Self-damping predictive current control method and system for LC filter type synchronous reluctance motor

Abstract

The present invention discloses an LC filter type self-damping predictive current control method and system for a synchronous reluctance motor, belonging to the field of power electronics and power transmission. By sampling the inductor current of the LC filter, the capacitor voltage, the stator current of the synchronous reluctance motor, and the rotor electrical angle, a stator current prediction model with self-damping characteristics is established, and a stator current tracking quadratic cost function without weight parameters is constructed based on this prediction model; then, combined with the quadratic function optimization theory, the optimal voltage reference analytical formula of the inverter that can minimize the current tracking error is derived; finally, pulse signals are generated by space vector pulse width modulation and applied to the inverter. The method of the present invention can automatically suppress system resonance without adding an additional damping loop, has a simple structure and does not require any parameter tuning, and can effectively improve the stability and reliability of system operation.

Description

Technical Field

[0001] The present invention relates to a self-damping predictive current control method and system for an LC-filtered synchronous reluctance motor, belonging to the fields of power electronics and electric drive. Background Art

[0002] Synchronous reluctance motors have been widely used in industrial production due to their advantages such as high efficiency, high power density, and high reliability. Nowadays, wide-bandgap semiconductor materials are developing rapidly. The synchronous reluctance motor drive system based on wide-bandgap semiconductor devices has a broader application prospect in high-speed fields such as high-speed electric spindles and flywheel energy storage due to its fast switching speed and high switching frequency, and it is one of the research hotspots in the international field. However, while improving the performance of the drive system, high-switching-frequency power devices will generate higher d v / d t , which causes serious common-mode interference problems, directly affects the normal operation of the motor and reduces its operating stability. In addition, it will also exacerbate the phenomenon of overvoltage at the motor terminal, accelerate the insulation damage of the motor or even burn out the motor, having an adverse impact on production operations. To address the above problems, adding an LC filter at the output terminal of the inverter driving the motor is an efficient and convenient solution, thus forming an LC-filtered synchronous reluctance motor drive system. However, the LC filter will form an LCL resonance effect with the stator inductance of the synchronous reluctance motor, directly affecting the stability and reliability of the system. Moreover, due to the increase in the control order of the system, it poses a greater challenge to the parameter tuning workload of traditional linear controllers. Therefore, there is an urgent need to explore a control method with a simple structure, stable and reliable operation for the LC-filtered synchronous reluctance motor drive system.

[0003] Predictive control has great superiority in multi-objective control and conditional constraint processing due to its simple principle and fast dynamic response, and it has been widely used in the field of synchronous reluctance motor drives. Currently, the predictive control for LC-filtered synchronous reluctance motors is mainly divided into two types: predictive control with an additional damping loop and predictive control based on multi-objective optimization. Predictive control with an additional damping loop needs to add an additional damping control loop to suppress the LCL resonance problem of the system, which undoubtedly increases the implementation complexity of the control system. And predictive control based on multi-objective optimization needs to introduce multiple weight parameters, and the tuning of weight parameters is still an unsolved problem at home and abroad. Therefore, both of these two predictive control schemes inevitably involve the selection of various design parameters (such as damping coefficients, weight parameters), with a large parameter tuning workload, further exacerbating the design complexity of the control system, thus reducing the reliability of system operation. Summary of the Invention

[0004] In view of the deficiencies of the prior art, a self-damping predictive current control method and system for an LC-filtered synchronous reluctance motor are provided. Without adding an additional damping loop, by deriving a stator current prediction model with self-damping characteristics and a cost function without weight parameters, while constraining the stator current, indirect regulation of the energy of the filter inductor and capacitor is achieved, successfully solving the resonance problem of the LC-filtered synchronous reluctance motor and improving the stability of system operation. Moreover, this method avoids the complicated process of adjusting weight parameters, has a simple structure and is easy to implement, and is very suitable for engineering applications.

[0005] To achieve the above technical objectives, the present invention provides a self-damping predictive current control method and system for an LC-filtered synchronous reluctance motor, including the following steps:

[0006] Step 1: Sample the state variables of the LC-filtered synchronous reluctance motor in each sampling period, including: the inductor current of the three-phase LC filter (the input end of the filter inductor of each phase is connected to the output end of each phase of the inverter, one end of the filter capacitor of each phase is connected to the output end of the filter inductor, and the other end is commonly connected to the star point), the capacitor voltage, the three-phase stator currents of the synchronous reluctance motor, and its electrical angle; θ Then, transform the three-phase sampling values of the above state variables through the Park transformation to obtain the state variable values in the d-q synchronous rotating coordinate system: , , ;

[0007] Step 2: According to the state variable values of the LC-filtered synchronous reluctance motor obtained in Step 1, establish discrete dynamic models corresponding to each state variable by means of the first-order forward Euler method: i f ( k + 1), v f ( k + 1), i s ( k + 1);

[0008] Step 3: According to the discrete dynamic model of the LC-filtered synchronous reluctance motor established in Step 2, further derive a stator current prediction model with self-damping characteristics: i s p ( k + 1);

[0009] Step 4: According to the stator current prediction model with self-damping characteristics derived in Step 3, construct a stator current tracking cost function without weight parameters. J；

[0010] Step 5: For the stator current tracking cost function without weight parameters constructed in Step 4, use the quadratic function optimization theory to solve the optimal voltage reference of the inverter in the d-q synchronous rotating coordinate system. v i * . Then perform the Park inverse transformation on it to obtain the optimal voltage reference expression of the inverter in the α-β stationary coordinate system. v iα * and v iβ * ；

[0011] Step 6: Apply the optimal voltage reference values of the inverter obtained in Step 5 v iα * and v iβ * , through the space vector pulse width modulation technology to generate control pulse signals and apply them to the inverter switching tubes to achieve self-damping predictive current control of the LC-filtered synchronous reluctance motor.

[0012] Furthermore, the discrete dynamic model of each state variable of the LC-filtered synchronous reluctance motor in Step 2 is established by the first-order forward Euler method of the following formula:

[0013]

[0014]

[0015]

[0016] In the formula, " k " and " k +1" respectively represent at the k and k +1 sampling instants, v i is the inverter output voltage in the d-q synchronous rotating coordinate system, A x , B x ([[]] x = 1, 2, 3) is the discrete model matrix, and its specific expression is:

[0017]

[0018]

[0019]

[0020] Wherein, L f is the filter inductor, R f is the parasitic resistance of the filter inductor, C f is the filter capacitor, ω e is the electrical angular velocity obtained by differentiating the electrical angular velocity θ e of the synchronous reluctance motor, R s is the stator resistance of the synchronous reluctance motor, L d and L q are the stator inductances in the d-q synchronous rotating coordinate system of the synchronous reluctance motor, T s is the sampling period. Wherein, L f is the filter inductor, R f is the parasitic resistance of the filter inductor, C f is the filter capacitor, ω e is the electrical angular velocity obtained by differentiating the electrical angular velocity θ e of the synchronous reluctance motor, R s is the stator resistance of the synchronous reluctance motor, L d and L q are the stator inductances in the d-q synchronous rotating coordinate system of the synchronous reluctance motor, T s is the sampling period. Wherein, L f is the filter inductor, R f is the parasitic resistance of the filter inductor, C f is the filter capacitor, ω e is the electrical angular velocity obtained by differentiating the electrical angular velocity θ e of the synchronous reluctance motor, R s is the stator resistance of the synchronous reluctance motor,L d and L q are the stator inductances in the d-q synchronous rotating coordinate system of the synchronous reluctance motor, T s is the sampling period.

[0021] Furthermore, in order to effectively predict the future state of the motor stator current, the k filtered inductor current value at time +1 i f ( k +1) is used to replace the i f ( k ) in the discrete dynamic model of the filtered capacitor voltage, so as to obtain the k predicted value of the filtered capacitor voltage at time +1 v f ( k +1). Then, this predicted value v f ( k +1) is used to replace the v f ( k ) in the discrete dynamic model of the motor stator current. Then, the predicted model of the motor stator current with self-damping characteristics in step 3 can be derived, and its specific expression is as follows:

[0022]

[0023] In the formula, i s p ( k +1) = i sd p ( k +1) i sq p ( k +1)] is the k+ prediction matrix of the motor stator current in the d-q synchronous rotating coordinate system at time 1.

[0024] Furthermore, according to the predicted model of the stator current with self-damping characteristics derived in step 3, it can be seen that this model inherently includes the filtered inductor current and filtered capacitor voltage variables. Therefore, while tracking and controlling the stator current, the resonant energy of the filter can be effectively regulated, thereby realizing resonant self-damping. Based on this principle, the stator current tracking cost function without weight parameters described in step 4 can be constructed as follows:

[0025]

[0026] In the formula, i s * = i sd * i sq * represents the stator current reference matrix of the motor in the d-q synchronous rotating coordinate system.

[0027] Furthermore, in step 5, the quadratic function optimization control theory is adopted to derive the optimal solution of the inverter voltage reference for the stator current tracking cost function without weight parameters constructed in step 4, so as to ensure that the stator current reaches its reference value in the next control cycle. Specifically, the cost function J is derived through the following formula to find the minimum value: J The minimum value of:

[0028]

[0029] Then the expression of the optimal voltage reference of the inverter can be obtained as:

[0030]

[0031] In the formula, v i * = v id * v iq * represents the inverter voltage reference matrix in the d-q synchronous rotating coordinate system.

[0032] An LC filter type synchronous reluctance motor self-damping predictive current control system used for an LC filter type synchronous reluctance motor self-damping predictive current control method, characterized in that it includes a self-damping characteristic stator current prediction model module, a stator current tracking cost function module without weight parameters, an inverter optimal voltage reference calculation module, a transformation module, and a pulse generation module connected in sequence. The self-damping characteristic stator current prediction model module inputs k The LC filter inductor current i fd ( k ) sampled at time i fq ( k ), the capacitor voltage v fd ( k ),v fq ( k ) and the stator current of the synchronous reluctance motor i sd ( k ), i sq ( k ), the predicted value of the motor stator current at the +1 moment is calculated; the stator current tracking cost function module without weight parameters establishes a cost function based on the stator current tracking with self-damping characteristics through the output of the self-damping characteristic stator current prediction model module; the optimal inverter voltage reference calculation module uses the stator current tracking cost function and the quadratic function optimization theory to calculate the optimal inverter voltage reference value; finally, the output of the optimal inverter voltage reference calculation module is transmitted to the pulse generation module through the transformation module. k +1 moment; the stator current tracking cost function module without weight parameters establishes a cost function based on the stator current tracking with self-damping characteristics through the output of the self-damping characteristic stator current prediction model module; the optimal inverter voltage reference calculation module uses the stator current tracking cost function and the quadratic function optimization theory to calculate the optimal inverter voltage reference value; finally, the output of the optimal inverter voltage reference calculation module is transmitted to the pulse generation module through the transformation module.

[0033] The self-damping characteristic stator current prediction model module is used to construct the stator current tracking cost function without weight parameters J；

[0034] The optimal inverter voltage reference calculation module is used to calculate the optimal inverter voltage reference that can make this cost function J obtain the minimum value;

[0035] The transformation module is used to convert the optimal inverter voltage reference value in the d-q synchronous rotating coordinate system to the α-β stationary coordinate system through the Park inverse transformation;

[0036] The pulse generation module is used to perform space vector pulse width modulation on the optimal inverter voltage reference value in the α-β stationary coordinate system to generate a pulse signal for controlling the inverter switching tubes.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. The method provided by the present invention does not require adding any additional damping loop. By deriving a stator current prediction model with self-damping characteristics, it realizes the indirect regulation of the energy of the filter inductor and capacitor, successfully solves the inherent resonance problem of the LC-filtered synchronous reluctance motor, and ensures the stability and reliability of the system operation;

[0039] 2. The stator current tracking cost function without weight parameters constructed by the method provided by the present invention avoids the complicated weight parameter adjustment process, so it has the advantages of simple structure and easy implementation, and is very suitable for engineering applications. Description of the Drawings

[0040] Figure 1 is the system control structure schematic diagram corresponding to the method provided by the present invention;

[0041] Figure 2 Schematic diagram of output waveforms of various state variables of an LC-filter type synchronous reluctance motor under different reference speeds and loads;

[0042] Figure 3 Schematic diagram of the steady-state waveform and harmonic spectrum of the stator current of the method provided by the present invention. Detailed implementation manners

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0044] Figure 1 Schematic diagram of the system structure corresponding to the method of the present invention. Among them, the DC bus voltage source is converted into an AC voltage square wave signal through a three-phase voltage source inverter, and then connected to the synchronous reluctance motor through an output LC filter (the input ends of the filtering inductors of each phase are connected to the output ends of each phase of the inverter, one end of the filtering capacitor of each phase is connected to the output end of the filtering inductor, and the other end is commonly connected to the star point); the three-phase stator current of the synchronous reluctance motor, the three-phase filtering capacitor voltage of the LC filter, the three-phase filtering inductor current, and the actual angular velocity of the motor are sampled in sequence ω r ; the outer speed loop still adopts the traditional proportional-integral PI control, and the inner current loop adopts the self-damping predictive current control method provided by the present invention.

[0045] In the figure, V dc represents the DC bus voltage; R f represents the equivalent series resistance inside the filtering inductor; L f represents the filtering inductor; C f represents the filtering capacitor; i f,abc represents the three-phase filtering inductor current; v f,acb represents the three-phase filtering capacitor voltage; i s,abc represents the three-phase stator current; θ and θ e respectively represent the mechanical angle and electrical angle of the motor rotor; P N represents the number of pole pairs of the motor; ω r * represents the reference speed; ω r represents the actual speed of the motor; i sd *is the reference current of the d-axis of the motor; i sq * is the reference current of the q-axis of the motor; d / dt represents the differential link; i s ( k )、 v f ( k ) and i f ( k ) respectively represent k the stator current, the filter capacitor voltage, and the filter inductor current in the d-q synchronous rotating coordinate system at time v i * represents the optimal voltage reference of the inverter in the d-q synchronous rotating coordinate system; v iα * 、 v iβ * respectively represent the components of the optimal voltage reference of the inverter in the α-β stationary coordinate system; S abc represents the switching state of the upper-bridge arm switch tube of the inverter.

[0046] An LC-filtered synchronous reluctance motor self-damping predictive current control method and system, characterized in that it includes a self-damping characteristic stator current prediction model module, a stator current tracking cost function module without weight parameters, an inverter optimal voltage reference calculation module, a transformation module, and a pulse generation module connected in sequence. The self-damping characteristic stator current prediction model module inputs k the LC filter inductor current in the d-q synchronous rotating coordinate system sampled at time i fd ( k ), i fq ( k ), the capacitor voltage v fd ( k ), v fq ( k ), and the stator current i sd ( k ), i sq ( k ), and calculates kThe predicted value of the motor stator current at the +1 moment; the stator current tracking cost function module without weight parameters establishes a cost function based on the stator current tracking with self-damping characteristics through the output of the self-damping characteristic stator current prediction model module; the optimal inverter voltage reference calculation module calculates the optimal inverter voltage reference value by using the stator current tracking cost function and the quadratic function optimization theory; finally, the output of the optimal inverter voltage reference calculation module is transmitted to the pulse generation module through the transformation module.

[0047] The self-damping characteristic stator current prediction model module is used to construct the stator current tracking cost function without weight parameters J；

[0048] The optimal inverter voltage reference calculation module is used to calculate the optimal inverter voltage reference that can make this cost function J obtain the minimum value;

[0049] The transformation module is used to convert the optimal inverter voltage reference value in the d-q synchronous rotating coordinate system to the α-β stationary coordinate system through the Park inverse transformation;

[0050] The pulse generation module is used to perform space vector pulse width modulation on the optimal inverter voltage reference value in the α-β stationary coordinate system to generate a pulse signal for controlling the inverter switching tubes.

[0051] A self-damping predictive current control method and system for an LC-filtered synchronous reluctance motor, including the following steps:

[0052] Step 1: Sample the state variables of the LC-filtered synchronous reluctance motor in each sampling period, including: the inductance current of the three-phase LC filter (the input end of the filtering inductance of each phase is connected to the output end of each phase of the inverter, one end of the filtering capacitor of each phase is connected to the output end of the filtering inductance, and the other end is commonly connected to the star point), the capacitor voltage, the three-phase stator current of the synchronous reluctance motor, and its electrical angle; θ Then, the three-phase sampled values of the above state variables are transformed through the Park transformation to obtain the state variable values in the d-q synchronous rotating coordinate system: , , ;

[0053] Step 2: The discrete dynamic model of each state variable of the LC-filtered synchronous reluctance motor is established by the first-order forward Euler method of the following formula:

[0054]

[0055]

[0056]

[0057] In the formula, " k " and " k +1" respectively represent the k th and k +1th sampling instants. v i is the inverter output voltage in the d-q synchronous rotating coordinate system. A x , B x ( x = 1, 2, 3) are the discrete model matrices, and their specific expressions are:

[0058]

[0059]

[0060]

[0061] In the formula, L f is the filter inductor. R f is the parasitic resistance of the filter inductor. C f is the filter capacitor. ω e is the electrical angular velocity obtained by differentiating the electrical angular velocity θ e of the synchronous reluctance motor. R s is the stator resistance of the synchronous reluctance motor. L d and L q are the stator inductances in the d-q synchronous rotating coordinate system of the synchronous reluctance motor. T s is the sampling period.

[0062] Step 3: To effectively predict the future state of the motor stator current, the value of the filter inductor current k at the i f ( k + 1) moment is used to replace i f ( k ) in the discrete dynamic model of the filter capacitor voltage, thereby obtaining the predicted value k of the filter capacitor voltage at the v f (k +1), and then use this predicted value v f ( k +1) to replace the v f ( k ) in the discrete dynamic model of the motor stator current, and the predicted model of the motor stator current with self-damping characteristics in step 3 can be derived. Its specific expression is as follows:

[0063]

[0064] In the formula, i s p ( k +1) = i sd p ( k +1) i sq p ( k +1)] is the k+ prediction matrix of the motor stator current in the d-q synchronous rotating coordinate system at time 1.

[0065] Step 4: According to the predicted model of the stator current with self-damping characteristics derived in step 3, it can be seen that this model inherently includes the filter inductor current and the filter capacitor voltage variables. Therefore, while tracking and controlling the stator current, the effective regulation of the filter resonance energy can be achieved, thereby realizing resonance self-damping. Based on this principle, the stator current tracking cost function without weight parameters described in step 4 can be constructed as follows:

[0066]

[0067] In the formula, i s * = i sd * i sq * represents the reference matrix of the motor stator current in the d-q synchronous rotating coordinate system.

[0068] Step 5: Use the quadratic function optimization control theory to derive the optimal solution of the inverter voltage reference for the stator current tracking cost function without weight parameters constructed in step 4 J to ensure that the stator current reaches its reference value in the next control cycle. Specifically, the minimum value of the cost function J is obtained through the following formula:

[0069]

[0070] The expression of the optimal voltage reference of the inverter can be obtained as follows:

[0071]

[0072] In the formula, v i * = v id * v iq * represents the voltage reference matrix of the inverter in the d-q synchronous rotating coordinate system.

[0073] Then, perform the inverse Park transformation on it to obtain the optimal solution of the voltage reference of the inverter in the α-β stationary coordinate system;

[0074] Step 6: Apply the optimal voltage reference value of the inverter in the α-β stationary coordinate system obtained in Step 5 through space vector pulse width modulation SVPWM to generate pulses with a constant switching frequency and apply them to the inverter to realize the control of the synchronous reluctance motor drive system with an LC filter.

[0075] To verify a self-damping predictive current control method and system for an LC-filtered synchronous reluctance motor provided by the present invention, the method provided by the present invention is applied to a synchronous reluctance motor drive system with an LC filter, and the system parameters are given in Table 1.

[0076] Table 1

[0077] Parameter Symbol Value DC bus voltage <![CDATA V dc > 540 V Rated speed <![CDATA n N > 1500 rpm Rated torque <![CDATA T e > 6 N·m Number of pole pairs <![CDATA P N > 2 Moment of inertia <![CDATA[7.6e-4 kg·m 2 > Stator resistance <![CDATA R s > 0.73 Ω d-axis inductance <![CDATA L d > 148 mH q-axis inductance <![CDATA L q > 32 mH Filter inductance <![CDATA L f > 2 mH Inductance equivalent series resistance <![CDATA R f > 0.2 Ω Filter capacitor <![CDATA C f > 9.5 uF Sampling frequency <![CDATA f s > 10 kHz Switching frequency <![CDATA f sw > 10 kHz

[0078] Set the speed reference as:

[0079]

[0080] Set the load torque as:

[0081]

[0082] Figure 2 The following are the system output waveforms corresponding to the control method proposed by the present invention applied to an LC-filtered synchronous reluctance motor drive system. From top to bottom, they are the motor speed, electromagnetic torque, stator current of the three-phase synchronous reluctance motor, filter capacitor voltage, and filter inductor current. From Figure 2It can be seen that since the method provided by the present invention uses a stator current prediction model with self-damping characteristics, the control system indirectly controls the energy of the inductor and capacitor of the filter while constraining the stator current. Therefore, it can effectively suppress system resonance, thereby ensuring the stable operation of the synchronous reluctance motor under various speeds and loading conditions.

[0083] Figure 3 It is a schematic diagram of the steady-state waveform and harmonic spectrum of the stator current corresponding to the control method proposed by the present invention. Figure 3 It can be seen that under the control method provided by the present invention, the steady-state waveform of the motor stator current has a high sinusoidality and a small harmonic content, thus ensuring high reliability and high control accuracy during motor operation.

Claims

1. A self-damping predictive current control method for an LC-filtered synchronous reluctance motor, characterized in that It includes the following steps: Step 1: Sample the state variables of the LC-filtered synchronous reluctance motor in each sampling period, including: the inductor current i of the three-phase LC filter f,abc = [i fa i fb i fc T , the capacitor voltage v f,abc = [v fa v fb v fc T , the three-phase stator current i of the synchronous reluctance motor s,abc = [i sa i sb i sc T and its electrical angle θ e ; Then, transform the three-phase sampled values of the above state variables through the Park transformation to obtain the state variable values in the d-q synchronous rotating coordinate system: i f (k) = [i fd (k) i fq (k)] T , v f (k) = [v fd (k) v fq (k)] T , i s (k) = [i sd (k) i sq (k)] T ;​​​ Step 2: According to the state variable values of the LC-filtered synchronous reluctance motor in the d-q synchronous rotating coordinate obtained in Step 1, establish the discrete dynamic models corresponding to each state variable by means of the first-order forward Euler method: i f (k + 1), v f (k + 1), i s (k + 1); Step 3: According to the discrete dynamic model of the LC-filtered synchronous reluctance motor established in Step 2, use the filtered inductor current value i f (k + 1) to replace i f (k) in the discrete dynamic model of the filtered capacitor voltage, so as to obtain the filtered capacitor voltage value v f (k + 1) at the (k + 1)-th moment. Then use this v f (k + 1) to replace v f (k) in the discrete dynamic model of the motor stator current, and the stator current prediction model with self-damping characteristics can be deduced: Step 4: According to the stator current prediction model with self-damping characteristics derived in Step 3, use the error between the predicted value of the stator current and the reference value of the stator current i s * to construct a stator current tracking cost function without weight parameters Step 5: For the stator current tracking cost function without weight parameters constructed in Step 4, use the quadratic function optimization theory to solve for the optimal voltage reference v of the inverter in the d-q synchronous rotating coordinate system, and then perform an inverse Park transformation on it to obtain the optimal voltage reference expressions v and v of the inverter in the α-β stationary coordinate system; i * and then perform an inverse Park transformation on it to obtain the optimal voltage reference expressions v and v of the inverter in the α-β stationary coordinate system; iα * and v iβ * ; Step 6: Apply the optimal voltage reference value v of the inverter obtained in Step 5 iα * and v iβ * , through space vector pulse width modulation technology, generate control pulse signals and apply them to the inverter switching tubes to achieve self-damping predictive current control of the LC-filtered synchronous reluctance motor.

2. The self-damping predictive current control method for an LC-filtered synchronous reluctance motor according to claim 1, wherein In step 2, the discrete dynamic model of each state variable of the LC filter type synchronous reluctance motor is established by the first-order forward Euler method of the following formula: i f (k + 1)= A1i f (k)+ B1[v i (k)- v f (k)] v f (k + 1) = A2v f (k) + B2[i f (k) - i s (k)] i s (k + 1) = A3i s (k) + B3v f (k) where "k" and "k + 1" represent the k-th and (k + 1)-th sampling instants respectively, and v i is the inverter output voltage in the d-q synchronous rotating coordinate system, A x , B x (x = 1, 2, 3) are discrete model matrices, and their specific expressions are as follows: Where, L f is the filter inductor, R f is the parasitic resistance of the filter inductor, C f is the filter capacitor, ω e is the electrical angular velocity obtained by differentiating the electrical angle θ e of the synchronous reluctance motor, R s is the stator resistance of the synchronous reluctance motor, L d and L q are the stator inductances in the d-q synchronous rotating coordinate system of the synchronous reluctance motor, T s is the sampling period.

3. A self-damping predictive current control method for an LC-filtered synchronous reluctance motor according to claim 1, characterized in that In step 5, the quadratic function optimization control theory is used to derive the optimal solution of the inverter voltage reference for the stator current tracking cost function J without weight parameters constructed in step 4, so as to ensure that the stator current reaches its reference value in the next control cycle. Specifically, the minimum value of the cost function J is obtained by the following formula: The expression of the optimal voltage reference of the inverter can be obtained as: where, v i * = [v id * v iq * represents the inverter voltage reference matrix in the d-q synchronous rotating coordinate system.

4. The LC filter type synchronous reluctance motor self-damping predictive current control system used in the LC filter type synchronous reluctance motor self-damping predictive current control method according to claim 1, characterized in that, It includes: Self-damping characteristic stator current prediction model module, which is used to establish a motor stator current prediction model with self-damping characteristics at the k+1 moment according to the inductor current i fd (k), i fq (k), the capacitor voltage v fd (k), v fq (k) and the stator current i sd (k), i sq (k) of the synchronous reluctance motor A stator current tracking cost function module without weight parameters, which is used to construct a stator current tracking cost function without weight parameters according to the stator current prediction model with self-damping characteristics and the error between the predicted value of the stator current at time k + 1 and the reference value of the stator current; An optimal inverter voltage reference calculation module, which is used to calculate the optimal inverter voltage reference that can make the cost function obtain the minimum value according to the quadratic function optimization theory; A transformation module, which is used to transform the optimal voltage reference value of the inverter in the d-q synchronous rotating coordinate system to the α-β stationary coordinate system through Park inverse transformation; A pulse generation module, which is used to generate a pulse signal for controlling the inverter switching tube by using space vector pulse width modulation for the optimal voltage reference value of the inverter in the α-β stationary coordinate system.

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