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

Abstract

The invention discloses a switch reluctance motor predictive current control method and device, electronic equipment and a medium. The method comprises the following steps: acquiring a first control period current of the switched reluctance motor; determining a first control period voltage corresponding to the switching 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 according to the first control period error potential model; constructing a second control period error potential equation according to the model parameters; solving the second control period error potential equation, and predicting a third control period current; and based on a preset finite set predictive current control target function, determining an optimal switching state of the second control period according to the third control period current and a preset third control period expected current, and controlling the switched reluctance motor according to the optimal switching state. According to the technical scheme, high-precision control of the switched reluctance motor can be realized.

Description

Technical Field

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

[0002] The switched reluctance motor is a new type of power motor for new energy passenger and freight electric vehicles, with advantages such as simple structure, low cost, and high reliability, and has great application prospects in application environments such as high load and large starting torque. The high-precision operation of the switched reluctance motor is the focus of attention of various research institutions and industries. Its precise current control directly affects the synthetic torque of the switched reluctance motor and thus determines the speed stability. Therefore, how to achieve high-precision control of the current of the switched reluctance motor is a key technical problem that needs to be solved urgently for it to have high rotational speed trajectory tracking accuracy.

[0003] Currently, the current control method for switched reluctance motors generally uses PI (Proportional Integral) control. However, due to the strong non-linearity and time-varying characteristics of the current model of the switched reluctance motor, it is difficult for conventional current control methods to achieve high-precision current control. Summary of the Invention

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

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

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

[0007] Determining the voltage of the first control period corresponding to the switch state in the switched reluctance motor;

[0008] Constructing a first control period error electromotive force model according to the current of the first control period and the voltage of the first control period;

[0009] Determining model parameters based on the first control period error electromotive force model; wherein, the model parameters are the pseudo partial derivatives of the second control period error electromotive force model;

[0010] Constructing a second control period error electromotive force equation according to the model parameters;

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

[0012] Based on a preset finite set predictive current control objective function, determine the optimal switching state of the second control period of the switched reluctance motor according to the current of the third control period and the preset desired current of the third control period, and control the switched reluctance motor according to the optimal switching state; wherein, the finite set predictive current control objective function is a finite set predictive current control objective function based on time delay compensation.

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

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

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

[0016] A first control period error electromotive force model construction module, configured to construct a first control period error electromotive force model according to the current of the first control period and the voltage of the first control period;

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

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

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

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

[0021] According to another aspect of the present invention, there is provided an electronic device, the electronic device 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 switched reluctance motor predictive current control method according to any embodiment of the present invention.

[0023] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the predictive current control method of the switched reluctance motor according to any embodiment of the present invention when executed.

[0024] The technical solution of the embodiment of the present invention solves the problem of low motion control accuracy caused by the current tracking error due to the inability of the current of the switched reluctance motor to reflect the current non-linearity and time-varying characteristics of the actual system under conventional linearized modeling through the finite set predictive current control with dynamic linearization considering model errors, and can achieve high-precision control of the switched reluctance motor.

[0025] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily 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 will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 is a flowchart of the predictive current control method of the switched reluctance motor according to Embodiment 1 of the present invention;

[0028] Figure 2 is a flowchart of the data-driven finite set predictive current control method of the switched reluctance motor according to Embodiment 1 of the present application;

[0029] Figure 3 is a schematic diagram of the power topology in the excitation stage according to Embodiment 1 of the present application;

[0030] Figure 4 is a schematic diagram of the power topology in the demagnetization stage according to Embodiment 1 of the present application;

[0031] Figure 5 is a schematic diagram of the power topology in the freewheeling stage according to Embodiment 1 of the present application;

[0032] Figure 6 is a schematic diagram of the predictive current control process of the switched reluctance motor according to Embodiment 2 of the present invention;

[0033] Figure 7 is a schematic diagram of the structure of the predictive current control device of the switched reluctance motor according to Embodiment 3 of the present invention;

[0034] Figure 8 It is a schematic structural diagram of an electronic device for implementing the predictive current control method of the switched reluctance motor according to the embodiments of the present invention. Specific embodiments

[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 accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope 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 do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0037] Embodiment 1

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

[0039] S101. Obtain the current of the first control period of the switched reluctance motor.

[0040] In this solution, the operation of the switched reluctance motor follows the principle of minimum reluctance, that is, the magnetic flux always closes along the path with the minimum reluctance. When the iron core with a certain shape moves to the position of minimum reluctance, its main axis will coincide with the axis of the magnetic field. When a certain phase winding of the stator is energized, the rotor salient pole will rotate in the direction that minimizes the reluctance of the phase winding, that is, the direction in which the rotor salient pole aligns with the salient pole of the energized phase of the stator. By controlling the amplitude, width of the current pulse applied to the motor winding and its relative position with respect to the rotor (i.e., conduction angle, turn-off angle), the magnitude and direction of the motor torque can be controlled, thus realizing speed control.

[0041] Among them, the current in the first control period can refer to the current at time K. The current in the first control period of the switched reluctance motor can be obtained based on a current sensor; it can also be measured based on a sampling resistor; or the current in the first control period of the switched reluctance motor can be obtained using a controller algorithm.

[0042] In this solution, Figure 2 is a flowchart of the data-driven finite set predictive current control method for a switched reluctance motor provided in the first embodiment of this application, as Figure 2 shown, obtain the current in the first control period of the switched reluctance motor .

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

[0044] Among them, the voltage in the first control period can refer to the voltage at time K.

[0045] In this embodiment, the switch state includes three switch states, which are respectively , and . Among them, is the excitation stage of the switched reluctance motor; is the freewheeling stage of the switched reluctance motor; is the demagnetization stage of the switched reluctance motor.

[0046] In this solution, the voltages of the switched reluctance motor in different switch states are different. The correlation between the switch state and the equivalent operating voltage is known. After obtaining the switch state in the switched reluctance motor, based on the correlation between the switch state and the equivalent operating voltage, determine the voltage in the first control period corresponding to the switch state in the switched reluctance motor.

[0047] Among them, as Figure 2 shown, obtain the switch state according to the stored equivalent operating voltage at time K and apply it.

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

[0049] Step A1, obtaining the switching state in the switched reluctance motor;

[0050] In this solution, the switching state in the switched reluctance motor can be determined based on the operating state, rotor position, or control strategy of the switched reluctance motor.

[0051] Step A2, determining the circuit topology corresponding to the switching state according to the switching state;

[0052] In this embodiment, Figure 3 is a schematic diagram of the power topology in the excitation stage provided in Embodiment 1 of the present application, Figure 4 is a schematic diagram of the power topology in the demagnetization stage provided in Embodiment 1 of the present application, Figure 5 is a schematic diagram of the power topology in the freewheeling stage provided in Embodiment 1 of the present application, as Figure 3 , Figure 4 , Figure 5 shown, different switching states correspond to different circuit topologies.

[0053] Furthermore, the circuit topology can be obtained based on the switching state in the switched reluctance motor.

[0054] Step A3, matching the first control cycle voltage corresponding to the circuit topology from the pre - determined correlation between the circuit topology and the equivalent action voltage.

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

[0056] Table 1

[0057]

[0058] Wherein, is the measured current; is the DC bus voltage; is the equivalent resistance of the circuit.

[0059] Specifically, after obtaining the switching state in the switched reluctance motor, the first control cycle voltage can be determined based on the switching state in the switched reluctance motor.

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

[0061] S103. Construct a first control cycle error electromotive force model based on the first control cycle current and the first control cycle voltage.

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

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

[0064] Furthermore, substitute the first control cycle current and the first control cycle voltage into the error electromotive force model to construct a first control cycle error electromotive force model.

[0065] Optionally, constructing a first control cycle error electromotive force model based on the first control cycle current and the first control cycle voltage includes steps B1 - B3:

[0066] Step B1. Construct a conventional linearized switched reluctance motor current model based on linear lossless energy conversion and linear distributed inductance curves;

[0067] Step B2. Split the conventional linearized switched reluctance motor current model to determine the error electromotive force model; among them, the error electromotive force model is composed of model error and the unmodeled part of the back electromotive force;

[0068] Step B3. Substitute the first control cycle current and the first control cycle voltage into the error electromotive force model to construct a first control cycle error electromotive force model.

[0069] Among them, linear lossless energy conversion means that in the energy conversion process, a linear relationship is satisfied and there is no energy loss; the linear distributed inductance curve is a curve that describes the linear relationship between the inductance value in an inductive element and certain variables.

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

[0071] ;

[0072] Among them, is the nominal resistance value of the winding; is the difference between the nominal resistance value and the actual resistance value; is the first - order derivative of the inductance function with respect to the rotor position; is the nominal value of the winding inductance; is the difference between the first derivative and the derivative of the actual inductance-position function; is the difference between the nominal inductance value and the actual inductance value; is the unmodeled part of the system.

[0073] The conventional linearized current model of the switched reluctance motor is split, that is, the difference in the conventional linearized current model of the switched reluctance motor is separated from the unmodeled part, and the specific split is as follows:

[0074] ;

[0075] ;

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

[0077] Specifically, considering the influence of nonlinearity and current on inductance, and the general characteristics of the actual system, 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. According to the controlled object being an L (inductance)-R (resistance) circuit system, and regarding current and position as functions of time, it is assumed that the functional characteristics of the ideal mathematical model parameters are first-order differentiable, and the second-order partial derivative exists and is continuous. Considering the system parameters and other variables in the error electromotive force model as constant values within the control period, then according to backward difference, we can obtain , that is, the error electromotive force model is and function. And according to the generalized Lipschitz condition ; where: and , , .

[0078] Furthermore, analyzing from the L-R circuit system, when the current switches from state to state , the model parameter error and the unmodeled back electromotive force error caused are both finite. Therefore, there exists a positive number b that satisfies the generalized Lipschitz condition, and the hypothesis can be proposed: satisfies the generalized Lipschitz condition.

[0079] In this solution, under the condition of meeting the optimization goal, the system parameters used in the control period are made to fit the actual system better. The error electromotive force model can be simplified to:

[0080] ;

[0081] Among them, ; ; is the sampling frequency of forward Euler discretization; is the current at time

[0082] Specifically, as Figure 2 shown, the first control cycle error potential model can be obtained according to the above formula as .

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

[0084] S104. Determine the model parameters according to the first control cycle error potential model; among them, the model parameters are the pseudo partial derivatives of the second control cycle error potential model.

[0085] In this embodiment, by iteratively solving the first control cycle error potential model, the second control cycle current is predicted, and the pseudo partial derivative of the second control cycle error potential model is predicted according to the second control cycle current. Among them, the second control cycle current may refer to the current at time .

[0086] In this solution, as Figure 2 shown, the model parameters are iteratively solved by the first control cycle error potential model .

[0087] S105. Construct a second control cycle 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 a second control error potential equation is 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 premise of CFDL (Computational Fluid Dynamics) is satisfied, an error potential equation is constructed based on the error potential model. The error potential equation is:

[0090] ;

[0091] Among them: , are the estimated error potential models 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] Furthermore, as Figure 2 shown, the second control cycle error potential equation can be obtained according to the above formula as . Among them, is the second control cycle error potential model, is the first control cycle error potential model, is the model parameter.

[0093] S106. Solve the second control cycle error potential equation to predict the current in the third control cycle.

[0094] In this solution, the current in the third control cycle is predicted by solving the second control cycle error potential equation. Among them, the current in the third control cycle can refer to the current at time .

[0095] Specifically, as Figure 2 shown, the calculation formula for the current in the third control cycle is: .

[0096] S107. Predict the current control objective function based on a preset finite set. Determine the optimal switching state of the switched reluctance motor in the second control cycle according to the current in the third control cycle and the expected current in the preset third control cycle, and control the switched reluctance motor according to the optimal switching state; among them, the finite set prediction current control objective function is a finite set prediction current control objective function based on time delay compensation.

[0097] Among them, the finite set prediction current control is based on the idea of model predictive control and considers the discrete models of the power converter and the motor. In each control cycle, according to the current motor state (such as current, voltage, etc.) and the system reference instruction (such as the given current value), the optimal switching state in the next control cycle is determined by solving a finite set optimization problem.

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

[0099] Specifically, substitute the third control cycle current, the third control cycle expected current, and the number of switch state transitions from the second control cycle switch state to the third control cycle switch state into the finite set predictive current control objective function, solve the optimal finite set predictive current control objective function, and then based on the optimal finite set predictive current control objective function, determine the switch state corresponding to the optimal finite set predictive current control objective function, and use this switch state as the optimal switch state. Furthermore, control the operation of the switched reluctance motor based on the optimal switch state.

[0100] Optionally, it includes:

[0101] Construct the finite set predictive current control objective function using the following formula;

[0102] ;

[0103] Wherein, is the finite set predictive current control objective function, is the third control cycle expected current, is the third control cycle current, is the number of switch state transitions from the second control cycle switch state to the third control cycle switch state, and are weight coefficients.

[0104] Specifically, as Figure 2 shown, considering delay compensation, construct the finite set predictive current control objective function based on the expected current, predicted current, and number of switch state transitions.

[0105] By constructing the 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 predictive current control objective function, determine the optimal switch 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, including steps C1 - C2:

[0107] Step C1: Substitute the third control cycle current and the preset third control cycle expected current into the preset finite set predictive current control objective function to determine the objective function value;

[0108] In this solution, substitute the third control cycle current and the preset third control cycle expected current into the preset finite set predictive current control objective function respectively to calculate the objective function values under different switch states. Among them, the number of objective function values is the same as the number of switch states.

[0109] Step C2: Determine the optimal objective function value from the objective function values, and determine the optimal switch state corresponding to the optimal objective function value.

[0110] In this embodiment, select an objective function value 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, the switch state corresponding to the optimal objective function value can be determined, and this switch state can be used as the optimal switch state.

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

[0113] The technical solution of the embodiment of the present invention is to obtain the current of the first control period of the switched reluctance motor, and determine the voltage of the first control period corresponding to the switch state in the switched reluctance motor. Then, according to the current of the first control period and the voltage of the first control period, a first control period error electromotive force model is constructed, and the first control period error electromotive force model is solved to predict the model parameters. And the current of the third control period is predicted according to the model parameters. Then, based on the preset finite set predictive current control objective function, according to the current of the third control period and the preset expected current of the third control period, the optimal switch state of the second control period of the switched reluctance motor is determined, and the switched reluctance motor is controlled according to the optimal switch state. By implementing this technical solution, through the finite set predictive current control considering 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 the conventional linearized modeling, thereby causing current tracking errors and resulting in low motion control accuracy, can be solved, and high-precision control of the switched reluctance motor can be achieved.

[0114] Embodiment 2

[0115] Figure 6 It is a schematic diagram of the predictive 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. As Figure 6 shown, the method includes:

[0116] S601: Obtain the current of the first control period of the switched reluctance motor.

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

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

[0119] S604. Construct the optimal problem model, where the optimal problem model is determined based on the error potential equation.

[0120] In this solution, the optimal problem model can be constructed based on the error potential equation at the historical moment. When the optimal problem model meets the optimization goal, the system parameters used in the previous control cycle are more in line with the actual system.

[0121] Specifically, the optimal problem model is:

[0122] .

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

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

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

[0126] Step D1. Transform the first control cycle error potential model to obtain the target first control cycle error potential model.

[0127] Step D2. Substitute the target first control cycle error potential model into the optimal problem model to obtain the target optimal problem model.

[0128] Step D3. Determine the model parameters according to the first - order partial derivative and the second - order partial derivative of the target optimal problem model.

[0129] Specifically, rewrite the error potential model as an expression at the k - 1 moment;

[0130] ;

[0131] ;

[0132] Where is the variable predicted value, is the variable actual value.

[0133] The above formula is a mathematical model composed of a linearized model and a data - driven error potential model, which reflects the relationship between the true error potential and the actual current. The current measurement error and the error under the voltage averaging effect of pulse width modulation will be ignored in this process.

[0134] Subtracting the above two formulas gives ;

[0135] The error potential model at the (k - 1)th moment obtained currently is:

[0136] ;

[0137] where Compared with , it should be such that 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. Therefore, using to replace and forming an optimal problem model.

[0138] Solving this optimal problem model by the gradient method, the target first - control - cycle error potential model is obtained, that is, The expression is:

[0139] ;

[0140] Specifically, substituting the target first - control - cycle error potential model into the optimal problem model, the target optimal problem model is obtained. Among them, the target optimal problem model is:

[0141] ;

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

[0143] ;

[0144] Let , a stationary - point equation is obtained, and then solving the second - order partial - derivative gives;

[0145] ;

[0146] Since the weight coefficient , the conclusion that the above formula is greater than zero holds, and this stationary point is a minimum value. The explicit expression obtained is:

[0147] .

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

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

[0150] S607. Solve the second control cycle error potential equation to predict the current in the third control cycle.

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

[0152] The technical solution of the embodiment of the present invention obtains the current in the first control cycle of the switched reluctance motor and determines the voltage in the first control cycle corresponding to the switching state in the switched reluctance motor. Then, according to the current in the first control cycle and the voltage in the first control cycle, construct a first control cycle error potential model, solve the first control cycle error potential model to predict the model parameters. And predict the current in the third control cycle according to the model parameters. Then, based on a preset finite set prediction current control objective function, according to the current in the third control cycle and the preset expected current in the third control cycle, determine the optimal switching state of the switched reluctance motor in the second control cycle, and control the switched reluctance motor according to the optimal switching state. By implementing this technical solution, through the finite set prediction current control considering 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 the conventional linearized modeling, thus causing current tracking errors and resulting in low motion control accuracy, can be solved, and high-precision control of the switched reluctance motor can be achieved.

[0153] Embodiment III

[0154] Figure 7 It is a schematic structural diagram of a switched reluctance motor predictive current control device provided in Embodiment III of the present invention. As Figure 7 shown, the device includes:

[0155] The first control cycle current acquisition module 701 is used to acquire the current in the first control cycle of the switched reluctance motor;

[0156] The first control cycle voltage determination module 702 is used to determine the voltage in the first control cycle corresponding to the switching state in the switched reluctance motor;

[0157] The first control cycle error potential model construction module 703 is used to construct a first control cycle error potential model according to the current in the first control cycle and the voltage in the first control cycle;

[0158] The model parameter determination module 704 is used to determine the model parameters according to the first control cycle error potential model; wherein, the model parameters are the pseudo partial derivatives of the second control cycle error potential model.

[0159] The second control cycle error electromotive force equation construction module 705 is configured to construct a second control cycle error electromotive force equation according to the model parameters;

[0160] The third control cycle current prediction module 706 is configured to solve the second control cycle error electromotive force equation to predict the third control cycle current;

[0161] The optimal switch state determination module 707 is configured to predict a current control objective function based on a preset finite set, determine an optimal switch state of a second control cycle of the switched reluctance motor according to the third control cycle current and a preset third control cycle desired current, and control the switched reluctance motor according to the optimal switch state; wherein, the finite set prediction current control objective function is a finite set prediction current control objective function based on time delay compensation.

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

[0163] Obtain the switch state in the switched reluctance motor;

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

[0165] Match a first control cycle voltage corresponding to the loop topology from a pre-determined association relationship between the loop topology and the equivalent acting voltage.

[0166] Optionally, the first control cycle error electromotive force model construction module 703 is specifically configured to:

[0167] Construct a conventional linearized switched reluctance motor current model based on linear lossless energy conversion and a linear distributed inductance curve;

[0168] Split the conventional linearized switched reluctance motor current model to determine an error electromotive force model; wherein, the error electromotive force model is composed of a model error and an unmodeled part in the back electromotive force;

[0169] Substitute the first control cycle current and the first control cycle voltage into the error electromotive force model to construct a first control cycle error electromotive force 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 electromotive force equation;

[0172] A model parameter determination unit configured to determine model parameters according to the first control cycle error electromotive force model and the optimal problem model.

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

[0174] Transform the first control cycle error potential model to obtain a target first control cycle error potential model;

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

[0176] Determine the model parameters according to the first-order partial derivative and second-order partial derivative of the target optimal problem model.

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

[0178] Construct a finite set predictive current control objective function using the following formula;

[0179] ;

[0180] Wherein, is the finite set predictive current control objective function, is the expected current in the third control cycle, is the current in the third control cycle, is the number of switchings from the switch state in the second control cycle to the switch state in the third control cycle, and are weight coefficients.

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

[0182] Substitute the current in the third control cycle and the preset expected current in the third control cycle into the preset finite set predictive current control objective function to determine the objective function value;

[0183] Determine the optimal objective function value from the objective function values, and determine the optimal switch state corresponding to the optimal objective function value.

[0184] The switched reluctance motor predictive current control device provided by the embodiments of the present invention can execute the switched reluctance motor predictive current control method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0185] Embodiment 4

[0186] Figure 8The structural schematic diagram of an electronic device 10 that can be used to implement the embodiments 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 processors, cellular phones, smart phones, 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] As Figure 8 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0188] A plurality of 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 through a computer network such as the Internet and / or various telecommunication networks.

[0189] The processor 11 can be various general-purpose and / or special-purpose processing components 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 dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any 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 may 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 may be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the switched reluctance motor predictive current control method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the switched reluctance motor predictive current control method by any other suitable means (e.g., by means of firmware).

[0191] The various embodiments of the systems and techniques described above in this document may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0192] The 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 apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs 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 can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0194] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, 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, speech input, or tactile input).

[0195] The systems and techniques described herein can be implemented in a computing system that includes backend 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 frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend 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: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0196] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs that run on respective computers and have a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0197] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed 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, and no limitation is made herein.

[0198] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A predictive current control method for a switched reluctance motor, characterized in that, Including: Obtain the current of the first control period of the switched reluctance motor; Determine the voltage of the first control period corresponding to the switch state in the switched reluctance motor; Construct a first control period error electromotive force model according to the current of the first control period and the voltage of the first control period; Determine model parameters according to the first control period error electromotive force model; wherein, the model parameters are the pseudo partial derivatives of the second control period error electromotive force model; Construct a second control period error electromotive force equation according to the model parameters; Solve the second control period error electromotive force equation to predict the current of the third control period; Based on a preset finite set prediction current control objective function, determine the optimal switch state of the second control period of the switched reluctance motor according to the current of the third control period and the preset expected current of the third control period, and control the switched reluctance motor according to the optimal switch state; wherein, the finite set prediction current control objective function is a finite set prediction current control objective function based on time delay compensation.

2. The method according to claim 1, wherein Determining the voltage of the first control period corresponding to the switch state in the switched reluctance motor includes: Obtain the switch state in the switched reluctance motor; Determine the loop topology corresponding to the switch state according to the switch state; Match the voltage of the first control period corresponding to the loop topology from the pre-determined association relationship between the loop topology and the equivalent action voltage.

3. The method according to claim 1, wherein Constructing a first control period error electromotive force model according to the current of the first control period and the voltage of the first control period includes: Construct a conventional linearized switched reluctance motor current model based on linear lossless energy conversion and linear distributed inductance curve; Split the conventional linearized switched reluctance motor current model to determine an error electromotive force model; wherein, the error electromotive force model is composed of model error and the unmodeled part in the back electromotive force; Substitute the current of the first control period and the voltage of the first control period into the error electromotive force model to construct a first control period error electromotive force model.

4. The method according to claim 1, wherein Determining model parameters according to the first control period error electromotive force model includes: Construct an optimal problem model; wherein, the optimal problem model is determined based on the error electromotive force equation; Determine model parameters according to the first control period error electromotive force model and the optimal problem model.

5. The method according to claim 4, wherein Determining model parameters according to the first control period error electromotive force model and the optimal problem model includes: Transform the first control period error electromotive force model to obtain a target first control period error electromotive force model; Substitute the target first control period error electromotive force model into the optimal problem model to obtain a target optimal problem model; Determine model parameters according to the first-order partial derivative and second-order partial derivative of the target optimal problem model.

6. The method according to claim 1, characterized in that, The finite set prediction current control objective function includes: Construct a finite set prediction current control objective function using the following formula; ； Among them, is the finite set predictive current control objective function, is the expected current in the third control period, is the current in the third control period, is the number of switch state transitions from the second control period to the third control period, and are weighting coefficients.

7. The method according to claim 1, wherein Based on a preset finite set prediction current control objective function, determining the optimal switch state of the second control period of the switched reluctance motor according to the current of the third control period and the preset expected current of the third control period includes: Substitute the third control cycle current and the preset expected current of the third control cycle into the preset finite set predictive current control objective function to determine the objective function value; Determine the optimal objective function value from the objective function values, and determine the optimal switching state corresponding to the optimal objective function value.

8. The predictive current control device for a switched reluctance motor is characterized in that, Including: A first control cycle current acquisition module for acquiring the first control cycle current of the switched reluctance motor; A first control cycle voltage determination module for determining the first control cycle voltage corresponding to the switching state in the switched reluctance motor; A first control cycle error electromotive force model construction module for constructing a first control cycle error electromotive force model according to the first control cycle current and the first control cycle voltage; A model parameter determination module for determining model parameters based on the first control cycle error electromotive force model; wherein, the model parameters are the pseudo partial derivatives of the second control cycle error electromotive force model; A second control cycle error electromotive force equation construction module for constructing a second control cycle error electromotive force equation according to the model parameters; A third control cycle current prediction module for solving the second control cycle error electromotive force equation to predict the third control cycle current; An optimal switching state determination module for determining the optimal switching state of the second control cycle of the switched reluctance motor based on a preset finite set predictive current control objective function, according to the third control cycle current and the preset expected current of the third control cycle, and controlling the switched reluctance motor according to the optimal switching state; wherein, the finite set predictive current control objective function is a finite set predictive current control objective function based on time delay compensation.

9. An electronic device, characterized in that, The electronic device includes: 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 switched reluctance motor predictive current control method according to any one of claims 1-7.

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

Images