Rotor control method, device, electronic device and storage medium of electric spindle

Abstract

The present application relates to the field of rotor control technology, and provides a rotor control method, device, electronic device and storage medium for an electric spindle. An operating parameter set is obtained by monitoring the operating state of a target electronic shaft, coordinate transformation of the operating parameter set is performed to generate an actual current component set, and the operating parameter set is adjusted to generate an adjustment reference standard value, and a stator flux prediction value and an electromagnetic torque prediction value are obtained by discrete prediction of the actual current component set, and then a cost function evaluation is performed based on the adjustment reference standard value, the stator flux prediction value and the electromagnetic torque prediction value to obtain the optimal voltage vector combination, and the optimal voltage vector combination is time-allocated to generate two adjustment voltage vectors, and finally, an inverter drive signal is generated in combination with space vector modulation to achieve precise drive of the rotor. Through the discrete prediction step, the present application can foresee and adjust the stator flux and electromagnetic torque, thereby achieving accurate and rapid control of the electric spindle rotor.

Description

Technical Field

[0001] The present application relates to the technical field of rotor control, and in particular to a rotor control method, device, electronic equipment and storage medium for an electric spindle. Background Art

[0002] As the core of high-precision machine tools, the electric spindle is crucial to the performance of the machine tool. The rotor control of the electric spindle can ensure the stability of the spindle at high speed rotation, directly improving the processing accuracy. At the same time, by regulating the start, acceleration and braking process of the rotor, the dynamic performance of the machine tool can be significantly enhanced, and the wear and failure of the rotor can be effectively reduced, thereby extending the service life of the electric spindle. Therefore, the rotor control of the electric spindle is of great significance to improving the overall performance and processing efficiency of the machine tool.

[0003] The rotor in the electric spindle is the main driving component. By controlling it, the performance of the electric spindle can be fully utilized to achieve efficient, stable and precise processing operations. However, the traditional rotor control method has hysteresis. The state of the high-speed rotating rotor changes every moment. When the existing control method makes an adjustment action according to the state of the rotor, the motion state of the rotor has changed compared to the state when the adjustment action was made, so that the motion state of the rotor can never be in the best state. Summary of the invention

[0004] In view of this, the present application provides a rotor control method, device, electronic device and storage medium for an electric spindle to solve the problem of rotor control lag.

[0005] In a first aspect, the present application provides a rotor control method for an electric spindle, the method comprising:

[0006] Step S1, driving the target electric spindle according to the received operation instruction;

[0007] Step S2, performing coordinate transformation on the operating parameter set of the target electric spindle collected in real time to obtain an actual current component set;

[0008] Step S3, performing proportional and integral adjustment on the operating parameter set to generate an adjustment reference standard value;

[0009] Step S4, obtaining a historical voltage vector set corresponding to the previous acquisition moment, and performing discrete prediction based on the actual current component set and the historical voltage vector set to obtain a stator flux linkage prediction value and an electromagnetic torque prediction value corresponding to the next acquisition moment;

[0010] Step S5, performing cost function evaluation according to the adjustment reference standard value, the stator flux prediction value and the electromagnetic torque prediction value to obtain an optimal voltage vector combination;

[0011] Step S6, performing time allocation on the optimal voltage vector combination to generate a first regulating voltage vector and a second regulating voltage vector;

[0012] Step S7: performing modulation processing according to the first adjustment voltage vector and the second adjustment voltage vector to generate an inverter drive signal, wherein the inverter drive signal is used to control the rotor of the target electric spindle.

[0013] In an optional embodiment, the actual current component set includes a first actual current component and a second actual current component, and the coordinate transformation of the operating parameter set of the target electric spindle acquired in real time to obtain the actual current component set includes:

[0014] Perform high-speed synchronous acquisition of the three-phase current of the target electric spindle to obtain the instantaneous current value in the operating parameter set at the current moment;

[0015] Decoding the rotor position pulse signal in the operating parameter set to obtain the original position angle of the rotor, and performing rotor dynamic compensation on the original position angle to generate the rotor position angle at the current moment;

[0016] Performing a first coordinate transformation on the instantaneous current value to generate a first current component and a second current component on an α-β axis;

[0017] A second coordinate transformation is performed on the first current component and the second current component to generate a first actual current component and a second actual current component on the dq axis.

[0018] In an optional implementation, the adjustment reference standard value includes an electromagnetic torque reference value and a stator flux reference value, and the proportional and integral adjustment of the operating parameter set to generate the adjustment reference standard value includes:

[0019] Calculate the speed error according to the target speed in the operation instruction and the measured speed in the operation parameter set to obtain a speed error signal;

[0020] Performing preset proportional and integral adjustments on the speed error signal to generate a primary electromagnetic torque reference value;

[0021] Performing output limiting processing on the primary electromagnetic torque reference value to generate the electromagnetic torque reference value;

[0022] The preset flux linkage reference value is dynamically adjusted in sections according to the measured rotation speed to generate the stator flux linkage reference value.

[0023] In an optional embodiment, the acquiring of the historical voltage vector set corresponding to the previous acquisition moment, and performing discrete prediction according to the actual current component set and the historical voltage vector set to acquire the stator flux linkage prediction value and the electromagnetic torque prediction value corresponding to the next acquisition moment includes:

[0024] According to the acquisition time tag in the operation parameter set, the operation parameter set corresponding to the last acquisition moment is acquired in a preset acquisition database to acquire the historical voltage vector set corresponding to the last acquisition moment;

[0025] Perform discrete prediction based on the historical voltage vector set and the actual current component set to obtain a predicted current component set corresponding to the next acquisition moment;

[0026] Calculating and synthesizing magnetic components of the predicted current component set to obtain the stator flux prediction value;

[0027] Torque calculation is performed on the current component set to obtain the electromagnetic torque prediction value.

[0028] In an optional embodiment, the optimal voltage vector combination includes a first optimal voltage vector and a second optimal voltage vector, and performing a cost function evaluation according to the adjustment reference standard value, the stator flux prediction value, and the electromagnetic torque prediction value to obtain the optimal voltage vector combination includes:

[0029] Traversing the inverter at the current moment to obtain a simulated voltage vector set that can be output by the inverter, and generating a prediction error data set according to the adjustment reference standard value and the simulated voltage vector set;

[0030] Performing a primary cost function weighted calculation on the prediction error data set to obtain a single vector cost function value set, and extracting a first optimal voltage vector from the voltage vector set according to the single vector cost function value set;

[0031] performing a double vector combination calculation on the first optimal voltage vector and the remaining voltage vectors in the voltage vector set to obtain a candidate combination cost function value set;

[0032] A second optimal voltage vector is extracted from the remaining voltage vectors according to the candidate combination cost function value set.

[0033] In an optional implementation, the time allocation of the optimal voltage vector combination to generate the first regulating voltage vector and the second regulating voltage vector includes:

[0034] Performing current slope modeling on the optimal voltage vector combination to obtain a current slope parameter set of the dq axes of each optimal voltage vector;

[0035] generating a time allocation optimization coefficient matrix according to the prediction error data set corresponding to the optimal voltage vector combination and the current slope parameter set;

[0036] Calculating a first action time of the first optimal voltage vector according to the predicted current component set, the current slope parameter set and the time allocation optimization coefficient matrix, and calculating a second action time of the second optimal voltage vector according to the first action time and a preset control period;

[0037] Performing time-domain synthesis on the optimal voltage vector combination according to the first action time and the second action time to generate an equivalent dq axis voltage component set;

[0038] The equivalent dq-axis voltage component set is inversely transformed according to the rotor position angle to generate the first regulating voltage vector and the second regulating voltage vector.

[0039] In an optional implementation, the modulation processing is performed according to the first adjustment voltage vector and the second adjustment voltage vector to generate an inverter drive signal, wherein the inverter drive signal is used to control the rotor of the target electric spindle, and includes:

[0040] performing polar coordinate angle calculation according to the first adjustment voltage vector and the second adjustment voltage vector to obtain a current sector number according to the polar coordinate angle;

[0041] performing adjacent vector decomposition on the first regulating voltage vector and the second regulating voltage vector according to the current sector number to obtain a basic vector action time set;

[0042] The basic vector action time set is symmetrically distributed according to a preset symmetry strategy to generate a PWM waveform timing sequence;

[0043] Perform switch state mapping according to the current sector number and the PWM waveform timing to obtain a PWM duty cycle signal;

[0044] Dead time compensation is performed on the PWM duty cycle signal to generate the inverter drive signal.

[0045] A second aspect of the present application provides a rotor control device for an electric spindle, the device comprising:

[0046] An instruction driving module, used for driving the target electric spindle according to the received operation instruction;

[0047] A coordinate transformation module, used for performing coordinate transformation on the operating parameter set of the target electric spindle collected in real time to obtain an actual current component set;

[0048] An adjustment standard module, used for performing proportional and integral adjustment on the operating parameter set to generate an adjustment reference standard value;

[0049] A discrete prediction module is used to obtain a historical voltage vector set corresponding to a previous acquisition moment, and to perform discrete prediction based on the actual current component set and the historical voltage vector set to obtain a stator flux linkage prediction value and an electromagnetic torque prediction value corresponding to a next acquisition moment;

[0050] A vector acquisition module, used for performing cost function evaluation according to the adjustment reference standard value, the stator flux prediction value and the electromagnetic torque prediction value to obtain an optimal voltage vector combination;

[0051] A time allocation module, used for allocating time for the optimal voltage vector combination to generate a first regulating voltage vector and a second regulating voltage vector;

[0052] A drive signal module is used to perform modulation processing according to the first adjustment voltage vector and the second adjustment voltage vector to generate an inverter drive signal, wherein the inverter drive signal is used to control the rotor of the target electric spindle.

[0053] A third aspect of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the rotor control method of the electric spindle as described above when executing the computer program.

[0054] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the rotor control method of the electric spindle as described above are implemented.

[0055] In summary, this application at least includes the following beneficial technical effects:

[0056] 1. Using the historical voltage vector set and the actual current component set for discrete prediction, the stator flux prediction value and electromagnetic torque prediction value of the next acquisition moment can be obtained in advance, and the optimal voltage vector combination can be quickly determined in combination with the cost function evaluation to quickly respond to changes in the rotor motion state.

[0057] 2. Monitor the running status of the electric spindle in real time, predict the deviation of the rotor and correct the deviation in time through adjustment means, thus enhancing the stability and reliability of the electric spindle operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 is a flow chart of a rotor control method of an electric spindle provided in an embodiment of the present application;

[0060] Figure 2 It is a functional module diagram of a rotor control device of an electric spindle provided in an embodiment of the present application;

[0061] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] like Figure 1 The figure is a flow chart of the rotor control method of the electric spindle provided in the embodiment of the present application. The rotor control method of the electric spindle provided in the embodiment of the present application includes the following steps.

[0064] Step S1, driving the target electric spindle according to the received operation instruction.

[0065] It should be understood that the operating instruction is an encrypted data packet including key control information such as target speed, rotation direction, acceleration curve, working mode and constraints. After receiving the instruction through the communication interface (for example, CAN bus), the data packet (i.e., operating instruction) is parsed and key parameters are extracted. The legitimacy of the parameters is thereby verified (for example, whether the speed exceeds the limit, whether the acceleration is executable), and an error code is fed back if abnormal. When the parameters pass the verification, the initial voltage vector is precalculated according to the target speed and rotation direction, and the PWM carrier frequency and dead time of the inverter are configured according to the initial voltage vector, thereby completing the configuration of the initial state. The parsed instruction generates the initial drive signal of the inverter (for example, six-way PWM wave), thereby triggering the power device to turn on, realizing the preliminary establishment of the rotor magnetic field through the current closed loop, and ensuring the smooth start of the motor.

[0066] Step S2: performing coordinate transformation on the operating parameter set of the target electric spindle collected in real time to obtain an actual current component set.

[0067] The three-way isolated Hall current sensor is used to synchronize the sampling frequency with the switching cycle of the insulated gate bipolar transistor (IGBT). (Typical =50 μ s), real-time synchronous sampling of the A, B, and C three-phase currents of the electric spindle. Each sensor output signal passes through an anti-aliasing filter (cutoff frequency =2kHz), and then the 12-bit analog to digital converter (ADC) module performs analog-to-digital conversion to quantify the discrete three-phase current instantaneous value i A (k), i B (k), i C (k).

[0068] At the same time, the rotor position pulse signal (A, B, Z phase) is collected through the quadrature encoder pulse circuit (Quadrature Encoder Pulse, QEP) interface of the incremental photoelectric encoder. The pulse edges are counted using the M-method speed measurement principle, and the original rotor position angle (θraw(k)) at the current acquisition time is decoded. In order to eliminate the coordinate transformation lag error caused by the dynamic rotation of the rotor, the original position angle is dynamically compensated based on the mechanical dynamics equation. In this embodiment of the application, the current acquisition time is represented by time k, and the rotor position angle at time k+1 is predicted by the following formula:

[0069]

[0070] in, is the mechanical angular velocity of the rotor at time k, To control the cycle, is the rotor moment of inertia, is the electromagnetic torque at time k, is the load torque at time k, is the preset viscous friction coefficient. By discretizing the rotor dynamics equations (i.e., ), predict the rotor position angle at the next acquisition time. After predicting the rotor position angle at the next acquisition time, calculate the compensated rotor position angle using the following formula:

[0071]

[0072] in, is the predicted rotor position angle at time k+1. The compensation angle is generated by linear interpolation to reduce the error during coordinate transformation.

[0073] Furthermore, the instantaneous values ​​of the three-phase currents (i.e., i A (k), i B (k), i C (k) Perform Clarke transformation (i.e., first coordinate transformation) to map the three-phase stationary coordinate system current to the two-phase stationary α-β coordinate system to generate the α-axis current component (ie, the first current component) and the β-axis current component (ie, the second current component). The first coordinate transformation is performed by the following formula:

[0074]

[0075] The three-phase current is projected into a two-phase stationary coordinate system through the first coordinate transformation, thereby simplifying the subsequent coordinate transformation calculation while ensuring power invariance.

[0076] Furthermore, based on the rotor position angle after dynamic compensation , construct the modified Parker transformation (i.e., the second coordinate transformation) matrix, transforming the α-β axis current component , Converted to the current component in the rotating dq coordinate system (i.e., the first actual current component), (ie, the second actual current component). The second coordinate transformation is performed using the following formula:

[0077]

[0078] Parker transformation decomposes the current into components that are synchronized with the rotor magnetic field, so that the first actual current component controls the flux amplitude and the second actual current component is directly related to the electromagnetic torque, thus achieving decoupling control. Increasing can directly increase the torque, and Adjust the magnetic link strength.

[0079] Step S3: Perform proportional and integral adjustments on the operating parameter set to generate an adjustment reference standard value.

[0080] The adjustment reference standard value includes an electromagnetic torque reference value and a stator flux reference value.

[0081] The actual mechanical speed of the electric spindle rotor is collected in real time through a high-precision photoelectric encoder. The encoder outputs a speed pulse signal every control cycle, which is converted into the angular velocity value corresponding to the current acquisition time after digital filtering and interpolation processing. The speed error signal is generated by calculating the difference between the target speed and the actual speed. The speed error signal reflects the deviation between the current speed and the target speed. The speed error signal is calculated by the following formula:

[0082]

[0083] in, is the speed error signal, is the target speed, The actual speed at time k. Speed ​​error is the core feedback quantity of closed-loop control, and its accuracy directly affects the response speed and steady-state accuracy of torque regulation. By calculating the speed deviation in real time, the system can dynamically adjust the electromagnetic torque to eliminate speed fluctuations and ensure the stability of the electric spindle under high load or variable speed conditions.

[0084] The speed error signal is input into the discrete PI controller. The PI regulator responds quickly to the speed deviation through the proportional term, and the integral term eliminates the long-term steady-state error. Its output is the primary electromagnetic torque reference value. The discrete implementation formula of the PI controller is as follows:

[0085]

[0086] in, is the primary electromagnetic torque reference value, is the preset scaling factor, is the integration coefficient, is the discrete integral term of the speed error. The discretization design ensures the real-time performance of the algorithm in the digital controller and avoids the calculation delay caused by continuous integration.

[0087] To prevent mechanical overload or reverse torque shock caused by PI output exceeding the limit, and protect the safety of the electric spindle structure (for example, when the load is suddenly unloaded, excessive positive torque may damage the transmission components.). According to the mechanical and thermodynamic limits of the electric spindle, the maximum allowable torque T is set. max With minimum torque T min The primary reference value is subjected to non-linear limiting as follows:

[0088]

[0089] in, It is the reference value of electromagnetic torque after limiting.

[0090] It should be understood that below the starting speed of the weak magnetic control (hereinafter referred to as the base speed), the electric spindle runs in the constant torque area, maintaining the rated flux to maximize the torque output; above the base speed, due to the inverter voltage limit, the flux amplitude needs to be reduced (i.e., weak magnetic control) to maintain voltage balance and avoid DC bus overvoltage. According to the weak magnetic control characteristics of the electric spindle, the measured speed is compared with the starting speed of the weak magnetic control (hereinafter referred to as the base speed), and the stator flux reference value is dynamically adjusted. The piecewise function formula is as follows:

[0091]

[0092] in, is the base speed, is the magnetic flux density (i.e., rated flux amplitude) corresponding to the rated working condition of the electric spindle is the stator flux reference value.

[0093] Step S4, obtaining a historical voltage vector set corresponding to a previous acquisition moment, and performing discrete prediction based on the actual current component set and the historical voltage vector set to obtain a stator flux prediction value and an electromagnetic torque prediction value corresponding to a next acquisition moment.

[0094] The historical voltage vector is the initial input condition of the discrete prediction model, and its accuracy directly affects the prediction accuracy of the subsequent current and flux. The continuity and traceability of data across control cycles are ensured through the database storage and retrieval mechanism. According to the time tag of the current control cycle, the corresponding operating parameter set of the previous acquisition moment is retrieved in the preset acquisition database to extract the historical voltage vector set. Among them, the historical voltage vector set includes a first historical voltage vector representing the voltage component acting on the d-axis of the electric spindle at the previous acquisition moment, and a second historical voltage vector representing the voltage component acting on the q-axis of the electric spindle at the previous acquisition moment.

[0095] Combine the first historical voltage vector and the second historical voltage vector obtained with the actual current component at the current acquisition time , , the predicted current component set at the next acquisition moment is calculated through the permanent magnet synchronous motor discrete prediction model. The permanent magnet synchronous motor discrete prediction model calculates the predicted current component set corresponding to the next acquisition moment through the following formula:

[0096]

[0097] in, is the d-axis stator inductance, is the q-axis stator inductance, is the stator resistance, is the electrical angular velocity at time k, is the magnetic flux of the rotor permanent magnet. ,in is the number of pole pairs. The d-axis predicted current component reflects the voltage action and electromagnetic coupling effect; the q-axis predicted current component reflects the influence of the back electromotive force term. The discrete prediction model converts continuous time dynamics into discrete recursive form through differential equations, providing computational feasibility for real-time control. The introduction of historical voltage vectors ensures the time continuity of the model and avoids cumulative errors caused by sampling delays.

[0098] Flux prediction is the core of direct torque control, and its accuracy directly affects the stability of torque control. Through split-axis calculation and synthesis, the magnetic field direction information is retained and the complexity of vector calculation is simplified. as well as Combined with motor parameters , as well as , calculate the d-axis and q-axis flux components respectively, and obtain the stator flux amplitude prediction value through vector synthesis. The stator flux amplitude prediction value is calculated by the following formula:

[0099]

[0100] in, is the predicted value of the stator flux amplitude.

[0101] based on , combined with the number of pole pairs And the rotor permanent magnet flux , the predicted value of electromagnetic torque at the next moment is calculated by the following formula:

[0102]

[0103] in, is the predicted value of electromagnetic torque. The pole pair number is used to quantify the mechanical torque generated by the interaction between the stator current and the permanent magnetic field. The predicted value of electromagnetic torque provides feedback for closed-loop control, ensuring that the output torque of the electric spindle accurately tracks the reference command to avoid overshoot or undershoot.

[0104] Step S5: performing cost function evaluation according to the adjustment reference standard value, the stator flux prediction value and the electromagnetic torque prediction value to obtain an optimal voltage vector combination.

[0105] According to the switch state combination of the three-phase voltage source inverter, all 8 voltage vectors are traversed. Among them, 6 valid vectors are represented by V1-V6, and 2 zero vectors are represented by V0 and V7. According to the voltage vectors obtained by traversal, the simulated voltage vector set V={ V 0, V 1, V 2, ..., V7}. For each voltage vector V(n)∈V, the predicted flux and predicted torque in the control cycle are calculated based on the discrete motor model, and a prediction error data set consisting of flux error and torque error is generated.

[0106] The predicted magnetic flux is calculated by the following formula:

[0107]

[0108] in, is the stator flux vector at the current acquisition moment, is the stator resistance, = is the dq axis current vector. By predicting the flux state after the voltage vector V(n) acts, it reflects the changes in the flux amplitude and direction.

[0109] The predicted torque is calculated using the following formula:

[0110]

[0111] in, To predict the conjugate of the current vector, The imaginary part is calculated by calculating the electromagnetic torque after the voltage vector V(n) acts to reflect the dynamic response of the torque.

[0112] The prediction error dataset is calculated using the following formula:

[0113]

[0114]

[0115] The prediction error dataset can be used to quantify the deviation between the prediction value and the reference value. The prediction error dataset is represented by { , }(n=0,1,…,7).

[0116] For each voltage vector V(n)∈V, the flux error and torque error are weighted and summed by the dynamic weight factor λ to generate a single vector cost function value J(n), and all J(n) are sorted in ascending order to select the first optimal voltage vector V corresponding to the minimum value. opt1 The cost function formula is as follows:

[0117]

[0118] in, is the dynamic flux weight factor, is the rated torque of the electric spindle. The tracking priority of balanced flux linkage and torque is calculated by cost weighting. When the load is heavy, λ is reduced to give priority to torque, and when the load is light, λ is increased to optimize the flux linkage.

[0119] After obtaining the single vector cost function value J(n) corresponding to each voltage vector V(n)∈V, the global optimal voltage vector (i.e., the first optimal voltage vector) is extracted from the single vector cost function value set through the following formula model:

[0120]

[0121] Wherein, argmin is the independent variable that returns the minimum function value. After obtaining the first optimal voltage vector, the system extracts the first optimal voltage vector from the simulation voltage vector set and updates the extracted simulation voltage vector set.

[0122] V opt1 As a benchmark, the 7 voltage vectors V(m) in the updated simulated voltage vector set are traversed, and the dual-vector combinations {Vopt1, V(m)} are generated one by one. The predicted flux and torque of each combination in the control cycle are calculated, and the total error is evaluated by the cost function.

[0123] For the combination {Vopt1, V(m)}, the following formulas are used to calculate its t Predicted flux and torque within t1 and t2=Ts−t1:

[0124]

[0125] in, For application V opt1 The predicted current after is the predicted current conjugate after applying V(m). Through the above combined prediction model calculation formula, the combined effect of the dual vector combination on flux and torque can be quantified.

[0126] After obtaining the predicted flux and torque for the combination {Vopt1, V(m)}, the flux error and torque error are weighted and summed by the dynamic weight factor λ to generate the combined cost function value The cost function formula is as follows:

[0127]

[0128] After obtaining the combined cost function value of each pair of combinations, a candidate combined cost function value set {J comb (0),J comb (1),…,J comb (6)}. Further, according to the candidate combination cost function value set, the dual vector combination {V opt1 ,V opt2The screening process of the optimal voltage vector combination is the same as the screening process of the first optimal voltage vector, which will not be described in detail. For details, please refer to the screening process of the first optimal voltage vector.

[0129] Step S6: Time-allocate the optimal voltage vector combination to generate a first regulating voltage vector and a second regulating voltage vector.

[0130] According to the selected optimal voltage vector combination {V opt1 ,V opt2} of the dq axis component V d1 、V q1 and V d2 、V q2 , combined with the current dq axis current i d(k) 、i q(k) , Motor parameters (inductance L d , L q And the stator resistance R s ) and electrical angular velocity ω e (k), the d-axis and q-axis current slope parameters of each vector are calculated by the following discretized differential equations:

[0131]

[0132]

[0133]

[0134]

[0135] in, V opt1 The d-axis current slope during action, is the electrical angular velocity at the current acquisition moment, V opt2 The d-axis current slope during action, V opt1 The q-axis current slope during action, V opt2 The q-axis current slope when the action is performed. According to the calculated d-axis and q-axis current slope parameters of each vector, a current slope parameter set { }.

[0136] According to the predicted flux error and predicted torque error, a nonlinear coupling model is constructed in combination with the current slope parameter set to generate the time allocation optimization coefficient matrix M and intermediate variables a and b. The intermediate variables a and b are calculated using the following formula:

[0137]

[0138]

[0139] Where a is the torque error weight factor, which indicates the contribution ratio of the torque error to the time allocation. b is the flux error weight factor, which indicates the contribution ratio of the flux error to the time allocation. , It is the reference value of electromagnetic torque and stator flux.

[0140] The time allocation optimization coefficient matrix M is calculated by the following formula:

[0141]

[0142] Where M is the regularization coefficient matrix for time allocation optimization, which is used to balance the coupling effects of current slope difference and flux / torque error.

[0143] Using the predicted current components id(k+1), iq(k+1), current slope parameter set { }, and the time allocation optimization coefficient matrix M is calculated by the current slope model V opt1 Action time distribution t1 and V opt2 The action time distribution is t2. The action time distribution formula is as follows:

[0144]

[0145] in, , To predict the current component set, L b , a, b are intermediate parameters related to flux linkage error and are used to balance the time allocation weight. t1 is V opt1 Action time, t2=T s -t1 is V opt2 Action time.

[0146] According to the action time t1 and t2, the optimal voltage vector combination {V opt1 ,V opt2 The dq axis components of the} are weighted in the time domain to generate equivalent d axis and q axis voltage components. The equivalent dq axis voltage component set is calculated by the following formula:

[0147]

[0148]

[0149] in, are the equivalent d-axis and q-axis voltage components, , V opt1 The d-axis and q-axis components of , V opt2 The d-axis and q-axis components of.

[0150] Based on the rotor position angle θ(k), the equivalent dq axis voltage components Perform the inverse Parker transform to generate the α-axis and β-axis voltage components in the stationary coordinate system (ie, the first regulated voltage vector), (ie, the second regulated voltage vector).

[0151]

[0152] Step S7: performing modulation processing according to the first adjustment voltage vector and the second adjustment voltage vector to generate an inverter drive signal, wherein the inverter drive signal is used to control the rotor of the target electric spindle.

[0153] according to , Obtaining the target voltage vector through vector synthesis .based on The α-β axis component of the polar coordinate angle is calculated , and determine the current sector number based on the angle range. The polar coordinate angle is calculated using the following formula:

[0154]

[0155]

[0156] in, , is the target voltage vector The position of the voltage vector on the complex plane is determined by the polar coordinate angle, providing a basis for sector division.

[0157] It should be understood that the complex plane in the embodiment of the present application is divided into 6 sectors Sn∈{1,2,3,4,5,6}, and each sector spans 60 degrees. Exemplarily, the sector number Sn is determined by the following formula:

[0158]

[0159] The continuous space is discretized through angle division, and the sector number is determined according to the polar coordinate angle to ensure the accuracy of subsequent vector decomposition and time allocation.

[0160] According to the sector number Sn, two adjacent valid vectors Vx and Vy corresponding to the sector are selected, and the target voltage vector Decompose into V x and V yThe target vector is decomposed into a linear combination of adjacent vectors by the following formula to ensure accurate tracking of the magnetic flux trajectory:

[0161]

[0162] in, = -60°. (Sn-1) is the local angle within the sector relative to the starting edge of the sector, U dc is the DC bus voltage, T1 and T2 are adjacent vectors, V x 、V y The basic vector action time set {T1, T2, T0} is generated according to the obtained T1, T2, T0, which is used as the input for PWM waveform generation.

[0163] This application adopts a seven-segment symmetrical allocation strategy, which allocates T1, T2, and T0 to the start, middle, and end stages of the PWM cycle in a symmetrical pattern to generate a seven-segment switching timing sequence. The specific allocation rules are as follows:

[0164]

[0165] Among them, T a , T b , T c is the conduction time of each bridge arm. According to the allocation rule, the PWM waveform timing {T a ,T b ,T c}.

[0166] The seven-stage symmetrical distribution strategy reduces switching losses and electromagnetic interference by evenly distributing switching actions while ensuring the symmetry of the output waveform.

[0167] At the same time, according to the sector number Sn, the preset switch state mapping table is queried to convert the PWM waveform timing {T a ,T b ,T c} is converted into a duty cycle signal of the three-phase bridge arm (i.e., phase A, phase B, and phase C). Three-phase duty cycle signal D A , D B , D C ∈[0,Ts], represents the conduction time of each bridge arm.

[0168] To avoid short circuit between the upper and lower bridge arms, a dead time T is inserted between the rising and falling edges of the PWM duty cycle signal. d , adjust the duty cycle signal to a driving signal with dead zone compensation.

[0169] Exemplarily, the dead time compensation of the phase A duty cycle signal is calculated by the following formula:

[0170]

[0171] in, , is the actual conduction time of the upper and lower bridge arms of phase A. Td is the dead time, usually Td=100ns. The dead time is determined by the IGBT switching characteristics. The six PWM signals generated in the end must meet =T s , to ensure time integrity. The dead zone compensation of the remaining B-phase duty cycle signal and the C-phase duty cycle signal is the same as the dead zone time compensation process of the A-phase duty cycle signal, which will not be repeated here. For details, please refer to the dead zone time compensation process of the A-phase duty cycle signal.

[0172] The dead time compensation rule offsets the conduction time of the upper and lower bridge arms of each phase in both directions to ensure that the upper and lower bridge arms are not turned on at the same time, thereby avoiding a direct short circuit. For example: the conduction time of the upper bridge arm is reduced by T d , used to close the upper bridge arm in advance and reserve a safe interval for the lower bridge arm to be turned on; the lower bridge arm conduction time increases T d , which is used to delay the opening of the lower bridge arm to ensure that the upper bridge arm is completely shut down.

[0173] Repeat steps S2 to S7 until the operation instruction is executed.

[0174] The present application is applied to the field of rotor control technology, and the operating parameter set is obtained by monitoring the operating state of the target electronic shaft, the coordinate transformation of the operating parameter set is performed to generate the actual current component set, and the operating parameter set is adjusted to generate the adjustment reference standard value, and the stator flux linkage prediction value and the electromagnetic torque prediction value are obtained by discrete prediction of the actual current component set, and then the cost function is evaluated according to the adjustment reference standard value, the stator flux linkage prediction value and the electromagnetic torque prediction value to obtain the optimal voltage vector combination, and the optimal voltage vector combination is time-allocated to generate two adjustment voltage vectors, and finally the inverter drive signal is generated in combination with space vector modulation to achieve precise drive of the rotor. Through the discrete prediction step, the present application can foresee and adjust the stator flux linkage and electromagnetic torque, thereby achieving accurate and rapid control of the electric spindle rotor.

[0175] like Figure 2 , which is a functional module diagram of a rotor control device for an electric spindle provided in an embodiment of the present application.

[0176] In some embodiments, the rotor control device 2 of the electric spindle may include a plurality of functional modules composed of computer program segments. The computer program of each program segment in the rotor control device 2 of the electric spindle may be stored in the memory of the server and executed by at least one processor to execute (see Figure 1 Description) Function of the rotor control method of the electric spindle.

[0177] In this embodiment, the rotor control device 2 of the electric spindle can be divided into multiple functional modules according to the functions it performs. The functional modules may include: an instruction drive module 21, a coordinate transformation module 22, an adjustment standard module 23, a discrete prediction module 24, a vector acquisition module 25, a time allocation module 26 and a drive signal module 27. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0178] The instruction driving module 21 is used to drive the target electric spindle according to the received operation instruction.

[0179] The coordinate transformation module 22 is used to perform coordinate transformation on the operating parameter set of the target electric spindle collected in real time to obtain an actual current component set.

[0180] In an optional implementation, the coordinate transformation module 22 is specifically used for:

[0181] Perform high-speed synchronous acquisition of the three-phase current of the target electric spindle to obtain the instantaneous current value in the operating parameter set at the current moment;

[0182] Decoding the rotor position pulse signal in the operating parameter set to obtain the original position angle of the rotor, and performing rotor dynamic compensation on the original position angle to generate the rotor position angle at the current moment;

[0183] Performing a first coordinate transformation on the instantaneous current value to generate a first current component and a second current component on an α-β axis;

[0184] A second coordinate transformation is performed on the first current component and the second current component to generate a first actual current component and a second actual current component on the dq axis.

[0185] The adjustment standard module 23 is used to perform proportional and integral adjustment on the operating parameter set to generate an adjustment reference standard value.

[0186] In an optional implementation, the adjustment standard module 23 is specifically used to:

[0187] Calculate the speed error according to the target speed in the operation instruction and the measured speed in the operation parameter set to obtain a speed error signal;

[0188] Performing preset proportional and integral adjustments on the speed error signal to generate a primary electromagnetic torque reference value;

[0189] Performing output limiting processing on the primary electromagnetic torque reference value to generate the electromagnetic torque reference value;

[0190] The preset flux linkage reference value is dynamically adjusted in sections according to the measured rotation speed to generate the stator flux linkage reference value.

[0191] The discrete prediction module 24 is used to obtain the historical voltage vector set corresponding to the previous acquisition moment, and perform discrete prediction based on the actual current component set and the historical voltage vector set to obtain the stator flux prediction value and electromagnetic torque prediction value corresponding to the next acquisition moment.

[0192] In an optional implementation, the discrete prediction module 24 is specifically configured to:

[0193] According to the acquisition time tag in the operation parameter set, the operation parameter set corresponding to the last acquisition moment is acquired in a preset acquisition database to acquire the historical voltage vector set corresponding to the last acquisition moment;

[0194] Perform discrete prediction based on the historical voltage vector set and the actual current component set to obtain a predicted current component set corresponding to the next acquisition moment;

[0195] Calculating and synthesizing magnetic components of the predicted current component set to obtain the stator flux prediction value;

[0196] Torque calculation is performed on the current component set to obtain the electromagnetic torque prediction value.

[0197] The vector acquisition module 25 is used to perform cost function evaluation according to the adjustment reference standard value, the stator flux prediction value and the electromagnetic torque prediction value to obtain the best voltage vector combination.

[0198] In an optional implementation, the vector acquisition module 25 is specifically used for:

[0199] Traversing the inverter at the current moment to obtain a simulated voltage vector set that can be output by the inverter, and generating a prediction error data set according to the adjustment reference standard value and the simulated voltage vector set;

[0200] Performing a primary cost function weighted calculation on the prediction error data set to obtain a single vector cost function value set, and extracting a first optimal voltage vector from the voltage vector set according to the single vector cost function value set;

[0201] performing a double vector combination calculation on the first optimal voltage vector and the remaining voltage vectors in the voltage vector set to obtain a candidate combination cost function value set;

[0202] A second optimal voltage vector is extracted from the remaining voltage vectors according to the candidate combination cost function value set.

[0203] The time allocation module 26 is used to allocate time for the optimal voltage vector combination to generate a first regulating voltage vector and a second regulating voltage vector.

[0204] In an optional implementation, the time allocation module 26 is specifically used to:

[0205] Performing current slope modeling on the optimal voltage vector combination to obtain a current slope parameter set of the dq axes of each optimal voltage vector;

[0206] generating a time allocation optimization coefficient matrix according to the prediction error data set corresponding to the optimal voltage vector combination and the current slope parameter set;

[0207] Calculating a first action time of the first optimal voltage vector according to the predicted current component set, the current slope parameter set and the time allocation optimization coefficient matrix, and calculating a second action time of the second optimal voltage vector according to the first action time and a preset control period;

[0208] Performing time-domain synthesis on the optimal voltage vector combination according to the first action time and the second action time to generate an equivalent dq axis voltage component set;

[0209] The equivalent dq-axis voltage component set is inversely transformed according to the rotor position angle to generate the first regulating voltage vector and the second regulating voltage vector.

[0210] The drive signal module 27 is used to perform modulation processing according to the first adjustment voltage vector and the second adjustment voltage vector to generate an inverter drive signal, wherein the inverter drive signal is used to control the rotor of the target electric spindle.

[0211] In an optional implementation, the driving signal module 27 is specifically used for:

[0212] performing polar coordinate angle calculation according to the first adjustment voltage vector and the second adjustment voltage vector to obtain a current sector number according to the polar coordinate angle;

[0213] performing adjacent vector decomposition on the first regulating voltage vector and the second regulating voltage vector according to the current sector number to obtain a basic vector action time set;

[0214] The basic vector action time set is symmetrically distributed according to a preset symmetry strategy to generate a PWM waveform timing sequence;

[0215] Perform switch state mapping according to the current sector number and the PWM waveform timing to obtain a PWM duty cycle signal;

[0216] Dead time compensation is performed on the PWM duty cycle signal to generate the inverter drive signal.

[0217] It should be understood that the various variations and specific embodiments of the methods provided in the above embodiments are also applicable to the rotor control device of the electric spindle of the present embodiment. Through the above detailed description of the rotor control method of the electric spindle, those skilled in the art can clearly know the implementation method of the rotor control device of the electric spindle of the present embodiment. For the sake of brevity of the specification, it will not be described in detail here.

[0218] like Figure 3 , which is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0219] In a preferred embodiment of the present invention, the electronic device 3 may include, but is not limited to: a memory 31 , at least one processor 32 and at least one communication bus 33 .

[0220] Those skilled in the art should understand that Figure 3 The structure of the electronic device 3 shown does not constitute a limitation of the embodiment of the present invention, and the electronic device 3 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components.

[0221] In some embodiments, the electronic device 3 is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits, programmable gate arrays, digital processors and embedded devices.

[0222] It should be noted that the electronic device 3 is only an example, and other existing or future electronic products that are suitable for the present application should also be included in the protection scope of the present application and included here by reference.

[0223] In some embodiments, the memory 31 stores a computer program, and when the computer program is executed by the at least one processor 32, all or part of the steps in the rotor control method of the electric spindle are implemented. The memory 31 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc memory, magnetic disk memory, magnetic tape memory, or any other computer-readable medium that can be used to carry or store data. Further, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, and the like.

[0224] In some embodiments, the at least one processor 32 is the control core (ControlUnit) of the electronic device 3, and uses various interfaces and lines to connect various components of the entire electronic device 3, and executes various functions and processes data of the electronic device 3 by running or executing programs or modules stored in the memory 31, and calling data stored in the memory 31. For example, when the at least one processor 32 executes the computer program stored in the memory 31, it implements all or part of the steps of the rotor control method of the electric spindle described in the embodiment of the present application; or implements all or part of the functions of the rotor control device of the electric spindle. The at least one processor 32 can be composed of an integrated circuit, for example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple integrated circuits with the same function or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips.

[0225] In some embodiments, the at least one communication bus 33 is configured to realize the connection and communication between the memory 31 and the at least one processor 32. Although not shown, the electronic device 3 may also include a power supply (such as a battery) for powering each component. Preferably, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to realize the functions of managing charging, discharging, and power consumption management through the power management device. The power supply may also include any components such as one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 3 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0226] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling an electronic device (which can be a personal computer, electronic device, or network device, etc.) or a processor to execute part of the method described in each embodiment of the present application.

[0227] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0228] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0229] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A rotor control method for an electric spindle, characterized in that: The method comprises: Step S1, driving the target electric spindle according to the received operation instruction; Step S2, performing coordinate transformation on the operating parameter set of the target electric spindle collected in real time to obtain an actual current component set; Step S3, performing proportional and integral regulation on the operating parameter set to generate a regulation reference standard value, wherein the regulation reference standard value includes an electromagnetic torque reference value and a stator flux linkage reference value; Step S4, obtaining a historical voltage vector set corresponding to the previous acquisition moment, and performing discrete prediction based on the actual current component set and the historical voltage vector set to obtain a stator flux linkage prediction value and an electromagnetic torque prediction value corresponding to the next acquisition moment; Step S5, performing a cost function evaluation according to the adjustment reference standard value, the stator flux prediction value, and the electromagnetic torque prediction value to obtain an optimal voltage vector combination, wherein the optimal voltage vector combination includes a first optimal voltage vector and a second optimal voltage vector; Traversing the inverter at the current moment to obtain a simulated voltage vector set that can be output by the inverter, and generating a prediction error data set according to the adjustment reference standard value and the simulated voltage vector set, wherein the prediction error data set is used to quantify the deviation between the stator flux prediction value and the stator flux reference value, and the deviation between the electromagnetic torque prediction value and the electromagnetic torque reference value; Performing a primary cost function weighted calculation on the prediction error data set to obtain a single vector cost function value set, and extracting a first optimal voltage vector from the voltage vector set according to the single vector cost function value set; performing a double vector combination calculation on the first optimal voltage vector and the remaining voltage vectors in the voltage vector set to obtain a candidate combination cost function value set; extracting a second optimal voltage vector from the remaining voltage vectors according to the candidate combination cost function value set; Step S6, performing time allocation on the optimal voltage vector combination to generate a first regulating voltage vector and a second regulating voltage vector; Performing current slope modeling on the optimal voltage vector combination to obtain a current slope parameter set of the dq axes of each optimal voltage vector; Generating a time allocation optimization coefficient matrix according to the prediction error data set corresponding to the optimal voltage vector combination and the current slope parameter set, wherein the time allocation optimization coefficient matrix is ​​used to balance the coupling effect of the current slope difference and the flux / torque error; Calculating a first action time of the first optimal voltage vector according to the predicted current component set, the current slope parameter set and the time allocation optimization coefficient matrix, and calculating a second action time of the second optimal voltage vector according to the first action time and a preset control period; Performing time-domain synthesis on the optimal voltage vector combination according to the first action time and the second action time to generate an equivalent dq axis voltage component set; Inversely transforming the equivalent dq-axis voltage component set according to the rotor position angle to generate the first regulating voltage vector and the second regulating voltage vector; Step S7: performing modulation processing according to the first adjustment voltage vector and the second adjustment voltage vector to generate an inverter drive signal, wherein the inverter drive signal is used to control the rotor of the target electric spindle.

2. The rotor control method of the electric spindle according to claim 1, characterized in that: The actual current component set includes a first actual current component and a second actual current component, and the coordinate transformation of the operating parameter set of the target electric spindle acquired in real time to obtain the actual current component set includes: Perform high-speed synchronous acquisition of the three-phase current of the target electric spindle to obtain the instantaneous current value in the operating parameter set at the current moment; Decoding the rotor position pulse signal in the operating parameter set to obtain the original position angle of the rotor, and performing rotor dynamic compensation on the original position angle to generate the rotor position angle at the current moment; Performing a first coordinate transformation on the instantaneous current value to generate a first current component and a second current component on an α-β axis; A second coordinate transformation is performed on the first current component and the second current component to generate a first actual current component and a second actual current component on the dq axis.

3. The rotor control method of the electric spindle according to claim 2, characterized in that: The adjustment reference standard value includes an electromagnetic torque reference value and a stator flux reference value, and the proportional and integral adjustment of the operating parameter set to generate the adjustment reference standard value includes: Calculate the speed error according to the target speed in the operation instruction and the measured speed in the operation parameter set to obtain a speed error signal; Performing preset proportional and integral adjustments on the speed error signal to generate a primary electromagnetic torque reference value; Performing output limiting processing on the primary electromagnetic torque reference value to generate the electromagnetic torque reference value; The preset flux linkage reference value is dynamically adjusted in sections according to the measured rotation speed to generate the stator flux linkage reference value.

4. The rotor control method of the electric spindle according to claim 3, characterized in that: The acquiring of the historical voltage vector set corresponding to the previous acquisition moment, and performing discrete prediction according to the actual current component set and the historical voltage vector set to acquire the stator flux linkage prediction value and the electromagnetic torque prediction value corresponding to the next acquisition moment includes: According to the acquisition time tag in the operation parameter set, the operation parameter set corresponding to the last acquisition moment is acquired in a preset acquisition database to acquire the historical voltage vector set corresponding to the last acquisition moment; Perform discrete prediction based on the historical voltage vector set and the actual current component set to obtain a predicted current component set corresponding to the next acquisition moment; Calculating and synthesizing magnetic components of the predicted current component set to obtain the stator flux prediction value; Torque calculation is performed on the current component set to obtain the electromagnetic torque prediction value.

5. The rotor control method of the electric spindle according to claim 1, characterized in that: The performing modulation processing according to the first adjustment voltage vector and the second adjustment voltage vector to generate an inverter drive signal, wherein the inverter drive signal is used to control the rotor of the target electric spindle, comprises: performing polar coordinate angle calculation according to the first adjustment voltage vector and the second adjustment voltage vector to obtain a current sector number according to the polar coordinate angle; performing adjacent vector decomposition on the first regulating voltage vector and the second regulating voltage vector according to the current sector number to obtain a basic vector action time set; The basic vector action time set is symmetrically distributed according to a preset symmetry strategy to generate a PWM waveform timing sequence; Perform switch state mapping according to the current sector number and the PWM waveform timing to obtain a PWM duty cycle signal; Dead time compensation is performed on the PWM duty cycle signal to generate the inverter drive signal.

6. A rotor control device for an electric spindle, characterized in that: The device comprises: An instruction driving module, used for driving the target electric spindle according to the received operation instruction; A coordinate transformation module, used for performing coordinate transformation on the operating parameter set of the target electric spindle collected in real time to obtain an actual current component set; An adjustment standard module, used for performing proportional and integral adjustment on the operating parameter set to generate an adjustment reference standard value, wherein the adjustment reference standard value includes an electromagnetic torque reference value and a stator flux reference value; A discrete prediction module is used to obtain a historical voltage vector set corresponding to a previous acquisition moment, and to perform discrete prediction based on the actual current component set and the historical voltage vector set to obtain a stator flux linkage prediction value and an electromagnetic torque prediction value corresponding to a next acquisition moment; A vector acquisition module, used for performing a cost function evaluation according to the adjustment reference standard value, the stator flux prediction value and the electromagnetic torque prediction value to obtain an optimal voltage vector combination, wherein the optimal voltage vector combination includes a first optimal voltage vector and a second optimal voltage vector; Traversing the inverter at the current moment to obtain a simulated voltage vector set that can be output by the inverter, and generating a prediction error data set according to the adjustment reference standard value and the simulated voltage vector set, wherein the prediction error data set is used to quantify the deviation between the stator flux prediction value and the stator flux reference value, and the deviation between the electromagnetic torque prediction value and the electromagnetic torque reference value; Performing a primary cost function weighted calculation on the prediction error data set to obtain a single vector cost function value set, and extracting a first optimal voltage vector from the voltage vector set according to the single vector cost function value set; performing a double vector combination calculation on the first optimal voltage vector and the remaining voltage vectors in the voltage vector set to obtain a candidate combination cost function value set; extracting a second optimal voltage vector from the remaining voltage vectors according to the candidate combination cost function value set; A time allocation module, used for allocating time for the optimal voltage vector combination to generate a first regulating voltage vector and a second regulating voltage vector; Performing current slope modeling on the optimal voltage vector combination to obtain a current slope parameter set of the dq axes of each optimal voltage vector; Generating a time allocation optimization coefficient matrix according to the prediction error data set corresponding to the optimal voltage vector combination and the current slope parameter set, wherein the time allocation optimization coefficient matrix is ​​used to balance the coupling effect of the current slope difference and the flux / torque error; Calculating a first action time of the first optimal voltage vector according to the predicted current component set, the current slope parameter set and the time allocation optimization coefficient matrix, and calculating a second action time of the second optimal voltage vector according to the first action time and a preset control period; Performing time-domain synthesis on the optimal voltage vector combination according to the first action time and the second action time to generate an equivalent dq axis voltage component set; Inversely transforming the equivalent dq-axis voltage component set according to the rotor position angle to generate the first regulating voltage vector and the second regulating voltage vector; A drive signal module is used to perform modulation processing according to the first adjustment voltage vector and the second adjustment voltage vector to generate an inverter drive signal, wherein the inverter drive signal is used to control the rotor of the target electric spindle.

7. An electronic device, characterized in that: The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the rotor control method of the electric spindle according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the rotor control method of the electric spindle according to any one of claims 1 to 5 are implemented.

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