Permanent magnet synchronous motor model prediction control method and system based on improved sliding mode disturbance observer

Abstract

The invention discloses a permanent magnet synchronous motor model prediction control method based on an improved sliding mode disturbance observer. The method comprises the steps of obtaining a current parameter of a permanent magnet synchronous motor at a k moment and a direct current side capacitor voltage of an inverter; calculating a q-axis predicted current iq (k + 1) at the k + 1 moment based on the current parameter; inputting the q-axis predicted current iq (k + 1) at the k + 1 moment and the current rotating speed omega into a sliding mode disturbance observer for estimation to obtain estimation parameters; performing reference voltage prediction based on a given reference rotating speed omega *, a reference current id * and an estimation parameter to obtain a d-axis reference voltage ud * and a q-axis reference voltage uq *; calculating the neutral-point voltage Uo (k + 1) of the inverter at the k + 1 moment by using a value function, wherein the value function is # imgabs0 #, wherein lambda is a balance weight coefficient; controlling the neutral-point potential of the inverter according to the neutral-point voltage Uo (k + 1) of the inverter at the k + 1 moment and the weight coefficient lambda; and controlling the inverter according to the voltage vector selected by the minimum value function. The invention provides a permanent magnet synchronous motor model prediction control method and system based on an improved sliding mode disturbance observer. The permanent magnet synchronous motor model prediction control method and system have better steady-state performance and dynamic performance.

Description

Technical Field

[0001] The invention relates to the technical field of permanent magnet synchronous motor control, in particular to a permanent magnet synchronous motor model predictive control method and system based on an improved sliding mode disturbance observer. Background Art

[0002] Permanent Magnet Synchronous Motor (PMSM) has attracted wide attention and application in many fields such as new energy vehicles and CNC machine tools due to its advantages of simple structure, high power density, high efficiency and wide speed regulation range. In addition, with the development of advanced manufacturing technologies such as high-speed machining technology, precision manufacturing technology and CNC technology, high speed, high efficiency and high precision have become the major development direction of current motor control.

[0003] As an important energy conversion facility that converts DC power into AC power to power the induction motor, the inverter is an indispensable component in motor control. For medium and high voltage drive systems, two-level inverters have disadvantages such as low withstand voltage and large switching losses, which cannot meet the use of medium and high voltage drive systems. Unlike two-level inverters, three-level inverters have the advantages of low harmonic generation, low voltage conversion rate, and high withstand voltage, so they are widely used in medium and high voltage drive systems.

[0004] Model predictive control (MPC) is an optimal control method based on the model of the controlled object. It has the advantages of simple control, flexibility and easy handling of nonlinear constraints, and has therefore attracted widespread attention from scholars at home and abroad in the field of motor control. The main way to control a permanent magnet synchronous motor based on the model predictive control method is: in the motor control process, the optimal voltage vector can be inferred through the discretized motor model, and then the inverter output voltage state closest to the optimal voltage vector is found through the ergodic method to calculate the set objective function, and the inverter is controlled to complete the motor control.

[0005] Model predictive control is highly sensitive to motor parameters. As a nonlinear time-varying system, the motor parameters will change with the running time and the external environment, resulting in prediction errors. When the sliding mode disturbance observer is used to compensate for the prediction error, the use of the switching function in the traditional exponential reaching law is the root cause of the jitter. Summary of the invention

[0006] In order to solve the deficiencies in the prior art, the present invention provides a permanent magnet synchronous motor model predictive control method and system based on an improved sliding mode disturbance observer, which has better steady-state performance and dynamic performance.

[0007] In order to achieve the above object, the specific scheme adopted by the present invention is: a permanent magnet synchronous motor model predictive control method based on an improved sliding mode disturbance observer, comprising the following steps: Get the current parameters of the permanent magnet synchronous motor and the DC side capacitor voltage of the inverter at time k. The current parameters include the current three-phase voltage U abc 、Current three-phase current I abc , the current speed ω and the current angle θ, the DC side capacitor voltage includes the first capacitor voltage U c1 and the second capacitor voltage U c2 ; Calculate the q-axis predicted current i at time k+1 based on the current three-phase voltage, current three-phase current and current angle q (k+1); The q-axis predicted current i at time k+1 q (k+1) and the current speed ω are input into the sliding mode disturbance observer to estimate the estimated parameters. The estimated parameters include the estimated speed D-axis equivalent current q-axis equivalent disturbance and d-axis equivalent disturbance Based on the given reference speed ω * , reference current i d * And the estimated parameters are used to predict the reference voltage, and the d-axis reference voltage and q-axis reference voltage u are obtained. q * ; The vector control is concentrated on the d-axis alternative voltage at time k and q-axis alternative voltage And the d-axis reference voltage u d * , q-axis reference voltage u q * , and according to the first capacitor voltage U c1 and the second capacitor voltage U c2 Predicted inverter midpoint voltage U o (k+1) is input into the cost function, and the alternative optimal voltage vector is calculated using the cost function. The cost function is: Where λ is the balance weight coefficient; According to the inverter midpoint voltage U at time k+1 o (k+1) and the weight coefficient are used to control the midpoint potential of the inverter.

[0008] As a further optimization of the above-mentioned permanent magnet synchronous motor model predictive control method based on the improved sliding mode disturbance observer: the specific method of calculating the optimal voltage vector using the value function is: Solve the cost function and obtain the optimal voltage vector by minimizing the cost function result.

[0009] As a further optimization of the above-mentioned permanent magnet synchronous motor model predictive control method based on the improved sliding mode disturbance observer: the q-axis predicted current is equivalent to: Among them, T s is the sampling period; i d (k) is the d-axis component of the stator current at time k, i q (k) is the q-axis component of the stator current at time k, R is the stator resistance, L is the stator inductance, ψ f is the permanent magnet flux, P is the number of pole pairs, T e is the electromagnetic torque.

[0010] As a further optimization of the above-mentioned permanent magnet synchronous motor model predictive control method based on the improved sliding mode disturbance observer: based on the given reference speed ω * , reference current i d * The specific method for predicting the reference voltage using the estimated parameters is: in, is the equivalent disturbance of the d-axis at time k+1, is the equivalent disturbance of the q axis at time k+1, J is the moment of inertia, T sp is the sampling period of the motion quantity in the formula.

[0011] As a further optimization of the above-mentioned permanent magnet synchronous motor model predictive control method based on the improved sliding mode disturbance observer: sp =mT s , where the value range of m is [9,11].

[0012] As a further optimization of the above-mentioned permanent magnet synchronous motor model predictive control method based on the improved sliding mode disturbance observer: according to the inverter midpoint voltage U at time k+1 o (k+1) The specific method for controlling the midpoint voltage of the inverter is: Where C is the DC side capacitance, S x Indicates the corresponding value of the switching state of the inverter, i x is the x-phase output current.

[0013] A permanent magnet synchronous motor control system is used to implement the above-mentioned permanent magnet synchronous motor model predictive control method based on an improved sliding mode disturbance observer, and the system includes:

[0014] A data acquisition module is used to obtain the current parameters of the permanent magnet synchronous motor and the DC side capacitor voltage of the inverter at time k;

[0015] The main control module is used to calculate the q-axis predicted current i at time k+1 q (k+1), estimate the estimated parameters, predict the reference voltage, and calculate the inverter midpoint voltage U at time k+1 o (k+1);

[0016] The control output module is used to control the inverter according to the optimal voltage vector selected by the value function.

[0017] Beneficial effects: The model predictive control of the present invention takes the midpoint voltage deviation into consideration, and can control the midpoint voltage deviation of the inverter within the required range; the sliding mode disturbance observer of the present invention adopts a new reaching law designed based on the exponential reaching law, which can smoothly cross the zero point, ensuring that both the steady-state performance and the dynamic performance of the system are maintained good; the present invention takes into account the effect of boundary layer thickness on the dynamic performance and jitter of sliding mode approaching motion, designs fuzzy rules based on the errors between the current and speed observed by the sliding mode disturbance observer and the actual current and speed, and uses a fuzzy controller to adaptively control the boundary layer thickness, ensuring that the system has smaller jitter, reducing the steady-state error of the system while having good dynamic performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is the main circuit topology diagram of the permanent magnet synchronous motor system; Figure 2 It is a schematic diagram of the control method of the present invention; Figure 3 It is a specific control block diagram of the model predictive control of the present invention; Figure 4 is an input membership function diagram of the fuzzy controller of the present invention; Figure 5 is an output membership function diagram of the fuzzy controller of the present invention; Figure 6 It is a block diagram of the disturbance observer d-axis control of the present invention; Figure 7 is a q-axis control block diagram of a disturbance observer of the present invention; Figure 8 It is a hybrid reaching law switching function diagram of the present invention; Fig. 9 It is a flow chart of the permanent magnet synchronous motor model predictive control method of the improved sliding mode disturbance observer of the present invention; Fig.10 It is a speed simulation effect diagram using the method of the present invention; Fig.11 It is a torque simulation effect diagram using the method of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] like Figure 1-3 As shown, first, in the surface-mounted permanent magnet synchronous motor, its d-axis inductance is equal to the q-axis inductance, that is, L d =L q =L. In the dq coordinate system, the mathematical model of the permanent magnet synchronous motor can be expressed as: where u d 、u q are the dq axis components of the stator voltage, i d 、i q are the dq axis components of the stator current, R is the stator resistance, L is the stator inductance, ψ f is the permanent magnet flux, ω is the electrical angular velocity, p is the number of pole pairs, T e is the electromagnetic torque.

[0021] Furthermore, the motion equation of the motor can be expressed as: Where T l is the load torque, B is the damping coefficient, and J is the moment of inertia.

[0022] According to the deadbeat prediction principle, the voltage equation and motion equation are discretized using the forward Euler method, and we can get:

[0023] In order to better track the reference speed and reference current at the next moment, the predicted speed ω(k+1) is used as the reference speed ω according to the deadbeat control principle. * , the d-axis predicted current i d (k+2) as reference current Combining equation (4) and equation (5), we can get the reference voltage equation: in and are the d-axis and q-axis components of the reference voltage respectively. Strategy.

[0024] When the motor is disturbed or runs for a long time, resulting in parameter mismatch, the voltage equation of the permanent magnet synchronous motor on the dq axis can be expressed as: where f d 、f q and are the equivalent disturbances when the parameters are mismatched, F d and F q f d and f q The rate of change of in, and f d and f q An estimated value of and are the estimated values ​​of d-axis current and speed respectively; G d and G q is the sliding mode parameter; U dsmo and U qsmo is the sliding mode control function.

[0025] Selecting the electrical angular velocity ω and the d-axis current estimation error as the linear sliding surface, we can get

[0026] In order to improve the dynamic performance of the system, the present invention designs a mixed reaching law containing a boundary layer: Where γ is the boundary layer thickness, σ(s,γ) is as follows Figure 8 shown.

[0027] Furthermore, there are

[0028] The difference between the observed value and the actual value of the sliding mode disturbance observer is used as the input variable of the fuzzy controller, and the boundary layer thickness γ is used as the output variable. The error between the observed value and the actual value of the d-axis current is defined as the input variable of the fuzzy controller, the domain of the d-axis current error is [-7,7], and the domain of the boundary layer thickness γ is [0,1.5]; the error between the observed value and the actual value of the speed ω is defined as the input variable of the q-axis fuzzy controller, the domain of the speed error is [-8,8], and the domain of the boundary layer thickness is [0,1.5]. The designed fuzzy rules are shown in Table 1:

[0029] Table 1 Fuzzy rules table

[0030] The Mamdani algorithm is used to fuzzify the input variables, and the Centroid algorithm is used to defuzzify them. The membership functions of the speed error and the q-axis boundary layer thickness are as follows: Figure 4 , Figure 5 As shown:

[0031] According to the selected sliding surface (9) and reaching law formula (10), the discrete equation of the reaching law can be obtained:

[0032] The error equation is obtained by subtracting equation (8) from equation (7): in e fd is the estimated error of the d-axis; e fq is the estimated error of the q-axis.

[0033] Combining equation (12) and equation (13), we can get

[0034] The discrete form of the sliding mode disturbance observer can be expressed as:

[0035] The structural block diagrams of the sliding mode disturbance observer are as follows: Figure 6 , Figure 7 shown.

[0036] In order to ensure the stability of the designed sliding mode disturbance observer, appropriate sliding mode parameters should be selected. According to the Lyapunov stability criterion, the stability condition for the sliding mode disturbance observer should be That should satisfy:

[0037] When the error is within the boundary layer, substituting into equation (10) yields the following Lyapunov first-order differential equation:

[0038] Since k1 and k2 are both greater than zero, is less than zero; when the error is outside the boundary layer, It is also always less than zero, satisfying the Lyapunov stability criterion.

[0039] From the above analysis, it can be seen that when the parameters are properly selected, the sliding mode disturbance observer is asymptotically stable. The error caused by parameter mismatch can be feedforward compensated to achieve accurate prediction of the reference voltage. The compensated reference voltage is shown in formula (18):

[0040] Based on the above theory, Figure 1 As shown, in the permanent magnet synchronous motor drive circuit, the inverter circuit includes two equalizing capacitors and an inverter bridge arm. The first equalizing capacitor C1 connects the positive pole of the DC power supply to the neutral point, and the second equalizing capacitor C2 connects the neutral point to the negative pole of the DC power supply. The inverter bridge arm includes 12 fully controlled switching devices and 6 clamping diodes. Among them, the cathode ends of the clamping diodes D1, D3, and D5 are connected to the emitters of the fully controlled switching devices S1, S5, and S9, and the anode ends are connected to the neutral line; the cathode ends of the clamping diodes D2, D4, and D6 are connected to the neutral line, and the anode ends are connected to the emitters of the fully controlled switching devices S3, S7, and S11.

[0041] The sensors include voltage sensors, current sensors and speed sensors, which are used to detect the stator voltage, stator current and speed of the permanent magnet synchronous motor respectively. The sensors output the collected signals to the controller for the controller to control according to the signals. The output end of the controller is connected to the drive circuit to control the diode clamped three-phase three-level inverter circuit.

[0042] According to the structure of the above-mentioned drive circuit, the present invention first provides a permanent magnet synchronous motor model predictive control method based on an improved sliding mode disturbance observer, including S1 to S6.

[0043] S1. Obtain the current parameters of the permanent magnet synchronous motor and the DC side capacitor voltage of the inverter at time k. The current parameters include the current three-phase voltage U abc 、Current three-phase current I abc , the current speed ω and the current angle θ, the DC side capacitor voltage includes the first capacitor voltage U c1 and the second capacitor voltage U c2 .

[0044] S2, calculate the q-axis predicted current i at time k+1 based on the current three-phase voltage, current three-phase current and current angle q (k+1). The q-axis predicted current is equivalent to: Among them, T s is the sampling period; i d (k) is the d-axis component of the stator current at time k, i q (k) is the q-axis component of the stator current at time k, R is the stator resistance, L is the stator inductance, ψ f is the permanent magnet flux, P is the number of pole pairs, T e is the electromagnetic torque.

[0045] S3, the q-axis predicted current i at time k+1 q (k+1) and the current speed ω are input into the sliding mode disturbance observer to estimate the estimated parameters. The estimated parameters include the estimated speed D-axis equivalent current q-axis equivalent disturbance and d-axis equivalent disturbance

[0046] S4, based on the given reference speed ω * , reference current i d * And the estimated parameters are used to predict the reference voltage and obtain the d-axis reference voltage u q * and q-axis reference voltage u d * Based on the given reference speed ω * , reference current i d * The specific method for predicting the reference voltage using the estimated parameters is: in, is the equivalent disturbance of the d-axis at time k+1, is the equivalent disturbance of the q axis at time k+1, J is the moment of inertia, T sp is the sampling period of the motion quantity in the formula. sp =mT s , where the value range of m is [9,11]. In this embodiment, m is 10.

[0047] S5. Focus the vector control on the d-axis alternative voltage at time k and q-axis alternative voltage And the d-axis reference voltage u d * , q-axis reference voltage u q * , and according to the first capacitor voltage U c1 and the second capacitor voltage U c2 Predicted inverter midpoint voltage U o (k+1) is input into the cost function, and the alternative optimal voltage vector is calculated using the cost function. The cost function is: Where λ is the balance weight coefficient.

[0048] S6, according to the inverter midpoint voltage U at time k+1 o (k+1) and the weight coefficient to control the inverter midpoint potential. According to the inverter midpoint voltage U at time k+1 o (k+1) The specific method for controlling the midpoint potential of the inverter is: Where C is the DC side capacitance, S xIndicates the corresponding value of the switching state of the inverter, i x is the x-phase output current.

[0049] More specifically, the specific method for calculating the optimal voltage vector using the value function is: solving the value function, and obtaining the optimal voltage vector by minimizing the result of the value function.

[0050] The control method of the present invention is verified by simulation below, and the simulation parameters are shown in Table 2.

[0051] Table 2 Simulation parameters

[0052] The design simulation conditions are no-load start, given speed of 500rpm, the speed suddenly increases to 1000rpm at 0.3s, and the load of 10Ngm is suddenly added at 0.6s. The simulation results are as follows Fig.10 and 11 As shown in the figure. It can be seen from the figure that when the speed reaches stability, the steady-state error is 1rpm; when the given speed is suddenly increased to 1000rpm at 0.3s, the speed reaches the given speed at 0.31s, and the response is fast; when the load is suddenly increased by 10Ngm at 0.6s, the speed drops by 10rpm, only 1%, and returns to steady state at 0.61s. When the motor is running stably, the torque pulsation is only about 0.7Nm. The motor has a fast dynamic response, small steady-state error, and strong anti-interference ability.

[0053] The present invention further provides a permanent magnet synchronous motor control system for realizing the above-mentioned permanent magnet synchronous motor model predictive control method based on improved sliding mode disturbance observer. The system includes a data acquisition module, a main control module and a control output module.

[0054] A data acquisition module is used to obtain the current parameters of the permanent magnet synchronous motor and the DC side capacitor voltage of the inverter at time k;

[0055] The main control module is used to calculate the q-axis predicted current i at time k+1 q (k+1), estimate the estimated parameters, predict the reference voltage, and calculate the inverter midpoint voltage U at time k+1 o (k+1);

[0056] The control output module is used to control the inverter according to the optimal voltage vector selected by the minimum value function.

[0057] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A permanent magnet synchronous motor model predictive control method based on an improved sliding mode disturbance observer, characterized in that: The steps include: Get the current parameters of the permanent magnet synchronous motor and the DC side capacitor voltage of the inverter at time k. The current parameters include the current three-phase voltage U abc 、Current three-phase current I abc , the current speed ω and the current angle θ, the DC side capacitor voltage includes the first capacitor voltage U c1 and the second capacitor voltage U c2 ; Calculate the q-axis predicted current i at time k+1 based on the current three-phase voltage, current three-phase current and current angle q (k+1); The q-axis predicted current i at time k+1 q (k+1) and the current speed ω are input into the sliding mode disturbance observer to estimate the estimated parameters. The estimated parameters include the estimated speed D-axis equivalent current q-axis equivalent disturbance and d-axis equivalent disturbance Based on the given reference speed ω * , reference current i d * And the estimated parameters are used to predict the reference voltage and obtain the d-axis reference voltage u d * and q-axis reference voltage u q * ; The vector control is concentrated on the d-axis alternative voltage at time k and q-axis alternative voltage And the d-axis reference voltage u d * , q-axis reference voltage u q * , and according to the first capacitor voltage U c1 and the second capacitor voltage U c2 Predicted inverter midpoint voltage U o (k+1) is input into the cost function, and the alternative optimal voltage vector is calculated using the cost function. The cost function is: Where λ is the balance weight coefficient; According to the inverter midpoint voltage U at time k+1 o (k+1) and the weight coefficient are used to control the midpoint potential of the inverter.

2. The permanent magnet synchronous motor model predictive control method based on the improved sliding mode disturbance observer according to claim 1 is characterized in that: The specific method of calculating the optimal voltage vector using the value function is: Solve the cost function and obtain the alternative optimal voltage vector by minimizing the cost function result.

3. The permanent magnet synchronous motor model predictive control method based on improved sliding mode disturbance observer according to claim 1, characterized in that: The q-axis predicted current is equivalent to: Among them, T s is the sampling period; i d (k) is the d-axis component of the stator current at time k, i q (k) is the q-axis component of the stator current at time k, R is the stator resistance, L is the stator inductance, ψ f is the permanent magnet flux, P is the number of pole pairs, T e is the electromagnetic torque.

4. The permanent magnet synchronous motor model predictive control method based on improved sliding mode disturbance observer as claimed in claim 3, characterized in that: Based on the given reference speed ω * , reference current i d * The specific method for predicting the reference voltage using the estimated parameters is: in, is the equivalent disturbance of the d-axis at time k+1, is the equivalent disturbance of the q axis at time k+1, J is the moment of inertia, T sp is the sampling period of the motion quantity in the formula.

5. The permanent magnet synchronous motor model predictive control method based on improved sliding mode disturbance observer according to claim 4, characterized in that: T sp =mT s , where the value range of m is [9,11].

6. The permanent magnet synchronous motor model predictive control method based on improved sliding mode disturbance observer according to claim 1, characterized in that: According to the inverter midpoint voltage U at time k+1 o (k+1) The specific method for controlling the midpoint potential of the inverter is: Where C is the DC side capacitance, S x Indicates the corresponding value of the switching state of the inverter, i x is the x-phase output current.

7. Permanent magnet synchronous motor control system, characterized in that: A system for implementing a permanent magnet synchronous motor model predictive control method based on an improved sliding mode disturbance observer as claimed in any one of claims 1 to 6, the system comprising: A data acquisition module is used to obtain the current parameters of the permanent magnet synchronous motor and the DC side capacitor voltage of the inverter at time k; The main control module is used to calculate the q-axis predicted current i at time k+1 q (k+1), estimate the estimated parameters, predict the reference voltage, and calculate the inverter midpoint voltage U at time k+1 o (k+1); The control output module is used to control the inverter according to the optimal voltage vector selected by the calculated minimum value function.

Images