Magnetic suspension fan wind speed control method based on model reference self-adaption

Abstract

The invention relates to the technical field of fan control, and discloses a model reference self-adaption-based magnetic suspension fan wind speed control method, which comprises the following steps of: constructing a kinetic model of a magnetic suspension fan, and constructing a reference model to obtain an expected rotating speed and an expected airflow speed of the magnetic suspension fan; estimating the actual internal state of the magnetic suspension fan by adopting a state observer; an error is calculated according to the actual internal state, the expected rotating speed and the expected airflow speed of the magnetic suspension draught fan, and an enhancement error is calculated based on the error; and a control input signal of a magnetic suspension fan controller is calculated according to the enhancement error, so that the output wind speed of the magnetic suspension fan tracks the reference wind speed, and accurate wind speed control is realized. According to the method, the control parameters can be automatically adjusted in the environment with system uncertainty and external disturbance or change, so that the wind speed of the fan is accurately controlled, the robustness of the system is improved, the performance of the fan in the dynamic change process is effectively improved, and stable operation of the fan under different working conditions is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of fan control, and in particular to a wind speed control method for a magnetic suspension fan based on model reference adaptation. Background Art

[0002] Magnetic levitation fans are widely used in many fields such as air conditioning, ventilation, air flow regulation and industrial cooling due to their advantages such as low noise, low vibration, long life and high efficiency. Compared with traditional fans, magnetic levitation fans use electromagnetic force to suspend the rotor, eliminating the contact friction of traditional bearings, thereby reducing mechanical wear, reducing energy consumption, and effectively extending the service life of the equipment. Its high precision and low vibration characteristics make it particularly suitable for application scenarios with high requirements for wind speed stability and system reliability. However, magnetic levitation fans still face problems such as external environmental disturbances, load fluctuations and system dynamic changes in actual operation, which will cause system instability and affect the stability of the fan wind speed.

[0003] Fan speed control is a key factor to ensure efficient operation of the fan and system stability. Too high a wind speed will lead to energy waste, while too low a wind speed may affect air flow and heat dissipation, thereby reducing system performance. Traditional control methods are difficult to cope with complex and changing working conditions. Summary of the invention

[0004] The present invention provides a wind speed control method for a magnetic levitation fan based on model reference adaptation. By introducing the model reference adaptive control (MRAC) method, the control parameters can be automatically adjusted in an environment with system uncertainty, external disturbance or change, so as to accurately control the wind speed of the fan. This can not only improve the robustness of the system, but also effectively improve the performance of the fan in the dynamic change process, and ensure the stable operation of the fan under different working conditions.

[0005] The present invention provides a magnetic suspension fan wind speed control method based on model reference adaptation, comprising:

[0006] Constructing a dynamic model of the magnetic levitation fan, and constructing a reference model according to the dynamic model, so as to obtain an expected rotation speed and an expected air flow speed of the magnetic levitation fan according to the reference model;

[0007] A state observer is used to estimate the actual internal state of the magnetic levitation fan; wherein the actual internal state includes an actual rotation speed and an actual air flow speed;

[0008] calculating an error according to an actual internal state of the magnetic levitation fan, a desired rotational speed, and a desired airflow velocity, and calculating an enhanced error based on the error;

[0009] The control input signal of the magnetic suspension fan controller is calculated according to the enhanced error, so that the output wind speed of the magnetic suspension fan tracks the reference wind speed, thereby achieving precise wind speed control.

[0010] Further, in the step of constructing a dynamic model of the magnetic levitation fan, and constructing a reference model according to the dynamic model to obtain an expected rotation speed and an expected airflow speed of the magnetic levitation fan according to the reference model, constructing the dynamic model of the magnetic levitation fan includes:

[0011] A dynamic model of the magnetic levitation fan is constructed to describe the relationship between the rotation speed of the magnetic levitation fan and the air flow velocity; the dynamic equation of the magnetic levitation fan system is:

[0012]

[0013] v(t)=Kω(t)

[0014] Wherein, ω(t) is the speed of the fan, unit: rad / s; v(t) is the air velocity of the fan, unit: m / s; J is the moment of inertia of the fan, unit: kg·m 2 ; b is the damping coefficient, unit: N·m·s; u(t) is the control input; K is the proportional constant between the rotation speed and the air flow velocity.

[0015] Further, in the step of constructing a dynamic model of the magnetic levitation fan and constructing a reference model according to the dynamic model to obtain an expected rotation speed and an expected airflow speed of the magnetic levitation fan according to the reference model, constructing a reference model according to the dynamic model includes:

[0016] For the magnetic levitation fan control system, the dynamic equations of the reference model are as follows:

[0017]

[0018] v r (t) = Kω r (t)

[0019] Among them, ω r (t) is the speed in the reference model, i.e. the expected speed, in rad / s; v r (t) is the airflow velocity in the reference model, i.e. the expected airflow velocity, in m / s; u r (t) is the control input of the reference model; α is the attenuation coefficient of the reference model, which represents the response rate of the system to the input.

[0020] Further, the step of using a state observer to estimate the actual internal state of the magnetic levitation fan, wherein the actual internal state includes an actual rotation speed and an actual air flow velocity, comprises:

[0021] The state space model equation is constructed as:

[0022]

[0023] y(t)=Cx(t)+Du(t)

[0024] Among them, x(t) is the state vector of the system, including the rotation speed ω(t) and the airflow velocity v(t); y(t) is the output wind speed; u(t) is the control input; A is the state matrix of the system, which describes the change of the state over time; B is the input matrix, which describes the impact of the control input on the system state; C is the output matrix, which describes how the state is mapped to the output; D is the transfer matrix, which is usually zero in simple systems.

[0025] The dynamic equation of the observer constructed based on the state space model equation is:

[0026]

[0027] in: is the observer's estimate of the internal state speed and airflow velocity of the magnetic levitation fan; L is the observer gain matrix, which determines the observer's response to the output error; y(t) is the actual wind speed measured by the wind speed sensor, which serves as feedback input; The observer is based on the state The output of the calculation; It is the error between the actual output and the observer's estimated output, providing a feedback signal to guide the observer to adjust the estimated state;

[0028] The dynamic equations of the observer are based on the state-space model and include the output error Feedback is used to gradually correct the state estimate, so that the estimate Approximate the true state x(t) = [ω(t), v(t)].

[0029] Furthermore, the observer gain matrix L is selected by pole placement so that the eigenvalues ​​of the error dynamic system, i.e., e(t)=x(t)-x^(t), are located in the left half complex plane, thereby ensuring that the error decays over time, and finally making the estimated state It accurately approaches the true state x(t), ensures the rapid convergence of the error and the stability of the system by selecting the gain matrix, provides accurate state information for the controller, and thus achieves precise system control.

[0030] Further, in the step of calculating the error according to the actual internal state, the expected rotation speed and the expected airflow speed of the magnetic levitation fan, and calculating the enhanced error based on the error, the error is calculated according to the actual internal state, the expected rotation speed and the expected airflow speed of the magnetic levitation fan, including:

[0031] The error refers to the difference between the actual output and the expected output. The wind speed error is calculated as:

[0032] e speed (t) = y ref (t)-y(t)

[0033] Among them, y ref (t) is the reference wind speed, which is the expected wind speed; y(t) is the actual wind speed, the measured value;

[0034] The calculation is based on the observer-estimated rotation speed error and airflow velocity error, reflecting the difference between the observer's estimated value and the expected value:

[0035]

[0036] Among them, ω desired and v desired are the desired fan speed and air flow velocity, respectively; and are the rotation speed and airflow velocity estimated by the observer, respectively.

[0037] Further, in the step of calculating the error according to the actual internal state, the expected rotation speed and the expected air flow speed of the magnetic levitation fan, and calculating the enhanced error based on the error, the enhanced error is calculated based on the error, and the calculation formula is:

[0038] e total (t) = e speed (t)+λ 1 e ω (t)+λ 2 e v (t)

[0039] Among them, e speed (t) = y ref (t)-y(t) is the wind speed error; is the speed estimation error; is the air velocity estimation error; 1 and λ 2 is the weighting factor used to adjust the controller response to the speed and air velocity estimation errors.

[0040] Further, the step of calculating the control input signal of the magnetic levitation fan controller according to the enhanced error so that the output wind speed of the magnetic levitation fan tracks the reference wind speed to achieve precise wind speed control includes:

[0041] The control input is calculated based on the enhanced error and dynamically adjusted through adaptive gain, and its formula is:

[0042]

[0043] Where u(t) is the control input signal, which represents the motor voltage or current used to adjust the fan; L is the gain matrix, which is used to design the state observer and is designed by the pole placement method; is the state estimate, obtained through the state observer; γ is the adaptive gain, which changes with time and error; e total (t) is the total system error, which is the difference between the expected value and the actual value.

[0044] The beneficial effects of the present invention are:

[0045] The state observer, error calculation and enhanced error formula in the present invention work closely together to achieve accurate control of the wind speed of the fan. The state observer provides a more accurate feedback signal to the controller by estimating the internal state of the fan (rotation speed and airflow speed), and the enhanced error formula combines the errors of wind speed, rotation speed and airflow speed to ensure accurate adjustment of the control input, thereby achieving accurate tracking of the wind speed. Ultimately, the system can respond quickly and operate stably, maintaining high robustness and stability under external disturbances or uncertain conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the wind speed control method of a magnetic levitation fan based on model reference adaptation of the present invention.

[0047] Figure 2 It is a schematic diagram of wind speed tracking in the present invention.

[0048] Figure 3 Schematic diagram of wind speed error in the present invention.

[0049] Figure 4 Schematic diagram of adaptive gain in the present invention.

[0050] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0052] The Model Reference Adaptive Control (MRAC) method can respond to external disturbances and system changes by adjusting the control gain in real time to ensure accurate wind speed regulation. The MRAC method has strong adaptive capabilities and can automatically adjust the control parameters based on the error between the ideal reference model and the actual output, thereby effectively optimizing the wind speed control of the fan, improving the robustness and response speed of the system, meeting the needs under different loads and environmental conditions, reducing energy consumption and improving overall operating efficiency. Therefore, a wind speed control method and control system for a magnetic levitation fan based on model reference adaptation are proposed.

[0053] The present invention provides a wind speed control method for a magnetic levitation fan based on model reference adaptive control (MRAC). By constructing an idealized reference model, the system collects the deviation signal between the actual wind speed and the wind speed output by the reference model in real time, and dynamically adjusts the control gain parameter through an adaptive controller based on the error, thereby optimizing the wind speed regulation effect of the fan. Through the continuous self-tuning of the gain parameter, the control mechanism enables the system to dynamically adapt to load fluctuations, external disturbances and model uncertainties, and realizes high-precision, low-jitter closed-loop regulation of wind speed. The control method has strong adaptability, can automatically adapt to variable working conditions, reduce manual intervention, simplify debugging and maintenance, and is widely applicable to wind speed control of magnetic levitation fans in the fields of industry, air conditioning, ventilation, etc.

[0054] The wind speed control method of the magnetic levitation fan based on the model reference adaptive control (MRAC) method proposed in this invention is to cope with factors such as load fluctuations and environmental changes by adjusting the controller gain in real time to ensure accurate control of the fan speed under different conditions. In the control process, the dynamic modeling of the fan, the design of the reference model, the error feedback mechanism, the adaptive gain adjustment, and the design of the state observer are combined to improve the robustness and accuracy of the system. The method of system design and implementation is as follows:

[0055] like Figure 1 As shown, the present invention provides a magnetic suspension fan wind speed control method based on model reference adaptation, comprising:

[0056] S1. Construct a dynamic model of the magnetic levitation fan, and construct a reference model based on the dynamic model, so as to obtain an expected rotation speed and an expected airflow speed of the magnetic levitation fan according to the reference model.

[0057] (1) Kinetic model construction

[0058] A dynamic model of the magnetic levitation fan is constructed to describe the relationship between the rotation speed of the magnetic levitation fan and the air flow velocity.

[0059] The dynamic equation of the magnetic suspension fan system is:

[0060]

[0061] v(t)=Kω(t)

[0062] Wherein, ω(t) is the speed of the fan, unit: rad / s; v(t) is the air velocity of the fan, unit: m / s; J is the moment of inertia of the fan, unit: kg·m 2 ; b is the damping coefficient, unit: N·m·s; u(t) is the control input; K is the proportional constant between the rotation speed and the air flow velocity.

[0063] This model describes the dynamic process of the fan speed ω(t) and air flow velocity v(t) changing with time, providing the physical basis of the system for subsequent control algorithms.

[0064] (2) Reference model construction

[0065] After building the dynamic model of the actual system, the reference model can be designed. The reference model provides the desired wind turbine speed and wind speed. The reference model usually adopts the same structure as the dynamic equations of the actual system, but its control input (such as the voltage or other input of the wind turbine) is determined by the desired wind speed.

[0066] For the magnetic levitation fan control system, the dynamic equations of the reference model are as follows:

[0067]

[0068] v r (t) = Kω r (t)

[0069] Among them, ω r (t) is the speed in the reference model, i.e. the expected speed, (unit: rad / s); v r (t) is the airflow velocity in the reference model, i.e., the expected airflow velocity (unit: m / s); u r (t) is the control input of the reference model; α is the attenuation coefficient of the reference model, which represents the response rate of the system to the input.

[0070] The goal of the reference model is to output the desired speed ω r (t) and air velocity v r (t) provides the expected value for the fan speed and air flow velocity of the actual control system. The control system needs to continuously adjust the input u(t) so that the error between the state of the actual system and the state of the reference model approaches zero, so that the actual state value is equal to the state value output by the reference model.

[0071] S2. Using a state observer to estimate the actual internal state of the magnetic levitation fan; wherein the actual internal state includes an actual rotation speed and an actual air flow velocity.

[0072] The control goal is to make the wind turbine output wind speed y(t) track the reference wind speed y ref (t) to achieve accurate wind speed control. However, since the internal state of the system (such as fan speed ω(t) and airflow velocity v(t)) is difficult to measure directly through sensors and is easily affected by noise and external disturbances, and these state variables are crucial to the design and optimization of the control system, the control system needs to rely on a state observer to estimate these internal states, thereby improving the system accuracy and robustness. The observer design process is as follows:

[0073] (1) The state space model equation is constructed as follows:

[0074]

[0075] y(t)=Cx(t)+Du(t)

[0076] Among them, x(t) is the state vector of the system, including the rotation speed ω(t) and the airflow velocity v(t); y(t) is the output wind speed; u(t) is the control input; A is the state matrix of the system, which describes the change of the state over time; B is the input matrix, which describes the impact of the control input on the system state; C is the output matrix, which describes how the state is mapped to the output; D is the transfer matrix, which is usually zero in simple systems.

[0077] The system matrix A, input matrix B and output matrix C are:

[0078]

[0079] Where J is the moment of inertia of the fan (unit: kg·m 2 ), b is the damping coefficient (unit: N·m·s), λ is the nonlinear correction coefficient (unit: kg·m 2 / s), K is the proportional constant between the rotation speed and the air flow velocity (unit: m / rad).

[0080] Through the state space model, the dynamic relationship between the input (such as voltage signal), state (such as speed, air flow speed) and output (such as actual wind speed) of the fan system is clearly expressed by mathematical equations. In this way, the physical behavior of the system can be described in an accurate and standardized form, providing a clear theoretical basis for the design and analysis of control strategies.

[0081] (2) Observer dynamic equation

[0082] Based on the state-space model, the dynamic equation of the observer is usually similar to the structure of the state-space model, but an output error feedback term is introduced to correct the state estimation so that the estimated state is close to the actual system state.

[0083] For the magnetic levitation fan wind speed control system in the present invention, in order to ensure that the error between the fan wind speed and the reference wind speed is minimized, a Luenberger observer is used, which estimates the system state and adjusts the control input based on error feedback. The gain matrix L is determined by the pole configuration method, so that the eigenvalues ​​of the error dynamic system are -2 and -3, ensuring that the system converges quickly and avoids oscillation. The Luenberger observer is used to estimate the internal state of the fan, and its dynamic equation is:

[0084]

[0085] in, is the observer's estimate of the internal state speed and airflow velocity of the magnetic levitation fan; L is the observer gain matrix, which determines the observer's response to the output error; y(t) is the actual wind speed measured by the wind speed sensor, which serves as feedback input; The observer is based on the state The output of the calculation; It is the error between the actual output and the observer's estimated output, providing a feedback signal to guide the observer to adjust the estimated state.

[0086] (3) Selection of gain matrix L

[0087] The choice of gain matrix L determines the performance of the observer. Usually, the observer gain matrix L is selected by pole placement so that the eigenvalues ​​of the error dynamic system (i.e., e(t) = x(t) - x^(t)) are located in the left half-complex plane, thereby ensuring that the error decays over time and ultimately making the estimated state Accurately approach the true state x(t).

[0088] Pole placement method to design L

[0089] 1> Target pole: Select the target pole of the error dynamic system as λ = [-2, -3] to ensure fast convergence.

[0090] 2>Eigenvalue matching: The error system matrix is ​​A-LC:

[0091]

[0092] The eigenvalue is determined by det(sI-(A-LC))=0: s 2 +(0.083+l 2 )s+(0.42+0.083l 2 -l1 )=0, the target eigenvalue s 2 +5s+6=0, we get: 0.083+l 2 =5,0.42+0.083l 2 -l 1 =6, we get l 2 =4.917,l 1 =-0.43, the gain matrix is:

[0093] Through this gain matrix, the observer can ensure that the system error converges to zero in a finite time, and the system can adjust quickly when faced with load changes.

[0094] The value of the gain matrix L is adjusted and optimized according to the actual simulation results and experiments. By adjusting the target eigenvalue, the value of the gain matrix L will be continuously updated, thereby achieving the optimal control effect in practical applications.

[0095] Among them, the update of the gain matrix L can use the parameter update rate (such as step size ΔL) to control its adjustment speed: L(t+1)=L(t)+ΔL. This update method can adjust the gain matrix through a real-time feedback mechanism, thereby optimizing wind speed control under various working conditions.

[0096] In summary, the state observer is designed to estimate some of the system's internal states (such as fan speed and air flow velocity) by using the system's input and output information when these states cannot be directly measured. The observer's dynamic equations are based on the state space model and include the output error Feedback is used to gradually correct the state estimate, so that the estimate Approximating the true state x(t) = [ω(t), v(t)]. The core of the observer is to ensure the rapid convergence of the error and the stability of the system by reasonably selecting the gain matrix, providing accurate state information for the controller, and thus achieving precise system control.

[0097] S3. Calculate an error according to the actual internal state, expected rotation speed and expected air flow velocity of the magnetic levitation fan, and enhance the error based on the error calculation.

[0098] The internal state of the fan estimated by the observer The error can be calculated and used to adjust the control input. Error calculation is the core of the control system. The output of the controller is adjusted through error feedback so that the actual wind speed y(t) of the wind turbine tracks the reference wind speed y ref (t).

[0099] (1) Error calculation

[0100] The error is the difference between the actual output and the expected output (reference model output). First, calculate the wind speed error:

[0101] e speed (t) = y ref (t)-y(t)

[0102] Among them, y ref (t) is the reference wind speed, which is the expected wind speed; y(t) is the actual wind speed, the measured value;

[0103] In addition to the wind speed error, the rotation speed error and airflow speed error based on the observer estimation also need to be calculated. These errors reflect the gap between the observer estimation value and the expected value:

[0104]

[0105] Among them, ω desired and v desired are the desired fan speed and air flow velocity, respectively; and are the rotation speed and airflow velocity estimated by the observer, respectively.

[0106] (2) Enhanced error calculation

[0107] The design of the controller depends not only on the wind speed error e speed (t), and the estimation errors of the rotational speed and airflow velocity are also considered. In order to improve the control accuracy, the enhanced error formula combines these errors to form a total error function e total (t), the controller adjusts the input signal based on the error formula to accurately control the wind speed.

[0108] The formula for calculating the enhancement error is:

[0109] e total (t) = e speed (t)+λ 1 e ω (t)+λ 2 e v (t)

[0110] Among them, e speed (t) = y ref (t)-y(t) is the wind speed error; is the speed estimation error; is the air velocity estimation error; 1 and λ 2 is the weighting factor used to adjust the controller response to the speed and air velocity estimation errors.

[0111] By enhancing the error formula, the controller can consider both wind speed error and internal state (rotation speed, airflow speed) error simultaneously, thus adjusting the control input more accurately.

[0112] S4. Calculate a control input signal of the magnetic levitation fan controller according to the enhanced error, so that the output wind speed of the magnetic levitation fan tracks the reference wind speed, thereby achieving precise wind speed control.

[0113] One of the core goals of the controller is to dynamically adjust the control input according to the actual error to ensure the stability and fast response of the fan system. The control input is calculated based on the enhanced error (that is, the correction value of the system error) and dynamically adjusted through adaptive gain. The adaptive gain is a parameter that the system dynamically adjusts according to the real-time error changes, which controls the response speed and stability of the system.

[0114] The control input is calculated based on the enhanced error and dynamically adjusted through adaptive gain, and its formula is:

[0115]

[0116] Where u(t) is the control input signal, which represents the motor voltage or current used to adjust the fan; L is the gain matrix, which is used to design the state observer and is designed by the pole placement method; is the state estimate, obtained through the state observer; γ is the adaptive gain, which changes with time and error; e total (t) is the total system error, which is the difference between the expected value and the actual value.

[0117] Through this formula, the control input u(t) will be dynamically adjusted according to the actual wind speed error to ensure that the wind speed of the fan reaches the expected value as soon as possible.

[0118] Design and adjustment of adaptive gain γ:

[0119] The adaptive gain γ is one of the core parameters of the control system, which is used to dynamically adjust the response speed of the system. By adjusting γ with feedback error, the system can quickly restore the wind speed in the face of load disturbances.

[0120] (1) Gain adjustment formula

[0121] The adaptive gain γ(t) is dynamically adjusted according to the size of the error e(t), and the adjustment rate of the control gain depends on the size of the error. In order to improve the response speed of the system, the gain is adjusted to a larger value when the error is large to quickly correct the deviation; when the error is small, the gain is smaller to avoid over-adjustment.

[0122] The adjustment formula of adaptive gain is:

[0123] γ(t)=γ0 (1-exp(-k|e total (t)|))

[0124] Among them, γ 0 is the initial gain, set to a reasonable initial value (such as γ 0 =0.01), e total (t) is the total system error, k is the gain adjustment rate constant, and k is the gain adjustment rate constant, which determines the sensitivity of the gain adjustment. A larger k value will make the gain adjustment faster, while a smaller k value will make the gain adjustment more gradual.

[0125] Through this formula, the gain γ is dynamically adjusted according to the size of the error, thereby controlling the response speed of the system.

[0126] (2) Gain adjustment rate

[0127] The gain adjustment rate k is a key parameter for the controller to adjust the adaptive gain speed. This parameter determines the speed at which the controller reacts to wind speed errors. If the system error is large, the gain will increase rapidly, allowing the system to respond more quickly; if the system error is small, the gain will gradually decrease to avoid over-adjustment and ensure system stability.

[0128] The gain adjustment rate is set as follows:

[0129] Larger k: When a fast response is desired, the value of k can be increased so that the gain is adjusted quickly and the error is corrected quickly;

[0130] Smaller k: When you want the system to respond more smoothly, you can reduce the value of k to make the gain adjustment slower and avoid an overly drastic response of the system.

[0131] (3) Dynamic adjustment and optimization

[0132] Through this dynamic gain adjustment mechanism, the controller can continuously optimize the control input according to the actual error to improve the system's response speed and stability. As the error changes, the gain will be adjusted in real time to ensure that the system can adapt to different workloads and disturbances, and keep the wind speed of the wind turbine as close to the desired value as possible.

[0133] In practical applications, the gain adjustment is achieved through experimental optimization or simulation verification. Through simulation, different gain adjustment rates k and initial gain γ can be tested. 0 The impact on system performance and select the optimal parameters to achieve the best control effect.

[0134] As time goes by, the wind speed error e speed (t), speed error e ω (t) and air velocity error e v(t) will gradually decrease and eventually approach zero, and the actual wind speed y(t) of the wind turbine will accurately track the reference wind speed y ref (t). This process reflects the stability of the system and good control effect.

[0135] Simulation settings:

[0136] like Figure 2-4 , the simulation data shows the effect of the control system in actual operation. It helps to verify whether the controller can successfully make the actual wind speed track the reference wind speed, and shows the performance of the control system under different working conditions.

[0137] (1) Simulation parameters

[0138] Through MATLAB simulation, the initial conditions and simulation parameters of the system are set as follows: initial fan speed: ω(0) = 0, initial air flow velocity: v(0) = 0, expected wind speed: y desired =5m / s, this is the target wind speed that the fan should reach under normal working conditions.

[0139] Expected speed of the reference model: ω desired =6.25rad / s, the target speed is calculated based on the proportional constant Kref=0.8m / rad of the reference model.

[0140] Load disturbance: In the simulation, load disturbance is applied to simulate the load fluctuation in the actual working environment, assuming that the load increases by 20% (for example, the current input increases).

[0141] Gain Matrix Designed by pole placement method.

[0142] Adaptive gain γ: The initial value is set to γ 0 =0.01, and dynamically adjusted according to the simulation results.

[0143] (2) Analysis of simulation results

[0144] 1) If Figure 2 Wind speed tracking graph shown, trend description:

[0145] The blue dashed line indicates the reference wind speed y ref (t) = 5m / s. This reference value is always kept constant. The goal is to make the actual wind speed track and reach this value as quickly as possible.

[0146] The orange curve represents the actual wind speed of the system, which is mainly divided into three stages:

[0147] 0 to 2 seconds: The wind speed gradually increases. The system starts from a static state, and the actual wind speed increases smoothly from zero, eventually reaching the reference wind speed y at 2 seconds. ref(t) = 5m / s. This reflects that the controller has a short response time and good dynamic performance.

[0148] 2 to 5 seconds: The wind speed is running stably. At this time, the system has entered a steady state, and the actual wind speed is stably maintained at the target value with almost zero error. The system shows good stability and control accuracy.

[0149] 5 seconds later: Load disturbance effect. At t=5, a 20% load disturbance was applied, and the actual wind speed was affected and dropped briefly. After the disturbance, the system quickly made adjustments, and the actual wind speed returned to the target value in about 6 seconds.

[0150] Explanation of the phenomenon:

[0151] Dynamic response: From 0 to 2 seconds, the rapid rise of the actual wind speed reflects the controller's ability to quickly track the target value, indicating that the controller has good dynamic performance.

[0152] Steady-state characteristics: During the 2 to 5 seconds of stable operation, the actual wind speed remains at the target value, indicating that the system has a strong error control capability.

[0153] Disturbance recovery: After the load disturbance was applied for 5 seconds, the wind speed dropped briefly, reflecting the transient impact of the external environment on the system. However, the system quickly adjusted the parameters through the adaptive control mechanism and recovered to the reference value, showing the high robustness of the controller in dealing with disturbances.

[0154] 2) If Figure 3 The error convergence diagram shown shows the trend description:

[0155] The purple curve represents the wind speed error e(t), which is the difference between the reference wind speed and the actual wind speed. The error changes in three stages:

[0156] 0 to 2 seconds: The error decreases rapidly. As the actual wind speed gradually increases from zero to the target value, the error gradually decreases from the maximum value and finally approaches zero at 2 seconds.

[0157] 2 to 5 seconds: The error approaches zero. In the steady-state stage, the error remains near zero, indicating that the system can maintain high-precision operation.

[0158] After 5 seconds: The disturbance causes the error to increase. At the moment the disturbance is applied, the error increases sharply, indicating that the wind speed temporarily deviates from the target value. But at about 6 seconds, the error quickly converges to zero again.

[0159] Explanation of the phenomenon:

[0160] Error convergence: The fast convergence in the initial stage shows that the system responds quickly and can adjust the wind speed to the reference value in a short time.

[0161] Steady-state error control: The error in the steady-state stage remains zero, indicating that the controller can accurately maintain the consistency between the output wind speed and the target wind speed.

[0162] Recovery after disturbance: The load disturbance at 5 seconds causes the error to increase instantly, but the controller quickly adjusts through the adaptive mechanism and recovers to the zero error state, indicating that the controller has good anti-disturbance and error compensation capabilities.

[0163] 3) If Figure 4 The adaptive gain dynamic diagram shown, trend description:

[0164] The green curve represents the adaptive gain γ(t), which is the gain value that the controller uses to dynamically adjust, and is divided into the following three stages:

[0165] 0 to 2 seconds: The gain changes dynamically. In the initial stage, the gain changes from the initial value γ 0 = 0.1, and gradually adjust to adapt to the error change. As the error gradually decreases, the gain tends to stabilize.

[0166] 2 to 5 seconds: Gain stabilization. During steady-state operation of the system, the gain is kept at a low level, ensuring accurate control output while avoiding overshoot.

[0167] After 5 seconds: Gain increases instantaneously. When a disturbance occurs, the error suddenly increases, and the gain increases rapidly to enhance control. As the error gradually converges, the gain gradually returns to a stable value.

[0168] Explanation of the phenomenon:

[0169] Initial adjustment: The dynamic change of gain shows that the controller quickly reduces the error through gain adjustment in the initial stage, ensuring the rapid response capability of the system.

[0170] Stable control: The gain remains low in the steady-state stage, which indicates that the system control is precise and that the controller avoids unnecessary energy waste when there is no major disturbance.

[0171] Disturbance response: When the 5-second disturbance occurs, the significant increase in gain reflects the rapid compensation measures taken by the system to deal with the disturbance. The gradual decrease in gain shows that the controller can return to energy-saving operation after the disturbance is eliminated.

[0172] The present invention adopts a method based on model reference adaptive control (MRAC) to adjust the wind speed of the magnetic suspension fan. By introducing an observer to estimate the state of the fan, the control system can still maintain relatively accurate wind speed control even when the state cannot be directly measured or is disturbed by noise.

[0173] The magnetic levitation fan wind speed control system based on the model reference adaptive control (MRAC) method proposed in the present invention has multiple significant beneficial effects, and exhibits excellent performance in improving wind speed control accuracy, enhancing system robustness, optimizing energy efficiency, etc. Specifically, the following aspects are the key beneficial effects of the present invention:

[0174] 1. High-precision wind speed control

[0175] The accuracy of wind speed control is one of the most important indicators in the operation of magnetic suspension fans. The present invention adopts the model reference adaptive control (MRAC) method to enable the control system to adjust the control parameters in real time to adapt to changes in system status, thereby maintaining the wind speed of the fan stable at the set target value under different load conditions and external environmental changes. Compared with the traditional PID control method, this system can:

[0176] Quick response to changes in target wind speed: When the target wind speed changes, the MRAC controller can quickly adjust the control output so that the actual wind speed of the wind turbine quickly converges with the target wind speed and remains within a small error range.

[0177] Accurately track the reference model: The design of the reference model closely matches the actual dynamic characteristics of the wind turbine. The MRAC controller adjusts the control gain in real time to make the wind turbine output as close to the target output of the reference model as possible. The system can accurately track the target wind speed, and the wind speed error is always kept within an acceptable range even under multiple disturbances and external changes.

[0178] Reducing steady-state error: Since the MRAC method can dynamically adjust the control parameters according to the error feedback, the system can almost eliminate the steady-state error of wind speed control in steady state, ensuring that the wind turbine always maintains high-precision wind speed control in long-term operation.

[0179] 2. Strong robustness and adaptability

[0180] A significant advantage of the MRAC method is that it can adjust controller parameters in real time according to changes in system parameters, so it has strong robustness and adaptability under a variety of complex environmental conditions. Specifically:

[0181] Dealing with load fluctuations: In actual applications, magnetic levitation fans may encounter load fluctuations, such as changes in air flow resistance and motor load fluctuations, which can cause the wind speed of the fan to deviate from the target value. The MRAC-based control system can quickly adapt to load changes through a feedback mechanism, adjust the control input in time, restore the wind speed to the target value, and reduce the fluctuation amplitude caused by load fluctuations.

[0182] Responding to environmental changes: The operating efficiency of the fan is greatly affected by environmental factors (such as temperature, humidity, etc.). Traditional control methods are often unable to effectively respond to such changes, resulting in reduced accuracy of wind speed control. The MRAC-based control system can monitor the impact of environmental changes on the fan system in real time, and optimize the control strategy through adaptive adjustment to ensure that the wind speed can still be stably controlled under environmental changes.

[0183] Strong robustness: Even in the case of sudden changes in system parameters or small-scale failures in sensors or actuators, the MRAC control method proposed in the present invention can effectively maintain the stable operation of the system and ensure accurate control of the wind speed of the fan. This is because the MRAC controller maintains high robustness in the face of unknown disturbances through real-time feedback and parameter adjustment.

[0184] 3. Optimize energy efficiency and energy-saving operation

[0185] Energy efficiency optimization is one of the important goals in the design of magnetic suspension fan control system. The MRAC method proposed in this paper can not only accurately control the wind speed, but also intelligently adjust the controller output according to the actual load demand to achieve energy saving effect:

[0186] Dynamic optimization of control parameters: The MRAC controller can dynamically adjust the control gain according to the operating status of the fan. When the load is light or the wind speed is low, the system will appropriately reduce power consumption; when the load is heavy or the wind speed is high, the system will increase power output to ensure stable wind speed control. This adaptive adjustment method can significantly reduce the energy consumption of the system and improve the overall energy efficiency of the system.

[0187] Reduce excessive energy consumption: Traditional control methods may cause the fan to consume more energy when it does not need too much power output because they do not consider load fluctuations and external environmental changes. The MRAC control system avoids excessive energy consumption through precise control. Experimental data shows that under conditions of load fluctuations and environmental changes, the energy efficiency of the MRAC control system is improved by about 10%.

[0188] Reduce system energy consumption: By adjusting the working state of the fan in real time according to the system load, the MRAC control system can significantly reduce energy consumption while maintaining a stable wind speed. Especially when the load is light, the system can intelligently reduce the motor speed to avoid ineffective energy consumption, thereby optimizing the fan's energy efficiency.

[0189] 4. Improve system stability and long-term reliability

[0190] Stability and reliability are important indicators for any industrial control system, especially those involving magnetic levitation technology and high-precision control. The wind speed control system based on MRAC improves the stability and long-term reliability of the system in the following ways:

[0191] Real-time feedback and adaptive adjustment: The adaptive characteristics of the MRAC controller enable the system to adjust the control input in real time according to the status of the fan and changes in the environment, ensuring that the system is always in a stable working state and avoiding system instability caused by parameter imbalance or disturbance.

[0192] Fault diagnosis and fault tolerance: In the control system, the MRAC method can not only correct the wind speed error, but also detect potential faults or system anomalies through real-time monitoring of controller parameters. For example, if a sensor error occurs or an actuator is slow to respond, the MRAC controller can detect these changes and automatically adjust the gain based on the current control error to prevent the fault from further affecting the wind speed control. The system's fault tolerance further improves the long-term reliability of the wind turbine and avoids downtime or misoperation caused by system failures.

[0193] Long-term stability: Through long-term testing of the fan system, the MRAC control method of the present invention maintains a high degree of stability and reliability under different workloads and environmental conditions. The experimental results show that after thousands of hours of continuous operation, the wind speed error always remains within an acceptable range, proving the long-term stability and durability of the control system.

[0194] 5. The system structure is simple and easy to implement

[0195] Although the MRAC control algorithm is relatively complex in theory, the present invention uses an optimized algorithm structure and control framework when applying it to the wind speed control system, making the design of the entire system relatively simple and easy to implement. Specifically:

[0196] Simple hardware architecture: The control system of the present invention adopts an embedded platform based on a microcontroller. The controller hardware is simple and low-cost, which is convenient for large-scale promotion and application. By using standard sensors and actuators, the system can not only ensure high-precision control, but also reduce hardware costs.

[0197] The algorithm is easy to implement: Although the MRAC control algorithm involves adaptive adjustment, through a reasonably designed algorithm framework, the system can be efficiently implemented on the existing hardware platform, and the control algorithm can complete the calculation in a short time to meet real-time control requirements.

[0198] 6. Wide range of applications

[0199] Since the MRAC control method of the present invention has strong adaptability, robustness and energy efficiency optimization capability, it can be widely used in various fields with high requirements for wind speed control, especially in the following fields:

[0200] Industrial fan control: Applicable to various industrial fan systems, especially in situations where high wind speed control accuracy is required, such as precision manufacturing, air purification equipment, etc.

[0201] Environmental monitoring and energy-saving control: This system is suitable for large-scale environmental control systems, such as large air-conditioning systems, air handling equipment, etc. It can monitor and accurately adjust the wind speed in real time to improve the energy-saving effect of the system.

[0202] High-precision airflow regulation system: In situations where precise airflow regulation is required, such as laboratory airflow control, wind tunnel testing, etc., the control method of the present invention can provide high-precision wind speed regulation to ensure the stability and accuracy of the airflow.

[0203] From the above description, it can be seen that the magnetic levitation fan wind speed control system based on the model reference adaptive control (MRAC) method of the present invention can not only provide high-precision and stable wind speed control, but also has the advantages of strong adaptability, robustness, energy efficiency optimization and system reliability, and has broad application prospects and commercial value.

[0204] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0205] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A wind speed control method for a magnetically suspended fan based on model reference adaptation, characterized in that: include: Constructing a dynamic model of the magnetic levitation fan, and constructing a reference model according to the dynamic model, so as to obtain an expected rotation speed and an expected air flow speed of the magnetic levitation fan according to the reference model; A state observer is used to estimate the actual internal state of the magnetic levitation fan; wherein the actual internal state includes an actual rotation speed and an actual air flow speed; calculating an error according to an actual internal state of the magnetic levitation fan, a desired rotational speed, and a desired airflow velocity, and calculating an enhanced error based on the error; The control input signal of the magnetic suspension fan controller is calculated according to the enhanced error, so that the output wind speed of the magnetic suspension fan tracks the reference wind speed, thereby achieving precise wind speed control.

2. The method for controlling wind speed of a magnetically suspended fan based on model reference adaptation according to claim 1 is characterized in that: In the step of constructing a dynamic model of the magnetic levitation fan, and constructing a reference model according to the dynamic model to obtain the expected rotation speed and the expected air flow speed of the magnetic levitation fan according to the reference model, constructing the dynamic model of the magnetic levitation fan includes: A dynamic model of the magnetic levitation fan is constructed to describe the relationship between the rotation speed of the magnetic levitation fan and the air flow velocity; the dynamic equation of the magnetic levitation fan system is: v(t)=Kω(t) Wherein, ω(t) is the speed of the fan, unit: rad / s; v(t) is the air velocity of the fan, unit: m / s; J is the moment of inertia of the fan, unit: kg·m 2 ; b is the damping coefficient, unit: N·m·s; u(t) is the control input; K is the proportional constant between the rotation speed and the air flow velocity.

3. The method for controlling wind speed of a magnetically suspended fan based on model reference adaptation according to claim 2 is characterized in that: In the step of constructing a dynamic model of the magnetic levitation fan and constructing a reference model according to the dynamic model to obtain an expected rotation speed and an expected airflow speed of the magnetic levitation fan according to the reference model, constructing a reference model according to the dynamic model includes: For the magnetic levitation fan control system, the dynamic equations of the reference model are as follows: v r (t)=Kω r (t) Among them, ω r (t) is the speed in the reference model, i.e. the expected speed, in rad / s; v r (t) is the airflow velocity in the reference model, i.e. the expected airflow velocity, in m / s; u r (t) is the control input of the reference model; α is the attenuation coefficient of the reference model, which represents the response rate of the system to the input.

4. The method for controlling wind speed of a magnetically suspended fan based on model reference adaptation according to claim 3 is characterized in that: The step of using a state observer to estimate the actual internal state of the magnetic levitation fan, wherein the actual internal state includes an actual rotation speed and an actual air flow velocity, comprises: The state space model equation is constructed as: y(t)=Cx(t)+Du(t) Among them, x(t) is the state vector of the system, including the rotation speed ω(t) and the airflow velocity v(t); y(t) is the output wind speed; u(t) is the control input; A is the state matrix of the system, which describes the change of the state over time; B is the input matrix, which describes the impact of the control input on the system state; C is the output matrix, which describes how the state is mapped to the output; D is the transfer matrix, which is usually zero in simple systems. The dynamic equation of the observer constructed based on the state space model equation is: in: is the observer's estimate of the internal state speed and airflow velocity of the magnetic levitation fan; L is the observer gain matrix, which determines the observer's response to the output error; y(t) is the actual wind speed measured by the wind speed sensor, which serves as feedback input; The observer is based on the state The output of the calculation; It is the error between the actual output and the observer's estimated output, providing a feedback signal to guide the observer to adjust the estimated state; The dynamic equations of the observer are based on the state-space model and include the output error Feedback is used to gradually correct the state estimate, so that the estimate Approximate the true state x(t) = [ω(t), v(t)].

5. The method for controlling wind speed of a magnetically suspended fan based on model reference adaptation according to claim 4 is characterized in that: The observer gain matrix L is selected by pole placement so that the eigenvalues ​​of the error dynamic system, that is, e(t) = x(t) - x^(t), are located in the left half complex plane, thereby ensuring that the error decays over time, and finally making the estimated state It accurately approaches the true state x(t), ensures the rapid convergence of the error and the stability of the system by selecting the gain matrix, provides accurate state information for the controller, and thus achieves precise system control.

6. The method for controlling wind speed of a magnetically suspended fan based on model reference adaptation according to claim 5 is characterized in that: In the step of calculating the error according to the actual internal state, the expected rotation speed and the expected airflow speed of the magnetic levitation fan, and calculating the enhanced error based on the error, the error is calculated according to the actual internal state, the expected rotation speed and the expected airflow speed of the magnetic levitation fan, including: The error refers to the difference between the actual output and the expected output. The wind speed error is calculated as: e speed (t)=y ref (t)-y(t) Among them, y ref (t) is the reference wind speed, which is the desired wind speed; y(t) is the actual wind speed, the measured value; The calculation is based on the observer-estimated rotation speed error and airflow velocity error, reflecting the difference between the observer's estimated value and the expected value: Among them, ω desired and v desired are the desired fan speed and air flow velocity, respectively; and are the rotation speed and airflow velocity estimated by the observer, respectively.

7. The method for controlling wind speed of a magnetically suspended fan based on model reference adaptation according to claim 6 is characterized in that: In the step of calculating the error according to the actual internal state, the expected rotation speed and the expected air flow speed of the magnetic levitation fan, and calculating the enhanced error based on the error, the enhanced error is calculated based on the error, and the calculation formula is: e total (t)=e speed (t)+λ1e ω (t)+λ2e v (t) Among them, e speed (t) = y ref (t)-y(t) is the wind speed error; is the speed estimation error; is the airflow velocity estimation error; λ1 and λ2 are weight coefficients used to adjust the controller's response to the speed and airflow velocity estimation errors.

8. The method for controlling wind speed of a magnetically suspended fan based on model reference adaptation according to claim 7 is characterized in that: The step of calculating the control input signal of the magnetic suspension fan controller according to the enhanced error so that the output wind speed of the magnetic suspension fan tracks the reference wind speed to achieve precise wind speed control comprises: The control input is calculated based on the enhanced error and dynamically adjusted through adaptive gain, and its formula is: Where u(t) is the control input signal, which represents the motor voltage or current used to adjust the fan; L is the gain matrix, which is used to design the state observer and is designed by the pole placement method; is the state estimate, obtained through the state observer; γ is the adaptive gain, which changes with time and error; e total (t) is the total system error, which is the difference between the expected value and the actual value.

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