CN117175988A - Global predictive control method for quasi-Z source inverter-permanent magnet synchronous motor system without weight coefficient - Google Patents

Abstract

The invention relates to a global predictive control method for a quasi-Z source inverter-permanent magnet synchronous motor system without a weight coefficient, which includes: real-time data acquisition of the quasi-Z source inverter-permanent magnet synchronous motor system; based on the quasi-Z source inverter ‑Permanent magnet synchronous motor system builds a quasi-Z source inverter prediction model and a permanent magnet synchronous motor prediction model; obtains the reference value of the quasi-Z source inverter‑permanent magnet synchronous motor system; performs unweighted model predictive control; converts the final The switching state corresponding to the optimal vector is applied to the inverter to achieve global predictive control, thereby realizing the control cycle. The present invention combines the sequencing principle with the quasi-Z source inverter prediction model, the permanent magnet synchronous motor prediction model and the cascade control structure to achieve the overall control of the quasi-Z source inverter-permanent magnet synchronous motor system; the present invention can reduce the model prediction The control algorithm is applied in the quasi-Z source inverter-permanent magnet synchronous motor system, while improving the smoothness of the system switching conditions.

Description

Global predictive control of quasi-Z source inverter-permanent magnet synchronous motor system without weight coefficient method

Technical field

The invention relates to the technical field of permanent magnet motor system control, in particular to a global predictive control method of a weightless coefficient quasi-Z source inverter-permanent magnet synchronous motor system.

Background technique

Traditional voltage source inverters are widely used in permanent magnet synchronous motor drive systems due to their simple control logic and small size. However, because they are step-down inverters, they are not suitable for occasions with large power supply voltage fluctuations. For example, in electric vehicles, battery power supply is affected by battery power and vehicle operating conditions, causing the input voltage of the inverter to fluctuate significantly, thereby adversely affecting the performance of the motor connected to the output side of the voltage source inverter, and thus Reduced power performance of electric vehicles. As a new type of inverter topology, the quasi-Z source inverter has unique advantages and built-in boost characteristics. Therefore, it has excellent applicability in fields such as electric vehicle drive and makes up for the shortcomings of traditional inverters. at.

Current control research methods for quasi-Z source inverters and permanent magnet synchronous motors mainly include traditional modulation methods and nonlinear methods represented by model predictive control. Compared with traditional modulation methods, model predictive control methods have significant advantages such as no need for modulation, simple control strategies, and excellent dynamic performance. Therefore, the model predictive control method for quasi-Z source inverter-permanent magnet synchronous motor system has extremely important research value.

Document "Finite set model predictive control method for quasi-Z sourceinverter-permanent magnet synchronous motor drive system" (Kangda Dong, TingnaShi, Shuxin Xiao et al. IET Electric Power Applications, 2019, 13(3): 302-309). In this document, finite set model predictive control is used to globally control the quasi-Z source inverter-permanent magnet synchronous motor system. After it is determined to be a non-shoot-through state, the capacitive voltage, electromagnetic torque and electromagnetic torque corresponding to the seven output vectors are The predicted value of the stator flux linkage amplitude is substituted into the cost function for calculation, and a voltage vector that minimizes the cost function is finally output. Although this literature is excellent in the control of quasi-Z source inverter-permanent magnet synchronous motor system, there is still a challenge: the cost function involves two weight coefficients that need to be adjusted simultaneously. There is currently a lack of appropriate design guidelines to guide the formulation of these weight coefficients. Therefore, when the cost function involves multiple weight coefficients, the adjustment of the weight coefficients becomes quite complicated. If the weight coefficient is improperly designed, the quasi-Z source inverter-permanent magnet synchronous motor system may not operate normally.

Contents of the invention

In order to solve the difficult and time-consuming problem of weight coefficient design in traditional model predictive control, the purpose of the present invention is to provide a method that can reduce the workload of applying a model predictive control algorithm in a quasi-Z source inverter-permanent magnet synchronous motor system, and at the same time improve Global predictive control method of quasi-Z source inverter-permanent magnet synchronous motor system without weight coefficient for smoothness during system operating condition switching.

In order to achieve the above objectives, the present invention adopts the following technical solution: a global predictive control method for a quasi-Z source inverter-permanent magnet synchronous motor system without weight coefficients. The method includes the following sequential steps:

(1) Real-time data acquisition of quasi-Z source inverter-permanent magnet synchronous motor system: In the current control cycle, recorded as the kth T _s moment, the current, voltage, rotation speed and position angle are sampled. The sampling content is specifically: Inductor current i _L1 , capacitor voltage u _C1 , rotation speed n _m , rotor position angle θ _m , and motor three-phase currents i _a , _ib and _ic , then transform the three-phase current from the three-phase static coordinate system to two-phase synchronization Under the dq coordinate system, the d and q axis currents i _d and i _q are obtained respectively;

(2) According to the real-time data obtained in step (1), build a quasi-Z source inverter prediction model and a permanent magnet synchronous motor prediction model based on the quasi-Z source inverter-permanent magnet synchronous motor system: the quasi-Z source inverter The device-permanent magnet synchronous motor system has 8 switch states, and the 8 switch states correspond to 8 basic vectors respectively. The 8 basic vectors include 6 effective vectors, 1 zero vector and 1 through vector; using sampling The obtained real-time data are respectively substituted into the quasi-Z source inverter prediction model and the permanent magnet synchronous motor prediction model to obtain the control variables of each basic vector at the next moment, including the capacitor voltage u on the quasi-Z source inverter side. _C1 (k+1) and inductor current i _L1 (k+1), as well as the electromagnetic torque T _e (k+1) on the permanent magnet synchronous motor side and the stator flux amplitude ψ _s (k+1);

(3) According to the real-time data obtained in step (1), obtain the reference value of the quasi-Z source inverter-permanent magnet synchronous motor system: obtain the inductor current reference value i L1 * through the load power feedforward link, and obtain the reference value i _L1 ^* through the power supply voltage and DC The capacitor voltage reference value u _C1 ^* is obtained from the bus voltage given value, and the torque reference value _Te ^* is obtained through the speed closed loop, and then the flux reference value ψ _s ^* is calculated based on the torque given value combined with i _d ^* = 0;

(4) Based on the real-time data obtained in step (1), the quasi-Z source inverter prediction model and permanent magnet synchronous motor prediction model constructed in step (2), and the quasi-Z source inverter-permanent model obtained in step (3) The reference value of the magnetic synchronous motor system is used for unweighted model predictive control: According to the inductor current ripple model and the inductor current value i _L1 , an inductor current boundary model is established to determine whether it is a through state at the next moment: if it is a through state, select The through vector is the optimal vector, otherwise it is a non-through vector, and the basic vector that minimizes the total cost function is selected as the optimal vector;

(5) Apply the switching state corresponding to the optimal vector to the inverter to achieve global predictive control, thereby realizing the control cycle.

In step (2), constructing a quasi-Z source inverter prediction model means:

In the straight-through state, the prediction model of the quasi-Z source inverter is:

In the formula, L ₁ and C ₁ are the inductance value and capacitance value of the impedance network respectively; i _L1 (k) and u _C1 (k) are the inductor current and capacitor voltage at the kth T _s moment respectively; T _s is the control period; i _L1 (k+1) and u _C1 (k+1) are the inductor current and capacitor voltage at the k+1th T _s moment respectively;

In the non-shoot-through state, the prediction model of quasi-Z source inverter is:

In the formula, u _in is the power supply voltage, i _inv (k+1) is the DC bus current value at the k+1th T _s moment, i _inv (k+1)=i _a (k)S ₁ +i _b (k )S ₃ + _ic (k)S ₅ ; i _a (k), i _b (k) and i _c (k) are the current values of phase A, phase B and phase C at the kth T _s time; S ₁ , S ₃ and S ₅ are the status of the upper bridge arm switch tubes of phase A, phase B and phase C respectively, on is 1 and off is 0;

The construction of a permanent magnet synchronous motor prediction model refers to:

In the formula, i _d (k+1) and i _q (k+1) are the d and q-axis currents at the k+1 T _s moment respectively; i _d (k) and i _q (k) are the k-th The d and q axis currents at T _s time; u _d (k) and u _q (k) are the d and q axis voltages at the kth T _s time respectively; R _s is the phase resistance; ω _e (k) is the kth T The electrical angular velocity at time _s ; ψ _f is the rotor flux amplitude; L _d and L _q are the d and q axis inductances of the permanent magnet synchronous motor respectively; p is the number of pole pairs of the motor, and T _e (k+1) represents the kth The electromagnetic torque on the motor side at time +1, ψ _s (k+1), represents the stator flux amplitude on the motor side at time k+1.

The step (3) specifically refers to:

Obtain the torque reference value _Te ^* : obtained through the speed closed loop;

Obtain the flux linkage reference value ψ _s ^* : The electromagnetic torque formula of the label-type permanent magnet synchronous motor is:

In the formula, T _e is the electromagnetic torque, p is the number of pole pairs of the motor, and ψ _f is the rotor flux amplitude;

The motor adopts i _d ^* = 0 control, combined with the torque reference value obtained by the speed closed loop, the flux reference value ψ _s ^* is obtained by the following formula:

In the formula, L _q is the q-axis inductance of the permanent magnet synchronous motor;

Get the capacitor voltage reference value u _C1 ^* :

u _C1 ^* =(u _in +u _pn )/2

In the formula, u _pn is the DC bus voltage; u _in is the power supply voltage;

Obtain the inductor current reference value i _L1 ^* : If the quasi-Z source inverter is connected to a three-phase fixed load, the inductor current reference value is obtained directly from the output power. Since the output power of the permanent magnet synchronous motor changes all the time after the permanent magnet synchronous motor is connected, the inductor current The reference value is obtained by load power feedforward compensation as follows:

i _L1 ^* =(P ₀ +k _p (u _C1 ^* -u _C1 )+k _i ∫(u _C1 ^* -u _C1 ))/u _in

In the formula, P ₀ is the output power generated by the mechanical angular velocity ω _m and the load torque T _L at the current moment, that is k _p and k _i are the proportional coefficient and integral coefficient of the power compensation link respectively, and u _C1 is the capacitor voltage.

The step (4) specifically includes the following steps:

(4a) Construct the inductor current boundary model:

The inductor current tolerance value is defined according to the current ripple model of the inductor current in one cycle:

In the formula, εi _L1 ^* is the inductor current tolerance value, T _s is the control period, u _C1 ^* is the capacitor voltage reference value, and L ₁ is the inductance value of the impedance network;

The inductor current error value is obtained by the following formula:

εi _L1 ＝|i _L1 ^* -i _L1 |

In the formula, εi _L1 is the inductor current error value; i _L1 ^* is the inductor current reference value;

(4b) According to the monotonic characteristics of the impedance network inductor current, that is, if the quasi-Z source inverter is in the through state, the inductor current increases, and when it is in the non-through state, the inductor current decreases; when the k T _s moment, the inductor current i _L1 When (k) is less than i _L1 ^* and εi _L1 is greater than εi _L1 ^* , it will enter the pass-through state at the next moment, otherwise it will enter the non-pass-through state;

(4c) If it is determined that the next moment is a pass-through state, then this state is directly selected as the optimal switching state and applied to the inverter; if it is determined to be a non-through state, proceed to the following steps:

First, calculate the cost function of the capacitor voltage separately. Cost function of stator flux linkage/> and cost function of electromagnetic torque/> Second, use/> Select the 4 states that minimize the capacitor voltage cost function from all non-through states, and press/> Determine the sorting positions of the four states in the stator flux linkage cost function; finally, pass/> Determine the switching state E ₁ that satisfies the minimum ordering sum of the two; a similar process is adopted for the electromagnetic torque and stator flux linkage, that is, another switching state E ₂ is obtained; finally, calculate the total cost function g in the switching states E ₁ and E respectively ₂ , select the switch state that makes the result of the total cost function g smaller as the optimal switch state, that is, the optimal vector output:

g＝|T _e ^* -T _e (k+1)|+|ψ _s ^* -ψ _s (k+1)|+|u _C1 ^* -u _C1 (k+1)|

In the formula, U _x is a non-through voltage vector, x=[1,7] and x∈N, N is a natural number; are the predicted value of capacitor voltage, the predicted value of stator flux linkage, and the predicted value of electromagnetic torque respectively; ψ _s ^* is the flux linkage reference value, and T _e ^* is the torque reference value;/> They are the cost function sorting values of capacitor voltage, the cost function sorting value of stator flux linkage, and the cost function sorting value of electromagnetic torque under 7 kinds of non-through vectors. The 7 kinds of non-through vectors include 6 kinds of effective vectors and 1 kind of zero vector;/> Corresponds to the action of one of the non-through vectors/> Both sorting and minimum value;/> Corresponds to the action of one of the non-through vectors/> Both sort and minimum.

It can be seen from the above technical solution that the beneficial effects of the present invention are: first, the present invention realizes a quasi-Z source inverter by combining the quasi-Z source inverter prediction model, the permanent magnet synchronous motor prediction model and the cascade control structure with the sequencing principle. -The overall control of the permanent magnet synchronous motor system; secondly, compared with the existing technology, the present invention can reduce the workload of applying the model predictive control algorithm in the quasi-Z source inverter-permanent magnet synchronous motor system, while improving the system efficiency. Smoothness when switching conditions.

Description of drawings

Figure 1 is a schematic structural diagram of a quasi-Z source inverter-permanent magnet synchronous motor system;

Figure 2 shows the DC side equivalent circuit in the through state;

Figure 3 shows the DC side equivalent circuit in the non-through state;

Figure 4 is a schematic diagram of the control method of the present invention;

Figure 5 is a simulation waveform diagram of the traditional predictive control method;

Figure 6 is a simulation waveform diagram of the present invention.

Detailed ways

As shown in Figure 4, a global predictive control method for a quasi-Z source inverter-permanent magnet synchronous motor system without weight coefficients includes the following sequential steps:

(1) Real-time data acquisition of quasi-Z source inverter-permanent magnet synchronous motor system 1: In the current control cycle, recorded as the kth T _s moment, the current, voltage, rotation speed and position angle are sampled. The sampling content is specifically: : inductor current i _L1 , capacitor voltage u _C1 , rotation speed n _m , rotor position angle θ _m , and motor three-phase currents i _a , _ib and i _c , and then transform the three-phase current from the three-phase static coordinate system to two-phase Under the synchronous dq coordinate system, the d and q axis currents i _d and i _q are obtained respectively;

(2) According to the real-time data obtained in step (1), a quasi-Z source inverter prediction model and a permanent magnet synchronous motor prediction model are constructed based on the quasi-Z source inverter-permanent magnet synchronous motor system 1: the quasi-Z source inverter prediction model The converter-permanent magnet synchronous motor system 1 has 8 switch states, the 8 switch states respectively correspond to 8 basic vectors, and the 8 basic vectors include 6 effective vectors, 1 zero vector and 1 through vector; The real-time data obtained through sampling are substituted into the quasi-Z source inverter prediction model and the permanent magnet synchronous motor prediction model to obtain the control variables of each basic vector at the next moment, including the capacitance on the quasi-Z source inverter side. Voltage u _C1 (k+1) and inductor current i _L1 (k+1), as well as electromagnetic torque T _e (k+1) on the permanent magnet synchronous motor side and stator flux amplitude ψ _s (k+1);

(3) According to the real-time data obtained in step (1), obtain the reference value of the quasi-Z source inverter-permanent magnet synchronous motor system 1: obtain the inductor current reference value i L1 * through the load power feedforward link, and obtain the inductor current reference value i _L1 ^* through the power supply voltage and The capacitor voltage reference value u _C1 ^* is obtained from the DC bus voltage given value, and the torque reference value _Te ^* is obtained through the speed closed loop, and then the flux reference value ψ _s ^* is calculated based on the torque given value combined with i _d ^* = 0 ;

(4) Based on the real-time data obtained in step (1), the quasi-Z source inverter prediction model and permanent magnet synchronous motor prediction model constructed in step (2), and the quasi-Z source inverter-permanent model obtained in step (3) The reference value of the magnetic synchronous motor system 1 is used for unweighted model predictive control: According to the inductor current ripple model and the inductor current value i _L1 , an inductor current boundary model is established to determine whether it is a through state at the next moment: if it is a through state, then Select the through vector as the optimal vector, otherwise it is a non-through vector, and select the basic vector that minimizes the total cost function as the optimal vector;

(5) Apply the switching state corresponding to the optimal vector to the inverter to achieve global predictive control, thereby realizing the control cycle.

In step (2), constructing a quasi-Z source inverter prediction model means:

In the straight-through state, the prediction model of the quasi-Z source inverter is:

In the formula, L ₁ and C ₁ are the inductance value and capacitance value of the impedance network respectively; i _L1 (k) and u _C1 (k) are the inductor current and capacitor voltage at the kth T _s moment respectively; T _s is the control period; i _L1 (k+1) and u _C1 (k+1) are the inductor current and capacitor voltage at the k+1th T _s moment respectively;

In the non-shoot-through state, the prediction model of quasi-Z source inverter is:

In the formula, u _in is the power supply voltage, i _inv (k+1) is the DC bus current value at the k+1th T _s moment, i _inv (k+1)=i _a (k)S ₁ +i _b (k )S ₃ + _ic (k)S ₅ ; i _a (k), i _b (k) and i _c (k) are the current values of phase A, phase B and phase C at the kth T _s time; S ₁ , S ₃ and S ₅ are the status of the upper bridge arm switch tubes of phase A, phase B and phase C respectively, on is 1 and off is 0;

The construction of a permanent magnet synchronous motor prediction model refers to:

In the formula, i _d (k+1) and i _q (k+1) are the d and q-axis currents at the k+1 T _s moment respectively; i _d (k) and i _q (k) are the k-th The d and q axis currents at T _s time; u _d (k) and u _q (k) are the d and q axis voltages at the kth T _s time respectively; R _s is the phase resistance; ω _e (k) is the kth T The electrical angular velocity at time _s ; ψ _f is the rotor flux amplitude; L _d and L _q are the d and q axis inductances of the permanent magnet synchronous motor respectively; p is the number of pole pairs of the motor, and T _e (k+1) represents the kth The electromagnetic torque on the motor side at time +1, ψ _s (k+1), represents the stator flux amplitude on the motor side at time k+1. Here the permanent magnet synchronous motor uses a surface-mounted permanent magnet synchronous motor.

The step (3) specifically refers to:

Obtain the torque reference value _Te ^* : obtained through the speed closed loop;

Obtain the flux linkage reference value ψ _s ^* : The electromagnetic torque formula of the label-type permanent magnet synchronous motor is:

In the formula, T _e is the electromagnetic torque, p is the number of pole pairs of the motor, and ψ _f is the rotor flux amplitude;

The motor adopts i _d ^* = 0 control, combined with the torque reference value obtained by the speed closed loop, the flux reference value ψ _s ^* is obtained by the following formula:

In the formula, L _q is the q-axis inductance of the permanent magnet synchronous motor;

Get the capacitor voltage reference value u _C1 ^* :

u _C1 ^* =(u _in +u _pn )/2

In the formula, u _pn is the DC bus voltage; u _in is the power supply voltage;

Obtain the inductor current reference value i _L1 ^* : If the quasi-Z source inverter is connected to a three-phase fixed load, the inductor current reference value is obtained directly from the output power. Since the output power of the permanent magnet synchronous motor changes all the time after the permanent magnet synchronous motor is connected, the inductor current The reference value is obtained by load power feedforward compensation as follows:

i _L1 ^* =(P ₀ +k _p (u _C1 ^* -u _C1 )+k _i ∫(u _C1 ^* -u _C1 ))/u _in

In the formula, P ₀ is the output power generated by the mechanical angular velocity ω _m and the load torque T _L at the current moment, that is k _p and k _i are the proportional coefficient and integral coefficient of the power compensation link respectively, and u _C1 is the capacitor voltage.

The step (4) specifically includes the following steps:

(4a) Construct the inductor current boundary model:

The inductor current tolerance value is defined according to the current ripple model of the inductor current in one cycle:

In the formula, εi _L1 ^* is the inductor current tolerance value, T _s is the control period, u _C1 ^* is the capacitor voltage reference value, and L ₁ is the inductance value of the impedance network;

The inductor current error value is obtained by the following formula:

εi _L1 ＝|i _L1 ^* -i _L1 |

In the formula, εi _L1 is the inductor current error value; i _L1 ^* is the inductor current reference value;

(4b) According to the monotonic characteristics of the impedance network inductor current, that is, if the quasi-Z source inverter is in the through state, the inductor current increases, and when it is in the non-through state, the inductor current decreases; when the k T _s moment, the inductor current i _L1 When (k) is less than i _L1 ^* and εi _L1 is greater than εi _L1 ^* , it will enter the pass-through state at the next moment, otherwise it will enter the non-pass-through state;

(4c) If it is determined that the next moment is a pass-through state, then this state is directly selected as the optimal switching state and applied to the inverter; if it is determined to be a non-through state, proceed to the following steps:

First, calculate the cost function of the capacitor voltage separately. Cost function of stator flux linkage/> and cost function of electromagnetic torque/> Second, use/> Select the 4 states that minimize the capacitor voltage cost function from all non-through states, and press/> Determine the sorting positions of the four states in the stator flux linkage cost function; finally, pass/> Determine the switching state E ₁ that satisfies the minimum ordering sum of the two; a similar process is adopted for the electromagnetic torque and stator flux linkage, that is, another switching state E ₂ is obtained; finally, calculate the total cost function g in the switching states E ₁ and E respectively ₂ , select the switch state that makes the result of the total cost function g smaller as the optimal switch state, that is, the optimal vector output:

g＝|T _e ^* -T _e (k+1)|+|ψ _s ^* -ψ _s (k+1)|+|u _C1 ^* -u _C1 (k+1)|

In the formula, U _x is a non-through voltage vector, x=[1,7] and x∈N, N is a natural number; are the predicted value of capacitor voltage, the predicted value of stator flux linkage, and the predicted value of electromagnetic torque respectively; ψ _s ^* is the flux linkage reference value, and T _e ^* is the torque reference value;/> They are the cost function sorting values of capacitor voltage, the cost function sorting value of stator flux linkage, and the cost function sorting value of electromagnetic torque under 7 kinds of non-through vectors. The 7 kinds of non-through vectors include 6 kinds of effective vectors and 1 kind of zero vector;/> Corresponds to the action of one of the non-through vectors/> Both sorting and minimum value;/> Corresponds to the action of one of the non-through vectors/> Both sort and minimum.

As shown in Figure 1, the quasi-Z source inverter-permanent magnet synchronous motor system 1 consists of a DC voltage source u _in , an impedance network, a three-phase inverter bridge and a permanent magnet synchronous motor PMSM. The impedance network is composed of inductors L ₁ and L ₂ , transistor S ₇ and capacitors C ₁ and C ₂ , and the three-phase inverter bridge is composed of transistors S ₁ to S ₆ .

The DC side equivalent circuit in the pass-through state is shown in Figure 2. It can be seen from Figure 2 that at this time, the upper and lower transistors of the same-phase bridge arm of the three-phase inverter bridge are turned on at the same time, transistor S ₇ is turned off, and the power supply and capacitor charge the inductor at the same time.

The DC side equivalent circuit in the non-through state is shown in Figure 3. From Figure 3, it can be seen that at this time, the transistor S ₇ is closed, the power supply and the inductor supply energy to the capacitor and the load at the same time, and the inverter bridge and AC load can be equivalent to a current source. i _inv .

Figures 5 and 6 are respectively the simulation waveform diagrams of the traditional predictive control method and the simulation waveform diagrams of the present invention. From top to bottom, they are stator current, electromagnetic torque, stator flux linkage and DC link voltage. It can be seen from Figures 5 and 6 that under steady-state conditions, the waveforms of the simulation diagrams of the two methods are similar, that is, the steady-state control performance of the two methods is the same; under the condition of load torque step, the stator current distortion of the control method proposed by the present invention And the electromagnetic torque ripple is significantly smaller than the traditional predictive control method, that is, the control method proposed in the present invention has stronger anti-interference performance. In summary, the control method proposed in the present invention further improves the anti-disturbance capability of the quasi-Z source inverter-permanent magnet synchronous motor system 1 while eliminating the need for cumbersome weight coefficient design work.

To sum up, the present invention realizes the overall control of the quasi-Z source inverter-permanent magnet synchronous motor system 1 through the quasi-Z source inverter prediction model, the permanent magnet synchronous motor prediction model and the cascade control structure combined with the sequencing principle; Compared with the existing technology, the present invention can reduce the workload of applying the model predictive control algorithm in the quasi-Z source inverter-permanent magnet synchronous motor system 1, and at the same time improve the smoothness of the system working condition switching.

Claims (4)

1. A global predictive control method for a quasi-Z source inverter-permanent magnet synchronous motor system without a weight coefficient is characterized by comprising the following steps of: the method comprises the following steps in sequence:

(1) Real-time data acquisition of a quasi-Z source inverter-permanent magnet synchronous motor system: in the current control period, marked as kth T _s At moment, sampling current, voltage, rotating speed and position angle, wherein the sampling content is specifically as follows: inductor current i _L1 Capacitance voltage u _C1 Rotational speed n _m Rotor position angle θ _m And motor three-phase current i _a 、i _b And i _c Then transforming the three-phase current from the three-phase static coordinate system to the two-phase synchronous dq coordinate system to obtain d-axis current and q-axis current i respectively _d And i _q ；

(2) According to the real-time data obtained in the step (1), a quasi-Z source inverter prediction model and a permanent magnet synchronous motor prediction model are built based on a quasi-Z source inverter-permanent magnet synchronous motor system: the quasi-Z source inverter-permanent magnet synchronous motor system is provided with 8 switch states, the 8 switch states respectively correspond to 8 basic vectors, and the 8 basic vectors comprise 6 effective vectors, 1 zero vector and 1 direct vector; the real-time data obtained by sampling are respectively substituted into a quasi-Z source inverter prediction model and a permanent magnet synchronous motor prediction model to obtain each control variable of each basic vector at the next moment, wherein each control variable comprises the capacitance voltage u of the quasi-Z source inverter side _C1 (k+1) and inductor current i _L1 (k+1) electromagnetic torque T on permanent magnet synchronous motor side _e (k+1) and stator flux linkage amplitude ψ _s (k+1)；

(3) According to the real-time data obtained in the step (1), obtaining a reference value of a quasi-Z source inverter-permanent magnet synchronous motor system: obtaining inductance current parameter through load power feedforward linkTest value i _L1 ^* Obtaining a capacitance voltage reference value u through a power supply voltage and a DC bus voltage given value _C1 ^* At the same time, the torque reference value T is obtained through the rotating speed closed loop _e ^* Then combine i according to the torque set point _d ^* Calculation of =0 to obtain flux linkage reference value ψ _s ^* ；

(4) According to the real-time data obtained in the step (1), the quasi-Z-source inverter prediction model and the permanent magnet synchronous motor prediction model constructed in the step (2) and the reference value of the quasi-Z-source inverter-permanent magnet synchronous motor system obtained in the step (3), carrying out model prediction control without weight: according to the inductor current ripple model and the inductor current value i _L1 Establishing an inductance current boundary model, and judging whether the inductance current boundary model is in a straight-through state or not at the next moment: if the direct state is the direct state, selecting the direct vector as an optimal vector, otherwise, selecting a basic vector with the minimum total cost function as the optimal vector, and selecting the basic vector with the minimum total cost function as a non-direct vector;

(5) And applying the switching state corresponding to the optimal vector to the inverter to realize global predictive control, thereby realizing control cycle.

2. The global predictive control method for a non-weight coefficient quasi-Z source inverter-permanent magnet synchronous motor system according to claim 1, wherein: in step (2), the building of the quasi-Z source inverter prediction model means:

in the straight-through state, the quasi-Z source inverter prediction model is as follows:

wherein L is ₁ And C ₁ The inductance value and the capacitance value of the impedance network are respectively; i.e _L1 (k) And u is equal to _C1 (k) Respectively the kth T _s Inductor current and capacitor voltage at moment; t (T) _s Is a control period; i.e _L1 (k+1) and u _C1 (k+1) is the (k+1) th T respectively _s Inductor current and capacitor voltage at moment;

in the non-through state, the quasi-Z source inverter prediction model is:

wherein u is _in For the supply voltage, i _inv (k+1) is the (k+1) th T _s Time direct current bus current value, i _inv (k+1)＝i _a (k)S ₁ +i _b (k)S ₃ +i _c (k)S ₅ ；i _a (k)、i _b (k) And i _c (k) Is the kth T _s Current values of the phase a, the phase B and the phase C at the moment; s is S ₁ 、S ₃ And S is ₅ The states of the upper bridge arm switch tubes of the phase A, the phase B and the phase C are respectively that the switch is turned on to be 1 and the switch is turned off to be 0;

the construction of the permanent magnet synchronous motor prediction model is as follows:

wherein i is _d (k+1) and i _q (k+1) is the (k+1) th T respectively _s Time d, q-axis current; i.e _d (k) And i _q (k) Respectively the kth T _s Time d, q-axis current; u (u) _d (k) And u is equal to _q (k) Respectively the kth T _s Time d, q axis voltage; r is R _s Is a phase resistance; omega _e (k) Is the kth T _s The electrical angular velocity at the moment; psi phi type _f The flux linkage amplitude of the rotor; l (L) _d And L is equal to _q D and q axis inductances of the permanent magnet synchronous motor respectively; p is the pole pair number of the motor, T _e (k+1) represents the electromagnetic field on the motor side at the (k+1) th timeTorque, ψ _s (k+1) represents the motor-side stator flux linkage amplitude at the k+1th time.

3. The global predictive control method for a non-weight coefficient quasi-Z source inverter-permanent magnet synchronous motor system according to claim 1, wherein: the step (3) specifically refers to:

obtaining a torque reference value T _e ^* : acquiring through a rotating speed closed loop;

obtaining a flux linkage reference value ψ _s ^* : the electromagnetic torque formula of the labeling permanent magnet synchronous motor is as follows:

wherein T is _e Is electromagnetic torque, p is the pole pair number of the motor, and psi _f The flux linkage amplitude of the rotor;

the motor adopts i _d ^* Control=0, combined with torque reference value, flux linkage reference value ψ, obtained by closed-loop rotation speed _s ^* Obtained by the following formula:

wherein L is _q The q-axis inductance is the q-axis inductance of the permanent magnet synchronous motor;

obtaining a capacitor voltage reference value u _C1 ^* ：

u _C1 ^* ＝(u _in +u _pn )/2

Wherein u is _pn Is the voltage of a direct current bus; u (u) _in Is the power supply voltage;

obtaining an inductance current reference value i _L1 ^* : if the quasi-Z source inverter is connected with a three-phase fixed load, the inductance current reference value is directly obtained by output power, and because the output power of the quasi-Z source inverter is changed at any moment after the quasi-Z source inverter is connected with the permanent magnet synchronous motor, the inductance current reference value is obtained through load power feedforward compensation as follows:

i _L1 ^* ＝(P ₀ +k _p (u _C1 ^* -u _C1 )+k _i ∫(u _C1 ^* -u _C1 ))/u _in

wherein P is ₀ For the current moment from the mechanical angular velocity omega _m And a load torque T _L The output power produced, i.ek _p And k _i The proportional coefficient and the integral coefficient of the power compensation link are respectively, u _C1 Is a capacitor voltage.

4. The global predictive control method for a non-weight coefficient quasi-Z source inverter-permanent magnet synchronous motor system according to claim 1, wherein: the step (4) specifically comprises the following steps:

(4a) Constructing an inductance current boundary model:

defining an inductor current tolerance value according to a current ripple model in one period of the inductor current:

wherein εi _L1 ^* Is the inductance current tolerance value, T _s To control the period u _C1 ^* Is the reference value of the capacitor voltage, L ₁ The inductance value of the impedance network;

the inductor current error value is obtained by the following formula:

εi _L1 ＝|i _L1 ^* -i _L1 |

wherein εi _L1 Is the inductance current error value; i.e _L1 ^* Is an inductance current reference value;

(4b) According to the monotonic characteristic of the impedance network inductance current, namely if the quasi-Z source inverter is in a through state, the inductance current rises, and when the quasi-Z source inverter is in a non-through state, the inductance current drops; when k T th _s Time inductance electricityStream i _L1 (k) Less than i _L1 ^* And εi _L1 Greater than epsilon i _L1 ^* When the device is in the non-straight-through state, the device enters the straight-through state at the next moment, and otherwise, the device enters the non-straight-through state;

(4c) If the next moment is judged to be the direct connection state, the state is directly selected to be the optimal switching state and is applied to the inverter; if the non-through state is judged, the following steps are carried out:

first, the cost function of the capacitor voltage is calculatedCost function of stator flux linkage>And the cost function of the electromagnetic torque->Second, use +.>Selecting 4 states minimizing the capacitor voltage cost function from all non-pass states, and selecting the 4 states according to +.>Determining the ordering positions of the 4 states in the stator flux cost function; finally, by->Determining a minimum switch state E satisfying both ordering ₁ The method comprises the steps of carrying out a first treatment on the surface of the A similar flow is adopted for the electromagnetic torque and the stator flux linkage, and another switch state E is obtained ₂ The method comprises the steps of carrying out a first treatment on the surface of the Finally, respectively calculating the total cost function g in the switching state E ₁ And E is ₂ And selecting a switching state with a smaller result value of the total cost function g as an optimal switching state, namely, an optimal vector output, according to the corresponding value:

g＝|T _e ^* -T _e (k+1)|+|ψ _s ^* -ψ _s (k+1)|+|u _C1 ^* -u _C1 (k+1)|

in U _x Is a non-pass voltage vector, x= [1,7]And x is N, N is a natural number; the method comprises the steps of respectively predicting a capacitor voltage, a stator flux linkage and an electromagnetic torque; psi phi type _s ^* For flux linkage reference value, T _e ^* Is a torque reference value; />The method comprises the steps of respectively sorting cost functions of capacitor voltage, stator flux linkage and electromagnetic torque under 7 non-through vectors, wherein the 7 non-through vectors comprise 6 effective vectors and 1 zero vector; />Is one of non-straight-through vectors corresponding to +.>Ordering and minimum value of the two; />Is one of non-straight-through vectors corresponding to +.>Both ordering and minima.