A PWM control method for reluctance motor based on torque distribution function

Abstract

The present invention belongs to the technical field of motor control, and specifically discloses a PWM control method for a reluctance motor based on a torque distribution function, comprising the following steps: determining the reference phase torque of each phase; obtaining a random predicted duty cycle; obtaining the phase current at time k and the rotor position at time k of the reluctance motor, and determining the phase flux linkage at time k of the reluctance motor; predicting the phase flux linkage at time k+1 and the rotor position at time k+1 of the reluctance motor; determining the phase current at time k+1 of the reluctance motor, and determining the phase torque at time k+1; establishing a third-order linear relationship expression and calculating a complex correlation coefficient; determining the predicted duty cycle and the predicted phase torque corresponding to the predicted duty cycle; determining the optimal phase torque of each phase, and determining the optimal duty cycle of each phase; converting the optimal duty cycle of each phase into a control signal of a switch tube to control the reluctance motor. The present invention can improve the following accuracy of the reference torque. The problem of inaccurate calculation of the following accuracy of the reference torque in the prior art is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of motor control, and in particular relates to a reluctance motor PWM control method based on a torque distribution function. Background Art

[0002] The reluctance motor has the advantages of simple structure, high reliability, wide speed regulation range, easy switching of working modes, and strong adaptability to harsh environments. It has become one of the most competitive motors in the field of electric vehicles and more electric aircraft and has broad development prospects.

[0003] Since the reluctance motor adopts a double-pole structure and usually operates in the deep saturation region of the magnetic circuit, its torque characteristics are highly nonlinear, and its instantaneous torque is difficult to directly express by analytical expression. This results in significant torque pulsation in the reluctance motor under traditional control methods, which is a potential threat to the stability of the electric drive system. The study of torque pulsation suppression has become one of the hot research areas of reluctance motors. Domestic and foreign scholars have successively proposed methods such as direct torque control, direct instantaneous torque control, torque distribution function and model predictive control. Among them, the combination of torque distribution function and model predictive control shows a good control effect. Although model predictive control can select the switching state that can minimize torque pulsation from three switching states, due to the limited switching state restrictions, the optimal switching state still has a certain gap with the actual optimal control signal. The instantaneous torque is difficult to accurately follow the reference torque, and the torque pulsation suppression effect needs to be further improved. Further research found that applying PWM signals to model predictive control can enrich the optimal selection of control signals. Based on the difference between different predicted torques and reference torques, the optimal duty cycle that can accurately follow the reference torque is output. So far, the application of torque distribution function and model predictive control on reluctance motor has achieved many results, but there is no research on applying PWM signal to model prediction to achieve accurate following of reference torque. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a reluctance motor PWM control method based on torque distribution function, which solves the problem of inaccurate calculation of the following precision of the reference torque in the prior art.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a reluctance motor PWM control method based on torque distribution function, comprising the following steps:

[0006] S1. Use a PI controller to obtain a reference torque of the reluctance motor, and distribute the reference torque to each phase of the reluctance motor through a sinusoidal torque distribution function to obtain a reference phase torque of each phase;

[0007] S2, obtain random prediction duty cycle within [-1,1] through Latin hypercube sampling;

[0008] The random prediction duty cycle is 9;

[0009] S3, using a current sensor and a position sensor to respectively obtain the phase current at time k and the rotor position at time k of the reluctance motor, and searching based on the first data lookup table to determine the phase flux linkage at time k of the reluctance motor;

[0010] S4, using a prediction formula to predict the phase flux linkage at time k+1 and the rotor position at time k+1 of the reluctance motor according to the phase current at time k, the rotor position at time k and the phase flux linkage at time k;

[0011] At time k+1, the phase flux linkage is 9;

[0012] S5. According to the phase flux at time k+1 and the rotor position at time k+1, a search is performed based on the second data lookup table to determine the phase current at time k+1 of the reluctance motor, and based on the phase current at time k+1 and the rotor position at time k+1, a search is performed in the third data lookup table to determine the phase torque at time k+1;

[0013] The phase current at time k+1 and the phase torque at time k+1 are both 9;

[0014] S6, based on the phase torque at time k+1 and the randomly predicted duty cycle corresponding to the phase torque at time k+1, establish a third-order linear relationship expression and calculate the complex correlation coefficient;

[0015] S7. According to the calculation result of the complex correlation coefficient and based on the third-order linear relationship expression, data expansion is performed on the random predicted duty cycle and the phase torque at time k+1 corresponding to the random predicted duty cycle to obtain the predicted duty cycle and the predicted phase torque corresponding to the predicted duty cycle;

[0016] S8. Based on the reference phase torque of each phase, determine the optimal phase torque of each phase from the predicted phase torque, and determine the predicted duty cycle corresponding to the optimal phase torque of each phase as the optimal duty cycle of each phase;

[0017] S9. Convert the optimal duty cycle of each phase into a control signal of the switch tube through a PWM modulation method, and apply the control signal to an asymmetric half-bridge power converter of a drive system of the reluctance motor to control the reluctance motor.

[0018] The beneficial effects of the above scheme are as follows: the present invention proposes a PWM control method for a reluctance motor based on a torque distribution function, which combines the PWM control signal with the torque distribution function and the model predictive control, enriches the selection range of the optimal control signal, outputs the predicted duty cycle that can minimize the difference between the instantaneous torque and the reference phase torque, effectively improves the tracking accuracy of the reference torque, and reduces the torque pulsation during the operation of the reluctance motor. At the same time, the method expands the predicted data through a one-dimensional linear interpolation method, replaces the cumbersome prediction process with a mathematical method, reduces the use of data lookup tables, and diversifies the candidate solutions, so that the output control signal is closer to the actual optimal solution.

[0019] Furthermore, in S4, the prediction formula is:

[0020]

[0021]

[0022] in, Indicates k+1 phase flux linkages, represents the phase flux at time k, represents the phase voltage at time k, represents the phase current at time k, represents the phase resistance, represents the control cycle, represents the rotor position at time k+1, represents the rotor position at time k, represents the rotor position at time k-1, Indicates A random prediction duty cycle.

[0023] Furthermore, before S6, the method further includes:

[0024] The coefficients of the third-order linear relationship expression are determined using the least square method, and the coefficients of the third-order linear relationship expression are substituted into the third-order linear relationship expression to determine the third-order linear relationship expression.

[0025] Furthermore, the third-order linear relationship expression is:

[0026]

[0027] in, Represents the coefficients of the preset third-order linear relationship expression, represents the predicted duty cycle, Represents the phase torque at time k+1.

[0028] Furthermore, in S6, the calculation formula for calculating the complex correlation coefficient is:

[0029]

[0030] in, represents the average value of the phase torque at 9 k+1 moments, The first The predicted phase torque, represents the multiple correlation coefficient, Indicates The phase torque at k+1 moments.

[0031] The beneficial effect of the above further scheme is that the fitting accuracy can be accurately evaluated through the complex correlation coefficient, which is convenient for determining the fitting effect under different fitting accuracies, and then taking different approaches to further determine the optimal duty cycle.

[0032] Further, in S7, according to the calculation result of the complex correlation coefficient and based on the third-order linear relationship expression, data expansion is performed on the random predicted duty cycle and the phase torque at the k+1 moment corresponding to the random predicted duty cycle, specifically including:

[0033] When the calculated result of the complex correlation coefficient is greater than 0.9, the predicted duty cycle is obtained within [-1,1] at intervals of 0.05, and the predicted duty cycle is substituted into the third-order linear relationship expression to obtain the phase torque at the k+1 moment corresponding to the predicted duty cycle, thereby respectively determining the data expansion result of the predicted duty cycle and the data expansion result of the phase torque at the k+1 moment corresponding to the predicted duty cycle;

[0034] When the calculated result of the complex correlation coefficient is less than or equal to 0.9, no data expansion is performed on the random prediction duty cycle and the phase torque at the k+1 moment corresponding to the random prediction duty cycle.

[0035] The beneficial effect of the above further scheme is: the duty cycle is converted into a switch control signal through the PWM modulation method and applied to the asymmetric half-bridge power converter, thereby achieving accurate following of the reference torque and effectively suppressing torque pulsation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The figure shows a flow chart of a PWM control method for a reluctance motor based on a torque distribution function.

[0037] Figure 2 Shown is a principle block diagram of a reluctance motor PWM control method based on torque distribution function.

[0038] Figure 3 Shown is 700rpm 2N Schematic diagram of the simulation verification results of the traditional prediction model under condition m.

[0039] Figure 4 Shown is 700rpm 2N Schematic diagram of the simulation verification results of the torque pulsation suppression performance of the present invention under the m condition.

[0040] Figure 5 Shown is 1000rpm 1N Schematic diagram of the simulation verification results of the traditional prediction model under condition m.

[0041] Figure 6 Shown is 1000rpm 1N Schematic diagram of the simulation verification results of the torque pulsation suppression performance of the present invention under the m condition. DETAILED DESCRIPTION

[0042] Now, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the accompanying drawings are only exemplary and are intended to explain the principles and spirit of the present invention, rather than to limit the scope of the present invention.

[0043] like Figure 1 As shown, a reluctance motor PWM control method based on torque distribution function includes the following steps:

[0044] S1. Use a PI controller to obtain a reference torque of the reluctance motor, and distribute the reference torque to each phase of the reluctance motor through a sinusoidal torque distribution function to obtain a reference phase torque of each phase.

[0045] For example, a switched reluctance motor (Switched Reluctance Motor, SRM) is a type of reluctance motor. Based on the basic principle of the reluctance motor, the operation of the motor is achieved by controlling the switching state of the stator winding (that is, controlling the on and off of the current). Taking the switched reluctance motor as an example, "each phase" refers to a part of the electromagnetic structure of the motor. Specifically for SRM, it usually refers to a group of excitation units consisting of stator windings and corresponding rotor teeth. The basic working principle of SRM relies on the switching control of the magnetic circuit between the stator and the rotor to generate a rotational torque. A basic reluctance motor usually contains multiple (such as single-phase, two-phase, three-phase, six-phase, etc.) such excitation units, each of which can be independently energized to attract or repel the corresponding rotor teeth, thereby driving the rotor to rotate.

[0046] The reference torque is distributed to each phase of the reluctance motor through the sinusoidal torque distribution function to obtain the distributed phase reference torque. The total desired torque (i.e., reference torque) can be divided into several parts through the sinusoidal torque distribution function and applied to different phases of the motor respectively. This can accurately control the total output torque and motion characteristics of the motor, and at the same time, by optimizing the current or voltage waveform of each phase, improve the operating efficiency of the motor and reduce noise and vibration, i.e., torque pulsation.

[0047] Optionally, the reference torque can be set according to actual conditions and is not specifically limited here.

[0048] S2. Obtain a random prediction duty cycle within [-1,1] through Latin hypercube sampling; the number of random prediction duty cycles is 9.

[0049] Exemplarily, "9 random predicted duty cycles" refers to a set of duty cycle values ​​for predictive control generated by the Latin hypercube sampling method. Duty cycle, which is the ratio of the high level time to the entire cycle time in the pulse width modulation (PWM) signal, is a key parameter for controlling motor current and torque. These 9 different duty cycle values ​​are used to explore possible current or torque outputs in the next control cycle, and then the optimal duty cycle is selected to achieve the best torque tracking performance and system response.

[0050] Specifically, the duty cycle represents the possible control level of the motor phase current. By simulating the expected behavior of the motor at these different levels, the actual operating performance of the motor can be optimized, torque fluctuations can be reduced, and control accuracy can be improved. Latin hypercube sampling is an efficient experimental design method that can better cover the entire parameter space with fewer sample points, so it can effectively find the optimal solution within limited computing resources.

[0051] S3. Use a current sensor and a position sensor to respectively obtain the phase current and the rotor position of the reluctance motor at time k, and perform a search based on the first data lookup table to determine the phase flux linkage of the reluctance motor at time k.

[0052] Exemplarily, the operating region of the reluctance motor can be divided into a single-phase region and a commutation region based on the allocated reference phase torque, and the operating region in the commutation region can also be divided into an excitation phase and a demagnetization phase.

[0053] In the single-phase region, only one stator winding (i.e., one phase) is energized. During this period, the magnetic field generated by this phase attracts the rotor and causes it to rotate. In the commutation region, two adjacent stator windings (i.e., two phases) are energized at the same time. During this period, the torque of one phase gradually decreases, and the torque of the other phase gradually increases to ensure the continuity and smoothness of the torque. The duty cycle refers to the proportion of the time that a phase is energized to the total time in a cycle. By adjusting the duty cycle, the average current and torque generated by the phase can be controlled. The excitation phase refers to the stator winding that is about to be energized or is being energized. At this stage, the phase generates a magnetic field through current, attracting the corresponding magnetic poles of the rotor, thereby generating torque; the magnetic field generated by the excitation phase will attract the rotor and rotate it to a position that is more conducive to reducing magnetic resistance. The demagnetization phase refers to the stator winding that is about to be deenergized or is being deenergized. At this stage, the current of this phase gradually decreases, and the magnetic field it generates also weakens accordingly; as the current of the demagnetization phase decreases, the magnetic field it generates weakens, and the attraction to the rotor gradually decreases, thereby reducing the torque; the existence of the demagnetization phase helps to smoothly switch from one phase to another, avoid sudden changes in torque, and maintain the smooth operation of the motor.

[0054] For example, at a certain position, the reference torque of phase A reaches its maximum value, and phase A enters the single-phase area; when the reference torque of phase A begins to decrease and the reference torque of phase B begins to increase, the motor enters the AB commutation area.

[0055] Exemplarily, the first data lookup table may be ψ ph ( i ph , θ ph ). The current sensor can be used to measure the phase current of the reluctance motor; the position sensor can be used to measure the position of the reluctance motor rotor. The first data lookup table can be a pre-established table, which stores the phase flux values ​​under different phase currents and rotor positions. Through this lookup table, the corresponding phase flux at time k can be found according to the phase current at time k and the rotor position at time k.

[0056] S4. According to the phase current at time k, the rotor position at time k and the phase flux at time k, use the prediction formula to predict the phase flux at time k+1 and the rotor position at time k+1 of the reluctance motor; the number of phase fluxes at time k+1 is 9.

[0057] In this embodiment, in S4, the prediction formula is:

[0058]

[0059]

[0060] in, Indicates k+1 phase flux linkages, represents the phase flux at time k, represents the phase voltage at time k, represents the phase current at time k, represents the phase resistance, represents the control cycle, represents the rotor position at time k+1, represents the rotor position at time k, represents the rotor position at time k-1, Indicates A random prediction duty cycle.

[0061] In a specific embodiment, the phase resistance may be 0.22Ω, and the control period may be 0.0001s.

[0062] Exemplarily, the prediction formula includes a random predicted duty cycle. Therefore, the 9 random predicted duty cycles obtained in S2 are substituted into the prediction formula respectively, and the phase flux at time k and the phase current at time k are used to obtain 9 phase fluxes at time k+1. At the same time, according to the prediction formula, the rotor position at time k+1 can also be obtained.

[0063] Optionally, the phase voltage may be measured by a voltage sensor. For example, a voltage sensor may be used to measure and obtain the phase voltage at time k.

[0064] S5. According to the phase flux at time k+1 and the rotor position at time k+1, a search is performed based on the second data lookup table to determine the phase current at time k+1 of the reluctance motor, and based on the phase current at time k+1 and the rotor position at time k+1, a search is performed in the third data lookup table to determine the phase torque at time k+1; the phase current at time k+1 and the phase torque at time k+1 are both 9.

[0065] Exemplarily, the second data lookup table may be i ph ( ψ ph , θ ph ). The second data lookup table may also be a pre-established table, which stores phase current values ​​under different phase flux and rotor positions. Through this lookup table, the corresponding phase current can be found according to the current phase flux and rotor position.

[0066] Exemplarily, the third data lookup table may be T ph ( i ph , θ ph). The third data lookup table may also be a pre-established table, which stores phase torques under different phase current values ​​and rotor positions. Through this lookup table, the corresponding phase torque can be found according to the current phase current value and rotor position.

[0067] S6. Based on the phase torque at time k+1 and the randomly predicted duty cycle corresponding to the phase torque at time k+1, a third-order linear relationship expression is established and the complex correlation coefficient is calculated.

[0068] In this embodiment, the third-order linear relationship expression is:

[0069]

[0070] in, Represents the coefficients of the preset third-order linear relationship expression, represents the predicted duty cycle, Represents the phase torque at time k+1.

[0071] In this embodiment, before S6, the method further includes:

[0072] The coefficients of the third-order linear relationship expression are determined using the least square method, and the coefficients of the third-order linear relationship expression are substituted into the third-order linear relationship expression to determine the third-order linear relationship expression.

[0073] Exemplarily, the least squares method can be used to first determine the coefficients of the third-order linear relationship expression and then determine the third-order linear relationship expression based on the 9 k+1 time phase torques and the random predicted duty cycles corresponding to the 9 k+1 time phase torques.

[0074] In this embodiment, in S6, the calculation formula for calculating the complex correlation coefficient is:

[0075]

[0076] in, represents the average value of the phase torque at 9 k+1 moments, The first The predicted phase torque, represents the multiple correlation coefficient, Indicates The phase torque at k+1 moments.

[0077] S7. According to the calculation result of the complex correlation coefficient and based on the third-order linear relationship expression, the random predicted duty cycle and the phase torque at time k+1 corresponding to the random predicted duty cycle are expanded to obtain the predicted duty cycle and the predicted phase torque corresponding to the predicted duty cycle.

[0078] In this embodiment, in S7, according to the calculation result of the complex correlation coefficient and based on the third-order linear relationship expression, data expansion is performed on the random predicted duty cycle and the phase torque at the k+1 moment corresponding to the random predicted duty cycle, specifically including:

[0079] When the calculated result of the complex correlation coefficient is greater than 0.9, the predicted duty cycle is obtained within [-1,1] at intervals of 0.05, and the predicted duty cycle is substituted into the third-order linear relationship expression to obtain the phase torque at the k+1 moment corresponding to the predicted duty cycle, thereby respectively determining the data expansion result of the predicted duty cycle and the data expansion result of the phase torque at the k+1 moment corresponding to the predicted duty cycle;

[0080] When the calculated result of the complex correlation coefficient is less than or equal to 0.9, no data expansion is performed on the random prediction duty cycle and the phase torque at the k+1 moment corresponding to the random prediction duty cycle.

[0081] For example, by obtaining the predicted duty cycle at intervals of 0.05 within [-1,1], 41 predicted duty cycles can be obtained. Therefore, by substituting the 41 predicted duty cycles into the third-order linear relationship expression respectively, the predicted phase torques corresponding to the 41 predicted duty cycles can be obtained.

[0082] S8. Based on the reference phase torque of each phase, determine the optimal phase torque of each phase from the predicted phase torque, and determine the predicted duty cycle corresponding to the optimal phase torque of each phase as the optimal duty cycle of each phase.

[0083] For example, in this embodiment, the predicted phase torque with the smallest difference from the reference phase torque of each phase can be used as the optimal phase torque of each phase, and the predicted duty cycle corresponding to the optimal phase torque of each phase is determined as the optimal duty cycle of each phase. Alternatively, by selecting a number of predicted phase torques with the smallest difference from the reference phase torque of each phase as the candidate phase torques of each phase, and based on actual experience, selecting the optimal phase torque from the candidate phase torques, and determining the predicted duty cycle corresponding to the optimal phase torque of each phase as the optimal duty cycle of each phase. No specific restrictions are made here.

[0084] S9. Convert the optimal duty cycle of each phase into a control signal of the switch tube through a PWM modulation method, and apply the control signal to an asymmetric half-bridge power converter of a drive system of the reluctance motor to control the reluctance motor.

[0085] like Figure 2 As shown, Figure 2 Shown is a principle block diagram of a reluctance motor PWM control method based on torque distribution function. Figure 2 The prediction model shown executes the steps described in S2-S8 of the present invention.

[0086] For example, PWM (Pulse Width Modulation) is a technique for controlling output voltage or current by changing the duty cycle of a pulse signal. The duty cycle refers to the proportion of the high level time to the entire cycle time in a cycle. For example, if a cycle is 10 milliseconds and the high level lasts for 5 milliseconds, the duty cycle is 50%.

[0087] The PWM modulator converts the calculated optimal duty cycle into a series of pulse signals. These pulse signals are used to control the switch tube (usually MOSFET or IGBT) in the power converter. Specifically: when the duty cycle is 50%, the switch tube is turned on for half of the time in a cycle and turned off for the other half. When the duty cycle is 80%, the switch tube is turned on for 80% of the time in a cycle and turned off for 20%.

[0088] The asymmetric half-bridge power converter is a common DC-AC converter, which is often used to drive a reluctance motor (SRM). It consists of two switches and a freewheeling diode. The characteristics of this converter are: asymmetry, that is, the conduction time of the two switches is not exactly the same, usually a main switch and an auxiliary switch; half-bridge, that is, only two switches are used instead of the four switches in the full-bridge structure.

[0089] The PWM modulated control signal is applied to the switch tube in the asymmetric half-bridge power converter to control the on and off of the main switch tube according to the high level and low level of the PWM signal. The auxiliary switch tube is usually turned on complementary to the main switch tube to ensure the continuity and stability of the circuit.

[0090] Optionally, in a specific embodiment, Figure 3 Shown is 700rpm 2N Schematic diagram of the simulation verification results of the traditional prediction model under condition m. Figure 4 Shown is 700rpm 2N Schematic diagram of the simulation verification results of the torque pulsation suppression performance of the present invention under the m condition. Figure 5 Shown is 1000rpm 1N Schematic diagram of the simulation verification results of the traditional prediction model under condition m. Figure 6 Shown is 1000rpm 1N Schematic diagram of the simulation verification results of the torque pulsation suppression performance of the present invention under the m condition.

[0091] Among them, the traditional prediction model predicts torque control in the following way:

[0092] (1) Using a PI controller to obtain the reference torque of the reluctance motor, and distributing the reference torque to each phase of the reluctance motor through a sinusoidal torque distribution function to obtain a reference phase torque of each phase;

[0093] (2) using a current sensor and a position sensor to respectively obtain a phase current and a rotor position of the reluctance motor at time k, and performing a search based on a first data lookup table to determine a phase flux linkage of the reluctance motor at time k;

[0094] (3) Based on the phase current at time k, the rotor position at time k, and the phase flux at time k, the traditional prediction formula is used to predict the phase flux at time k+1 and the rotor position at time k+1 of the reluctance motor; the traditional prediction formula is:

[0095]

[0096]

[0097] in, Indicates the three switching states of the reluctance motor. =1 means the winding is turned on, =0 means winding zero voltage follow current, =-1 means the winding is turned off. Therefore, the phase flux linkage is 3 at time k+1.

[0098] (4) According to the phase flux at time k+1 and the rotor position at time k+1, the second data lookup table is searched to determine the phase current at time k+1 of the reluctance motor, and based on the phase current at time k+1 and the rotor position at time k+1, the third data lookup table is searched to determine the phase torque at time k+1. There are three phase currents at time k+1 and three phase torques at time k+1.

[0099] (5) A cost function is established based on the phase torque at time k+1, and the switch state corresponding to the minimum cost function is selected as the optimal switch state at time k+1, and it is converted into a switch control signal for application. Cost function as follows:

[0100]

[0101] in, Indicates the number of phase windings, represents the phase torque at time k+1, Indicates the phase torque that can be used as a reference.

[0102] The comparison between the simulation verification results of the torque pulsation suppression performance of the present invention and the simulation verification results of the traditional prediction model can be performed by quantifying the torque pulsation:

[0103] Torque ripple Its effective value can be calculated as:

[0104]

[0105] in, and are the starting and ending rotor positions of a rotor cycle, respectively. is the total torque output by the switched reluctance motor, is the average value of the total torque in one rotor cycle, and .

[0106] According to the above calculation method, we can get 700rpm 2N The torque ripple of the torque control predicted by the traditional model under the m condition is 0.41N m, the torque pulsation of the present invention is 0.26N m. 1000rpm 1N The torque ripple of the traditional predictive torque control under the m condition is 0.19N m, the torque pulsation of the present invention is 0.06N m.

[0107] According to the comparison of simulation verification results of the present invention and the traditional prediction model under the same working conditions, it can be seen that the present invention can achieve accurate following of the instantaneous torque with the reference torque and maximize the suppression of torque pulsation during the operation of the reluctance motor.

[0108] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A reluctance motor PWM control method based on torque distribution function, characterized in that: The method comprises: S1. Using a PI controller to obtain a reference torque of the reluctance motor, and distributing the reference torque to each phase of the reluctance motor through a sinusoidal torque distribution function to obtain a reference phase torque of each phase; S2, obtain random prediction duty cycle within [-1,1] through Latin hypercube sampling; The random prediction duty cycle is 9; S3, using a current sensor and a position sensor to respectively obtain the phase current at time k and the rotor position at time k of the reluctance motor, and performing a search based on a first data lookup table to determine the phase flux linkage at time k of the reluctance motor; S4, predicting the phase flux linkage at time k+1 and the rotor position at time k+1 of the reluctance motor using a prediction formula according to the phase current at time k, the rotor position at time k and the phase flux linkage at time k; The phase flux linkage at the k+1 moment is 9; S5, according to the phase flux at time k+1 and the rotor position at time k+1, searching based on the second data lookup table to determine the phase current at time k+1 of the reluctance motor, and based on the phase current at time k+1 and the rotor position at time k+1, searching in the third data lookup table to determine the phase torque at time k+1; The phase current at time k+1 and the phase torque at time k+1 are both 9; S6. Based on the phase torque at time k+1 and the randomly predicted duty cycle corresponding to the phase torque at time k+1, a third-order linear relationship expression is established and a complex correlation coefficient is calculated; S7. According to the calculation result of the complex correlation coefficient and based on the third-order linear relationship expression, data expansion is performed on the random predicted duty cycle and the phase torque at time k+1 corresponding to the random predicted duty cycle to obtain the predicted duty cycle and the predicted phase torque corresponding to the predicted duty cycle; S8. Based on the reference phase torque of each phase, determine the optimal phase torque of each phase from the predicted phase torque, and determine the predicted duty cycle corresponding to the optimal phase torque of each phase as the optimal duty cycle of each phase; S9. Convert the optimal duty cycle of each phase into a control signal of a switch tube through a PWM modulation method, and apply the control signal to an asymmetric half-bridge power converter of a drive system of the reluctance motor to control the reluctance motor.

2. The method according to claim 1, characterized in that In S4, the prediction formula is: in, Indicates k+1 phase flux linkages, represents the phase flux at time k, represents the phase voltage at time k, represents the phase current at time k, represents the phase resistance, represents the control cycle, represents the rotor position at time k+1, represents the rotor position at time k, represents the rotor position at time k-1, Indicates A random prediction duty cycle.

3. The method according to claim 2, characterized in that Before S6, the method further includes: The coefficients of the third-order linear relationship expression are determined using the least squares method, and the coefficients of the third-order linear relationship expression are substituted into the third-order linear relationship expression to determine the third-order linear relationship expression.

4. The method according to claim 3, characterized in that The third-order linear relationship expression is: in, Represents the coefficients of the preset third-order linear relationship expression, represents the predicted duty cycle, Represents the phase torque at time k+1.

5. The method according to claim 4, characterized in that In S6, the calculation formula for calculating the complex correlation coefficient is: in, represents the average value of the phase torque at 9 k+1 moments, The first The predicted phase torque, represents the multiple correlation coefficient, Indicates The phase torque at k+1 moments.

6. The method according to claim 5, characterized in that In S7, according to the calculation result of the complex correlation coefficient and based on the third-order linear relationship expression, data expansion is performed on the random predicted duty cycle and the phase torque at the k+1 moment corresponding to the random predicted duty cycle, specifically including: When the calculation result of the complex correlation coefficient is greater than 0.9, the predicted duty cycle is obtained within [-1, 1] at intervals of 0.05, and the predicted duty cycle is substituted into the third-order linear relationship expression to obtain the phase torque at the time k+1 corresponding to the predicted duty cycle, thereby respectively determining the data expansion result of the predicted duty cycle and the data expansion result of the phase torque at the time k+1 corresponding to the predicted duty cycle; When the calculated result of the complex correlation coefficient is less than or equal to 0.9, no data expansion is performed on the random predicted duty cycle and the k+1 time phase torque corresponding to the random predicted duty cycle.

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