A model predictive control method and system for a permanent magnet synchronous motor of an air compressor

Abstract

The application provides a kind of air compressor permanent magnet synchronous motor model prediction control method and system, including the improved speed controller based on speed loop disturbance observation, target current corrector, double-step ideal voltage calculator, double-step current prediction controller based on current loop disturbance observation.The improved speed controller based on speed loop disturbance observation outputs target quadrature-axis current;The target current corrector corrects target current value in real time;The double-step ideal voltage calculator calculates the ideal voltage vector considering two-step prediction;The double-step current prediction controller based on current loop disturbance observation adds the change of speed in sampling period to the prediction equation, and carries out two-step prediction.The application effectively solves a series of problems caused by parameter mismatch of air compressor drive motor due to ultra-high speed, temperature rise and other reasons, ultimately reduces the fluctuation of motor current, realizes the precise control of air compressor speed.

Description

Technical Field

[0001] This invention relates to the field of electromechanical coupling control technology for air compressors used in fuel cell systems, and in particular to a model predictive control method and system for a permanent magnet synchronous motor used in an air compressor. Background Technology

[0002] Turbo air compressors are widely used in industrial fields due to their high efficiency, large flow rate, low vibration, low noise, and strong stability. A turbo air compressor accelerates gas through a high-speed rotating impeller, increasing its kinetic energy. This kinetic energy is then converted into pressure energy by a diffuser, thus achieving the purpose of compressing the gas.

[0003] The drive motor of a turbo air compressor is usually an ultra-high-speed permanent magnet synchronous motor. Under normal circumstances, ultra-high-speed permanent magnet synchronous motors are characterized by extremely low inductance and extremely small flux linkage. In actual operation, due to changes in speed, temperature rise, external interference, fatigue aging, and other factors, ultra-high-speed permanent magnet synchronous motors often experience significant parameter mismatch, which leads to increased speed fluctuations and reduced control accuracy. This greatly affects the adaptability of the air compressor control algorithm to different operating conditions. Therefore, how to improve the control algorithm to cope with parameter mismatch of the air compressor drive motor, and thus adapt to complex operating conditions and improve control accuracy, has become a current research hotspot. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a model predictive control method and system for a permanent magnet synchronous motor for air compressors, which overcomes the influence of speed error and current fluctuation caused by parameter mismatch, improves the robustness of the system, enhances the control accuracy of the system, alleviates system fatigue caused by current fluctuation, and extends the life of the system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a model predictive control method for a permanent magnet synchronous motor for an air compressor, wherein an improved speed controller based on speed loop disturbance observation receives the quadrature-axis current signal and speed error signal from the current sensor at the current moment, and based on an improved sliding mode observation algorithm, simulates the parameter mismatch problem generated by the air compressor drive motor as a load torque, adds it to the improved sliding mode control equation, and outputs the target quadrature-axis current value.

[0006] In a preferred embodiment, the following is included:

[0007] 1) Define the improved sliding mode speed control reaching law according to the following equation:

[0008]

[0009] In the formula, s ω For the speed error sliding surface, e ω For speed error, k lωFor parameters greater than 0, k tω For parameters greater than 0, α ω Let k be the parameter between 0 and 1. fω For parameters greater than 0, ξ ω Let δ be the parameter between 0 and 1. ω For parameters greater than 0, σ ω Parameters that are greater than 0;

[0010] 2) Define the improved sliding mode speed control saturation function y according to the following equation:

[0011]

[0012] In the formula, β ω The boundary layer is a saturated function with a value greater than 0;

[0013] 3) Based on the augmented state-space equation that defines the speed loop disturbance as the speed loop disturbance torque, an observation equation for the speed loop disturbance torque is established, and the approach rate of the speed loop disturbance torque observer is defined according to the following equation:

[0014]

[0015] In the formula, λ smo For observer switching gain greater than 1, J cp For the rotational inertia of the air compressor, e T To perturb the observation error, s smo The sliding surface is used for observing rotational speed error.

[0016] In a preferred embodiment, the target current corrector receives the current signal from the current sensor at the current moment, the target quadrature-axis current signal from the improved speed controller based on speed loop disturbance observation, and the target direct-axis current signal which is always 0, and outputs a target current correction signal through an improved sliding mode control algorithm.

[0017] In a preferred embodiment, the following is included:

[0018] 1) Define the direct-axis current static error sliding surface s according to the following equation. id Cross-axis current static error sliding surface s iq :

[0019]

[0020] In the formula, e id The difference between the target direct-axis current value and the current sampled direct-axis current value, e iq The difference between the target quadrature-axis current value and the current sampled quadrature-axis current value, δ i For integral sliding surface gain, g d For the direct-axis sliding mode constraint function, gq This is the cross-axis sliding mode constraint function;

[0021] 2) Define the orthogonal axis sliding mode constraint function g according to the following equation:

[0022]

[0023] In the formula, α i Parameters that are greater than 0;

[0024] 3) Define the differential equation for the static error compensation value of the current according to the following equation:

[0025]

[0026] In the formula, i drc For the direct-axis target current compensation value, i qrc For the cross-axis target current compensation value, L d For direct-axis inductors, L q For quadrature axis inductance, R s For the line resistance of the air compressor drive motor, k i For parameters greater than 0, θ i ri is a parameter greater than 0, and ri is a parameter located in the interval between 0 and 1.

[0027] In a preferred embodiment, the two-step ideal voltage calculator receives the orthogonal axis current correction target current signal of the target current corrector and the orthogonal axis voltage signal of the motor voltage sensor at the current moment. Based on the extended model current prediction algorithm that takes into account disturbances, it back-calculates the ideal voltage vector that can reach the orthogonal axis current target correction signal value at times k+1 and k+2, and outputs it to the two-step current prediction controller based on current loop disturbance observation.

[0028] In a preferred embodiment, the following is included:

[0029] 1) Define the speed prediction equation according to the following equation:

[0030]

[0031] In the formula, "~" represents prediction, T SG For controller sampling time;

[0032] 2) Derive the orthogonal-axis ideal voltage vector U that reaches the target current value at time k+1 according to the following equation. id1 U iq1 The orthogonal-axis ideal voltage vector U that reaches the target current value at time k+2 is derived. id2 U iq2 ;

[0033]

[0034] In the formula, A U B U D U To account for speed variations and current loop disturbances, the extended current prediction state space matrix, d U This is the system disturbance vector.

[0035] In a preferred embodiment, the dual-step current prediction controller based on current loop disturbance observation receives the corrected orthogonal axis target current signal from the target current corrector and the ideal voltage vector signal from the dual-step ideal voltage calculator, generates a voltage pulse signal, and outputs it to the inverter to drive the air compressor to rotate.

[0036] In a preferred embodiment, the following is included:

[0037] 1) Based on the deadbeat current control principle, and considering the current disturbance caused by motor parameter mismatch, an extended model current prediction algorithm considering the disturbance is designed according to the following equations:

[0038]

[0039] In the formula, U d For the direct-axis voltage value of the motor voltage sensor, U q For the quadrature axis voltage value of the motor voltage sensor, f d For the direct-axis current disturbance observation, f q For cross-axis current disturbance observations, γ d For observer parameters greater than 0, γ q For observer parameters greater than 0, F d For the direct-axis sliding mode control function, F q This is the cross-axis sliding mode control function;

[0040] 2) Define the current loop disturbance sliding mode observation function according to the following equation:

[0041]

[0042] In the formula, s dsmo For the sliding mode surface of the direct-axis observation error, s qsmo For cross-axis observation error sliding surface, M d For the direct-axis boundary layer adaptive saturation function, M q For the cross-axis boundary layer adaptive saturation function, R d For the adaptive reaching law of the direct-axis boundary layer, R q This is the adaptive reaching law for the cross-axis boundary layer;

[0043] 3) Define the boundary layer adaptive reaching law R according to the following equation:

[0044]

[0045] In the formula, ε c For the approach rate gain greater than 0, e smo For observation error, s smo For observation error sliding surface, β c Boundary layer gain greater than 0, η c For parameters greater than 0, λ c Parameters that are greater than 0;

[0046] 4) Define the boundary layer adaptive saturation function M according to the following equation:

[0047]

[0048] The boundary layer adaptive saturation function uses the arctangent function to ensure that the boundary layer thickness does not increase indefinitely. When the sliding mode observation error approaches 0, the boundary layer thickness also approaches 0.

[0049] 5) According to the extended model current prediction algorithm considering disturbances, U d (k), U q (k) is defined as the ideal voltage vector from the two-step ideal voltage calculator, performing current predictions in steps k+1 and k+2, and evaluated according to the following metrics:

[0050]

[0051] Find g i The ideal voltage vector corresponding to the smallest value is the first optimal voltage vector;

[0052] 6) Based on the introduction of duty cycle calculation, the duty cycle is calculated first to obtain the first optimal vector action time. The zero voltage vector corresponding to the remaining voltage vector action time is replaced with 7 basic voltage vectors. The combined voltage vector is calculated. The orthogonal axis predicted current corresponding to the combined voltage vector in step k+1 and step k+2 is calculated by the extended model current prediction algorithm considering disturbances. The second optimal voltage vector is obtained by substituting it into the evaluation index shown.

[0053] The present invention also provides a model predictive control system for a permanent magnet synchronous motor for an air compressor, which operates as described in the model predictive control method for a permanent magnet synchronous motor for an air compressor, including an improved speed controller (1) based on speed loop disturbance observation, a target current corrector (2), a two-step ideal voltage calculator (3), and a two-step current predictive controller (4) based on current loop disturbance observation.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] This application provides a model predictive control method and system for a permanent magnet synchronous motor used in air compressors. Compared with the traditional PI speed controller, it adopts an improved speed controller based on speed loop disturbance observation. This reduces overshoot and increases response speed, overcoming the problem of large overshoot in traditional PI speed controllers when dealing with ultra-high-speed permanent magnet synchronous motors. This ensures that the components of the ultra-high-speed permanent magnet synchronous motor are not damaged due to excessive overshoot. At the same time, it monitors the speed loop disturbance caused by parameter mismatch due to changes in operating conditions and fatigue aging during air compressor operation in real time, thus overcoming the impact of parameter mismatch on control accuracy and speed fluctuation.

[0056] This application provides a model predictive control method and system for a permanent magnet synchronous motor used in air compressors. It employs a target current corrector, which, compared to the traditional target current correction algorithm with direct feedback from a low-pass filter, uses a nonlinear integral sliding surface. This prevents the speed loop and current loop from competing for control of the system before the system stabilizes, ensuring stable system operation. At the same time, without affecting speed and current fluctuations, it smooths out the current control error caused by the model predictive algorithm, further improving control accuracy.

[0057] This application provides a model predictive control method and system for a permanent magnet synchronous motor used in air compressors. It employs a two-step ideal voltage calculator and derives an ideal voltage vector based on an extended current prediction equation that considers current loop disturbances and speed changes within the sampling period. This reduces current fluctuations while saving on controller computation costs.

[0058] This application provides a model predictive control method and system for a permanent magnet synchronous motor used in air compressors. It employs a two-step current predictive controller based on current loop disturbance observation. Compared to traditional current disturbance observation algorithms, this effectively reduces system jitter caused by current loop disturbance observation. Furthermore, based on an extended model current prediction algorithm that considers disturbances, it derives the predicted current of the candidate voltage vector in steps k+1 and k+2, and substitutes a new evaluation index to ultimately select the first and second optimal voltage vectors. This better adapts to the real-world control delay problem of two steps between the controller and inverter, and between the inverter and motor. It also overcomes the impact of current fluctuations caused by parameter mismatch, improving system robustness, reducing system fatigue caused by current fluctuations, and extending system lifespan. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of a model predictive control system for a permanent magnet synchronous motor used in an air compressor, according to a preferred embodiment of the present invention.

[0060] Figure 2 This is a schematic diagram of the internal structure of an improved speed controller based on speed loop disturbance observation, according to a preferred embodiment of the present invention.

[0061] Figure 3 This is a schematic diagram of the internal workings of the target current corrector according to a preferred embodiment of the present invention.

[0062] Figure 4 This is a schematic diagram of the internal structure of a two-step current prediction controller based on current loop disturbance observation, which is a preferred embodiment of the present invention.

[0063] Figure 5 The diagram shows the control effect of the preferred embodiment of the present invention under the condition of parameter mismatch, which is to deal with the speed step response and load step disturbance.

[0064] Figure 6 The diagram shows the control effect of the preferred embodiment of the present invention under the condition of parameter mismatch, which is to deal with the cosine response of the rotational speed and the sinusoidal disturbance of the load.

[0065] In the figure: 1- Improved speed controller based on speed loop disturbance observation, 2- Target current corrector, 3- Two-step ideal voltage calculator, 4- Two-step current prediction controller based on current loop disturbance observation. Detailed Implementation

[0066] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0067] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0068] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations according to this application; as used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise; furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0069] Please see Figure 1 This invention provides a model predictive control method and system for a permanent magnet synchronous motor for air compressors, including an improved speed controller 1 based on speed loop disturbance observation, a target current corrector 2, a two-step ideal voltage calculator 3, and a two-step current predictive controller 4 based on current loop disturbance observation.

[0070] The improved speed controller 1 based on speed loop disturbance observation receives the quadrature-axis current signal and speed error signal from the motor current sensor at the current moment. Based on the improved sliding mode observation algorithm, it incorporates the load disturbance into the improved sliding mode control equation and outputs the target quadrature-axis current value. The target current corrector 2 receives the current signal from the current sensor at the current moment, the target quadrature-axis current signal from the improved speed controller 1 based on speed loop disturbance observation, and the target direct-axis current signal which is always 0. Through the improved sliding mode control algorithm, it outputs the target current correction signal. The two-step ideal voltage calculator 3 receives the target... The target current signal of the right quadrature axis current correction from the target current corrector 2 and the right quadrature axis voltage signal of the motor voltage sensor at the current moment are used to deduce the ideal voltage vector that can reach the target current correction signal value at times k+1 and k+2, and output it to the dual-step current prediction controller 4 based on current loop disturbance observation; the dual-step current prediction controller 4 receives the corrected right quadrature axis target current signal from the target current corrector 2 and the ideal voltage vector signal from the dual-step ideal voltage calculator 3, generates a voltage pulse signal, outputs it to the inverter, and drives the air compressor to rotate.

[0071] Please see Figure 2 The working principle and design method of the improved speed controller 1 based on speed loop disturbance observation are as follows:

[0072] 1) Design the disturbance observation equations based on the following equations:

[0073]

[0074] In the formula, ω m For the mechanical angular velocity of the air compressor, T d B is the speed-cycle disturbance torque, and J is the viscous friction coefficient of the air compressor drive motor. cp For the rotational inertia of the air compressor, P n The number of pole pairs of the air compressor drive motor, For the nameplate magnetic flux of the air compressor drive motor, i q (k) represents the current sampled value of the quadrature axis current of the motor current sensor at the current moment.

[0075] 2) Design the disturbance observation error equation based on the following equation:

[0076]

[0077] In the formula, "^" represents observation, l d Z is the observer gain greater than 0, and Z is the observer control law.

[0078] 3) Design the observer sliding surface s based on the following equation. smo :

[0079]

[0080] In the formula, c ωsmo Integral parameters.

[0081] 4) Define the sliding mode convergence rate according to the following equation:

[0082]

[0083] In the formula, ξ smo These are the controllable parameters of the observer.

[0084] 5) Define the observer control law Z according to the following equation:

[0085]

[0086] In the formula, μ is a parameter greater than 1.

[0087] 6) Establish the rotational differential equation of the air compressor for control purposes:

[0088]

[0089] In the formula, T e For the electromagnetic torque of the air compressor drive motor, T cp The compression torque (T) at the compression end of the air compressor exp This refers to the expansion torque at the expansion end of the air compressor.

[0090] 7) Establish the air compressor speed error sliding surface according to the following equation:

[0091]

[0092] In the formula, e ω =ω ref -ω m ω ref For the target angular velocity, c ω This is the integral coefficient for the rotational speed error.

[0093] 8) Define the speed equation considering the speed loop disturbance as follows:

[0094]

[0095] 9) Define the improved sliding mode speed control reaching law according to the following equation:

[0096]

[0097] In the formula, k lω For parameters greater than 0, k tω For parameters greater than 0, α ω Let k be the parameter between 0 and 2.fω For parameters greater than 0, ξ ω Let δ be the parameter between 0 and 1. ω For parameters greater than 0, σ ω Parameters that are greater than 0.

[0098] 10) Establish the improved sliding mode speed control saturation function y according to the following equation:

[0099]

[0100] In the formula, β ω Parameters that are greater than 0.

[0101] 11) Output the target quadrature-axis current signal i according to the following equation. qr :

[0102]

[0103] Please see Figure 3 The working principle and design method of the target current corrector 2 are as follows:

[0104] 1) Define the direct-axis current error e according to the following equation. id Cross-axis current error e iq :

[0105]

[0106] 2) Define the direct-axis current static error sliding surface s according to the following equation. id Cross-axis current error sliding surface s iq :

[0107]

[0108] In the formula, δ i For sliding mode gain, g d For the linear sliding mode control rate, g q This refers to the cross-axis sliding mode control rate.

[0109] 3) Define the orthogonal axis sliding mode control law g according to the following equation:

[0110]

[0111] 4) Define the sliding mode convergence rate according to the following equation:

[0112]

[0113] In the formula, k i For parameters greater than 0, θ i is a parameter greater than 0, and ri is a parameter located in the interval between 0 and 1.

[0114] 5) Output the direct-axis target current compensation value i according to the following equation. drc Cross-axis target current compensation value i qrc :

[0115]

[0116] In the formula, L d For direct-axis inductors, L q For quadrature axis inductance, R s This refers to the line resistance of the air compressor drive motor.

[0117] 6) Output the direct-axis target current correction value i according to the following equation. drr Cross-axis target current correction value i qrr :

[0118]

[0119] Please see Figure 4 The working principle and design method of the two-step current prediction controller 4 based on current loop disturbance observation are as follows:

[0120] 1) Based on the fundamental voltage-current equations of a permanent magnet synchronous motor, define the sliding mode disturbance observation differential equation according to the following equations:

[0121]

[0122] In the formula, γ d For parameters greater than 0, γ q For parameters greater than 0, F d For the direct-axis sliding mode control function, F q This is the cross-axis sliding mode control function.

[0123] 2) Define the direct-axis sliding mode observation error e according to the following equation. dsmo Cross-axis sliding mode observation error e qsmo :

[0124]

[0125] 3) Define the direct-axis integral sliding surface s according to the following equation. dsmo Cross-axis integral sliding surface s qsmo :

[0126]

[0127] In the formula, c smo This is the observer integral gain parameter.

[0128] 4) Define the sliding mode observer convergence rate according to the following equation:

[0129]

[0130] In the formula, M d For the direct-axis boundary layer adaptive saturation function, M q For the cross-axis boundary layer adaptive saturation function, R d For the adaptive reaching law of the direct-axis boundary layer, R q This is the adaptive reaching law for the cross-axis boundary layer.

[0131] 5) Define the boundary layer adaptive reaching law R according to the following equation:

[0132]

[0133] In the formula, ε c For the rate of approach gain greater than 0, e smo For observation error, s smo For observation error sliding surface, β c Boundary layer gain greater than 0, η c For parameters greater than 0, λ c Parameters that are greater than 0.

[0134] 6) Define the boundary layer adaptive saturation function M according to the following equation:

[0135]

[0136] 7) The sliding mode control function is derived using the following equation:

[0137]

[0138] 8) Based on the deadbeat current control principle, and considering motor parameter mismatch, design an extended model current prediction algorithm that considers dynamics according to the following equations:

[0139]

[0140] In the formula, "~" indicates prediction, U d For the direct-axis voltage value of the motor voltage sensor, U q This is the quadrature-axis voltage value of the motor voltage sensor.

[0141] 9) Retrieve the first optimal voltage vector U according to the following procedure. pot1 :

[0142] The voltage component U of the ideal voltage vector of the two-step ideal voltage calculator 3 on the orthogonal axis is known. id1 U iq1 U id2 U iq2 U is obtained through the inverse Park transform. alpha1 U beta1 U alpha2U beta2 This leads to the theoretically optimal voltage vector sector N. i1 N i2 .

[0143] According to N i1 N i2 The first optimal candidate voltage vector U is obtained. A1 U A2 U A3 U A4 Calculate the current component U of the current component along the orthogonal axis. Ad1 U Aq1 U Ad2 U Aq2 U Ad3 U Aq3 U Ad4 U Aq4 Then, let them be respectively:

[0144]

[0145] Substituting these values ​​into the state-space equation for the predicted current, we obtain the predicted current values ​​at time k+1 and k+2. Substituting these predicted current values ​​into the current prediction evaluation index shown below:

[0146]

[0147] Let g i The candidate first optimal voltage vector corresponding to the smallest value is the first optimal voltage vector.

[0148] 10) Calculate i when the zero voltage vector is applied according to the following equation. q The slope S0:

[0149]

[0150] 11) Based on the basic principle of a two-level three-bridge inverter, there are 8 spatial basic voltage vectors. After removing one duplicate zero voltage vector, let the remaining 7 spatial basic voltage vectors be the second optimal candidate voltage vectors.

[0151] 12) Calculate i when the seven second optimal voltage vectors are applied according to the following equations. q slope S opt2 :

[0152]

[0153] In the formula, U qq These are the quadrature-axis voltage components corresponding to the second optimal candidate voltage vector.

[0154] 13) Calculate the first optimal voltage vector action time T according to the following equation. opt1 :

[0155]

[0156] 14) According to the following equation, the direct-axis voltage component U corresponding to the seven synthesized voltage vectors formed by the seven second-option optimal voltage vectors and the first optimal voltage vector is obtained. mixd Cross-axis voltage component U mixq :

[0157]

[0158] In the formula, U opt1d For U opt1 The direct-axis voltage component, U dd These are the direct-axis voltage components corresponding to the second optimal candidate voltage vector.

[0159] 15) Substitute the seven synthesized voltage vectors into the predicted current state-space equation to obtain 14 two-step current prediction values. Substitute these values ​​into the current prediction evaluation index, g. i The combined voltage vector corresponding to the smallest value is the optimal combined voltage vector, and the second optimal alternative voltage vector corresponding to the optimal combined voltage vector is the second optimal voltage vector.

[0160] 16) Output the first optimal voltage vector and the second optimal voltage vector signals and apply them to the inverter to ultimately drive the air compressor to rotate.

[0161] Preferably, an extended Kalman filter speed observer is designed to receive the current signal from the motor current sensor and the voltage signal from the motor voltage sensor at the current moment, and output the observed value of the air compressor's angular velocity. This enables sensorless vector control of the air compressor, saving the manufacturing cost of the air compressor drive motor position sensor and reducing the overall size of the air compressor. The specific design method includes the following steps:

[0162] 1) Based on the voltage and current equations of the permanent magnet synchronous motor, establish the current equations of the air compressor drive motor in the stationary coordinate system:

[0163]

[0164] In the formula, i α For the current component along the Alpha axis, i β For the current component along the Beta axis, u α For the voltage component along the Alpha axis, u β For the voltage component along the Beta axis, L s For motor inductance, θ e The electric angle is the motor angle.

[0165] 2) Based on the fundamental principles of model prediction, establish the prediction equation:

[0166]

[0167] In the formula, “~” represents prediction and “~” represents state observation.

[0168] 3) Calculate the predicted output value:

[0169]

[0170] 4) Calculate the error covariance matrix:

[0171]

[0172] 5) Define the Q and R matrices:

[0173] Q = cos(V)

[0174] R = cos(W)

[0175] In the formula, V is the system noise with a mathematical expectation of 0, and W is the measurement noise with a mathematical expectation of 0.

[0176] 6) Calculate the gain matrix K:

[0177]

[0178] 7) Calculate the optimal state estimation vector:

[0179]

[0180] 8) Calculate the error covariance matrix for the next prediction step, iterate sequentially, and finally output the observed angular velocity values ​​of the air compressor:

[0181]

[0182] Preferably, the model predictive control method and system for a permanent magnet synchronous motor for an air compressor provided by this invention requires the permanent magnet synchronous motor control integrator to employ a DSP multi-core processor with 1MB program memory, 256KB data memory, 8KB stack memory, a main frequency of 200MHz, a bit width of 32 bits, and support for single-precision floating-point operations; to employ a high-precision ADC with 32-bit resolution, a sampling rate of 200kHz, and a signal bandwidth of 50kHz; to employ MOSFET power devices with a switching frequency of 100kHz, a power voltage of 600V, and using SiC material; to employ filters and protection circuits to reduce the impact of noise and vibration; to employ the EtherCAT communication protocol, requiring a transmission rate of up to 50Mbps; and to employ current and voltage sensors with a frequency of 50kHz or higher. This provides a solid hardware foundation for the model predictive control system for a permanent magnet synchronous motor for an air compressor proposed by this invention, ensuring the efficient, stable, and safe operation of the system.

[0183] A simulation was conducted with parameter mismatches: the drive motor flux linkage was 0.5 times the nominal value, the inductance was 0.5 times the nominal value, and the resistance was 1.5 times the nominal value. The simulation time was 2 seconds, and the target speed was 80,000 rpm. Starting from a traditional control system, the improved speed controller 1 based on speed loop disturbance observation, the target current corrector 2, the two-step ideal voltage calculator 3, and the two-step current prediction controller 4 based on current loop disturbance observation, as described in this invention, were added sequentially. The system was defined as entering a steady state when the absolute value of the speed error was less than 100 rpm and no longer changed by more than 100 rpm over time. The speed error, quadrature-axis current fluctuation, maximum speed overshoot, and stabilization time data and their analysis after the system stabilized are as follows:

[0184] As shown in Table 1, system number (i) represents an air compressor system using traditional PI speed control and traditional model predictive current control; system number (ii) represents an air compressor system using an improved speed controller 1 based on speed loop disturbance observation and traditional model predictive current control; system number (iii) represents an air compressor system using an improved speed controller 1 based on speed loop disturbance observation and a dual-step current predictive controller 4 based on current loop disturbance observation; system number (iv) represents an air compressor system using an improved speed controller 1 based on speed loop disturbance observation, a dual-step current predictive controller 4 based on current loop disturbance observation, and a dual-step ideal voltage calculator 3; and system number (v) represents the air compressor system of the present invention.

[0185] As shown in Table 1, starting with the traditional control system, with the sequential addition of the improved speed controller 1 based on speed loop disturbance observation, the target current corrector 2, the two-step ideal voltage calculator 3, and the two-step current prediction controller 4 based on current loop disturbance observation described in this invention, the system settling time, the mean absolute error of speed (MAE) after stabilization, the mean relative error of speed (MRE) after stabilization, the root mean square error of speed (RMSE) after stabilization, the maximum overshoot, the MAE of the quadrature axis current after stabilization, the MRE of the quadrature axis current after stabilization, and the RMSE of the quadrature axis current after stabilization all show a decreasing trend; among them, from system number (III) to system number (IV), all data increase, the reason being the addition of the two-step... The reduced current fluctuations after the ideal voltage calculator 3 resulted in a larger gap between the target current and the actual current, indicating an increase in error. From system number (I) to system number (V), the system of this invention reduced the stabilization time by 66.30%, the maximum overshoot by 99.9909%, the stabilized speed MAE by 95.72%, the stabilized speed MRE by 96.07%, the stabilized speed RMSE by 90.27%, the stabilized quadrature axis current MAE by 72.32%, the stabilized quadrature axis current MRE by 64.32%, and the stabilized quadrature axis current RMSE by 72.49%. This invention demonstrates good adaptability to operating conditions in response to parameter mismatch problems.

[0186] Table 1. Simulation results of each system under parameter mismatch conditions.

[0187]

[0188] Please see Figure 5 Simulations of this invention were conducted under the following conditions: the drive motor flux linkage was 0.5 times the nominal value; the inductance was 0.5 times the nominal value; the resistance was 1.5 times the nominal value; the target speed started at 0.4s, increased by 10,000 rpm every 0.2s to 100,000 rpm, and then decreased by 10,000 rpm every 0.2s; a cyclic stepped load disturbance was applied, starting at 0.3s with a sudden increase of 3 Nm of load torque, followed by a sudden decrease of 3 Nm of load torque every 0.2s, and then a sudden increase of 3 Nm of load torque every 0.2s. The results are as follows. Figure 5 As shown, the present invention can always maintain high-precision tracking of the target speed, and the speed error returns to 0 in a very short time when faced with sudden load changes.

[0189] Please see Figure 6Simulations of this invention were conducted under the following conditions: the drive motor flux linkage was 0.5 times the nominal value; the inductance was 0.5 times the nominal value; the resistance was 1.5 times the nominal value; the target speed was a cosine signal with an amplitude of 25,000 rpm, a bias of 55,000 rpm, and a frequency of 7.85 Hz; and the load disturbance was a sine signal with an amplitude of 3 Nm, a bias of 1.5 Nm, and a frequency of 7.85 Hz. The results are as follows... Figure 6 As shown, the present invention can always maintain high-precision tracking of the target rotational speed and the target current curve, demonstrating good adaptability to operating conditions.

[0190] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A predictive control method for a permanent magnet synchronous motor used in an air compressor, characterized in that, An improved speed controller based on speed loop disturbance observation receives the quadrature-axis current signal and speed error signal from the current sensor at the current moment. Based on the improved sliding mode observation algorithm, the parameter mismatch problem generated by the air compressor drive motor is simulated as a load torque and added to the improved sliding mode control equation to output the target quadrature-axis current value. Includes the following: A1) Define the improved sliding mode speed control reaching law according to the following equation: In the formula, s ω For the speed error sliding surface, e ω For speed error, k lω For controller parameters greater than 0, k tω For parameters greater than 0, α ω Let k be the parameter between 0 and 1. fω For parameters greater than 0, ξ ω Let δ be the parameter between 0 and 1. ω For parameters greater than 0, σ ω Parameters that are greater than 0; A2) Define the improved sliding mode speed control saturation function y according to the following equation: In the formula, β ω The boundary layer is a saturated function with a value greater than 0; A3) Based on the augmented state-space equation that defines the speed loop disturbance as the speed loop disturbance torque, the speed loop disturbance torque observation equation is established, and the speed loop disturbance torque observer convergence rate is defined according to the following equation: In the formula, λ smo For observer gain greater than 1, J cp For the rotational inertia of the air compressor, e T To perturb the observation error, s smo For the rotational speed observation error sliding surface; The target current corrector receives the current signal from the current sensor at the current moment, the target quadrature axis current signal from the improved speed controller based on speed loop disturbance observation, and the target direct axis current signal which is always 0. Based on the improved sliding mode control algorithm, it outputs the target current correction signal. Includes the following: B1) Define the direct-axis current static error sliding surface s according to the following equation. id Cross-axis current static error sliding surface s iq : In the formula, e id The difference between the target direct-axis current value and the current sampled direct-axis current value, e iq The difference between the target quadrature-axis current value and the current sampled quadrature-axis current value, δ i For integral sliding surface gain, g d For the direct-axis sliding mode constraint function, g q This is the cross-axis sliding mode constraint function; B2) Define the orthogonal axis sliding mode constraint function g according to the following equation: In the formula, α i Parameters that are greater than 0; B3) Define the differential equation for the static error compensation value of the current according to the following equation: In the formula, i drc For the direct-axis target current compensation value, i qrc For the cross-axis target current compensation value, L d For direct-axis inductors, L q For quadrature axis inductance, R s For the line resistance of the air compressor drive motor, k i For parameters greater than 0, θ i ri is a parameter greater than 0, and ri is a parameter located in the interval between 0 and 1.

2. The predictive control method for a permanent magnet synchronous motor for an air compressor according to claim 1, characterized in that, The two-step ideal voltage calculator receives the orthogonal axis current correction target current signal from the target current corrector and the orthogonal axis voltage signal from the voltage sensor at the current moment. Based on the extended model current prediction algorithm that takes into account disturbances, it back-calculates the ideal voltage vector that can reach the orthogonal axis current target correction signal value at times k+1 and k+2, and outputs it to the two-step current prediction controller based on current loop disturbance observation.

3. The predictive control method for a permanent magnet synchronous motor for an air compressor according to claim 2, characterized in that, Includes the following: 1) Define the speed prediction equation according to the following equation: In the formula, " "Represents prediction, T" SG For controller sampling time; 2) Derive the orthogonal-axis ideal voltage vector U that reaches the target current value at time k+1 according to the following equation. id1 U iq1 The orthogonal-axis ideal voltage vector U that reaches the target current value at time k+2 is derived. id2 U iq2 ; In the formula, A U B U D U To account for speed variations and current loop disturbances, the extended current prediction state space matrix, d U This is the system disturbance vector.

4. The predictive control method for a permanent magnet synchronous motor for an air compressor according to claim 1, characterized in that, The dual-step current prediction controller based on current loop disturbance observation receives the corrected orthogonal axis target current signal from the target current corrector and the ideal voltage vector signal from the dual-step ideal voltage calculator, generates a voltage pulse signal, and outputs it to the inverter to drive the air compressor to rotate.

5. The predictive control method for a permanent magnet synchronous motor for an air compressor according to claim 4, characterized in that, Includes the following: 1) Based on the deadbeat current control principle, and considering the current loop disturbance caused by motor parameter mismatch, an extended model current prediction algorithm considering the disturbance is designed according to the following equations: In the formula, U d For the direct-axis voltage value of the motor voltage sensor, U q For the quadrature axis voltage value of the motor voltage sensor, f d For the direct-axis current disturbance observation, f q For cross-axis current disturbance observations, γ d For parameters greater than 0, γ q For parameters greater than 0, F d For the direct-axis sliding mode control function, F q This is the cross-axis sliding mode control function; 2) Define the current loop disturbance sliding mode observation function according to the following equation: In the formula, s dsmo For the sliding mode surface of the direct-axis observation error, s qsmo For cross-axis observation error sliding surface, M d For the direct-axis boundary layer adaptive saturation function, M q For the cross-axis boundary layer adaptive saturation function, R d For the adaptive reaching law of the direct-axis boundary layer, R q This is the adaptive reaching law for the cross-axis boundary layer; 3) Define the boundary layer adaptive reaching law R according to the following equation: In the formula, ε c For the rate of approach gain greater than 0, e smo For observation error, s smo For observation error sliding surface, β c Boundary layer gain greater than 0, η c For parameters greater than 0, λ c Parameters that are greater than 0; 4) Define the boundary layer adaptive saturation function M according to the following equation: 5) According to the extended model current prediction algorithm considering disturbances, U d (k), U q (k) is defined as the ideal voltage vector from the two-step ideal voltage calculator, performing current predictions in steps k+1 and k+2, and evaluated according to the following metrics: Find g i The ideal voltage vector corresponding to the smallest value is the first optimal voltage vector; 6) Based on the introduction of duty cycle calculation, the duty cycle is calculated first to obtain the first optimal vector action time. The zero voltage vector corresponding to the remaining voltage vector action time is replaced with 7 basic voltage vectors. The combined voltage vector is calculated. The orthogonal axis predicted current corresponding to the combined voltage vector in step k+1 and step k+2 is calculated by the extended model current prediction algorithm considering disturbances. The second optimal voltage vector is obtained by substituting it into the evaluation index shown.

6. A predictive control system for a permanent magnet synchronous motor used in an air compressor, characterized in that, The method for predictive control of a permanent magnet synchronous motor for an air compressor as described in any one of claims 1-5 includes an improved speed controller (1) based on speed loop disturbance observation, a target current corrector (2), a two-step ideal voltage calculator (3), and a two-step current predictive controller (4) based on current loop disturbance observation.

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