Predictive control method of current increment for permanent magnet synchronous motor under high-speed operation

Abstract

The present disclosure provides a predictive control method of current increment for a permanent magnet synchronous motor includes: substituting a mathematical expression of a stator voltage during one control period into a continuous time domain current model to obtain a discrete current prediction model and a predicted current at the next time point; obtaining a predicted current increment from a current increment prediction model by subtracting a predictive current at a present time point from a predictive current at a next time point; establishing a cost function according to a preset reference current increment and the predicted current increment; obtaining an optimal voltage increment by minimizing the cost function; superposing the optimal voltage increment on a stator voltage of a present control period to obtain an optimal stator voltage of a next control period for controlling control the permanent magnet synchronous motor.

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of China application serial no. 202110648245.5, filed on Jun. 10, 2021. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification.

TECHNICAL FIELD

The present disclosure relates to a predictive control method for permanent magnet synchronous motor. More particularly, it relates to a predictive control method of current increment to improve the current control performance for the motor working in a high-speed condition.

RELATED ART

Due to the limitations of space and service environment, electric vehicles have higher requirements for motors used for driving the electric vehicles. Due to the advantages of high-power density, high efficiency, wide speed range, etc., the permanent magnet synchronous motor is chosen as power source in most enterprises of electric vehicles, such as the Leaf of Nissan and the RAVE EV of Toyota. Since permanent magnet synchronous motor is a typical nonlinear system, the nonlinear control method can achieve better control performance than linear control method (e.g., PI control), such as fuzzy control, sliding mode control and model predictive control. Model predictive control has received more attention and been widely studied for permanent magnet synchronous motor drive systems because of its advantages of fast response, appropriate for multi-variable systems and easy implementation.

Model predictive control is a kind of model-based control method. The existing model predictive control methods commonly employ the prediction model derived from the one-order forward Euler approximation method which ignores the rotor movement during one control period. In particular, the one-order forward Euler approximation method is only suitable for the rotor operated in low-speed condition. However, the electric vehicle requires good performance of the permanent magnet synchronous motor drives under the high-speed condition. When the permanent magnet synchronous motor is under the high-speed operation, the change of the rotor position angle becomes larger during one control period, which results in a large deviation between the actual values of the d axis/q axis stator voltages and the discrete values of the d axis/q axis stator voltages used in the control algorithm, and deteriorates the control performance of the model predictive control. In the practical application of the permanent magnet synchronous motor driving system, the parameters vary with the stator current and temperature of permanent magnet synchronous motor, which causes prediction errors of the predictive model. Besides, the dead-time effect causes the error between the actual voltage outputted by the inverter and the reference voltages, which deteriorates the control performance of the model predictive control. Therefore, the existing technology lacks a predictive control method that can both improve the prediction accuracy for the motor under the high-speed operation and reduce the system parameter sensitivity.

SUMMARY OF INVENTION

The technical problem to be solved by the disclosure is to provide a predictive control method of current increment suitable for the permanent magnet synchronous motor under the high-speed operation.

The technical scheme of the disclosure is:

1) establishing a mathematical expression of a stator voltage during one control period according to a position change of a rotor of the permanent magnet synchronous motor during the one control period;

2) obtaining a continuous time domain current model of the permanent magnet synchronous motor by solving a continuous time domain equation of the permanent magnet synchronous motor;

3) ignoring a stator resistance voltage drop, and substituting the mathematical expression of the stator voltage during the one control period into the continuous time domain current model of the permanent magnet synchronous motor for solving solutions to obtain a discrete current predictive model suitable for the permanent magnet synchronous motor under the high-speed operation, and then obtaining a prediction current at a next time point by using the discrete current predictive model;

4) obtaining a current increment prediction model suitable for the permanent magnet synchronous motor under the high-speed operation by subtracting a predictive current at a present time point from a predictive current at a next time point, and obtaining a prediction current increment calculated from the current increment prediction model;

5) establishing a cost function by taking a squared error at an end of each control period between a preset reference current increment and a predictive current increment as an evaluation criterion; evaluating an error of a stator current increment at the end of each control period corresponding to a stator voltage increment by using the cost function; obtaining an optimal voltage increment which minimizing the cost function by solving a convex optimization problem for the cost function;

6) superposing the optimal voltage increment on a stator voltage of a present control period to obtain an optimal stator voltage of a next control period, and applying the optimal stator voltage to the permanent magnet synchronous motor.

In step 1, the mathematical expression of the stator voltage during one control period is:

$\left[\begin{array}{c}{u}_d\left(t\right)\\ u_q\left(t\right)\end{array}\right]=\left[\begin{array}{cc}\cos \left[\left(t-k{T}_s\right){\omega }_r\right]& \sin \left[\left(t-k{T}_s\right){\omega }_r\right]\\ -\sin \left[\left(t-k{T}_s\right){\omega }_r\right]& \cos \left[\left(t-k{T}_s\right){\omega }_r\right]\end{array}\right]\left[\begin{array}{c}{u}_{d,k}\\ u_{q,k}\end{array}\right]$

where, u_d(t) and u_q(t) are the d-axis stator voltage and the q-axis stator voltage, respectively; T_s is the control period; u_d,k is the d-axis component of the stator voltage vector at time point kT_s, and u_q,k is the q-axis component of the stator voltage, where the subscript d represents d-axis, the subscript q represents q-axis, and the subscript k represents the ordinal number of the control period; ω_r is the electrical angular velocity; k represents the ordinal number of the control period; t represents the present time point.

In step 3, the discrete current prediction model is:

$i_s\left(k+1\right)=A_0\left(k\right)i_s\left(k\right)+B_0\left(k\right)u_s\left(k\right)+D_0\left(k\right)$ $A_0\left(k\right)=\left[\begin{array}{cc}\cos \left({\omega }_{r,k}{T}_s\right)& \frac{L_{q0}}{L_{d0}}\sin \left({\omega }_{r,k}{T}_s\right)\\ -\frac{L_{d0}}{L_{q0}}\sin \left({\omega }_{r,k}{T}_s\right)& \cos \left({\omega }_{r,k}{T}_s\right)\end{array}\right]$ $B_0\left(k\right)=\left[\begin{array}{cc}\frac{T_s}{L_{d0}}\cos \left({\omega }_{r,k}{T}_s\right)& \frac{T_s}{L_{d0}}\sin \left({\omega }_{r,k}{T}_s\right)\\ -\frac{T_s}{L_{q0}}\sin \left({\omega }_{r,k}{T}_s\right)& \frac{T_s}{L_{q0}}\cos \left({\omega }_{r,k}{T}_s\right)\end{array}\right]$ $D_0\left(k\right)={\left[\frac{{\psi }_{f0}\left[\cos \left({\omega }_{r,k}{T}_s\right)-1\right]}{L_{d0}}-\frac{{\psi }_{f0}\sin \left({\omega }_{r,k}{T}_s\right)}{L_{q0}}\right]}^T$

where, i_s(k+1) represents the predictive current vector at time point (k+1)T_s; i_s(k) represents the stator current vector at time point kT_s; u_s(k) represents the stator voltage vector at time point kT_s; Δ₀(k) represents the coefficient matrix of i_s(k); B₀(k) represents the coefficient matrix of u_s(k); D₀(k) represents the coefficient matrix of the back electromotive force; ω_r,k is the electrical angular velocity at time point kT_s, where r represents the symbol related to the rotor; k represents the ordinal number of the control period; L_d0, L_q0, and ψ_f0 are the nominal values of d-axis stator inductances, q-axis stator inductances, and a permanent magnet flux linkage, respectively.

In step 4, the current increment prediction model is:Δi_s(k+1)=A₀(k)Δi_s(k)+B₀(k)Δu_s(k)Δi_s(k)=i_s(k)−i_s(k−1)Δu_s(k)=u_s(k)−u_s(k−1)

where Δi_s(k+1) represents the predicted current increment calculated from the current increment prediction model; Δi_s(k) represents the stator current increment between the stator current at time point kT_s and the stator current at time point (k−1)T_s; Δu_s(k) represents the stator voltage increment between the stator voltage at time point kT_s and the stator voltage at time point (k−1)T_s; i_s(k−1) represents the stator current vector at time point (k−1)T_s u_s(k−1) represents the stator voltage vector at time point (k−1)T_s; Δ₀(k) represents the coefficient matrix of Δi_s(k); B₀(k) represents the coefficient matrix of Δu_s(k).

In step 5, the cost function is established as:

$\underset{\Delta u_s}{\min }J={\left[\Delta i_s^{\mathrm{ref}}-\Delta i_s\left(k+2\right)\right]}^T\left[\Delta i_s^{\mathrm{ref}}-\Delta i_s\left(k+2\right)\right]+\Delta {u_s\left(k+1\right)}^{T}P\Delta u_s\left(k+1\right)$ $\mathrm{Satisfy}:\{\begin{array}{c}❘u_s\left(k\right)+\Delta u_s\left(k+1\right)❘\le U_{\max }\\ ❘i_s\left(k+1\right)+\Delta i_s\left(k+2\right)❘\le I_{\max }\end{array}$

where Δi_s^ref represents the reference current increment; P is the weight factor matrix; U_max and I_max are the maximal voltage and maximal current of the permanent magnet synchronous motor driving system; the superscript T represents the matrix transpose operation; “Satisfy” represents the constraint conditions; Δi_s(k+2) represents the predicted current increment calculated from the current increment prediction model; Δu_s(k+1) represents the stator voltage increment from time point kT_s to time point (k+1)T_s; J is the value of the cost function.

In step 6, the optimal voltage increment is added to the stator voltage of the present control period to obtain an optimal stator voltage of the next control period, and the optimal stator voltage is:u_s^opt(k+1)=u_s(k)+Δu_s^opt(k+1)

where, u_s(k) represents the stator voltage at time point kTs; u_s^opt(k+1) represents the optimal stator voltage at time point (k+1)T_s; Δu_s^opt(k+1) represents the optimal voltage increment from time point kT_s to time point (k+1)T_s.

The method of the present disclosure has the following beneficial effects:

1. The method of the present disclosure establishes a current increment prediction model by considering the variation of rotor position angle during one control period. Compared with the conventional current prediction model obtained from the first-order forward Euler approximation, the present disclosure makes the current prediction result more accurate and reduces the current ripple of the predictive control method for the permanent magnet synchronous motor under the high-speed operation.

2. The disclosure takes the stator current increment as the state variable and takes the stator voltage increment as the control variable so that the current tracking performance of the predictive current control method based on the current increment prediction model is little affected by the motor parameter variation and the inverter dead-time effect. In addition, the inductance change has little impact on the current fluctuation during the practical operation of the motor.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is the control block diagram which illustrates the predictive control method of current increment suitable for the permanent magnet synchronous motor under the high-speed operation.

FIG. 2 is the simulation current waveforms of the conventional current predictive model when the deadtime is set to 3 μs and the simulation current waveforms of the current increment prediction model.

FIG. 3 is the current prediction error waveforms of the current increment prediction model with inductance mismatch under different motor operating conditions.

FIG. 4 is the experimental waveforms of d-axis current, q-axis current and a phase current under different motor operating conditions for the predictive current control method based on the conventional current predictive model and for the current increment prediction model.

DESCRIPTION OF EMBODIMENTS

Embodiments of the predictive control method of the current increment suitable for the permanent magnet synchronous motor under high-speed operation are explained with reference to the drawings.

In the following, the method of the disclosure is further introduced based on the detailed principle and situation:

1. Establish the Model of the Permanent Magnet Synchronous Motor:

The rotating coordinate is established and the d-axis aligns on the rotor flux. The continuous time domain model of the permanent magnet synchronous motor is

$\begin{array}{cc}\frac{{\mathrm{di}}_s}{\mathrm{dt}}=A_s{i}_s+B_s{u}_s+D_s& \left(1\right)\end{array}$

In the equation (1),

$\begin{array}{ccc}{A}_s=\left[\begin{array}{cc}-\frac{r_{s0}}{L_{d0}}& {\omega }_r\frac{L_{q0}}{L_{d0}}\\ -{\omega }_r\frac{L_{d0}}{L_{q0}}& -\frac{r_{s0}}{L_{q0}}\end{array}\right];& B_s\left[\begin{array}{cc}\frac{1}{L_{d0}}& 0\\ 0& -\frac{r_{s0}}{L_{q0}}\end{array}\right];& D_s=\left[\begin{array}{c}0\\ -\frac{{\omega }_r{\psi }_{f0}}{L_{q0}}\end{array}\right]\end{array};$

where r_s0, L_d0, L_q0, and ψ_f0 are the nominal values of stator resistance, d-axis inductance, q-axis inductance, and permanent magnet flux linkage, respectively; ω_r is the electrical angular velocity; A_s represents the coefficient matrix of the current item; B_s represents the coefficient matrix of the voltage item; D_s represents the coefficient matrix related to the back electromotive force; i_s(t)=[i_d(t), i_q(t)]^T, where i_d(t), and i_q(t) are the d-axis stator current and the q-axis stator current, respectively; u_s(t)=[u_d(t), u_q(t)]^T, where u_d(t) and u_q(t) are the d-axis stator voltage and the q-axis stator voltage, respectively; t represents the time.

By solving (1), the current model of the permanent magnet synchronous motor in continuous time domain is expressed as

$\begin{array}{cc}{i}_s\left(t\right)=e^{\left(t-{\mathrm{kT}}_s\right)A_s}{i}_s\left(k\right)+\left[e^{\left(t-{\mathrm{kT}}_s\right)A_s}-I\right]A_s^{-1}{D}_s+{\int }_{{\mathrm{kT}}_s}^t{e}^{\left(t-\tau \right)A_s}{B}_s{u}_s\left(\tau \right)d\tau & \left(2\right)\end{array}$

In equation (2), T_s is the control period; I is the identity matrix.

2. Establish the Discrete Current Prediction Model of the Permanent Magnet Synchronous Motor Under High-Speed Operation Condition:

The existing predictive current control methods commonly employ the prediction model derived from the first-order forward Euler approximation method which assumes that the value of ω_rT_s is small enough so that the variation of rotor position angle during one control period can be ignored. The exponential term e^(t-kTs)As is equivalently simplified as (t−kT_s)A_s+I. Substituting the above assumptions into equation (2) and discretizing equation (2), the conventional current prediction model obtained by using the first-order forward Euler approximation method is discretized as

$\begin{array}{cc}{i}_s\left(k+1\right)=A_{c0}\left(k\right)i_s\left(k\right)+B_{c0}\left(k\right)u_s\left(k\right)+D_{c0}\left(k\right)& \left(3\right)\end{array}$ $\begin{array}{cc}{A}_{c0}\left(k\right)=\left[\begin{array}{cc}1-\frac{T_s{R}_{s0}}{L_{d0}}& {\omega }_{r,k}\frac{T_s{L}_{q0}}{L_{q0}}\\ -{\omega }_{r,k}\frac{T_s{L}_{d0}}{L_{q0}}& 1-\frac{T_s{R}_{s0}}{L_{q0}}\end{array}\right]\text{⁠};& B_{c0}\left(k\right)=\left[\begin{array}{cc}\frac{T_s}{L_{d0}}& 0\\ 0& \frac{T_s}{L_{q0}}\end{array}\right];\end{array}$ $D_c\left(k\right)={\left[\begin{array}{cc}0& -\frac{T_s{\omega }_{r,k}{\psi }_{f0}}{L_{q0}}\end{array}\right]}^T;$

In equation (3), i_s(k+1)=[i_d,k+1, i_q,k+1]^T represents the predicted current vector at time point (k+1)T_s, and i_s(k+1)=[i_d,k+1, i_q,k+1]^T, where i_d,k+1 and i_q,k+1 are the d-axis predictive current and q-axis predicted current at time point (k+1)T_s, where the subscript d represents d-axis, the subscript q represents q-axis, and the subscript (k+1) represents the (k+1)th control period; i_s(k) represents the stator current vector at kT_s time point, and i_s(k)=[i_d,k, i_q,k]^T, where i_d,k and i_q,k are d-axis current and q-axis current at time point kT_s, where the subscript k represents the kth control period t; u_s(k) represents the stator voltage vector at time point kT_s, and u_s(k)=[u_d,k, u_q,k]^T, where u_d,k and u_q,k are d-axis stator voltage and q-axis stator voltage at time point kT_s; A_c0(k) represents the coefficient matrix of the stator current item at time point kT_s; B_c0(k) represents the coefficient matrix of the stator voltage item at time point kT_s; D_c0(k) represents the coefficient matrix related to the back electromotive force at time point kT_s; ω_r,k is the electrical angular velocity at time point kT_s, where r indicates a symbol related to the rotor; k represents the ordinal number of the control period.

However, the assumption of e^(t-kTs)As≈(t−kT_s)A_s+I is invalid when the motor works under the high-speed operation, and the variation of rotor position angle during one control period cannot be ignored. This disclosure considers the variation of rotor position angle during one control period, and the stator voltage u_s(t)=[u_d(t),u_q(t)]^T in equation (2) during one control period can be expressed as

$\begin{array}{cc}\left[\begin{array}{c}{u}_d\left(t\right)\\ u_q\left(t\right)\end{array}\right]=\left[\begin{array}{cc}\cos \left[\left(t-{\mathrm{kT}}_s\right){\omega }_r\right]& \sin \left[\left(t-{\mathrm{kT}}_s\right){\omega }_r\right]\\ -\sin \left[\left(t-{\mathrm{kT}}_s\right){\omega }_r\right]& \cos \left[\left(t-{\mathrm{kT}}_s\right){\omega }_r\right]\end{array}\right]\left[\begin{array}{c}{u}_{d,k}\\ u_{q,k}\end{array}\right]& \left(4\right)\end{array}$

In equation (4), u_d,k and u_q,k are d-axis voltage and q-axis voltage at time point kT_s, respectively; kT_s≤t≤(k+1)T_s.

Ignoring the stator resistance voltage drop and substituting equation (4) into equation (2), the discrete current prediction model is obtained

$\begin{array}{cc}{i}_s\left(k+1\right)=A_0\left(k\right)i_s\left(k\right)+B_0\left(k\right)u_s\left(k\right)+D_0\left(k\right)& \left(5\right)\end{array}$ $A_0\left(k\right)=\left[\begin{array}{cc}\cos \left({\omega }_{r,k}{T}_s\right)& \frac{L_{q0}}{L_{d0}}\sin \left({\omega }_{r,k}{T}_s\right)\\ -\frac{L_{d0}}{L_{q0}}\sin \left({\omega }_{r,k}{T}_s\right)& \cos \left({\omega }_{r,k}{T}_s\right)\end{array}\right];B_0\left(k\right)=\left[\begin{array}{cc}\frac{T_s}{L_{d0}}\cos \left({\omega }_{r,k}{T}_s\right)& \frac{T_s}{L_{d0}}\sin \left({\omega }_{r,k}{T}_s\right)\\ -\frac{T_s}{L_{q0}}\sin \left({\omega }_{r,k}{T}_s\right)& \frac{T_s}{L_{q0}}\cos \left({\omega }_{r,k}{T}_s\right)\end{array}\right]$ $D_0\left(k\right)=\left[\begin{array}{c}\frac{{\psi }_{f0}}{L_{d0}}[\cos \left({\omega }_{r,k}{T}_s\right)-1\\ -\frac{{\psi }_{f0}}{L_{q0}}\sin \left({\omega }_{r,k}{T}_s\right)\end{array}\right].$

In equation (5), A₀(k) represents the coefficient matrix of the stator current item at time point kT_s; B₀(k) represents the coefficient matrix of the stator voltage item at time point kT_s; D₀(k) represents the coefficient matrix related to the back electromotive force at time point kT_s.

Compared with equation (3), equation (5) considers the influence of rotor movement in each control period on the actual operation trajectories of stator current and voltage, so that it can reflect the change of the stator current in one control period more accurately. However, the dead-time effect and motor parameter mismatch still cause the prediction error.

3. Establish the Current Increment Prediction Model of the Permanent Magnet Synchronous Motor Under the High-Speed Operation.

The inverter output voltage error caused by the dead-time effect is related to the three-phase switching states of the inverter and the directions of three-phase currents. Because the three-phase switching mode of the inverter is fixed, and the directions of the three-phase currents do not change frequently, so the voltage errors between two adjacent control periods caused by the dead-time effect can be seemed to be equal. Therefore, the voltage error caused by the dead-time effect can be eliminated to a certain extent by subtracting the stator voltages from another one stator voltage in two adjacent control period. In the motor drives, ω_r can be seemed to be constant during two adjacent control periods since the control period is short enough, so A₀(k), B₀(k), and D₀(k) can be seemed to be constant during two adjacent control periods. Subtracting the predicted current at time point (k−1)T_s from the predictive current at time point kT_s based on equation (5), the current increment predictive model appropriate for permanent magnet synchronous motor under the high-speed operation is obtained as:Δi_s(k+1)=A₀(k)Δi_s(k)+B₀(k)Δu_s(k)  (6)

In equation (6), Δi_s(k+1)=[Δi_d,k+1, Δ_q,k+1]^T represents the predicted current increment calculated from the current increment prediction model, where Δi_d,k+1 and Δi_q,k+1 are d-axis predictive current increment and q-axis predictive current increment, respectively; Δi_s(k) represents the stator current increment between the stator current at time point kT_s and the stator current at time point (k−1)T_s, i.e., Δi_s(k)=i_s(k)−i_s(k−1), and Δi_s(k)=[Δi_d,k, Δi_q,k]^T, where Δi_d,k is d-axis stator current increment between the d-axis stator current at time point kT_s and the d-axis stator current at time point (k−1)T_s, and Δi_q,k is q-axis stator current increment between the q-axis stator current at time point kT_s and the q-axis stator current at time point (k−1)T_s; Δu_s(k) represents the stator voltage increment between the stator voltage at time point kT_s and the stator voltage at time point (k−1)T_s, i.e., Δu_s(k)=u_s(k)−u_s(k−1), and Δu_s(k)=[Δu_d,k, Δu_q,k]^T where Δu_d,k is d-axis stator voltage increment between the d-axis stator voltage at time point kT_s and the d-axis stator voltage at time point (k−1)T_s, and Δu_q,k is q-axis stator voltage increment between the q-axis stator voltage at time point kT_s and the q-axis stator voltage at time point (k−1)T_s; u_s(k−1) represents the stator voltage at time point (k−1)T_s.

The control variable in equation (6) is the stator voltage increment Δu_s(k), which indicates that the current increment prediction model can reduce the output voltage error caused by the inverter dead-time effect. Comparing equation (6) with equation (5), it can be seen that the coefficients Δ₀(k) and B₀(k) in equation (5) and equation (6) are equal, but the back electromotive force item is eliminated in equation (6), i.e., the current increment prediction model is independent of the permanent magnet flux linkage, and is only affected by the stator inductance.

4. Establish the Cost Function

The cost function is established by taking the squared error between the preset reference current increment and the predictive current increment as the evaluation criterion. The cost function is applied to evaluate the error of the stator current increment at the end of each control period corresponding to the stator voltage increment. Considering the delay compensation problem of the predictive current control, the cost function is established as:

$\begin{array}{cc}\underset{\Delta u_s}{\min }J={\left[\Delta i_s^{\mathrm{ref}}-\Delta i_s\left(k+2\right)\right]}^T\left[\Delta i_s^{\mathrm{ref}}-\Delta i_s\left(k+2\right)\right]+\Delta {u_s\left(k+1\right)}^{T}P\Delta u_s\left(k+1\right)& \left(7\right)\end{array}$ $\mathrm{Satisfy}:\{\begin{array}{c}❘u_s\left(k\right)+\Delta u_s\left(k+1\right)❘\le U_{\max }\\ ❘i_s\left(k+1\right)+\Delta i_s\left(k+2\right)❘\le I_{\max }\end{array}$

where Δi_s^ref represents the reference current increment; P is the weight factor matrix which is used to determine the importance of voltage increment; U_max and I_max are the maximal voltage and the maximal current of the permanent magnet synchronous motor driving system; the superscript T represents the matrix transpose operation; “Satisfy” represents the constraint conditions; Δi_s(k+2) represents the predicted current increment calculated from the current increment prediction model; Δu_s(k+1) represents the stator voltage increment between the stator voltage at time point (k+1)T_s and the stator voltage at time point kT_s; J is the value of the cost function. The voltage increment item in the cost function is used to reduce the dynamic overshoot of the motor, and to prevent the motor and power switching suffering from voltage surge and current surge.

5. Obtain the Optimal Stator Voltage Vector

Substituting equation (6) into equation (7), the cost function is established as:

$\begin{array}{cc}\underset{\Delta u_s}{\min }J=\Delta {u_s\left(k\right)}^T\left(B_0^T{B}_0+P\right)\Delta u_s\left(k\right)-2\Delta {u_s\left(k\right)}^T{B}_0^T\left[\Delta i_s^{\mathrm{ref}}-A_0\left(k\right)\Delta i_s\left(k\right)\right]+{\left(\Delta i_s^{\mathrm{ref}}\right)}^T\Delta i_s^{\mathrm{ref}}-2{\left(\Delta i_s^{\mathrm{ref}}\right)}^T{A}_0\left(k\right)\Delta i_s\left(k\right)+\Delta {i_s\left(k\right)}^T{A}_0^T{A}_0\left(k\right)\Delta i_s\left(k\right)& \left(8\right)\end{array}$

According to the convex optimization theory, the extreme value of the cost function J can be obtained by calculating the partial derivative of formula (8) with respect to Δu_s(k) and make it zero:

$\begin{array}{cc}\frac{\partial J}{\partial \Delta u\left(k\right)}=0& \left(9\right)\end{array}$

By solving equation (9), the optimal voltage increment minimizing the value of J can be derived asΔ_s^opt=[B₀^TB₀+P⁻]⁻¹B₀^T[ΔAi_s^ref−A₀Δi_s(k+1)]  (10)

By superposing the optimal voltage increment on the stator voltage at the present control period, the optimal stator voltage is obtained as:u_s^opt(k+1)=u_s(k)+Δ_s^opt(k+1)  (11)

In equation (11), Δu_s^opt(k+1) represents the optimal voltage increment from time point kT_s to time point (k+1)T_s.

The detailed implementation process of the disclosure is shown in FIG. 1 in the form of a control block diagram. Referring to FIG. 1, MTPA represents the maximum torque per ampere, θr represents the angle of the rotor of the motor, SVPWM represents the space vector pulse width modulation. In addition, S_A, S_B and S_C represent the switching signals of inverter legs with respect to A, B, and C phases, respectively. This method demonstrated in FIG. 1 improves the current prediction accuracy under high-speed operation and eliminates the use of the permanent magnet flux during implementing the predictive control method of the current increment.

The feasibility of the proposed method is verified by combining detailed simulation and experimental data shown in FIG. 2, FIG. 3, and FIG. 4.

To verify the practicability and validity of the proposed predictive current control method based on the current increment prediction model, the simulations and experiments are carried out on a 20-kW permanent-magnet synchronous motor system. The parameters of the tested motor are shown in TABLE I. In the experimental platform, the controller taking the DSP (TMS320F28335) as the core is employed for algorithm implementation, and the dynamometer is an induction motor controlled by S120 produced by Siemens.

TABLE I
parameter	Symbols	Values	Units
Number of pole pairs	np	4
Rated speed	nN	3000	r/min
Rated torque	TN	64	Nm
Rotor flux linkage	Ψf0	0.07574	Wb
Stator resistance	rs0	0.00114	Ω
d-axis stator inductance	Ld0	0.2	mH
q-axis stator inductance	Lq0	0.555	mH

1. The Influence of the Dead-Time Effect on the Current Control Performance

To eliminate the effect of parameter mismatch, this disclosure checks the effect of the dead-time on the current control performances of the predictive current control method based on the conventional current prediction model and predictive current increment control method in this disclosure by simulation. In the simulation, the conventional current prediction model is shown in (3), the control period T_s is set to 200 μs, and the dead-time t_d is set to 3 μs. FIG. 2 shows the simulation waveforms of preset d-axis reference current i_d^ref, d-axis current measured value i_d, preset q-axis current i_q^ref and q-axis current measured value i_q of the predictive current control method based on the conventional current prediction model and predictive control method of the current increment, respectively when the permanent magnet synchronous motor works under different rotating speed n and different load torque T_L.

From FIG. 2, it can be seen that there is tracking error of between i_d^ref and i_d of the predictive current control method based on the conventional current predictive model when the motor works at 300 r/min with 64 Nm load torque, and the i_d can track i_d^ref in other operation conditions. However, the tracking error between i_q and its reference of the predictive current control method based on the conventional current predictive model is significant in different operation conditions. From FIG. 2, it can also be seen that both i_d and i_q of the predictive current increment control method can track their references well.

2. The Analysis of Current Prediction Error Under Parameter Mismatch

In the practical motor drives, there is the errors between the nominal inductances (L_d0 and L_d0) and the actual inductances (L_d and L_d). Define ΔL_d and ΔL_q as the perturbation values of the inductances. Substituting L_d=L_d0+ΔL_d and L_q=L_q0+ΔL_q into equation (6), the predictive current increment of current increment prediction model considering inductance mismatch is obtained as:

$\begin{array}{cc}\Delta i_{\mathrm{sp}}\left(k+1\right)=\Delta i_s\left(k+1\right)+e\left(k+1\right)& \left(12\right)\end{array}$ $\begin{array}{c}\left(13\right)\end{array}$ $\{\begin{array}{c}e\left(k+1\right)=\left[\begin{array}{c}{e}_{d,k+1}\\ e_{q,k+1}\end{array}\right]=\Delta A\left(k\right)\left[\begin{array}{c}\Delta i_{d,k}\\ \Delta i_{q,k}\end{array}\right]+\Delta B\left(k\right)\left[\begin{array}{c}\Delta u_{d,k}\\ \Delta u_{q,k}\end{array}\right]\\ \Delta A\left(k\right)=\left[\begin{array}{cc}0& \frac{\Delta L_q{L}_{d0}-\Delta L_d{L}_{q0}}{L_{d0}\left(L_{d0}+\Delta L_d\right)}\sin \left({\omega }_{r,k}{T}_s\right)\\ \frac{\Delta L_q{L}_{d0}-\Delta L_d{L}_{q0}}{L_{q0}\left(L_{q0}+\Delta L_q\right)}\sin \left({\omega }_{r,k}{T}_s\right)& 0\end{array}\right]\\ \Delta B\left(k\right)=\left[\begin{array}{cc}-\frac{T_s\Delta L_d}{L_{d0}\left(L_{d0}+\Delta L_d\right)}\cos \left({\omega }_{r,k}{T}_s\right)& -\frac{T_s\Delta L_d}{L_{d0}\left(L_{d0}+\Delta L_d\right)}\sin \left({\omega }_{r,k}{T}_s\right)\\ \frac{T_s\Delta L_q}{L_{q0}\left(L_{q0}+\Delta L_q\right)}\sin \left({\omega }_{r,k}{T}_s\right)& -\frac{T_s\Delta L_q}{L_{q0}\left(L_{q0}+\Delta L_q\right)}\cos \left({\omega }_{r,k}{T}_s\right)\end{array}\right]\end{array}$

In equation (12), Δi_sp(k+1) represents the predicted current increment considering inductance mismatch; e(k+1)=[e_d,k+1, e_q,k+1]^T represents the current prediction error vector caused by inductance mismatch, where e_d,k+1 and e_q,k+1 are the d-axis current prediction error and q-axis current prediction error, respectively; Δi_s(k+1) has been shown in equation (6).

In equation (13), ΔL_d represents the error between d-axis actual inductance La and d-axis nominal inductance L_d0; ΔL_q represents the error between q-axis actual inductance L_q and q-axis nominal inductance L_q0; ΔA(k) represents the coefficient matrix of the stator current increment item; ΔB(k) represents the coefficient matrix of the stator voltage increment item.

From equation (13), it can be seen that the amplitude and sign of current prediction error (e_d,k+1 and e_q,k+1) are affected by the state variables (Δi_d,k and Δi_q,k), the control variables (Δu_d,k and Δu_q,k) and the angular velocity ω_r. To intuitively illustrate the influence of inductance mismatch on the current prediction error of the current increment prediction model, waveforms of e_d,k+1 and e_d,k+1 are shown in FIG. 3 for the current increment prediction model under different operation conditions (motor rotational speed n and load torque T_L) when L_d and L_q increase up 20% and 50%, respectively.

For the current increment prediction model, e_d,k+1 and e_q,k+1 always fluctuate around zero as shown in FIG. 3. Such current prediction errors mainly impact the current ripples, but have almost no effect on the current tracking error. In addition, the larger the fluctuation range of e_d,k+1 (or e_q,k+1) is, the larger the effect of inductance mismatch on the d-axis current ripple (or q-axis current ripple) is. From FIG. 3, it can also be seen the fluctuation ranges of e_d,k+1 and e_q,k+1 change very little under different motor operation conditions. When the load torque is light, the amplitudes of d-axis current and q-axis current are not large enough compared with the fluctuation ranges of e_d,k+1 and e_q,k+1, so the effects of inductance mismatches on the d-axis current ripple and q-axis current ripple are great. However, as the torque increases, the amplitudes of d- and q-axis currents become large, and the fluctuation ranges of e_d and e_q become small compared with amplitudes of d- and q-axis currents. So the effects of inductance mismatch on the current ripples reduce. The changes of the inductances under the conditions with different load torque in above analysis are set the same (ΔL_d=20% L_d0 and ΔL_q=50% L_q0). However, there are a positive correlation between the inductances and stator currents. In other words, the changes of the inductances in light load are much smaller than the setting values. Therefore, the effect of the inductance mismatch on the current ripples of predictive current increment control is not great in the practical motor operating.

In summary, inductance mismatch has the impact on the current ripple of the predictive control method of the current increment, but the effect of the inductances mismatch is small in the practical motor operating. In addition, the inductance mismatch has almost no effect on the current tracking error.

3. The Comparison of Steady-State Performance

This disclosure compares the steady-state performance of predictive current increment control with the steady-state performance of predictive current control based on conventional current predictive model in a 20 kW PMSM drives. The parameter of the tested PMSM is shown in TABLE I. In the experiment, the motor works at 300 r/min and 7500 r/min, and the output power of the motor is 20 kW.

FIG. 4 shows the steady-state waveforms of the presuppose d-axis reference current i_d^ref, the d-axis current i_d, the presuppose q-axis reference current i_q^ref the q-axis current i_q and a-phase current i_a of the predictive current control method based on the conventional current prediction model and current increment prediction model, respectively under different operation condition. From the experimental results, it can be seen that the current ripples of the predictive current increment control do not increase compared with the predictive current control method based on the conventional current predictive model when the motor works at 300 r/min, although current increment prediction model ignores the stator resistance. However, both the d-axis current ripple and q-axis current ripple of predictive current increment control are significantly less than the d-axis current ripple and q-axis current ripple of predictive current control method based on the conventional current prediction model when the motor works at 7500 r/min. In addition, it can be seen that i_d and i_q of predictive current increment control can track their references well, but there are obvious d-axis current tracking error and q-axis current tracking error of predictive current control method based on the conventional current prediction model caused by the dead-time affect and parameter mismatch.

The disclosure does not limit the type of each device except for a special description. so long as the device can complete the above functions.

Technical personnel in this field can understand that the attached figure is only a schematic diagram, and the serial number of the above disclosure implementation cases is only for description, which does not represent the advantages and disadvantages of the implementation cases.

The above is only a better implementation case of the invention, which is not used to limit the invention. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the invention should be included in the protection scope of the invention.

Claims

1. A predictive control method of current increment for a permanent magnet synchronous motor under a high-speed operation, wherein the high-speed operation is operated at a speed equal to or higher than 3000 r/min, and the predictive control method of the current increment for the permanent magnet synchronous motor under the high-speed operation comprising the following steps: (1) establishing a mathematical expression of a stator voltage during one control period according to a position change of a rotor of the permanent magnet synchronous motor during the one control period;(2) obtaining a continuous time domain current model of the permanent magnet synchronous motor by solving a continuous time domain equation of the permanent magnet synchronous motor;(3) substituting the mathematical expression of the stator voltage during the one control period into the continuous time domain current model of the permanent magnet synchronous motor for solving solutions to obtain a discrete current prediction model for the permanent magnet synchronous motor under the high-speed operation, and then obtaining a predicted current at a next time point by using the discrete current prediction model;(4) subtracting a predictive current at a present time point from a predictive current at a next time point to obtain a current increment prediction model for the permanent magnet synchronous motor under the high-speed region, and then obtaining a predicted current increment calculated from the current increment prediction model;(5) establishing a cost function by taking a squared error at an end of each control period between a preset reference current increment and a predicted current increment as an evaluation criterion, and then minimizing the cost function by solving a convex optimization problem for the cost function to obtain an optimal voltage increment;(6) superposing the optimal voltage increment on a stator voltage of a present control period to obtain an optimal stator voltage of a next control period, and then apply the optimal stator voltage of the next control period to the permanent magnet synchronous motor.

2. The predictive control method of the current increment for the permanent magnet synchronous motor under the high-speed operation according to claim 1, wherein in step (1), the mathematical expression of the stator voltage during the one control period is:

3. The predictive control method of the current increment for the permanent magnet synchronous motor under the high-speed operation according to claim 1, wherein in step (3), the discrete current prediction model is:

4. The predictive control method of the current increment for the permanent magnet synchronous motor under the high-speed operation according to claim 1, wherein in step (4), the current increment prediction model is: Δis(k+1)=A0(k)Δis(k)Δu s(k)Δis(k)=is(k)−is(k−1)Δus(k)=us(k)−us(k−1)where, Δis(k+1) represents the predicted current increment calculated from the current increment prediction model; Δis(k) represents a stator current increment between a stator current at time point kTs and a stator current at time point (k−1)Ts; Δus(k) represents a stator voltage increment between a stator voltage at time point kTs and a stator voltage at time point (k−1)Ts; is(k−1) represents a stator current vector at time point; (k−1)Ts; us(k−1) represents a stator voltage vector at time point (k−1)Ts; A0(k) represents a coefficient matrix of Δis(k); B0(k) represents a coefficient matrix of Δus(k).

5. The predictive control method of the current increment for the permanent magnet synchronous motor under the high-speed operation according to claim 1, wherein in step (5), the cost function is established as:

6. The predictive control method of the current increment for the permanent magnet synchronous motor under the high-speed operation according to claim 1, wherein in step (6), the optimal voltage increment is added to the stator voltage of the present control period to obtain the optimal stator voltage of the next control period, and the optimal stator voltage is: usopt(k+1)=us(k)+Δusopt(k+1)where us(k) represents a stator voltage at time point kTs; usopt(k+1) represents an optimal stator voltage at time point (k+1)Ts; Δusopt(k+1) represents an optimal voltage increment from time point kTs to time point (k+1)Ts.