A method, system and medium for improving robust performance of predictive control of permanent magnet motor

Abstract

This invention discloses a method, system, and medium for improving the robustness of predictive control of permanent magnet motors. The method includes calculating the cumulative torque error S at time k+1. t (k+1) and cumulative flux error S f (k+1); Predicting the cumulative torque error S at time k+2 t (k+2) and cumulative flux linkage error S f (k+2) The torque value function J1 and flux linkage value function J2 have been generated respectively. In the voltage vector space, the four voltage vectors that minimize the values ​​of torque value function J1 and flux linkage value function J2 are selected, and the common voltage vector among them is further selected as the final output voltage vector used to control the permanent magnet synchronous motor. This invention can improve the robustness of the system under the condition of mismatch in the resistance, inductance, and flux linkage parameters of the permanent magnet synchronous motor, and removes the weighting factor, thereby improving control efficiency.

Description

Technical Field

[0001] This invention relates to the field of control technology for permanent magnet synchronous motors, and specifically to a method, system, and medium for improving the predictive control robustness of permanent magnet motors. Background Technology

[0002] In recent years, model predictive control has been increasingly widely used in the fields of power converters and motor systems due to its flexibility, simplicity, and ability to handle nonlinear multivariable systems in the discrete-time domain. Model predictive control can be divided into two categories: (1) continuous control set model predictive control; (2) finite control set model predictive control. The basic principle of finite set model predictive control for permanent magnet synchronous motors is: based on the predictive model that includes the mathematical model of the inverter, the future torque or current response of the motor under a finite number of switching states is derived, and the future response corresponding to each switching state is evaluated through the value function, so as to select the optimal switching state to control the motor. The model is one of the most important members in finite control set model predictive control. The reference variables in the predictive torque control value function are electromagnetic torque and stator flux amplitude. Predictive torque control is mainly affected by parameter mismatch. However, parameter mismatch and model uncertainty are common in practice. Under such undesirable conditions, the control performance will deteriorate. The challenges of parameter mismatch and model uncertainty in predictive torque control are attracting more and more attention. At the same time, the weighting factor is also a major problem faced by predictive torque control. At present, there are several methods to solve the weighting factor problem. First, weighting factors are calculated using complex algorithms. Second, the weighting factors are removed by processing the cost function accordingly. Currently, commonly used methods to improve the robustness of predictive torque control mainly fall into three categories: observer-based, model-free, and parameter identification. Implementing an observer-based method introduces a higher computational burden and more complex control strategies. Model-free methods also have a large computational load. Parameter identification methods also suffer from high computational costs, and their control strategies are relatively complex. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method, system and medium for improving the robustness of predictive control of permanent magnet motors, which addresses the above-mentioned problems of the prior art. The present invention can improve the robustness of the system when the resistance, inductance and flux linkage parameters of the permanent magnet synchronous motor are mismatched.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0005] A method for improving the robustness of predictive control of a permanent magnet motor includes:

[0006] S101, Calculate the cumulative torque error S at time k+1. t (k+1) and cumulative flux error S f (k+1);

[0007] S102, based on the cumulative torque error S at time k+1 t (k+1) and cumulative flux error S f (k+1), predicting the cumulative torque error S at time k+2. t (k+2) and cumulative flux linkage error S f (k+2);

[0008] S103, based on the cumulative torque error S at time k+2. t (k+2) and cumulative flux linkage error S f (k+2) Generates the torque value function J1 and the flux linkage value function J2;

[0009] S104, in the voltage vector space, select the first four voltage vectors that minimize the value of the torque value function J1 and the first four voltage vectors that minimize the value of the flux linkage value function J2 respectively;

[0010] S105: Select the common voltage vector from the four voltage vectors that minimize the torque value function J1 and the four voltage vectors that minimize the flux linkage value function J2 as the final output voltage vector for controlling the permanent magnet synchronous motor.

[0011] Optionally, the cumulative torque error S at time k+1 in step S101 t (k+1) and cumulative flux error S f The expression for the computation function of (k+1) is:

[0012] S t (k+1)=S t (k)+(e t (k+1)-e t (k))+K t e t (k+1)T s ,

[0013] S f (k+1)=S f (k)+(e f (k+1)-e f (k))+K f e f (k+1)T s ,

[0014] In the above formula, S t (k) and S f (k) represents the cumulative torque error and cumulative flux linkage error at time k, respectively. t (k+1) and e f(k+1) represent the torque and flux linkage error at time k+1, respectively, e t (k) and e f (k) represents the torque and flux linkage error at time k, respectively. t and K f T is the coefficient of the integral term. s The sampling period.

[0015] Optionally, in step S102, the cumulative torque error S at time k+2 is predicted. t (k+2) and cumulative flux linkage error S f The function expression for (k+2) is:

[0016] S t (k+2)=S t (k+1)+(e t (k+2)-e t (k+1))+K t e t (k+2)T s ,

[0017] S f (k+2)=S f (k+1)+(e f (k+2)-e f (k+1))+K f e f (k+2)T s ,

[0018] In the above formula, e t (k+2) and e f (k+2) represents the torque and flux linkage error at time k+2, respectively.

[0019] Optionally, the calculation function expressions for the torque and flux linkage errors are as follows:

[0020]

[0021]

[0022] In the above formula, e t (k) and e f (k) represents the torque and flux linkage error at time k, respectively. and T represents the reference values ​​for torque and flux linkage, respectively. e (k) and ψ s (k) represents the torque and flux linkage at time k.

[0023] Optionally, the calculation function expression for the torque and flux linkage at the current time k is:

[0024] T e (k)=1.5n p [ψ f i q (k)+(L d -L q )i q (k)i d (k)],

[0025]

[0026] In the above formula, n p Let ψ be the number of pole pairs of the permanent magnet synchronous motor. f For permanent magnet flux linkage, i q (k) and i q (k) represents the predicted dq-axis current value at time k, L d and L q These are the d-axis and q-axis inductances, respectively.

[0027] Optionally, the calculation function expression for the predicted dq-axis current is:

[0028]

[0029]

[0030] In the above formula, i q (k+1) and i q (k+1) represent the predicted dq-axis current values ​​at time k+1, i q (k) and i q (k) represents the predicted dq-axis current value at time k, and T s For the sampling period, L d and L q These are the d-q axis inductances, and u... d (k) and u q (k) is the dq-axis voltage, R s For the stator resistance, ω r Let ψ be the electric angular velocity. f It is a permanent magnet flux linkage.

[0031] Optionally, the functional expression for generating the torque value function J1 in step S103 is:

[0032] J1=(S t (k+2)) 2 ,

[0033] In the above formula, S t (k+2) represents the cumulative torque error at time k+2.

[0034] Optionally, the functional expression for generating the magnetic flux linkage value function J2 in step S103 is:

[0035] J2=(S f (k+2)) 2 ,

[0036] In the above formula, S f (k+2) represents the cumulative flux linkage error at time k+2.

[0037] Furthermore, the present invention also provides a system for improving the robustness of predictive control of a permanent magnet motor, comprising a microprocessor and a memory interconnected thereto, the microprocessor being programmed or configured to perform the method for improving the robustness of predictive control of a permanent magnet motor.

[0038] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program for being programmed or configured by a microprocessor to perform the method for improving the robustness of predictive control of a permanent magnet motor.

[0039] Compared with the prior art, the present invention has the following main advantages:

[0040] 1. This invention utilizes the torque cumulative error S based on time k+2. t (k+2) and cumulative flux linkage error S f (k+2) generates the torque value function J1 and the flux linkage value function J2, which not only considers the error at the current moment, but also accumulates the error at past time and weights it over time, thereby improving the robustness of the system parameters without interfering with the observer.

[0041] 2. This invention involves selecting the four voltage vectors that minimize the torque value function J1 and the four voltage vectors that minimize the flux linkage value function J2 in the voltage vector space. The common voltage vector among these four voltage vectors is then selected as the final output voltage vector used to control the permanent magnet synchronous motor. Through this parallel approach, the value function is divided into two separate value functions: torque and flux linkage. Simultaneously, both torque and flux linkage are optimized, selecting the four voltage vectors with the minimum torque and flux linkage value functions respectively. The common voltage vector is then taken as the final voltage vector, thereby eliminating the weighting factor, avoiding the complex design process of the weighting factor, and improving control efficiency. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the basic process of the method in an embodiment of the present invention.

[0043] Figure 2This is a schematic diagram illustrating the control principle of the method in an embodiment of the present invention.

[0044] Figure 3 This is a simulation waveform of the A-phase current of the permanent magnet synchronous motor in an embodiment of the present invention when there is a two-fold parameter mismatch.

[0045] Figure 4 This is a simulation waveform diagram of the permanent magnet synchronous motor at twice the parameter mismatch speed in an embodiment of the present invention.

[0046] Figure 5 This is a simulation waveform diagram of the permanent magnet synchronous motor under two times parameter mismatch torque in an embodiment of the present invention. Detailed Implementation

[0047] like Figure 1 and Figure 2 As shown, the method for improving the robustness of predictive control of a permanent magnet motor in this embodiment includes:

[0048] S101, Calculate the cumulative torque error S at time k+1. t (k+1) and cumulative flux error S f (k+1);

[0049] S102, based on the cumulative torque error S at time k+1 t (k+1) and cumulative flux error S f (k+1), predicting the cumulative torque error S at time k+2. t (k+2) and cumulative flux linkage error S f (k+2);

[0050] S103, based on the cumulative torque error S at time k+2. t (k+2) and cumulative flux linkage error S f (k+2) Generates the torque value function J1 and the flux linkage value function J2;

[0051] S104, in the voltage vector space, select the first four voltage vectors that minimize the value of the torque value function J1 and the first four voltage vectors that minimize the value of the flux linkage value function J2 respectively;

[0052] S105: Select the common voltage vector from the four voltage vectors that minimize the torque value function J1 and the four voltage vectors that minimize the flux linkage value function J2 as the final output voltage vector for controlling the permanent magnet synchronous motor.

[0053] In this embodiment, the cumulative torque error S at time k+1 in step S101 t (k+1) and cumulative flux error S f The expression for the computation function of (k+1) is:

[0054] S t (k+1)=S t (k)+(e t (k+1)-e t (k))+K t e t (k+1)T s ,

[0055] S f (k+1)=S f (k)+(e f (k+1)-e f (k))+K f e f (k+1)T s ,

[0056] In the above formula, S t (k) and S f (k) represents the cumulative torque error and cumulative flux linkage error at time k, respectively. t (k+1) and e f (k+1) represent the torque and flux linkage error at time k+1, respectively, e t (k) and e f (k) represents the torque and flux linkage error at time k, respectively. t and K f T is the coefficient of the integral term. s The sampling period.

[0057] Correspondingly, in step S102 of this embodiment, the cumulative torque error S at time k+2 is predicted. t (k+2) and cumulative flux linkage error S f The function expression for (k+2) is:

[0058] S t (k+2)=S t (k+1)+(e t (k+2)-e t (k+1))+K t e t (k+2)T s ,

[0059] S f (k+2)=S f (k+1)+(e f (k+2)-e f (k+1))+K f e f (k+2)T s ,

[0060] In the above formula, et (k+2) and e f (k+2) represents the torque and flux linkage error at time k+2, respectively.

[0061] S t (k)=S t (k-1)+(e t (k)-e t (k-1))+K t e t (k)T s ,

[0062] S f (k)=S f (k-1)+(e f (k)-e f (k-1))+K f e f (k)T s .

[0063] In this embodiment, the calculation function expressions for torque and flux linkage error are as follows:

[0064]

[0065]

[0066] In the above formula, e t (k) and e f (k) represents the torque and flux linkage error at time k, respectively. and T represents the reference values ​​for torque and flux linkage, respectively. e (k) and ψ s (k) represents the torque and flux linkage at time k, respectively. For different times, we have:

[0067]

[0068]

[0069]

[0070]

[0071] And so on.

[0072] In this embodiment, the calculation function expressions for the torque and flux linkage at the current time k are as follows:

[0073] T e (k)=1.5n p [ f i q (k)+(Ld -L q )i q (k)i d (k)],

[0074]

[0075] In the above formula, n p Let ψ be the number of pole pairs of the permanent magnet synchronous motor. f For permanent magnet flux linkage, i q (k) and i q (k) represents the predicted dq-axis current value at time k, L d and L q These are the d-axis and q-axis inductances, respectively.

[0076] In this embodiment, the calculation function expression for the predicted dq-axis current is:

[0077]

[0078]

[0079] In the above formula, i q (k+1) and i q (k+1) represent the predicted dq-axis current values ​​at time k+1, i q (k) and i q (k) represents the predicted dq-axis current value at time k, and T s For the sampling period, L d and L q These are the d-q axis inductances, and u... d (k) and u q (k) is the dq-axis voltage, R s For the stator resistance, ω r Let ψ be the electric angular velocity. f It is a permanent magnet flux linkage.

[0080] In this embodiment, the functional expression for generating the torque value function J1 in step S103 is as follows:

[0081] J1=(S t (k+2)) 2 ,

[0082] In the above formula, S t (k+2) represents the cumulative torque error at time k+2.

[0083] In this embodiment, the functional expression for generating the magnetic flux linkage value function J2 in step S103 is as follows:

[0084] J2=(S f (k+2)) 2,

[0085] In the above formula, S f (k+2) represents the cumulative flux linkage error at time k+2.

[0086] See Figure 2 In this embodiment, a parallel structure is used to address the cumulative torque error S at time k+2. t (k+2) and cumulative flux linkage error S f (k+2) is processed, including: the cumulative torque error S based on time k+2. t (k+2) and cumulative flux linkage error S f (k+2) Generate the torque value function J1 and the flux linkage value function J2; select the four voltage vectors in the voltage vector space that minimize the torque value function J1 and the four voltage vectors that minimize the flux linkage value function J2, respectively. Finally, select the common voltage vector among the four voltage vectors that minimize the torque value function J1 and the four voltage vectors that minimize the flux linkage value function J2 as the final output voltage vector used to control the permanent magnet synchronous motor.

[0087] Figure 3 This is a simulation waveform of the A-phase current of the permanent magnet synchronous motor in this embodiment under a two-fold parameter mismatch. Figure 3 It can be seen that the harmonic content of the A-phase current is relatively small when the parameters are mismatched. Figure 4 The following is a simulation waveform of the permanent magnet synchronous motor at twice the parameter mismatch speed in this implementation, provided by... Figure 4 It can be seen that even with mismatched parameters, the rotational speed can remain stable at the rated speed of 1500 r / min with minimal pulsation. Figure 5 The following is a simulation waveform of the permanent magnet synchronous motor under double parameter mismatch torque in this implementation. Figure 5 It can be seen that the torque ripple value is very small even with parameter mismatch, which effectively improves robustness.

[0088] In summary, the method in this embodiment designs a novel proportional-integral (PI) form value function that considers not only the error at the current moment but also accumulates errors from past times and weights them over time. This strategy improves the robustness of system parameters without interfering observers. Then, through a parallel structure, the value function is divided into two separate value functions: torque and flux linkage. Both torque and flux linkage are optimized simultaneously, selecting the four voltage vectors with the smallest torque and flux linkage value function values ​​respectively. The common voltage vector is then taken as the final voltage vector (in the absence of a common voltage vector, the voltage vector that minimizes the torque error is selected), thus eliminating the weighting factor. This embodiment enhances the robustness of the system when system parameters are mismatched and avoids the complex design process of weighting factors, thereby improving control efficiency.

[0089] Furthermore, this embodiment also provides a system for improving the robustness of predictive control of a permanent magnet motor, including a microprocessor and a memory interconnected, wherein the microprocessor is programmed or configured to execute the aforementioned method for improving the robustness of predictive control of a permanent magnet motor. Additionally, this embodiment also provides a computer-readable storage medium storing a computer program for being programmed or configured by the microprocessor to execute the aforementioned method for improving the robustness of predictive control of a permanent magnet motor.

[0090] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for improving the robustness of predictive control of a permanent magnet motor, characterized in that, include: S101, Calculate the cumulative torque error at time k+1. Cumulative error of magnetic flux ; S102, Accumulated torque error based on time k+1 Cumulative error of magnetic flux Predicting the cumulative error of torque at time k+2 Cumulative error of magnetic flux : ， ， In the above formula, and Let n be the torque and flux linkage error at time k+2, respectively. and Let n be the torque and flux linkage error at time k+1, respectively. and The coefficient of the integral term, The sampling period; S103, based on the cumulative torque error at time k+2. Cumulative error of magnetic flux Generating torque value function J 1 and magnetic flux linkage value function J 2: ， ； S104, in the voltage vector space, respectively select functions that enable torque value functions. J The four voltage vectors with the smallest 1 value, and the flux linkage value function J The four voltage vectors with the smallest 2 values; S105, making the torque value function J The four voltage vectors with the smallest 1 value and the flux linkage value function J The four voltage vectors with the smallest 2 values ​​are selected as the common voltage vector and used as the final output voltage vector to control the permanent magnet synchronous motor.

2. The method for improving the robustness of predictive control of a permanent magnet motor according to claim 1, characterized in that, Accumulated torque error at time k+1 in step S101 Cumulative error of magnetic flux The expression for the computation function is: ， ， In the above formula, and These represent the cumulative torque error and cumulative flux linkage error at time k, respectively. and Let n be the torque and flux linkage error at time k+1, respectively. and Let n be the torque and flux linkage error at time k, respectively. and The coefficient of the integral term, The sampling period.

3. The method for improving the robustness of predictive control of a permanent magnet motor according to claim 2, characterized in that, The calculation function expressions for the torque and flux linkage error are as follows: ， ， In the above formula, and Let n be the torque and flux linkage error at time k, respectively. and These are reference values ​​for torque and flux linkage, respectively. and Let $\mathbf$ be the torque and $\mathbf$ at time $k$, respectively.

4. The method for improving the robustness of predictive control of a permanent magnet motor according to claim 3, characterized in that, The calculation function expressions for the torque and flux linkage at the current time k are as follows: ， ， In the above formula, This represents the number of pole pairs in a permanent magnet synchronous motor. It is a permanent magnet flux chain. and These are the predicted dq-axis current values ​​at time k, respectively. and These are the d-axis and q-axis inductances, respectively.

5. The method for improving the robustness of predictive control of a permanent magnet motor according to claim 4, characterized in that, The calculation function expression for the predicted dq-axis current is: ， ， In the above formula, and These are the predicted dq-axis current values ​​at time k+1. and These are the predicted dq-axis current values ​​at time k, respectively. The sampling period is and These are the d-q axis inductors, and This is the dq-axis voltage. For stator resistance, Electric angular velocity, It is a permanent magnet flux linkage.

6. A system for improving the robustness of predictive control of a permanent magnet motor, comprising a microprocessor and a memory interconnected, characterized in that, The microprocessor is programmed or configured to perform the method for improving the robustness of predictive control of a permanent magnet motor as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that, The computer program is used to be programmed or configured by a microprocessor to perform the method for improving the robustness of predictive control of a permanent magnet motor as described in any one of claims 1 to 5.

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