ωmin is the inertia weight at the termination of the algorithm, a is the dynamic adjustment factor, iter is the current iteration number, ger is the maximum iteration number, and pavg is the average value of the position change of each particle.

5. A control method for a high-speed motor and an electromagnetic bearing system according to claim 3, characterized in that In the sigmoid function, the dynamic adjustment factor a is adjusted according to the fitness change rate Δf. When Δf < 0.01, ; When Δf≥0.01, where a is the dynamic adjustment factor; Δf is the fitness change rate.

6. The control method of a high-speed motor and an electromagnetic bearing system according to claim 3, wherein The calculation formulas for the individual learning factor c1 and the swarm learning factor c2 are as follows: ; ; where c1 is the individual learning factor; c2 is the swarm learning factor; c1max is the initial individual learning factor, c1min is the individual learning factor at the end of the algorithm; c2min is the initial swarm learning factor, c2max is the swarm learning factor at the end of the algorithm; a is the dynamic adjustment factor; iter is the current iteration number; ger is the maximum iteration number.

7. A control system for a high-speed motor and an electromagnetic bearing system, characterized in that, It includes: A data acquisition module for collecting displacement errors; A model establishment module for building a model of the particle swarm optimization algorithm and inputting the error value E into the improved particle swarm optimization algorithm model; A parameter setting module for setting the initial parameters of the particle swarm optimization algorithm and initializing the particle swarm and the initial optimal value; A weight adjustment module for adjusting the inertia weight ω according to the iteration progress and the particle displacement; A range setting module for setting the update speed and the update position range; A calculation module for calculating the fitness value fSwarm of each particle, updating the individual best position pbest and the global historical best position gbest of the swarm, and at the same time calculating the current global best fitness value fgbest and the corresponding PID parameters; A judgment module for judging whether the optimized particle meets the termination condition. If not, re-enter the model establishment module for calculation until the evaluation value of the optimal particle reaches the design requirement, and then use the output result as the initial control signal of the control system.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the control method of the high-speed motor and electromagnetic bearing system according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the control method of the high-speed motor and electromagnetic bearing system according to any one of claims 1-6.

10. A computer program product comprising a computer-readable medium, characterized in that, On the computer-readable medium, there is computer-readable program code, and the program code executes the control method of the high-speed motor and electromagnetic bearing system according to any one of claims 1-6.

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