Switched reluctance motor predictive current control method, device, electronic equipment and medium

Abstract

The present invention discloses a method, device, electronic device, and medium for predictive current control of a switched reluctance motor. The method includes obtaining a first control cycle current of the switched reluctance motor; determining a first control cycle voltage corresponding to a switch state in the switched reluctance motor; constructing a first control cycle error potential model based on the first control cycle current and the first control cycle voltage; determining model parameters based on the first control cycle error potential model; constructing a second control cycle error potential equation based on the model parameters; solving the second control cycle error potential equation to predict a third control cycle current; determining an optimal switching state for the second control cycle based on the third control cycle current and a preset third control cycle expected current based on a preset finite set predictive current control objective function, and controlling the switched reluctance motor based on the optimal switching state. This technical solution can achieve high-precision control of the switched reluctance motor.

Description

Technical Field

[0001] The present invention relates to the technical field of predicted current control of switched reluctance motors, and in particular to a method, device, electronic equipment and medium for predicted current control of switched reluctance motors. Background Art

[0002] The switched reluctance motor (SRM) is a new type of power motor for new energy passenger and freight electric vehicles. It boasts a simple structure, low cost, and high reliability, making it a promising candidate for applications requiring high loads and large starting torques. High-precision operation of SRMs is a key focus of research institutions and industry. Precise current control directly impacts the SRM's resulting torque and, consequently, speed stability. Therefore, achieving high-precision current control is a critical technical challenge that must be addressed to ensure high-speed trajectory tracking accuracy.

[0003] Currently, the current control method of the switched reluctance motor generally uses PI (proportional integral) control. However, since the current model of the switched reluctance motor has strong nonlinear and time-varying characteristics, conventional current control methods are difficult to achieve high-precision current control. Summary of the Invention

[0004] The present invention provides a method, device, electronic equipment and medium for predictive current control of a switched reluctance motor, which can realize high-precision control of the switched reluctance motor by considering the finite set predictive current control of the dynamic linearization of the model error.

[0005] According to one aspect of the present invention, a method for predictive current control of a switched reluctance motor is provided, the method comprising:

[0006] Obtaining a first control cycle current of the switched reluctance motor;

[0007] determining a first control cycle voltage corresponding to a switch state in the switched reluctance motor;

[0008] constructing a first control period error potential model according to the first control period current and the first control period voltage;

[0009] Determining model parameters based on the first control cycle error potential model; wherein the model parameters are pseudo partial derivatives of the second control cycle error potential model;

[0010] constructing a second control period error potential equation according to the model parameters;

[0011] Solving the error potential equation of the second control period to predict the current of the third control period;

[0012] Based on a preset finite set predicted current control objective function, the optimal switching state of the second control cycle of the switched reluctance motor is determined according to the third control cycle current and the preset third control cycle expected current, and the switched reluctance motor is controlled according to the optimal switching state; wherein the finite set predicted current control objective function is a finite set predicted current control objective function based on time delay compensation.

[0013] According to another aspect of the present invention, a switch reluctance motor predictive current control device is provided, the device comprising:

[0014] A first control period current acquisition module, configured to acquire a first control period current of the switched reluctance motor;

[0015] A first control period voltage determination module, configured to determine a first control period voltage corresponding to a switch state in the switched reluctance motor;

[0016] a first control period error potential model building module, configured to build a first control period error potential model according to the first control period current and the first control period voltage;

[0017] A model parameter determination module, configured to determine model parameters based on the first control period error potential model; wherein the model parameters are pseudo partial derivatives of the second control period error potential model;

[0018] A second control period error potential equation construction module, configured to construct a second control period error potential equation according to the model parameters;

[0019] A third control period current prediction module, configured to solve the second control period error potential equation and predict the third control period current;

[0020] An optimal switching state determination module is used to determine the optimal switching state of the second control cycle of the switched reluctance motor based on a preset finite set predicted current control objective function, according to the third control cycle current and the preset third control cycle expected current, and control the switched reluctance motor according to the optimal switching state; wherein the finite set predicted current control objective function is a finite set predicted current control objective function based on time delay compensation.

[0021] According to another aspect of the present invention, an electronic device is provided, comprising:

[0022] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the switch reluctance motor predictive current control method described in any embodiment of the present invention.

[0023] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the switch reluctance motor predictive current control method described in any embodiment of the present invention when executed.

[0024] The technical solution of the embodiment of the present invention solves the problem that the current of the switched reluctance motor cannot reflect the current nonlinearity and time-varying characteristics of the actual system under conventional linearization modeling, thereby causing current tracking errors and resulting in low motion control accuracy, through finite set predictive current control that takes into account the dynamic linearization of the model error. It can achieve high-precision control of the switched reluctance motor.

[0025] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 is a flow chart of a method for predictive current control of a switched reluctance motor according to a first embodiment of the present invention;

[0028] Figure 2 A flow chart of a data-driven finite set predictive current control method for a switched reluctance motor provided in Example 1 of the present application;

[0029] Figure 3 This is a schematic diagram of the power topology in the excitation stage provided in Example 1 of the present application;

[0030] Figure 4 This is a schematic diagram of the power topology in the demagnetization stage provided in Example 1 of the present application;

[0031] Figure 5 Schematic diagram of the power topology in the freewheeling phase provided in Example 1 of the present application;

[0032] Figure 6 A schematic diagram of a predicted current control process of a switched reluctance motor provided in the second embodiment of the present invention;

[0033] Figure 7 A schematic diagram of the structure of a predicted current control device for a switched reluctance motor provided in a third embodiment of the present invention;

[0034] Figure 8 It is a structural diagram of an electronic device for implementing the switch reluctance motor predictive current control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described 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 making creative efforts should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] Example 1

[0038] Figure 1 This is a flow chart of a method for predicting current control of a switched reluctance motor according to a first embodiment of the present invention. This embodiment is applicable to high-precision control of a switched reluctance motor. The method can be executed by a switch reluctance motor predictive current control device. The switch reluctance motor predictive current control device can be implemented in the form of hardware and / or software. The switch reluctance motor predictive current control device can be configured in a device. For example, the device can be a background server or other device with communication and computing capabilities. Figure 1 As shown, the method includes:

[0039] S101 : Obtain a first control cycle current of a switched reluctance motor.

[0040] In this scheme, the switched reluctance motor operates according to the principle of minimum reluctance, which states that the magnetic flux always closes along the path of minimum reluctance. When the shaped iron core moves to the minimum reluctance position, its main axis aligns with the axis of the magnetic field. When power is applied to a stator phase winding, the rotor's salient poles rotate in the direction that minimizes the reluctance of that phase winding—that is, they align with the salient poles of the energized stator phase. By controlling the amplitude and width of the current pulses applied to the motor windings, as well as their relative position to the rotor (i.e., the conduction angle and the cut-off angle), the magnitude and direction of the motor torque can be controlled, thereby achieving speed regulation.

[0041] The first control cycle current may refer to the current at time K. The first control cycle current of the switched reluctance motor may be obtained using a current sensor, measured using a sampling resistor, or obtained using a controller algorithm.

[0042] In this plan, Figure 2 This is a flow chart of a data-driven finite set predictive current control method for a switched reluctance motor provided in Example 1 of the present application, as shown in FIG. Figure 2 As shown, the first control cycle current of the switched reluctance motor is obtained .

[0043] S102: Determine a first control cycle voltage corresponding to a switch state in the switched reluctance motor.

[0044] The first control period voltage may refer to the voltage at time K.

[0045] In this embodiment, the switch state includes three switch states: 、 and .in, is the excitation stage of the switched reluctance motor; It is the freewheeling stage of the switched reluctance motor; This is the demagnetization stage of the switched reluctance motor.

[0046] In this solution, the voltage of the switched reluctance motor varies in different switching states. The correlation between the switching state and the equivalent applied voltage is known. After obtaining the switching state of the switched reluctance motor, the first control cycle voltage corresponding to the switching state of the switched reluctance motor is determined based on the correlation between the switching state and the equivalent applied voltage.

[0047] Among them, Figure 2 As shown, according to the storage equivalent voltage at time K Get the switch status and apply it.

[0048] Optionally, determining a first control cycle voltage corresponding to a switch state in the switched reluctance motor includes steps A1-A3:

[0049] Step A1, obtaining the switch state of the switched reluctance motor;

[0050] In this solution, the switch state in the switched reluctance motor can be determined according to the working state, rotor position or control strategy of the switched reluctance motor.

[0051] Step A2: determining a loop topology corresponding to the switch state according to the switch state;

[0052] In this embodiment, Figure 3 This is a schematic diagram of the power topology in the excitation stage provided in the first embodiment of the present application. Figure 4 Schematic diagram of the power topology in the demagnetization stage provided in the first embodiment of the present application. Figure 5 This is a schematic diagram of the power topology in the freewheeling phase provided in the first embodiment of the present application. Figure 3 、 Figure 4 、 Figure 5 As shown, different switch states correspond to different loop topologies.

[0053] Furthermore, the loop topology can be obtained based on the switch states in the switched reluctance motor.

[0054] Step A3: Match a first control period voltage corresponding to the loop topology from a predetermined association relationship between the loop topology and the equivalent action voltage.

[0055] In this embodiment, the loop topology is obtained according to the switch state, and the equivalent operating voltage is obtained. The correlation between the switch state and the equivalent operating voltage is shown in Table 1.

[0056] Table 1

[0057]

[0058] in, To measure current; is the DC bus voltage; is the equivalent resistance of the loop.

[0059] Specifically, after the switch state in the switched reluctance motor is obtained, the first control period voltage may be determined based on the switch state in the switched reluctance motor.

[0060] By acquiring the first control period voltage, the first control period error potential model can be predicted based on the first control period current and the first control period voltage.

[0061] S103: Construct a first control period error potential model according to the first control period current and the first control period voltage.

[0062] Among them, the error potential model is a mathematical model used to describe and analyze the potential error caused by various factors in various physical systems.

[0063] In this embodiment, a conventional linearized switched reluctance motor current model may be constructed based on linear lossless energy conversion and a linear distributed inductance curve, and the conventional linearized switched reluctance motor current model may be split to construct an error potential model.

[0064] Furthermore, the first control period current and the first control period voltage are substituted into the error potential model to construct the first control period error potential model.

[0065] Optionally, constructing a first control period error potential model according to the first control period current and the first control period voltage includes steps B1-B3:

[0066] Step B1: constructing a conventional linearized switched reluctance motor current model based on linear lossless energy conversion and linear distributed inductance curve;

[0067] Step B2: Decomposing the conventional linearized switched reluctance motor current model to determine an error potential model; wherein the error potential model is composed of the model error and the unmodeled portion of the back electromotive force;

[0068] Step B3: Substitute the first control period current and the first control period voltage into the error potential model to construct a first control period error potential model.

[0069] Among them, linear lossless energy conversion refers to the situation in which a linear relationship is satisfied and there is no energy loss during the energy conversion process; the linear distributed inductance curve is a curve that describes the linear relationship between the inductance of an inductor element and certain variables.

[0070] Specifically, based on the assumptions of linear lossless energy conversion and linear distributed inductance curves, a conventional linearized switched reluctance motor current model is constructed as a baseline model to reduce the data-driven model parameter fluctuations caused by system IO (input and output) data measurement errors. The conventional linearized switched reluctance motor current model is:

[0071] ;

[0072] in, is the nominal resistance of the winding; Is the difference between the nominal resistance and the actual resistance; is the first-order derivative of the inductance function with respect to the position of the mover; is the nominal value of the winding inductance; is the difference between the modified first-order derivative and the actual inductance-position function derivative; is the difference between the nominal inductance value and the actual inductance value; The unmodeled part of the system.

[0073] The conventional linearized switched reluctance motor current model is split, that is, the difference value and the unmodeled part in the conventional linearized switched reluctance motor current model are separated. Specifically, the split is:

[0074] ;

[0075] ;

[0076] in, It is composed of the system model error and the unmodeled part of the back electromotive force, which is called the error potential model.

[0077] Specifically, considering the impact of nonlinearity and current on inductance, as well as the general characteristics of actual systems, it is assumed that the derivative of the inductance function with respect to current is first-order differentiable, and the second-order partial derivative exists and is continuous. Since the controlled object is an L (inductance)-R (resistance) circuit system, and the current and position are considered as functions of time, it is assumed that the function characteristics of the ideal mathematical model parameters are first-order differentiable, and the second-order partial derivative exists and is continuous. The system parameters and other variables in the error potential model are considered to be constant within the control period, and then according to the backward difference, it can be obtained , that is, the error potential model for and function. And according to the generalized Lipschitz condition ;in: and , , .

[0078] Further, from the analysis of the LR circuit system, the current is from the state Switch to state , the model parameter errors and unmodeled back EMF errors caused by it are both finite, so there exists a positive number b that satisfies the generalized Lipschitz condition, and the hypothesis can be proposed: The generalized Lipschitz condition is satisfied.

[0079] In this solution, the system parameters used in the control cycle are made more consistent with the actual system while meeting the optimization objectives. The error potential model can be simplified as:

[0080] ;

[0081] in, ; ; is the sampling frequency of forward Euler discretization; for Current at the moment.

[0082] Specifically, such as Figure 2 As shown, the error potential model of the first control period can be obtained according to the above formula: .

[0083] By determining the first control period error potential model, the pseudo partial derivative of the second control period error potential model can be predicted based on the first control period error potential model.

[0084] S104. Determine model parameters based on the first control cycle error potential model; wherein the model parameters are pseudo partial derivatives of the second control cycle error potential model.

[0085] In this embodiment, the second control cycle current is predicted by iteratively solving the first control cycle error potential model, and the pseudo partial derivative of the second control cycle error potential model is predicted based on the second control cycle current. Current at the moment .

[0086] In this program, if Figure 2 As shown, the model parameters are solved by iteratively solving the error potential model of the first control period .

[0087] S105 . Construct a second control period error potential equation according to the model parameters.

[0088] In this embodiment, the second control cycle error potential model can be determined according to the first control cycle error potential model, and then the second control error potential equation can be constructed based on the first control cycle error potential model, the second control cycle error potential model and the model parameters.

[0089] In this solution, assuming that the modeling prerequisites of CFDL (Computational Fluid Dynamics) are met, the error potential equation is constructed based on the error potential model. The error potential equation is:

[0090] ;

[0091] in: 、 is the estimated error potential model obtained at time k and time k-1; 、 is the actual current at time k and time k-1; is the pseudo partial derivative at time k.

[0092] Further, such as Figure 2 As shown, the error potential equation of the second control period can be obtained according to the above formula: .in, is the error potential model of the second control cycle, is the error potential model of the first control cycle, are model parameters.

[0093] S106 : Solve the error potential equation of the second control period to predict the current of the third control period.

[0094] In this solution, the third control period current is predicted by solving the second control period error potential equation. Current at the moment .

[0095] Specifically, such as Figure 2 As shown, the third control cycle current The calculation formula is: .

[0096] S107. Based on a preset finite set predicted current control objective function, determine the optimal switching state of the second control cycle of the switched reluctance motor according to the third control cycle current and the preset third control cycle expected current, and control the switched reluctance motor according to the optimal switching state; wherein the finite set predicted current control objective function is a finite set predicted current control objective function based on time delay compensation.

[0097] Finite set predictive current control (FPC) is based on the concept of model predictive control and takes into account the discrete models of the power converter and motor. Within each control cycle, the optimal switching state for the next control cycle is determined by solving a finite set optimization problem based on the current motor state (such as current and voltage) and the system reference command (such as a given current value).

[0098] In this solution, a finite set predictive current control objective function can be constructed based on the expected current, predicted current, and the number of switching times.

[0099] Specifically, the third control period current, the third control period expected current, and the number of switching cycles from the second control period switching state to the third control period switching state are substituted into the finite set predictive current control objective function to obtain the optimal finite set predictive current control objective function. Based on the optimal finite set predictive current control objective function, the switching state corresponding to the optimal finite set predictive current control objective function is determined and used as the optimal switching state. The switched reluctance motor is then controlled based on the optimal switching state.

[0100] Optional, including:

[0101] The finite set predictive current control objective function is constructed using the following formula;

[0102] ;

[0103] in, For a finite set of predictive current control objective functions, is the expected current in the third control cycle, is the third control cycle current, is the number of switching times from the second control period switching state to the third control period switching state, and is the weight coefficient.

[0104] Specifically, such as Figure 2 As shown in FIG, considering the time delay compensation, a finite set predictive current control objective function is constructed based on the expected current, predicted current and the number of switching times.

[0105] By constructing a finite set predictive current control objective function, high-precision control of the switched reluctance motor can be achieved.

[0106] Optionally, based on a preset finite set predicted current control objective function, determining the optimal switching state of the second control period of the switched reluctance motor according to the third control period current and a preset third control period expected current includes steps C1-C2:

[0107] Step C1, substituting the third control period current and the preset third control period expected current into a preset finite set predicted current control objective function to determine an objective function value;

[0108] In this solution, the third control period current and the preset third control period expected current are respectively substituted into the preset finite set predicted current control objective function to calculate the objective function values ​​under different switching states. The number of objective function values ​​is the same as the number of switching states.

[0109] Step C2: determining an optimal objective function value from the objective function values, and determining an optimal switching state corresponding to the optimal objective function value.

[0110] In this embodiment, an objective function value is selected from the objective function values ​​as the optimal objective function value. Specifically, the minimum objective function value can be selected as the optimal objective function value.

[0111] Furthermore, after determining the optimal objective function value, a switch state corresponding to the optimal objective function value may be determined, and the switch state may be used as the optimal switch state.

[0112] By constructing a finite set predictive current control objective function to determine the optimal switching state, the operation of the switched reluctance motor can be controlled based on the optimal switching state, thereby achieving high-precision control of the switched reluctance motor.

[0113] The technical solution of the embodiment of the present invention obtains the first control cycle current of the switched reluctance motor and determines the first control cycle voltage corresponding to the switch state in the switched reluctance motor. Then, based on the first control cycle current and the first control cycle voltage, a first control cycle error potential model is constructed, the first control cycle error potential model is solved, and the model parameters are predicted. The third control cycle current is predicted based on the model parameters. Then, based on a preset finite set predicted current control objective function, the optimal switch state of the second control cycle of the switched reluctance motor is determined according to the third control cycle current and the preset third control cycle expected current, and the switched reluctance motor is controlled according to the optimal switch state. By implementing this technical solution, through the finite set predicted current control that takes into account the dynamic linearization of the model error, the problem that the current of the switched reluctance motor cannot reflect the current nonlinearity and time-varying characteristics of the actual system under conventional linearization modeling, thereby causing current tracking error and resulting in low motion control accuracy is solved, and high-precision control of the switched reluctance motor can be achieved.

[0114] Example 2

[0115] Figure 6 This is a schematic diagram of the predicted current control process of the switched reluctance motor provided by the second embodiment of the present invention. The relationship between this embodiment and the above embodiment is a detailed description of the model parameter determination process. Figure 6 As shown, the method includes:

[0116] S601 : Obtain a first control cycle current of a switched reluctance motor.

[0117] S602: Determine a first control cycle voltage corresponding to a switch state in the switched reluctance motor.

[0118] S603: Construct a first control period error potential model according to the first control period current and the first control period voltage.

[0119] S604, constructing an optimal problem model; wherein the optimal problem model is determined based on the error potential equation.

[0120] In this solution, an optimal problem model can be constructed based on the error potential equation at each historical moment. This optimal problem model satisfies the optimization objective and makes the system parameters used in the previous control cycle more closely match the actual system.

[0121] Specifically, the optimal problem model is:

[0122] .

[0123] S605 : Determine model parameters according to the first control period error potential model and the optimal problem model.

[0124] In this embodiment, the first control period error potential model may be substituted into the optimal problem model to construct a new optimal problem model, and then the new optimal problem model may be solved to determine the model parameters.

[0125] Optionally, determining model parameters according to the first control period error potential model and the optimal problem model includes steps D1-D3:

[0126] Step D1, transforming the first control period error potential model to obtain a target first control period error potential model;

[0127] Step D2: Substitute the target first control period error potential model into the optimal problem model to obtain the target optimal problem model;

[0128] Step D3: Determine model parameters based on the first-order partial derivatives and second-order partial derivatives of the target optimal problem model.

[0129] Specifically, the error potential model is rewritten as the expression at time k-1;

[0130] ;

[0131] ;

[0132] in, is the predicted value of the variable, The actual value of the variable.

[0133] The above formula is a mathematical model constructed from a linearized model and a data-driven error potential model, reflecting the relationship between the true error potential and the actual current. This process ignores the current measurement error and the error caused by the voltage averaging effect of pulse width modulation.

[0134] Subtracting the above two formulas, we can get ;

[0135] The error potential model at time k-1 obtained from the current is:

[0136] ;

[0137] in, Compared to , should make and The gap between them is as small as possible, thus constituting the first term of the cost function, that is, the pseudo partial derivative obtained by the data-driven method should make the difference between the estimated error potential model obtained by combining historical data and the actual error potential model smaller, so Alternative And constitute the optimal problem model.

[0138] The optimal problem model is solved by the gradient method to obtain the target first control cycle error potential model, that is, The expression is:

[0139] ;

[0140] Specifically, the target first control cycle error potential model is substituted into the optimal problem model to obtain the target optimal problem model. The target optimal problem model is:

[0141] ;

[0142] Solve the first-order partial derivative expression to make it zero;

[0143] ;

[0144] make , we get the stationary point equation, and then solve the second-order partial derivative to get;

[0145] ;

[0146] Due to the weight coefficient , so the conclusion that the above formula is greater than zero holds true, and the stationary point is a minimum value. The resulting explicit expression is:

[0147] .

[0148] By solving the model parameters, the second control period error potential equation can be constructed based on the model parameters.

[0149] S606: Construct a second control period error potential equation according to the model parameters.

[0150] S607: Solve the error potential equation of the second control period to predict the current of the third control period.

[0151] S608. Based on a preset finite set predicted current control objective function, determine the optimal switching state of the second control cycle of the switched reluctance motor according to the third control cycle current and the preset third control cycle expected current, and control the switched reluctance motor according to the optimal switching state; wherein the finite set predicted current control objective function is a finite set predicted current control objective function based on time delay compensation.

[0152] The technical solution of the embodiment of the present invention obtains the first control cycle current of the switched reluctance motor and determines the first control cycle voltage corresponding to the switch state in the switched reluctance motor. Then, based on the first control cycle current and the first control cycle voltage, a first control cycle error potential model is constructed, the first control cycle error potential model is solved, and the model parameters are predicted. The third control cycle current is predicted based on the model parameters. Then, based on a preset finite set predicted current control objective function, the optimal switch state of the second control cycle of the switched reluctance motor is determined according to the third control cycle current and the preset third control cycle expected current, and the switched reluctance motor is controlled according to the optimal switch state. By implementing this technical solution, through the finite set predicted current control that takes into account the dynamic linearization of the model error, the problem that the current of the switched reluctance motor cannot reflect the current nonlinearity and time-varying characteristics of the actual system under conventional linearization modeling, thereby causing current tracking error and resulting in low motion control accuracy is solved, and high-precision control of the switched reluctance motor can be achieved.

[0153] Example 3

[0154] Figure 7 This is a schematic diagram of the structure of the switch reluctance motor predictive current control device provided by the third embodiment of the present invention. Figure 7 As shown, the device includes:

[0155] A first control period current acquisition module 701 is used to acquire a first control period current of the switched reluctance motor;

[0156] A first control cycle voltage determination module 702 is configured to determine a first control cycle voltage corresponding to a switch state in the switched reluctance motor;

[0157] A first control period error potential model building module 703 is configured to build a first control period error potential model according to the first control period current and the first control period voltage;

[0158] A model parameter determination module 704 is configured to determine model parameters based on the first control cycle error potential model; wherein the model parameters are pseudo partial derivatives of the second control cycle error potential model;

[0159] A second control period error potential equation construction module 705 is used to construct a second control period error potential equation according to the model parameters;

[0160] A third control period current prediction module 706 is configured to solve the second control period error potential equation and predict the third control period current;

[0161] The optimal switching state determination module 707 is used to determine the optimal switching state of the second control cycle of the switched reluctance motor based on a preset finite set predicted current control objective function, according to the third control cycle current and the preset third control cycle expected current, and control the switched reluctance motor according to the optimal switching state; wherein the finite set predicted current control objective function is a finite set predicted current control objective function based on time delay compensation.

[0162] Optionally, the first control period voltage determination module 702 is specifically configured to:

[0163] Get the switch status in the switched reluctance motor;

[0164] determining a loop topology corresponding to the switch state according to the switch state;

[0165] The first control cycle voltage corresponding to the loop topology is matched based on a predetermined correlation between the loop topology and the equivalent action voltage.

[0166] Optionally, the first control period error potential model building module 703 is specifically configured to:

[0167] Based on linear lossless energy conversion and linear distributed inductance curve, a conventional linearized switched reluctance motor current model is constructed;

[0168] The conventional linearized switched reluctance motor current model is split to determine an error potential model; wherein the error potential model is composed of the model error and the unmodeled portion of the back electromotive force;

[0169] The first control period current and the first control period voltage are substituted into the error potential model to construct a first control period error potential model.

[0170] Optionally, the model parameter determination module 704 includes:

[0171] An optimal problem model construction unit, configured to construct an optimal problem model; wherein the optimal problem model is determined based on an error potential equation;

[0172] A model parameter determination unit is used to determine model parameters according to the first control period error potential model and the optimal problem model.

[0173] Optionally, the model parameter determination unit is specifically used to:

[0174] transforming the first control period error potential model to obtain a target first control period error potential model;

[0175] Substituting the target first control period error potential model into the optimal problem model to obtain a target optimal problem model;

[0176] Model parameters are determined based on the first-order partial derivatives and the second-order partial derivatives of the target optimal problem model.

[0177] Optionally, the optimal switch state determination module 707 is specifically configured to:

[0178] The finite set predictive current control objective function is constructed using the following formula;

[0179] ;

[0180] in, For a finite set of predictive current control objective functions, is the expected current in the third control cycle, is the third control cycle current, is the number of switching times from the second control period switching state to the third control period switching state, and is the weight coefficient.

[0181] Optionally, the optimal switch state determination module 707 is further configured to:

[0182] Substituting the third control period current and the preset third control period expected current into a preset finite set predicted current control objective function to determine an objective function value;

[0183] An optimal objective function value is determined from the objective function values, and an optimal switching state corresponding to the optimal objective function value is determined.

[0184] The switch reluctance motor predictive current control device provided in the embodiment of the present invention can execute the switch reluctance motor predictive current control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0185] Example 4

[0186] Figure 8A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0187] like Figure 8 As shown, electronic device 10 includes at least one processor 11 and memory, such as read-only memory (ROM) 12 and random access memory (RAM) 13, communicatively connected to at least one processor 11. The memory stores computer programs executable by the at least one processor. Processor 11 can perform various appropriate actions and processes based on the computer programs stored in ROM 12 or loaded from storage unit 18 into RAM 13. RAM 13 can also store various programs and data required for the operation of electronic device 10. Processor 11, ROM 12, and RAM 13 are interconnected via bus 14. An input / output (I / O) interface 15 is also connected to bus 14.

[0188] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0189] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the switched reluctance motor predictive current control method.

[0190] In some embodiments, the switched reluctance motor predictive current control method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the switched reluctance motor predictive current control method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the switched reluctance motor predictive current control method in any other suitable manner (e.g., via firmware).

[0191] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0192] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0193] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0194] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0195] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0196] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0197] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0198] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for predictive current control of a switched reluctance motor, characterized in that: include: Obtaining a first control cycle current of the switched reluctance motor; determining a first control cycle voltage corresponding to a switch state in the switched reluctance motor; constructing a first control period error potential model according to the first control period current and the first control period voltage; Determining model parameters based on the first control cycle error potential model; wherein the model parameters are pseudo partial derivatives of the second control cycle error potential model; constructing a second control period error potential equation according to the model parameters; Solving the error potential equation for the second control period to predict the current for the third control period; Based on a preset finite set predicted current control objective function, the optimal switching state of the second control cycle of the switched reluctance motor is determined according to the third control cycle current and the preset third control cycle expected current, and the switched reluctance motor is controlled according to the optimal switching state; wherein the finite set predicted current control objective function is a finite set predicted current control objective function based on time delay compensation.

2. The method according to claim 1, characterized in that Determining a first control cycle voltage corresponding to a switch state in the switched reluctance motor includes: Get the switch status in the switched reluctance motor; determining a loop topology corresponding to the switch state according to the switch state; The first control cycle voltage corresponding to the loop topology is matched based on a predetermined correlation between the loop topology and the equivalent action voltage.

3. The method according to claim 1, characterized in that Constructing a first control period error potential model according to the first control period current and the first control period voltage, including: Based on linear lossless energy conversion and linear distributed inductance curve, a conventional linearized switched reluctance motor current model is constructed; The conventional linearized switched reluctance motor current model is split to determine an error potential model; wherein the error potential model is composed of the model error and the unmodeled portion of the back electromotive force; The first control period current and the first control period voltage are substituted into the error potential model to construct a first control period error potential model.

4. The method according to claim 1, wherein Determining model parameters based on the first control period error potential model includes: Constructing an optimal problem model; wherein the optimal problem model is determined based on an error potential equation; Model parameters are determined according to the first control period error potential model and the optimal problem model.

5. The method according to claim 4, characterized in that Determining model parameters based on the first control period error potential model and the optimal problem model includes: transforming the first control period error potential model to obtain a target first control period error potential model; Substituting the target first control period error potential model into the optimal problem model to obtain a target optimal problem model; Model parameters are determined based on the first-order partial derivatives and the second-order partial derivatives of the target optimal problem model.

6. The method according to claim 1, characterized in that The finite set predicted current control objective function includes: The finite set predictive current control objective function is constructed using the following formula; ； in, For a finite set of predictive current control objective functions, is the expected current in the third control cycle, is the third control cycle current, is the number of switching times from the second control period switching state to the third control period switching state, and is the weight coefficient.

7. The method according to claim 1, characterized in that Determining an optimal switching state of the second control period of the switched reluctance motor based on a preset finite set predicted current control objective function and according to the third control period current and a preset third control period expected current includes: Substituting the third control period current and the preset third control period expected current into a preset finite set predicted current control objective function to determine an objective function value; An optimal objective function value is determined from the objective function values, and an optimal switching state corresponding to the optimal objective function value is determined.

8. A switch reluctance motor predictive current control device, characterized in that: include: A first control period current acquisition module, configured to acquire a first control period current of the switched reluctance motor; A first control period voltage determination module, configured to determine a first control period voltage corresponding to a switch state in the switched reluctance motor; a first control period error potential model building module, configured to build a first control period error potential model according to the first control period current and the first control period voltage; A model parameter determination module, configured to determine model parameters based on the first control period error potential model; wherein the model parameters are pseudo partial derivatives of the second control period error potential model; A second control period error potential equation construction module, configured to construct a second control period error potential equation according to the model parameters; A third control period current prediction module, configured to solve the second control period error potential equation and predict the third control period current; An optimal switching state determination module is used to determine the optimal switching state of the second control cycle of the switched reluctance motor based on a preset finite set predicted current control objective function, according to the third control cycle current and the preset third control cycle expected current, and control the switched reluctance motor according to the optimal switching state; wherein the finite set predicted current control objective function is a finite set predicted current control objective function based on time delay compensation.

9. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the switched reluctance motor predictive current control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the switch reluctance motor predictive current control method according to any one of claims 1 to 7 when executed.

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