Permanent magnet synchronous motor variable gain sliding mode and variable gain robust model prediction control method

Abstract

The invention discloses a permanent magnet synchronous motor variable gain sliding mode and variable gain robust model prediction control method. The method comprises the following steps: step 1, sampling a motor state; 2, setting a speed controller and parameters thereof, and obtaining a d-axis current reference value and a q-axis current reference value; 3, a current controller and parameters thereof are set, and in the current controller, reference current and feedback current are input into the prediction model improved based on the variable gain robust item at the same time; and 4, dividing the space vector into sectors, obtaining a three-phase full-bridge driving pulse of the inverter through a modulation module according to different voltage vector action time, and outputting a control signal by the inverter to complete the current and speed double-closed-loop control of the permanent magnet synchronous motor. The method belongs to the technical field of permanent magnet synchronous motor control, and by dynamically adjusting the sliding mode gain and predicting the robust item of the model, the dynamic and steady-state performance of the system can be remarkably improved when parameters are mismatched and the load is suddenly changed, and no hardware is needed to be added.

Description

Technical Field

[0001] The present invention belongs to the technical field of permanent magnet synchronous motor control, and relates to a variable gain sliding mode and variable gain robust model predictive control method for a permanent magnet synchronous motor. Background Art

[0002] With the widespread application of permanent magnet synchronous motors (PMSMs) in industrial servo systems, electric vehicles, aerospace, and other fields, the research on high-performance control strategies has become crucial. Traditional control methods (such as proportional-integral control) have gained widespread application due to their simple structure. However, they suffer from slow dynamic response and insufficient steady-state accuracy when faced with the nonlinear characteristics of PMSMs, parameter uncertainties (such as the time-varying characteristics of resistance, inductance, and flux), and external disturbances (such as sudden load changes). Sliding mode control (SMC) offers good robustness against uncertainties, but traditional SMC suffers from severe chattering, which affects control accuracy. Terminal sliding mode control can achieve finite-time convergence but can suffer from singularities that lead to control failure. Non-singular fast terminal sliding mode control avoids singularities while accelerating system response, but it struggles to strike a balance between dynamic response speed and steady-state chattering. Model Predictive Control (MPC) demonstrates its advantages in motor control due to its multi-objective optimization capabilities. However, existing MPC methods, such as finite control set MPC and continuous control set MPC, are highly dependent on parameter accuracy, and their performance degrades significantly when parameters mismatch occurs. To improve robustness, existing MPC techniques have primarily employed parameter identification or disturbance observation compensation, but these approaches inevitably increase the computational burden on the processor.

[0003] Therefore, there is an urgent need to improve the high-performance control technology for PMSM and combine the speed loop variable gain nonsingular fast terminal sliding mode control (VGNFTSMC) with the current loop variable gain robust model predictive current control (VGRMPCC) to take into account both dynamic response and steady-state accuracy. Without significantly increasing the processor's computing burden, it is robust to parameter uncertainty and time-varying properties, and significantly improves the control performance of PMSM under complex working conditions. Summary of the Invention

[0004] The purpose of the present invention is to provide a variable gain sliding mode and variable gain robust model predictive control method for a permanent magnet synchronous motor, which solves the problem that in the prior art PMSM control process, the control performance is significantly degraded when there is parameter mismatch, load mutation and external disturbance.

[0005] The technical solution adopted by the present invention is a variable gain sliding mode and variable gain robust model predictive control method for a permanent magnet synchronous motor, which is implemented according to the following steps: Step 1: Sampling the motor status; Step 2: Set the speed controller and its parameters to obtain d Shaft current reference and q Shaft current reference value; Step 3: Set the current controller and its parameters. In the current controller, the reference current i dref 、 i qref With feedback current i d ( k ), i q ( k ) are simultaneously input into the prediction model based on the improved variable gain robust term; Step 4: Divide the space vector into sectors. According to the different voltage vector action times, the three-phase full-bridge drive pulse of the inverter is obtained through the modulation module. The inverter then outputs the control signal to complete the dual closed-loop control of the current and speed of the permanent magnet synchronous motor.

[0006] The beneficial effect of the present invention is that the composite control strategy for permanent magnet synchronous motors based on VGNFTSMC and VGRMPCC can ensure high dynamic response when parameter mismatch, load mutation, and external disturbance occur during PMSM control, while significantly improving the steady-state performance of the system, including control variable oscillation and output accuracy. This strategy is suitable for high-precision control scenarios such as electric vehicle drives and industrial servos, and has the following advantages: 1) VGNFTSMC adopts a new variable gain reaching law, which obtains a large gain when far from the sliding mode surface to accelerate convergence, and reduces the gain when close to the sliding mode surface to suppress chattering, thereby enhancing the system's dynamic response capability and steady-state accuracy; 2) VGRMPCC compensates for motor parameter uncertainty by dynamically adjusting robust term gains, improving system control performance under parameter mismatch conditions. It eliminates the need for online parameter identification and has computational complexity comparable to basic model predictive control, reducing the computational burden on the processor. 3) Only the control algorithm needs to be improved, without adding additional sensors. The control performance of PMSM is significantly improved without increasing the complexity and cost of the system hardware. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 This is a block diagram of the permanent magnet synchronous motor control system on which the method of the present invention relies; Figure 2 This is a block diagram of voltage vector synthesis used in the method of the present invention; Figure 3 This is a comparison waveform of the speed during the acceleration phase between the method of the present invention and the traditional non-singular fast terminal sliding mode control method; Figure 4 This is a comparison waveform of the speed during the loading phase between the method of the present invention and the traditional non-singular fast terminal sliding mode control method; Figure 5 It is the steady-state speed waveform of the method of the present invention under the parameter matching working condition; Figure 6 It is the steady-state speed waveform of the traditional three-vector model predictive current control method under parameter matching conditions; Figure 7 It is the steady-state torque waveform of the method of the present invention under the parameter matching working condition; Figure 8 This is the steady-state torque waveform of the traditional three-vector model predictive current control method under parameter matching conditions; Figure 9 It is the steady-state speed waveform of the method of the present invention under the inductance parameter mismatch condition; Figure 10 This is the steady-state speed waveform of the traditional three-vector model predictive current control method under the inductance parameter mismatch condition; Figure 11 It is the steady-state torque waveform of the method of the present invention under the inductance parameter mismatch condition; Figure 12 This is the steady-state torque waveform of the traditional three-vector model predictive current control method under the inductance parameter mismatch condition; Figure 13 The method of the present invention has different parameter values k 1 and k Speed ​​waveform in the acceleration phase under case 2; Figure 14 The method of the present invention has different parameter values k 1 and k Speed ​​waveform during the loading phase in case 2; Figure 15 The method of the present invention is k 1=2000, k Speed ​​controller output waveform when 2=200; Figure 16 The method of the present invention is k 1=2000, k Speed ​​controller output waveform when 2=0; Figure 17 The method of the present invention is k 1=5000, k Speed ​​controller output waveform when 2=0; Figure 18 Different parameters under constant current reference value conditions k q1 、 k q2 Dynamic current waveform of the value; Figure 19 Different parameters under constant current reference value conditions k q1 、 k q2 The steady-state current waveform of the value; Figure 20 This is the steady-state speed waveform of the method of the present invention under different nominal values ​​of resistance and inductance parameters; Figure 21 This is the steady-state torque waveform of the method of the present invention under different nominal values ​​of resistance and inductance parameters; Figure 22 This is the steady-state speed waveform of the method of the present invention under different nominal values ​​of resistance, inductance and flux linkage parameters; Figure 23 It is the steady-state torque waveform of the method of the present invention under different nominal values ​​of resistance, inductance and flux linkage parameters.

[0008] In the figure, 1. speed controller, 2. current controller, 3. modulation module, 4. inverter, 5. three-phase current sensor, 6. permanent magnet synchronous motor, 7. coordinate transformation module, 8. magnetic encoder, 9. microprocessor. DETAILED DESCRIPTION

[0009] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0010] Reference Figure 1The overall structure of the permanent magnet synchronous motor control system relied upon by the method of the present invention includes a speed controller 1 (full name "variable gain non-singular fast terminal sliding mode speed controller", abbreviated as VGNFTSMC), a current controller 2 (full name "variable gain robust model predictive current controller", abbreviated as VGRMPCC), a modulation module 3 (full name "SVPWM modulation module", abbreviated as SVPWM), an inverter 4 (DC / AC converter), a three-phase current sensor 5, and a permanent magnet synchronous motor 6 (abbreviated as PMSM), which are connected in sequence. The three-phase current sensor 5 is connected to a coordinate transformation module 7 after A / D conversion, and the coordinate transformation module 7 is then connected to the current controller 2. A magnetic encoder 8 (or "position encoder") is connected to the coordinate transformation module 7 and the speed controller 1 using SPI communication. The coordinate transformation module 7, speed controller 1, current controller 2, and modulation module 3 are all integrated into a microprocessor 9, and the corresponding control functions are implemented through a preset internal control program.

[0011] All input interfaces and input signals (including current sensors and position sensors) of the microprocessor 9 , as well as all output ports and output signals (SVPWM drive signals) of the microprocessor 9 , are all implemented using existing technical means.

[0012] according to Figure 1 The control relationship of the permanent magnet synchronous motor control system is shown in the figure. i d 、 i q is the state quantity, u d 、 u q As the input quantity, the current discretization state equation of the permanent magnet synchronous motor 6 is obtained, and the expression is as follows: , in, T s is the sampling period, R is the stator resistance, L is the stator inductance, u d ( k ) is the stator voltage d Axis component, u q ( k ) is the stator voltage q Axis component; i d ( k ) is the stator current d Axis component, i q ( k ) is the stator currentq Axis component; ψ f is the magnetic flux of the rotor permanent magnet, ω e ( k ) is the electrical angular velocity; In addition, the PMSM electromagnetic torque equation and motion equation are as follows: , in, T e ( k )for k The electromagnetic torque at the moment, p n is the pole pair number, ψ f is the magnetic flux of the rotor permanent magnet, T s is the sampling period, ω ( k )for k Time speed, J is the motor moment of inertia, T L is the load torque, b m is the friction coefficient.

[0013] (The above two operational equations can be regarded as preliminary steps of the method of the present invention) Reference Figure 1 The control principle of the permanent magnet synchronous motor control system referred to in the present invention is: First, the three-phase current of the permanent magnet synchronous motor is collected by the three-phase current sensor 5 and the magnetic encoder 8. i a ( k ) 、i b ( k ) 、i c ( k ) and mechanical angle θ ( k ), are sent to the coordinate transformation module 7, and after data processing is completed inside the coordinate transformation module 7, the current in the two-phase rotating coordinate system is obtained. i d ( k ), i q ( k ); In addition, for mechanical angle θ ( k ) Take the differential to get the speed ω ( k ), leading to step 1 below; Then, the speed ω ( k ) and speed reference ω ref After the difference is made, it is sent to the speed controller 1 to generate q Shaft current reference value i qref , leading to step 2 below; Secondly, let d Shaft current reference value i dref =0, d Shaft current reference value i dref 、 q Shaft current reference value i qref 、 d Shaft current measurement i d ( k ), q Shaft current measurement i q ( k ) The four signals are sent to the current controller 2 together, and the prediction model based on variable gain improvement is used to calculate the current under different voltage vectors. d Shaft current change rate s dn and q Shaft current change rate s qn , n Corresponding to different voltage vectors, the action time of each voltage vector is further calculated, which leads to step 3 below; Finally, based on the voltage vectors selected for each sector and the action time of each voltage vector calculated in step 3, the three-phase full-bridge drive pulse of the inverter 4 is obtained through the modulation module 3 to control the inverter 4, output current and speed control signals, and ultimately achieve real-time control of the permanent magnet synchronous motor 6, leading to step 4 below.

[0014] The method of the present invention is based on the structural principle of the above-mentioned permanent magnet synchronous motor control system and is implemented according to the following steps: Step 1: Sampling the motor status. The three-phase current sensors are used to collect the current of the permanent magnet synchronous motor 6. a 、 b 、 c Three-phase current i a ( k ) 、i b ( k ) 、i c (k ), k Indicates the sampling moment; at the same time, the mechanical angle of the permanent magnet synchronous motor 6 is sampled by the magnetic encoder 8 θ ( k ); Mechanical angle θ ( k ) Take the differential and obtain the speed ω ( k ), then multiply by the pole logarithm p n Get the electrical angular velocity ω e ( k ), the three-phase current is converted into the two-phase rotating coordinate system through coordinate transformation d Axis (direct axis) current i d ( k )and q Axis (quadrature axis) current i q ( k ), the transformation formula is shown in formula (1): , (1) in, θ e ( k ) is the electrical angle, which is determined by the mechanical angle of the permanent magnet synchronous motor 6 θ ( k ) multiplied by the number of pole pairs p n get; i a ( k ) 、i b ( k ) 、i c ( k ) are respectively the permanent magnet synchronous motor 6 a 、 b 、 c Three-phase current in k The sampling value at the moment.

[0015] Step 2: Set speed controller 1 and its parameters. The design goal of speed controller 1 (ie VGNFTSMC) is to obtain d Shaft current reference and q Shaft current reference value, setting d Shaft current reference value i dref =0, then q Shaft current reference value i qrefThe expression of is shown in formula (2): , (2) Where, J is the motor moment of inertia, p n is the number of motor pole pairs, ψ f is the magnetic flux of the rotor permanent magnet, b m is the friction coefficient, Indicates from 0 to t The definite integral of q , p , h , l are all positive odd numbers, and satisfy 1< p / q <2, l / h > p / q ; α and β are positive design parameters, where α Affects the convergence speed of the sliding surface, β Affects the dynamic response capability of the system and satisfies α >0, β >0; ε Responsible for adjusting the convergence speed of the control law and satisfying 0< ε <1, to balance response speed and chattering suppression; γ Responsible for adjusting the smoothness of the control law and satisfying γ >0; δ Responsible for adjusting the decay rate of the exponential term and satisfying 0≤ δ ≤1; k 1>0 is a fixed gain, ensuring that even if the sliding surface error s ( k ) is small, the controller can still output sufficient control quantity; k 2>0 is the adaptive gain, which can enhance the ability to suppress large errors and improve the dynamic response capability of the system; ( k 1+ k 2| s ( k )|) constitutes the variable gain coefficient of the robust term, which is a unique innovation of the speed controller of the present invention; s ( k ) is the sliding surface of speed controller 1; x 1( k ) 、x 2( k ) are the state variables of the system, and the two state variables x 1( k )、x 2( k ) is expressed as formula (3): , (3) In formula (3), ω ref is the reference value of the given speed, ω ( k ) is the rotation speed, T s is the sampling period; in addition, s ( k ) is expressed as formula (4): (4) Step 3: Set the current controller 2 and its parameters. In current controller 2 (VGRMPCC), the reference current i dref 、 i qref With feedback current i d ( k ), i q ( k ) are simultaneously input into the prediction model based on the variable gain robust term. The prediction model improves the robustness of the permanent magnet synchronous motor control system under parameter mismatch by introducing the variable gain robust term for the motor parameters. Calculation under different voltage vectors d - q The shaft current change rate is shown in equations (5) to (7): , (5) , (6) , (7) In formula (5) to formula (7), s d0 and s q0 They represent the zero voltage vector, corresponding to d Axis and q The rate of change of the shaft current; u j and u k Respectively represent the effective voltage vector in a specific sector; s dj and s qj Respectively represented in the voltage vector u jUnder the action, corresponding d Axis and q The rate of change of the shaft current; s dk and s qk Respectively represented in the voltage vector u k Under the action, corresponding d Axis and q The rate of change of the shaft current; S d ( k )= i dref − i d ( k )and S q ( k )= i qref − i q ( k ) respectively represent d Axis and q Tracking error of shaft current; k d f ( S d ( k ), ϕ )and k q f ( S q ( k ), ϕ ) are defined as d Axis and q The robust term of the axis prediction model, represents the thickness of the boundary layer; 、 、 Represents stator resistance R , stator inductance L and permanent magnet flux ψ f The nominal value of k d1 ≥1, k d2 ≥0, k q1 ≥1, k q2 ≥0, 2>0, 1> ε 2>0,0≤ 2≤1, η 1. η 2 is a small positive constant; 、 、 The stator resistance is R , stator inductance L and permanent magnet flux ψ f The uncertainty range of , their respective expressions are shown in formula (8): , (8) In formula (8), 、 、 Represents stator resistance R , stator inductance L and permanent magnet flux ψ f The maximum possible value of 、 、 Represents stator resistance R , stator inductance L and permanent magnet flux ψ f The minimum possible value of Here, and are the variable gain terms of the robust terms, which are the unique innovation of the present invention. The two values ​​are large when they are far away from the corresponding robust sliding mode surface, and small when they are close to the sliding mode surface, which well takes into account the speed and steady-state oscillation; Calculate the action time of the three voltage vectors in each sector t j 、 t k 、 t 0, the expression is shown in formula (9): , (9) In formula (9), i d ( k )express k time d Shaft current measurement value, i q ( k )express k time q Shaft current measurement value; s d0 and s q0 They represent respectively the zero voltage vector, d Axis and q The rate of change of the shaft current; s dj ands qj Respectively represented in the voltage vector u j Under the effect, d Axis and q The rate of change of the shaft current; s dk and s qk Respectively represented in the voltage vector u k Under the effect, d Axis and q The rate of change of the shaft current; From this, we get three voltage vectors for each sector: u j , u k , u 0 action time t j 、 t k 、 t 0.

[0016] Step 4: Divide the space vector into sectors. According to the different voltage vector action times, the three-phase full-bridge drive pulse of the inverter 4 is obtained through the modulation module 3 (SVPWM). The inverter 4 then outputs the control signal to complete the current and speed dual closed-loop control of the permanent magnet synchronous motor 6. The specific process is: The selection of voltage vector is based on the principle of space vector modulation, which divides the voltage plane into six sectors with an interval of 60°. In each control cycle, the system first determines the sector position of the reference voltage vector, and then selects two adjacent effective vectors corresponding to the sector and combines them with the zero vector for synthesis. The corresponding relationship between each sector and the action vector is shown in Table 1. The synthesis principle of voltage vector is shown in Figure 2 As shown, in Figure 2 In the example, the synthetic voltage vector in sector I is u Ⅰ , effective vector j =1, k =2, it is not difficult to see that the synthetic vector is passed through the effective voltage vector u 1 and u 2. Respective action time t j and t k , and zero voltage vector action time t 0 is used to realize synthesis; the corresponding relationship between the values ​​of the effective voltage vectors and the zero voltage vectors of other sectors is shown in Table 1, and so on. This is a mature technology in this field and will not be described in detail.

[0017] Table 1. Correspondence between sectors and applied voltage vectors

[0018] To verify the control effect of the method of the present invention, a model was built on MATLAB / Simulink for simulation verification. The parameter settings of the permanent magnet synchronous motor used in the simulation process are shown in Table 2.

[0019] Table 2. Verification parameters for setting permanent magnet synchronous motor

[0020] Example 1 In this embodiment 1, the following steps are implemented: Step 1: Sampling the motor status. The three-phase current sensors are used to collect the current of the permanent magnet synchronous motor 6. a 、 b 、 c Three-phase current i a ( k ) 、i b ( k ) 、i c ( k ), k Indicates the sampling moment; at the same time, the mechanical angle of the permanent magnet synchronous motor 6 is sampled by the magnetic encoder 8 θ ( k ); Mechanical angle θ ( k ) Take the differential and obtain the speed ω ( k ), then multiply by the pole logarithm p n Get the electrical angular velocity ω e ( k ), the three-phase current is converted into the two-phase rotating coordinate system through coordinate transformation d Axis (direct axis) current i d ( k )and q Axis (quadrature axis) current i q ( k ).

[0021] Step 2: Set speed controller 1 and its parameters. The design goal of speed controller 1 (ie VGNFTSMC) is to obtain d Shaft current reference and q Shaft current reference value, settingd Shaft current reference value i dref =0, we get q Shaft current reference value i qref .

[0022] Step 3: Set the current controller 2 and its parameters. In current controller 2 (VGRMPCC), the reference current i dref 、 i qref With feedback current i d ( k ), i q ( k ) are simultaneously input into the prediction model based on the variable gain robust term to calculate the d - q The shaft current change rate is shown in equations (5) to (7): , (5) , (6) , (7) In formula (5) to formula (7), s d0 and s q0 They represent the zero voltage vector, corresponding to d Axis and q The rate of change of the shaft current; u j and u k Respectively represent the effective voltage vector in a specific sector; s dj and s qj Respectively represented in the voltage vector u j Under the action, corresponding d Axis and q The rate of change of the shaft current; s dk and s qk Respectively represented in the voltage vector u k Under the action, corresponding d Axis and q The rate of change of the shaft current; S d ( k )= idref − i d ( k )and S q ( k )= i qref − i q ( k ) respectively represent d Axis and q Tracking error of shaft current; k d f ( S d ( k ), ϕ )and k q f ( S q ( k ), ϕ ) are defined as d Axis and q The robust term of the axis prediction model, represents the thickness of the boundary layer; 、 、 Represents stator resistance R , stator inductance L and permanent magnet flux ψ f The nominal value of k d1 ≥1, k d2 ≥0, k q1 ≥1, k q2 ≥0, 2>0, 1> ε 2>0,0≤ 2≤1, η 1. η 2 is a small positive constant; 、 、 The stator resistance is R , stator inductance L and permanent magnet flux ψ f The uncertainty range of , their respective expressions are shown in formula (8): , (8) In formula (8), 、 、 Represents stator resistance R , stator inductance L and permanent magnet flux ψ f The maximum possible value of 、 、 Represents stator resistance R , stator inductance L and permanent magnet flux ψ f The minimum possible value of Here, and are the variable gain terms of the robust terms, which are the unique innovation of the present invention. The two values ​​are large when they are far away from the corresponding robust sliding mode surface, and small when they are close to the sliding mode surface, which well takes into account the speed and steady-state oscillation; Calculate the action time of the three voltage vectors in each sector t j 、 t k 、 t 0, the expression is as follows: , (9) In formula (9), i d ( k )express k time d Shaft current measurement value, i q ( k )express k time q Shaft current measurement value; s d0 and s q0 They represent respectively the zero voltage vector, d Axis and q The rate of change of the shaft current; s dj and s qj Respectively represented in the voltage vector u j Under the effect, d Axis and q The rate of change of the shaft current; s dk and s qk Respectively represented in the voltage vector u k Under the effect, d Axis and q The rate of change of the shaft current; From this, we get three voltage vectors for each sector: u j , u k , u 0 action time t j 、 t k 、 t 0.

[0023] Step 4: Divide the space vector into sectors. Based on the different voltage vector action times, the modulation module 3 (SVPWM) obtains the three-phase full-bridge drive pulse of the inverter 4. The inverter 4 then outputs the control signal to complete the dual closed-loop control of the current and speed of the permanent magnet synchronous motor 6. The specific process is as follows: The selection of voltage vector is based on the principle of space vector modulation, which divides the voltage plane into six sectors with an interval of 60°. In each control cycle, the system first determines the sector position of the reference voltage vector, and then selects two adjacent effective vectors corresponding to the sector and combines them with the zero vector for synthesis. The corresponding relationship between each sector and the action vector is shown in Table 1. The synthesis principle of voltage vector is shown in Figure 2 As shown, in Figure 2 In the example, the synthetic voltage vector in sector I is u Ⅰ , effective vector j =1, k =2, it is not difficult to see that the synthetic vector is passed through the effective voltage vector u 1 and u 2. Respective action time t j and t k , and zero voltage vector action time t 0 is used to realize synthesis; the corresponding relationship between the effective voltage vector and the zero voltage vector for other sectors is shown in Table 1, and so on.

[0024] When calculating the action time of the voltage vector, it is necessary to determine whether the action time of the voltage vector is within a reasonable range and discard the voltage vectors that do not meet the requirements. In this embodiment 1, the following is achieved: The control method of the present invention was compared with a conventional non-singular fast terminal sliding mode control method. Both current controllers employed the control method of the present invention to independently verify the improved speed controller performance. Because the performance differences of speed controllers are primarily reflected in the system's speed dynamic response, simulations focused on analyzing the motor's dynamic performance during startup and load surges.

[0025] The simulation conditions are as follows: the motor starts at no load, the speed reference value is stepped from 0 rpm to 1000 rpm, and the load torque is stepped from 0 N·m to 0.5 N·m at 0.5 s. Using the aforementioned method of the present invention, the parameters of the two controllers ("speed controller 1" and "current controller 2") are set as follows during the simulation: The speed controller 1 of the present invention, namely VGNFTSMC, has the following parameters: k 1=2000, k 2=200, ε =0.5, γ =1, δ =0.5, p =35, q =31, h =51, l =71, α =0.001, β =0.001.

[0026] The current controller 2 of the present invention, namely VGRMPCC, has the following parameters: ε 2=0.5, γ 2=1, δ 2=0.5, k d1 =1, k d2 =1, k q1 =1, k q2 =1, =14mΩ, =5.49μH, =0.00134Wb, =7mΩ, =1.83μH, =0.00033Wb.

[0027] The traditional speed controller, that is, the traditional non-singular fast terminal sliding mode control expression is as follows:

[0028] Where, sgn( s ) is a sign function.

[0029] The corresponding parameter settings of the traditional control method are as follows: k =2000, ε =0.5, and the other parameters remain consistent with the control method of the present invention.

[0030] The speed comparison waveforms of the startup phase of the method of the present invention and the traditional non-singular fast terminal sliding mode control method are as follows: Figure 3 As shown, the speed comparison waveform in the loading stage is as follows Figure 4 As shown in FIG. 3 , the comparison of the waveforms shows that the method of the present invention improves the dynamic response speed of the speed.

[0031] Example 2 Following the four steps of the aforementioned method, the control method was compared with a conventional three-vector model predictive current control method. Both speed controllers employed the proposed speed control method to independently verify the improved current controller performance. Because the performance differences of current controllers are primarily reflected in the system's steady-state performance, simulations focused on analyzing the torque and speed fluctuations of the motor during steady-state operation.

[0032] The simulation working conditions are designed as follows: under parameter matching and inductance parameter mismatch conditions, the speed reference value is set to 1000 r / min and the load torque is set to 0.5 N·m. After the speed stabilizes, steady-state performance analysis is performed.

[0033] The control parameters of the method of the present invention under the parameter matching condition (controller parameters are consistent with the actual parameters of the motor) are set as: =7mΩ, =3.66μH, =0.00167Wb, and the other parameters remain the same as those in Example 1.

[0034] The control parameters of the method of the present invention under the inductance parameter mismatch condition are set as follows: =7mΩ, =5.49μH, = 0.00167Wb, the other parameters remain the same as in Example 1; The current prediction formula of the traditional three-vector model predictive current control method is as follows:

[0035] The corresponding parameters are as follows: (1) Parameter matching: =7mΩ, =3.66μH, =0.00167Wb; (2) Parameter mismatch: =7mΩ, =5.49μH, =0.00167Wb, at this time only the inductance parameters are adapted.

[0036] Under the parameter matching condition, the speed waveform of the method in the steady state stage is as follows: Figure 5 As shown, the speed waveform of the traditional three-vector model prediction current control method in the steady state stage is as follows Figure 6 As shown, compared Figure 5 and Figure 6 It can be seen that under the same speed controller conditions, the method of the present invention has a steady-state speed fluctuation that is basically consistent with that of the traditional three-vector model predictive current control method.

[0037] The torque waveform of the method of the present invention in the steady state stage is as follows: Figure 7 As shown, the torque waveform of the traditional three-vector model predictive current control method in the steady state stage is as follows Figure 8 As shown, compared Figure 7 and Figure 8 It can be seen that under the same speed controller conditions, the method of the present invention has a steady-state torque fluctuation that is basically equivalent to that of the traditional three-vector model predictive current control method.

[0038] pass Figure 5 and Figure 6 , Figure 7 and Figure 8 The comparison shows that the control method of the present invention has a performance that is basically equivalent to that of the traditional three-vector model predictive current control under parameter matching conditions.

[0039] Under the parameter mismatch condition (only the inductance parameter mismatch condition as described above), the speed waveform of the steady-state stage of the method of the present invention is as follows: Figure 9 As shown, the speed waveform of the traditional three-vector model prediction current control method in the steady state stage is as follows Figure 10 As shown, compared Figure 9 and Figure 10 It can be seen that under the same speed controller conditions, the method of the present invention has smaller steady-state speed fluctuations. The torque waveform of the steady-state stage of the method of the present invention is as follows: Figure 11 As shown, the torque waveform of the traditional three-vector model predictive current control method in the steady state stage is as follows Figure 12 As shown, compared Figure 11 and Figure 12 It can be seen that under the same speed controller conditions, the method of the present invention has smaller steady-state torque fluctuation, and the correspondence between steady-state torque fluctuation and steady-state speed fluctuation is consistent. The comparison results verify that the control method of the present invention has smaller steady-state fluctuation than the traditional current control method under parameter mismatch conditions, and has better control performance.

[0040] Example 3 The implementation process is the same as that of Example 1. In order to illustrate the different k 1. k The impact of the value of 2 on system performance is analyzed through simulation.

[0041] The simulation condition is designed as follows: the motor starts at no load, the speed reference value jumps from 0r / min to 1000r / min, and at 0.6s, the load torque jumps from 0 N·m to 0.5N·m. k 1, k 2 values ​​are simulated and verified, except k 1, k 2, the other controller parameters are consistent with those in Example 1. The three groups of parameters of the speed controller 1 are selected as follows: (1) k 1=2000, k 2=200;(2) k 1=2000, k 2=0;(3) k 1=5000, k 2=0.

[0042] The speed waveform during the startup phase is as follows: Figure 13 As shown in the figure, the speed waveform in the loading stage is as follows: Figure 14 As shown, the speed controller output in steady state is i qref The waveforms are as follows Figure 15 、 Figure 16 、 Figure 17 shown.

[0043] According to the simulation results, the following conclusions can be drawn: k 1The same situation, k The larger the 2, the faster the dynamic response of the system, and k 2. The output fluctuation of the controller is less affected in steady state; k 2 o'clock, k The larger the value of 1, the faster the dynamic response speed of the system, but the greater the output fluctuation of the controller in steady state. Therefore, the present invention innovatively proposes to reduce k 1. Increase k 2 product term is beneficial to taking into account both dynamic response speed and reducing steady-state oscillation.

[0044] Example 4 The implementation process is the same as that of Example 2. In order to illustrate the influence of the variable gain term in the current controller 2 on the system performance, this Example 4 uses k q1 、 k q2 As an example, analysis is performed through simulation.

[0045] The simulation condition is designed as follows: q The shaft current reference value is 30A, and the load torque is 0N·m. During the simulation, different k q2The value is simulated and verified, except k q1 、 k q2 Except for the above, the other controller parameters are consistent with those in Example 1. The parameter design method of the current controller 2 is as follows: (1) k q1 =1, k q2 =1;(2) k q1 =1, k q2 =0;(3) k q1 =2, k q2 =0; Dynamic current waveform Figure 18 As shown, the steady-state current waveform is as follows Figure 19 As shown, it can be seen that k q1 The larger the current, the faster the dynamic response, but the larger the steady-state fluctuation; k q2 The product term can effectively speed up the current response speed and has little effect on steady-state fluctuations. This is also the advantage of the variable gain robust term proposed in the present invention.

[0046] The direct-axis current control also has similar results, which will not be described here.

[0047] Example 5 The implementation process is the same as that of Example 2. In order to illustrate the different nominal values ​​of resistance parameters in the current controller 2, this Example 5 and nominal values ​​of inductance parameters The impact on the steady-state performance of the system is analyzed by simulation of the steady-state stage of the motor.

[0048] The simulation working condition is designed as follows: the speed reference value is 1000r / min, the load torque is 0.5N·m, and the steady-state performance analysis is performed after the speed stabilizes. =14mΩ, =5.49μH, and the design methods of other control parameters are consistent with those in Example 1.

[0049] Steady-state speed waveform Figure 20 As shown, the steady-state torque waveform is as follows Figure 21 shown.

[0050] Example 6 The implementation process is the same as that of Example 2. In order to illustrate the different nominal values ​​of resistance parameters in the current controller 2, this Example 6 , inductance parameter nominal value And the nominal parameters of permanent magnet flux The impact on the steady-state performance of the system is analyzed by simulation of the steady-state stage of the motor.

[0051] The simulation working condition is designed as follows: the speed reference value is 1000r / min, the load torque is 0.5N·m, and the steady-state performance analysis is performed after the speed stabilizes. =14mΩ, =5.49μH, =0.00134Wb, and the design methods of other control parameters are consistent with those in Example 1.

[0052] Steady-state speed waveform Figure 22 As shown, the steady-state torque waveform is as follows Figure 23 shown.

[0053] Comparing the simulation results of Example 5 and Example 6 with Example 2, it can be seen that when there is parameter mismatch, the control method of the present invention maintains substantially unchanged steady-state speed fluctuations and torque fluctuations for different parameter mismatch (degrees), showing good robustness.

Claims

1. A variable gain sliding mode and variable gain robust model predictive control method for a permanent magnet synchronous motor, characterized in that: Follow these steps to implement: Step 1: Sampling the motor status; Step 2: Set the speed controller (1) and its parameters to obtain d Shaft current reference and q Shaft current reference value; Step 3: Set the current controller (2) and its parameters. In the current controller (2), the reference current i dref 、 i qref With feedback current i d ( k ), i q ( k ) are simultaneously input into the prediction model based on the improved variable gain robust term; Step 4: Divide the space vector into sectors, and obtain the three-phase full-bridge drive pulse of the inverter (4) through the modulation module (3) according to the action time of different voltage vectors. The inverter (4) then outputs the control signal to complete the dual closed-loop control of the current and speed of the permanent magnet synchronous motor (6).

2. The variable gain sliding mode and variable gain robust model predictive control method for a permanent magnet synchronous motor according to claim 1, characterized in that: The overall structure of the permanent magnet synchronous motor control system relied upon by the method comprises a speed controller (1), a current controller (2), a modulation module (3), an inverter (4), a three-phase current sensor 5 and a permanent magnet synchronous motor (6) connected in sequence, the three-phase current sensor (5) being connected to a coordinate transformation module (7) after A / D conversion, the coordinate transformation module (7) being connected to the current controller (2), the magnetic encoder (8) being connected to the coordinate transformation module (7) and the speed controller (1), and the above-mentioned coordinate transformation module (7), speed controller (1), current controller (2) and modulation module (3) being all integrated in a microprocessor (9).

3. The variable gain sliding mode and variable gain robust model predictive control method for a permanent magnet synchronous motor according to claim 1, characterized in that: In step 1, the specific process is: The three-phase current sensors are used to collect the current of the permanent magnet synchronous motor (6). a 、 b 、 c Three-phase current i a ( k ) 、i b ( k ) 、i c ( k ), k Indicates the sampling moment; at the same time, the mechanical angle of the permanent magnet synchronous motor (6) is sampled by the magnetic encoder (8) θ ( k ); Mechanical angle θ ( k ) Take the differential and obtain the speed ω ( k ), then multiply by the pole logarithm p n Get the electrical angular velocity ω e ( k ), the three-phase current is converted into the two-phase rotating coordinate system through coordinate transformation d Shaft current i d ( k )and q Shaft current i q ( k ), the transformation formula is shown in formula (1): ，(1) in, θ e ( k ) is the electrical angle, which is determined by the mechanical angle of the permanent magnet synchronous motor (6) θ ( k ) multiplied by the number of pole pairs p n get; i a ( k ) 、i b ( k ) 、i c ( k ) are respectively the permanent magnet synchronous motor (6) a 、 b 、 c Three-phase current in k The sampling value at the moment.

4. The variable gain sliding mode and variable gain robust model predictive control method for a permanent magnet synchronous motor according to claim 1, characterized in that: In step 2, the specific process is: set up d Shaft current reference value i dref =0, then q Shaft current reference value i qref The expression of is as follows: ，(2) Where, J is the motor moment of inertia, p n is the number of motor pole pairs, ψ f is the magnetic flux of the rotor permanent magnet, b m is the friction coefficient, Indicates from 0 to t The definite integral of q , p , h , l are all positive odd numbers, and satisfy 1< p / q <2, l / h > p / q ; α and β are positive design parameters, where α Affects the convergence speed of the sliding surface, β Affects the dynamic response capability of the system and satisfies α >0, β >0; ε Responsible for adjusting the convergence speed of the control law and satisfying 0< ε <1, to balance response speed and chattering suppression; γ Responsible for adjusting the smoothness of the control law and satisfying γ >0; δ Responsible for adjusting the decay rate of the exponential term and satisfying 0≤ δ ≤1; k 1>0 is a fixed gain, ensuring that even if the sliding surface error s ( k ) is small, the controller can still output sufficient control quantity; k 2>0 is the adaptive gain, which can enhance the ability to suppress large errors and improve the dynamic response capability of the system; ( k 1+ k 2| s ( k )|) constitutes the variable gain coefficient of the robust term; s ( k ) is the sliding surface of the speed controller (1); x 1( k ) 、x 2( k ) are the state variables of the system, and the two state variables x 1( k ) 、x 2( k ) is expressed as formula (3): ，(3) In formula (3), ω ref is the reference value of the given speed, ω ( k ) is the rotation speed, T s is the sampling period; in addition, s ( k ) is: .

5. The variable gain sliding mode and variable gain robust model predictive control method for a permanent magnet synchronous motor according to claim 1, characterized in that: In step 3, the specific process is: Calculation under different voltage vectors d - q The shaft current change rate is shown in equations (5) to (7): ，(5) ，(6) ，(7) In formula (5) to formula (7), s d0 and s q0 They represent the zero voltage vector, corresponding to d Axis and q The rate of change of the shaft current; u j and u k Respectively represent the effective voltage vector in a specific sector; s dj and s qj Respectively represented in the voltage vector u j Under the action, corresponding d Axis and q The rate of change of the shaft current; s dk and s qk Respectively represented in the voltage vector u k Under the action, corresponding d Axis and q The rate of change of the shaft current; S d ( k )= i dref − i d ( k )and S q ( k )= i qref − i q ( k ) respectively represent d Axis and q Tracking error of shaft current; k d f ( S d ( k ), ϕ )and k q f ( S q ( k ), ϕ ) are defined as d Axis and q The robust term of the axis prediction model, represents the thickness of the boundary layer; 、 、 Represents stator resistance R , stator inductance L and permanent magnet flux ψ f The nominal value of k d1 ≥1, k d2 ≥0, k q1 ≥1, k q2 ≥0, 2>0, 1> ε 2>0,0≤ 2≤1, η 1. η 2 is a small positive constant; 、 、 The stator resistance is R , stator inductance L and permanent magnet flux ψ f The uncertainty range of , their respective expressions are shown in formula (8): ，(8) In formula (8), 、 、 Represents stator resistance R , stator inductance L and permanent magnet flux ψ f The maximum possible value of 、 、 Represents stator resistance R , stator inductance L and permanent magnet flux ψ f The minimum possible value of and are the variable gain terms of the robust term respectively; Calculate the action time of the three voltage vectors in each sector t j 、 t k 、 t 0, the expression is as follows: ，(9) In formula (9), i d ( k )express k time d Shaft current measurement value, i q ( k )express k time q Shaft current measurement value; s d0 and s q0 They represent respectively the zero voltage vector, d Axis and q The rate of change of the shaft current; s dj and s qj Respectively represented in the voltage vector u j Under the effect, d Axis and q The rate of change of the shaft current; s dk and s qk Respectively represented in the voltage vector u k Under the effect, d Axis and q The rate of change of the shaft current; From this, we get three voltage vectors for each sector: u j , u k , u 0 action time t j 、 t k 、 t 0.

6. The variable gain sliding mode and variable gain robust model predictive control method for a permanent magnet synchronous motor according to claim 5, characterized in that: The variable gain term of the robust term takes a large value when the two values ​​are far away from the corresponding robust sliding mode surface, and takes a small value when close to the sliding mode surface.

7. The variable gain sliding mode and variable gain robust model predictive control method for a permanent magnet synchronous motor according to claim 1, characterized in that: In step 4, the specific process is: The voltage vector is selected based on the principle of space vector modulation, which divides the voltage plane into six sectors spaced 60 degrees apart. In each control cycle, the sector where the reference voltage vector is located is first determined, and then two adjacent valid vectors corresponding to the sector are selected and combined with the zero vector to form a composite. When calculating the action time of the voltage vector, it is necessary to determine whether the action time of the voltage vector is within a reasonable range and discard the voltage vector that does not meet the requirements.

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