Model predictive speed control method, system, chip and device for synchronous reluctance motor

Abstract

The application discloses a synchronous reluctance motor model prediction speed control method, system, chip and equipment, a mathematical model of a synchronous reluctance motor is modeled, the synchronous reluctance motor is modeled based on a two-phase rotating coordinate system and a two-phase static coordinate system, and a synchronous reluctance motor mathematical model is obtained; a two-phase static coordinate system model of the synchronous reluctance motor is taken as a reference model to design an effective flux linkage observer; a prediction model is established by using the mathematical model of the two-phase rotating coordinate system; the stator current, speed and effective flux linkage are controlled by using the prediction model, the number of switching changes required from a current voltage vector to a predicted voltage vector is added to a cost function, and a determination method of a variable reference value in the cost function is given; a new cost function is used to obtain an optimal switching state; and the speed loop and the current loop are omitted in the whole motor control system, so that the speed response is accelerated, and the dynamic and steady states of the system are improved, and the motor operation performance is improved.

Description

Technical Field

[0001] This invention belongs to the field of high-performance synchronous reluctance motor control technology, specifically relating to a synchronous reluctance motor model prediction speed control method, system, chip, and device. Background Technology

[0002] Synchronous reluctance motors are widely used in industrial and agricultural production due to their robustness, reliability, high efficiency, energy saving, wide speed range, and cost-effectiveness. With technological advancements, market demands for motor performance are increasing, leading to in-depth research into high-performance AC speed control technology, with model predictive control becoming an important research branch.

[0003] Most current research on model predictive control focuses primarily on current, torque, and flux linkage control. However, the inherent cascaded structure limits the dynamic response performance of the motor; therefore, improvements to the dual-loop model predictive control scheme are needed. Model predictive speed control combines the advantages of model predictive control and direct speed control, overcoming the limitations of the cascaded structure and achieving high dynamic performance control of the speed loop.

[0004] Predictive speed control of a synchronous reluctance motor (SRRM) consists of two stages: prediction and optimization. In the prediction stage, the required control variables are predicted using a constructed voltage and current model. Different predicted variables inevitably affect the system. Therefore, multiple optimization objectives can be designed in the cost function to evaluate the similarity between predicted and reference values, thereby achieving multi-objective optimization. The reference value is the given value of the control variable; a smaller cost function value indicates that the predicted value is closer to the given value. Calculating reasonable variable setpoints when designing the cost function helps improve prediction accuracy, facilitates the selection of a more accurate voltage vector, and improves the dynamic speed response, load response, and tracking performance of the SRRM. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a synchronous reluctance motor model predictive speed control method, system, chip and device to address the shortcomings of the prior art, thereby solving the technical problem of cost function design in synchronous reluctance motor model predictive speed control and improving the speed dynamic response, load response and tracking performance of the synchronous reluctance motor.

[0006] The present invention adopts the following technical solution:

[0007] A model-based predictive speed control method for synchronous reluctance motors includes the following steps:

[0008] S1. The mathematical model of the synchronous reluctance motor is modeled based on the two-phase rotating coordinate system and the two-phase stationary coordinate system to obtain the mathematical model of the synchronous reluctance motor.

[0009] S2. Construct an effective flux linkage observer based on the mathematical model of the synchronous reluctance motor obtained in step S1, and obtain an estimated value of the rotational speed through the effective flux linkage observer;

[0010] S3. Establish a prediction model based on the mathematical model of the synchronous reluctance motor obtained in step S1. Use the estimated speed of the synchronous reluctance motor obtained in step S2 as the input of the prediction model to predict the stator current, speed, effective flux linkage and number of switching changes at time k+1. Calculate the predicted values ​​of stator current, speed, effective flux linkage and number of switching changes at time k+2 using the predicted values ​​at time k+1.

[0011] S4. Based on the predicted values ​​of stator current, speed, effective flux linkage, and number of switching changes at time k+2 obtained in step S3, design a cost function, calculate the cost function to directly determine the selected optimal vector, and determine the control performance of the synchronous reluctance motor.

[0012] Specifically, in step S1, the mathematical expression for the synchronous reluctance motor in the two-phase rotating coordinate system is as follows:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] The mathematical expression for a synchronous reluctance motor in a two-phase stationary coordinate system is as follows:

[0019]

[0020]

[0021] Among them, u d u q Represents the stator voltage components along the d-axis and q-axis; R s Indicates the stator resistance of the motor; i d i q Represents the stator current components along the d-axis and q-axis; ψ d ψ q ω represents the stator flux linkage component along the d-axis and the stator flux linkage component along the q-axis; e L represents the electric speed of the motor. d L q Represents the stator inductance components along the d-axis and q-axis; ωm T represents the mechanical speed of the electric motor. e T represents electromagnetic torque. l J represents the load torque; B represents the moment of inertia; and n represents the damping coefficient. p Represents the extreme logarithm; u α u β Represents the stator voltage components along the α-axis and β-axis; i α i β Represents the stator current components along the α-axis and β-axis; ψ α ψ β This represents the stator flux linkage component under the α-axis and the stator flux linkage component under the β-axis.

[0022] Specifically, in step S2, the estimated value of the rotational speed ω e for:

[0023]

[0024] Where θ is the rotor position and t is time.

[0025] Furthermore, the torque formula for a synchronous reluctance motor is defined in relation to i. q The product of the flux linkages is the effective flux linkage, specifically:

[0026]

[0027] in, ψ represents the effective flux linkage component along the d-axis and the effective flux linkage component along the q-axis. d ψ q L represents the stator flux linkage component along the d-axis and the stator flux linkage component along the q-axis. d L q i represents the stator inductance component along the d-axis and the stator inductance component along the q-axis. d i q ψ represents the stator current component along the d-axis. a For effective magnetic flux linkage.

[0028] Specifically, in step S3, the predicted values ​​of stator current, rotational speed, effective flux linkage, and number of switching changes at time k+2 are as follows:

[0029] The predicted stator current is expressed as follows:

[0030]

[0031] The predicted rotor mechanical speed at time k+2 is:

[0032]

[0033] The predicted effective flux linkage ψ at time k+2 ap (k+2) is:

[0034] ψ ap (k+2)=(L d -L q )i d (k+2)

[0035] Number of switch changes n s for:

[0036] n s =|S a (k)-S a (k+2) j |+|S b (k)-S b (k+2) j |+|S c (k)-S c (k+2) j |

[0037] Among them, T s For sampling time, u d (k), u q (k) represents the selected voltage vector component along the d-axis and the selected voltage vector component along the q-axis at time k, L d L q i represents the stator inductance component along the d-axis and the stator inductance component along the q-axis. d i q This represents the stator current component along the d-axis, where J is the moment of inertia and T is the stator current component along the d-axis. e (k) represents the electromagnetic torque at time k, T L (k) represents the load torque at time k, B is the damping coefficient, and ω e (k) is the electric angular velocity at time k, ω e (k+1) is the electric angular velocity at time k+1, T e (k+1) represents the electromagnetic torque at time k+1, T L (k+1) represents the load torque at time k+1.

[0038] Specifically, in step S4, the d-axis and q-axis stator current components i under the action of the basic voltage vector are predicted. dj (k+2), i qj (k+2), rotational speed ω rj (k+2), effective flux linkage ψ aj (k+2), switch state S a (k+2) j S b (k+2) j S c(k+2) j (j=0,1,…,7); then calculate the corresponding cost function based on the predicted value; finally, select the voltage vector that minimizes the cost function as the optimal output of the inverter.

[0039] Furthermore, the cost function is:

[0040]

[0041] Define a cost function to minimize speed error; the cost function g s The expression is as follows:

[0042] g s =α[(ω mj (k+2)-ω m,ref ) 2 +(ω mj (k+1)-ω m,ref ) 2 ]+β[T e (k)-T l (k)] 2

[0043] Where λ1, λ2, λ3, and λ4 are weighting factors, i d,ref i q,ref These are the reference values ​​for the d-axis and q-axis stator current components, ω m,ref , This is a reference value for rotational speed and effective flux linkage, S a (k), S b (k), S c (k) represents the current switch state, i d,ref ω m,ref i is a constant value dj (k+2) is the predicted value of the d-axis current at time k+2 under the voltage vector, i qj (k+2) is the predicted value of the q-axis current at time k+2 under the voltage vector, ψ ap (k+2) is the predicted value of the effective flux linkage at time k+2 under the voltage vector, n s The number of switching changes is ω, where α and β are weighting factors. mj (k+2) is the predicted rotational speed at time k+2 under this voltage vector, ω mj (k+1) is the predicted rotational speed at time k+1 under this voltage vector, ω m,ref T is the reference value for rotational speed. e (k) represents the electromagnetic torque, T l (k) represents the load torque.

[0044] Secondly, embodiments of the present invention provide a synchronous reluctance motor model predictive speed control system, comprising:

[0045] The module is constructed to model the mathematical model of the synchronous reluctance motor based on a two-phase rotating coordinate system and a two-phase stationary coordinate system, thus obtaining the mathematical model of the synchronous reluctance motor.

[0046] The estimation module constructs an effective flux linkage observer based on the mathematical model of the synchronous reluctance motor obtained from the construction module, and obtains the estimated value of the rotational speed through the effective flux linkage observer;

[0047] The prediction module establishes a prediction model based on the mathematical model of the synchronous reluctance motor obtained from the construction module. It uses the estimated value of the synchronous reluctance motor speed obtained from the estimation module as the input of the prediction model to predict the stator current, speed, effective flux linkage and number of switching changes at time k+1. It calculates the predicted values ​​of stator current, speed, effective flux linkage and number of switching changes at time k+2 based on the predicted values ​​at time k+1.

[0048] The control module designs a cost function based on the predicted values ​​of stator current, speed, effective flux linkage, and number of switching changes at time k+2 obtained from the prediction module. The cost function is then used to directly determine the selected optimal vector and thus the control performance of the synchronous reluctance motor.

[0049] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described synchronous reluctance motor model predictive speed control method.

[0050] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described synchronous reluctance motor model predictive speed control method.

[0051] Compared with the prior art, the present invention has at least the following beneficial effects:

[0052] This invention presents a model-based predictive speed control method for synchronous reluctance motors, incorporating an effective flux linkage observer to estimate the motor's speed. The predictive model predicts and controls stator current, speed, effective flux linkage, and the number of switching changes. A novel cost function is designed, and a method for calculating the given values ​​within this cost function is provided. This novel cost function is used to obtain the optimal switching state, thereby improving the system's dynamic and steady-state performance, reducing switching losses, and enhancing the motor's dynamic speed response, load response, and tracking performance.

[0053] Furthermore, the dynamic mathematical model expression of the synchronous reluctance motor in the three-phase natural coordinate system is relatively complex and strongly coupled. In order to facilitate control, it is necessary to select the simplified mathematical model of the synchronous reluctance motor in the two-phase rotating coordinate system and the two-phase stationary coordinate system.

[0054] Furthermore, it will be with i q The multiplied flux linkage is set as the effective flux linkage, thus transforming the motor model into an ideal salient-pole synchronous motor. The effective flux linkage is set as follows:

[0055] ψ a =(L d -L q )i d

[0056] As can be seen from the above formula, the position of the effective flux linkage is always consistent with the d-axis. Therefore, the rotor position of the synchronous reluctance motor can be obtained directly by observing the effective flux linkage.

[0057] Furthermore, the cost function is a tool for evaluating the error between the given value and the predicted value in model predictive speed control. Therefore, the cost function is closely related to the optimal switching state selected by the control system. In addition, the appropriateness of the cost function selection is a crucial factor in judging the effectiveness of model predictive speed control. If the cost function is not selected properly, it directly affects the selection of the optimal switching state, causing the control system to use an inappropriate switching state as the optimal switching state, thus affecting the control performance of the synchronous reluctance motor. Setting the stator current prediction value, speed prediction value, effective flux prediction value, and the number of switching changes as cost functions allows for simultaneous control of the motor's stator current, speed, effective flux, and the number of switching changes, improving the motor's dynamic and steady-state performance. Digital control systems suffer from sampling and filtering time delays, resulting in a one-step delay. The selected optimal switching state acts on the system at time k+1. To eliminate this one-step delay, it is necessary to predict the control variable value at time k+2.

[0058] Furthermore, the voltage vector used in each cycle of the model predictive speed control is obtained by online calculation and optimization of the cost function, which is more accurate and effective than the method of selecting the optimal voltage vector by offline design of the switching table.

[0059] Furthermore, by setting an appropriate cost function, it can be used not only for closed-loop control, but also for pulse width modulation optimization and speed estimation in sensorless systems, thus expanding its feasibility.

[0060] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0061] In summary, this invention introduces an effective flux linkage observer to estimate the motor speed. A predictive model is used to predict and control stator current, speed, effective flux linkage, and the number of switching changes. A novel cost function is designed, and a method for calculating the given values ​​in the cost function is provided. This novel cost function is used to obtain the optimal switching state, thereby improving the dynamic and steady-state performance of the system, reducing current ripple and switching losses, and enhancing the motor's dynamic speed response, load response, and tracking performance.

[0062] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0063] Figure 1 This is a control block diagram of the present invention;

[0064] Figure 2 This is a basic voltage vector block diagram of the two-level inverter of the present invention;

[0065] Figure 3 This is a block diagram of the two-level voltage source inverter of the present invention;

[0066] Figure 4 A block diagram of a chip provided by the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0069] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0070] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0071] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0072] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0073] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0074] This invention provides a model predictive speed control method for a synchronous reluctance motor, which uses the reconstructed stator voltage and the measured stator current as inputs to an effective flux linkage observer to obtain an estimate of the rotational speed; then, through methods such as... Figure 2 The eight basic voltage vectors shown are substituted one by one into the current prediction equation to predict the current. Then, for each voltage vector, the corresponding stator current, effective flux linkage, rotational speed, and number of switching changes are predicted. Finally, the optimal voltage vector is selected according to the principle of minimizing the errors in rotational speed, effective flux linkage, stator current, and number of switching changes, and its corresponding switching signal is output in the next control cycle to act as follows. Figure 3 The two-level inverter shown drives the motor. The speed loop is omitted in the entire motor control system, which speeds up the speed response and makes the control approach more direct.

[0075] Please see Figure 1 The present invention provides a model predictive speed control method for a synchronous reluctance motor, comprising the following steps:

[0076] S1. Model the mathematical model of the synchronous reluctance motor by modeling the synchronous reluctance motor based on a two-phase rotating coordinate system and a two-phase stationary coordinate system, and obtain the mathematical model of the synchronous reluctance motor.

[0077] In a two-phase rotating coordinate system, the mathematical expression for a synchronous reluctance motor is:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] In a two-phase stationary coordinate system, the mathematical expression for a synchronous reluctance motor is:

[0084]

[0085]

[0086] Among them, u d u q Represents the stator voltage components along the d-axis and q-axis; R s Indicates the stator resistance of the motor; i d i q Represents the stator current components along the d-axis and q-axis; ψ d ψ q L represents the stator flux linkage component along the d-axis and the stator flux linkage component along the q-axis; d L q Represents the stator inductance components along the d-axis and q-axis; ω m T represents the mechanical speed of the electric motor. e T represents electromagnetic torque. l J represents the load torque; B represents the moment of inertia; and n represents the damping coefficient. p Represents the extreme logarithm; u α u β Represents the stator voltage components along the α-axis and β-axis; i α i β Represents the stator current components along the α-axis and β-axis; ψ α ψβ This represents the stator flux linkage component under the α-axis and the stator flux linkage component under the β-axis.

[0087] S2. Based on the mathematical model of the synchronous reluctance motor in the two-phase stationary coordinate system obtained in step S1, construct an effective flux linkage observer and obtain the estimated value of the rotational speed through the designed effective flux linkage observer.

[0088] The effective flux observer is a flux observer structure belonging to the category of closed-loop flux estimation. It provides a unified flux / position observer design for sensorless AC motors. Its basic idea is to define the flux and position observer in the torque formula of a synchronous reluctance motor. q The multiplied flux linkage is the effective flux linkage, thus transforming the motor model into an ideal salient-pole synchronous motor. The effective flux linkage is always parallel to the d-axis and represents the excitation flux linkage that generates torque; therefore, observing the effective flux linkage yields the rotor position angle and speed. Based on this idea, a model of the effective flux linkage observer is established using the mathematical model of a synchronous reluctance motor:

[0089] For synchronous reluctance motors, referring to the torque expression (5), the effective flux linkage is defined as follows:

[0090]

[0091] ψ represents the effective flux linkage component along the d-axis and the effective flux linkage component along the q-axis; a Indicates a valid magnetic flux linkage.

[0092] Therefore, the torque formula becomes:

[0093]

[0094] The voltage equations for the α and β axes become:

[0095]

[0096] The expression for estimating the flux linkage of a synchronous reluctance motor is:

[0097]

[0098]

[0099] The rotor position expression is:

[0100]

[0101] Rotor speed expression:

[0102]

[0103] Where t is time.

[0104] S3. Based on the mathematical model of the synchronous reluctance motor in the two-phase rotating coordinate system obtained in step S1, establish a prediction model and use the estimated value of the synchronous reluctance motor speed obtained in step S2 as the input of the prediction model. Let the current time be k. Predict the stator current, speed, effective flux linkage and number of switching changes at time k+1. Calculate the predicted values ​​of stator current, speed, effective flux linkage and number of switching changes at time k+2 using the predicted values ​​at time k+1.

[0105] In the finite set model predictive speed control system of synchronous reluctance motor, the stator current state equation of the synchronous reluctance motor is used as the model for prediction. Using the forward Euler discretization formula, the predicted stator current value is expressed as:

[0106]

[0107] Based on the equation of motion of the synchronous reluctance motor, the predicted speed at time k+2 is obtained:

[0108]

[0109] The expression for the predicted value of effective flux linkage k+2 is:

[0110] ψ ap (k+2)=(L d -L q )i d (k+2) (17)

[0111] Among them, T s For sampling time, u d (k), u q (k) represents the selected voltage vector component along the d-axis and the selected voltage vector component along the q-axis at time k.

[0112] S4. Based on the predicted values ​​of stator current, speed, effective flux linkage, and number of switching changes at time k+2 obtained in step S3, design a cost function to evaluate the similarity between the predicted values ​​and the reference values. Provide a method for calculating the reference values ​​in the cost function. Finally, calculate the cost function to directly determine the selected optimal vector, thereby determining the control performance of the synchronous reluctance motor.

[0113] The cost function is a tool used to evaluate the similarity between predicted and reference values; therefore, the cost function directly determines the selected optimal vector, and thus the control performance of the synchronous reluctance motor. In finite set model predictive speed control, to better adjust the average switching frequency of the system, the number of switching changes required to reach the desired voltage vector from the current voltage vector is added to the cost function; the expression for the cost function is as follows:

[0114]

[0115] Where, n s It can be expressed by the following formula:

[0116] n s =(|S a (k)-S a (k+2) j |+|S b (k)-S b (k+2) j |+|S c (k)-S c (k+2) j |) (19)

[0117] Where λ1, λ2, λ3, and λ4 are weighting factors, i d,ref i q,ref These are the reference values ​​for the d-axis and q-axis stator current components, ω m,ref , This is a reference value for rotational speed and effective flux linkage, S a (k), S b (k), S c (k) represents the current switch state, i d,ref ω m,ref Set to a constant value.

[0118] The expression for the effective flux linkage reference value is as follows:

[0119]

[0120] Define a cost function to minimize speed error, as shown in the following expression:

[0121] g s =α[(ω m (k+2)-ω m,ref ) 2 +(ω m (k+1)-ω m,ref ) 2 ]+β[T e (k)-T l (k)] 2 (twenty one)

[0122] Where α and β are weighting factors, the cost function can be minimized by setting the derivative of (21) to zero. Let T l If it is a constant, then the derivative result is expressed as:

[0123]

[0124] The expression for the electromagnetic torque reference value is as follows:

[0125]

[0126] Where α and β are weighting factors.

[0127] The expression for the reference value of the q-axis stator current component is as follows:

[0128]

[0129] For a two-level voltage source inverter, there are eight basic switching states; first, predict the d-axis and q-axis stator current components i under the action of the basic voltage vector. dj (k+2), i qj (k+2), rotational speed ω rj (k+2), effective flux linkage ψ aj (k+2), switch state S a (k+2) j S b (k+2) j S c (k+2) j (j=0,1,…,7); then calculate the corresponding cost function based on the predicted value; finally, select the voltage vector that minimizes the cost function as the optimal output of the inverter.

[0130] In another embodiment of the present invention, a synchronous reluctance motor model predictive speed control system is provided. This system can be used to implement the above-mentioned synchronous reluctance motor model predictive speed control method. Specifically, the synchronous reluctance motor model predictive speed control system includes a construction module, an estimation module, a prediction module, and a control module.

[0131] Among them, the construction module models the mathematical model of the synchronous reluctance motor based on a two-phase rotating coordinate system and a two-phase stationary coordinate system, and obtains the mathematical model of the synchronous reluctance motor.

[0132] The estimation module constructs an effective flux linkage observer based on the mathematical model of the synchronous reluctance motor obtained from the construction module, and obtains the estimated value of the rotational speed through the effective flux linkage observer;

[0133] The prediction module establishes a prediction model based on the mathematical model of the synchronous reluctance motor obtained from the construction module. It uses the estimated value of the synchronous reluctance motor speed obtained from the estimation module as the input of the prediction model to predict the stator current, speed, effective flux linkage and number of switching changes at time k+1. It calculates the predicted values ​​of stator current, speed, effective flux linkage and number of switching changes at time k+2 based on the predicted values ​​at time k+1.

[0134] The control module designs a cost function based on the predicted values ​​of stator current, speed, effective flux linkage, and number of switching changes at time k+2 obtained from the prediction module. The cost function is then used to directly determine the selected optimal vector, thus determining the control performance of the synchronous reluctance motor.

[0135] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a synchronous reluctance motor model predictive speed control method, including:

[0136] A mathematical model of the synchronous reluctance motor is constructed based on a two-phase rotating coordinate system and a two-phase stationary coordinate system. An effective flux linkage observer is then built based on this model to obtain an estimated rotational speed. A prediction model is established using the estimated rotational speed as input to predict the stator current, rotational speed, effective flux linkage, and number of switching changes at time k+1. The predicted values ​​at time k+1 are then used to calculate the predicted values ​​for stator current, rotational speed, effective flux linkage, and number of switching changes at time k+2. A cost function is designed based on these predictions, and the calculation of the cost function directly determines the selected optimal vector, thus determining the control performance of the synchronous reluctance motor.

[0137] Please see Figure 4The terminal device includes a chip 600, which includes one or more processors 622, and a memory 632 for storing computer programs executable by the processors 622. The computer programs stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 622 may be configured to execute the computer program to perform the aforementioned synchronous reluctance motor model predictive speed control method.

[0138] Additionally, chip 600 may also include a power supply component 626 and a communication component 650. The power supply component 626 can be configured to perform power management of chip 600, and the communication component 650 can be configured to enable communication of chip 600, such as wired or wireless communication. Furthermore, chip 600 may also include an input / output (I / O) interface 658. Chip 600 can operate on an operating system stored in memory 632.

[0139] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0140] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the synchronous reluctance motor model predictive speed control method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps:

[0141] A mathematical model of the synchronous reluctance motor is constructed based on a two-phase rotating coordinate system and a two-phase stationary coordinate system. An effective flux linkage observer is then built based on this model to obtain an estimated rotational speed. A prediction model is established using the estimated rotational speed as input to predict the stator current, rotational speed, effective flux linkage, and number of switching changes at time k+1. The predicted values ​​at time k+1 are then used to calculate the predicted values ​​for stator current, rotational speed, effective flux linkage, and number of switching changes at time k+2. A cost function is designed based on these predictions, and the calculation of the cost function directly determines the selected optimal vector, thus determining the control performance of the synchronous reluctance motor.

[0142] In summary, this invention provides a model predictive speed control method, system, chip, and device for a synchronous reluctance motor. It introduces an effective flux linkage observer to estimate the motor's speed. Through a predictive model, it performs predictive control on stator current, speed, effective flux linkage, and the number of switching changes. A novel cost function is designed, and a method for calculating the given values ​​in the cost function is provided. This novel cost function is used to obtain the optimal switching state, thereby improving the system's dynamic and steady-state performance, reducing switching losses, and enhancing the motor's dynamic speed response, load response, and tracking performance.

[0143] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0144] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0145] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0146] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0147] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0149] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0150] 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These 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 function 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 function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment 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.

[0153] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A model predictive speed control method for synchronous reluctance motors, characterized in that, Includes the following steps: S1. A mathematical model of the synchronous reluctance motor is constructed based on a two-phase rotating coordinate system and a two-phase stationary coordinate system, resulting in the synchronous reluctance motor model. The mathematical expression for the synchronous reluctance motor in the two-phase rotating coordinate system is as follows: In a two-phase stationary coordinate system, the mathematical expression for a synchronous reluctance motor is: in, , express d Under-shaft stator voltage component, q Off-axis stator voltage component; Indicates the stator resistance of the motor; , express d Under-shaft stator current component q Under-shaft stator current component; , express d Lower stator flux linkage component q Off-axis stator flux linkage component; , express d Shaft-mounted stator inductance component, q The stator inductance component below the shaft; Indicates the electric speed of the motor; Indicates the mechanical speed of the motor; Indicates electromagnetic torque. Indicates load torque; Indicates the moment of inertia; Indicates the damping coefficient; Represents the extreme logarithm; , express Under-shaft stator voltage component, Off-axis stator voltage component; , express Under-shaft stator current component Under-shaft stator current component; , express Lower stator flux linkage component Off-axis stator flux linkage component; S2. Construct an effective flux linkage observer based on the synchronous reluctance motor model obtained in step S1, and obtain an estimated value of the rotational speed through the effective flux linkage observer; S3. Based on the synchronous reluctance motor model obtained in step S1, establish a prediction model, using the estimated synchronous reluctance motor speed obtained in step S2 as the input to the prediction model. k Predict the stator current, rotational speed, effective flux linkage, and number of switching changes at time +1. k The predicted value at time +1 is calculated. k Predicted values ​​of stator current, rotational speed, effective flux linkage, and number of switching changes at +2 time. k The predicted values ​​for stator current, rotational speed, effective flux linkage, and number of switching changes at time +2 are as follows: The predicted stator current is expressed as follows: k+2 The predicted value of the rotor mechanical speed at any given time is: k+2 Effective flux prediction value at time 1 for: Number of switch changes n s for: in, Sampling time, express k time d Selected voltage vector components under the axis, q The selected voltage vector component under the axis, , express d Shaft-mounted stator inductance component, q The stator inductance component below the shaft, , express d Under-shaft stator current component, For rotational inertia, for k Electromagnetic torque at time t, for k Load torque at any given moment The damping coefficient is... for k electric angular velocity at time t, for k The electric angular velocity at time +1, for k Electromagnetic torque at time +1 for k Load torque at time +1; S4, based on the result obtained in step S3 k The design cost function is based on the predicted stator current, speed, effective flux linkage, and number of switching changes at +2 time. The cost function directly determines the selected optimal vector, thus determining the control performance of the synchronous reluctance motor and predicting the effect of the basic voltage vector. d, q Shaft stator current components Rotation speed Effective magnetic flux Switch status ( Then, calculate the corresponding cost function based on the predicted value; finally, select the voltage vector that minimizes the cost function as the optimal output of the inverter. The cost function is: Define a cost function to minimize speed error. The expression is as follows: in, As a weighting factor, , yes d, q Reference values ​​for shaft stator current components. These are reference values ​​for rotational speed and effective flux linkage. This is the current on / off state. It is a constant value. Under voltage vector k +2 time d Predicted value of shaft current, Under voltage vector k +2 time q Predicted value of shaft current, Under voltage vector k Predicted effective flux linkage at time +2 The number of times the switch changes. It is a weighting factor. For this voltage vector k Predicted rotational speed at +2 time. For this voltage vector k The predicted value of the rotational speed at time +1. This is a reference value for the rotational speed. For electromagnetic torque, This represents the load torque.

2. The synchronous reluctance motor model predictive speed control method according to claim 1, characterized in that, In step S2, the estimated value of the rotational speed. for: in, For rotor position, For time.

3. The synchronous reluctance motor model predictive speed control method according to claim 2, characterized in that, In the torque formula of a synchronous reluctance motor, and The product of the flux linkages is the effective flux linkage, specifically: in, , express d Off-axis effective flux linkage component, q Off-axis effective flux linkage component, , express d Lower stator flux linkage component q The stator flux linkage component below the shaft, , express d Shaft-mounted stator inductance component, q The stator inductance component below the shaft, , express d Under-shaft stator current component, For effective magnetic flux linkage.

4. A model predictive speed control system for a synchronous reluctance motor, characterized in that, This system is used to execute the synchronous reluctance motor model predictive speed control method as described in claim 1, comprising: The module is constructed to model the mathematical model of the synchronous reluctance motor based on a two-phase rotating coordinate system and a two-phase stationary coordinate system, thus obtaining the synchronous reluctance motor model. The estimation module constructs an effective flux linkage observer based on the synchronous reluctance motor model obtained from the construction module, and obtains the estimated value of the rotational speed through the effective flux linkage observer; The prediction module establishes a prediction model based on the synchronous reluctance motor model obtained from the construction module. It uses the estimated synchronous reluctance motor speed obtained from the estimation module as input to the prediction model. k Predict the stator current, rotational speed, effective flux linkage, and number of switching changes at time +1. k The predicted value at time +1 is calculated. k Predicted values ​​of stator current, rotational speed, effective flux linkage, and number of switching changes at +2 time; The control module, based on the prediction module... k The cost function is designed based on the predicted values ​​of stator current, speed, effective flux linkage, and number of switching changes at time +2. The cost function is then used to directly determine the selected optimal vector and thus the control performance of the synchronous reluctance motor.

5. A chip, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method of claim 1, 2, or 3.

6. An electronic device, characterized in that, Includes the chip as described in claim 5.

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