Switched reluctance motor current control method and system based on fuzzy logic

Abstract

The application belongs to the field of motor control and provides a fuzzy logic-based switched reluctance motor current control method and system, which adopts a control structure of a speed loop combining a fuzzy logic-based cost function to optimize duty ratio, wherein the speed loop adopts a preprocessed speed deviation as input to adjust speed, and outputs as reference current and reference torque; meanwhile, current, position and voltage information are used to predict current of the next period, and actual torque is obtained by looking up a table according to current and position information to compensate reference current output by the speed loop; then, error processing is performed on the compensated control current and actual current, and the sum of squares of the error is combined with reference torque and actual torque of each phase as input of the cost function, and the fuzzy logic-based cost function is used to optimize duty ratio, so as to realize accurate control of three-phase current and further realize suppression of motor output torque ripple.

Description

Technical Field

[0001] This invention belongs to the field of motor control technology, specifically relating to a current control method and system for a switched reluctance motor based on fuzzy logic. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Switched reluctance motors (SRMs) have been successfully applied in many fields due to their simple structure, low manufacturing cost, strong fault tolerance, high operational reliability, and high efficiency over a wide speed range. However, the unique doubly salient pole structure and the high saturation characteristics of the magnetic circuit during operation of SRMs make their electromagnetic torque a nonlinear function of the rotor position angle and stator phase current. This characteristic of SRMs results in large instantaneous output torque ripple. Reducing the torque ripple of SRMs has become a research hotspot in the fields of electrical drives and motor control.

[0004] Most current control strategies for torque ripple are based on traditional current hysteresis control methods, which suffer from problems such as inconsistent switching frequencies and noise disturbances. Limited by the switching frequency of power devices, traditional hysteresis control strategies also exhibit issues such as the actual current not accurately following the reference current and significant overshoot. Furthermore, using a fixed switching frequency and duty cycle means the switching device remains in an on or off state throughout a switching cycle, resulting in low current control and the occurrence of high-frequency current pulse peaks, especially at locations with low inductance, causing current spikes that can easily damage power devices. Generally, to achieve maximum torque in the shortest time, the current must rise to its peak value quickly. However, using a current chopping strategy with a fixed duty cycle in the PWM control cycle leads to significant current overshoot within a single sampling cycle, especially under heavy loads or low speeds, where the current can rise to very high levels and damage power devices. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a current control method and system for switched reluctance motors based on fuzzy logic. The invention proposes a method to predict the current at the next moment based on current speed, current, voltage, and other information. Then, error processing is performed between the reference current output by the speed loop and the predicted current to obtain the current error to be compensated. By compensating the reference current, the torque output capability of the winding in the commutation interval is enhanced. To address the poor anti-interference performance of a fixed duty cycle, a method based on a fuzzy logic cost function is proposed to optimize the duty cycle, thereby optimizing the actual output torque of the motor and reducing torque ripple.

[0006] According to some embodiments, the first aspect of the present invention provides a current control method for a switched reluctance motor based on fuzzy logic, employing the following technical solution:

[0007] A current control method for switched reluctance motors based on fuzzy logic includes:

[0008] Obtain the actual speed of the switched reluctance motor, determine the speed deviation based on the given reference speed, and perform preprocessing.

[0009] Based on the pre-processed speed deviation control speed closed loop, the reference current and reference torque of the switched reluctance motor are obtained;

[0010] Based on the current rotor position signal of the switched reluctance motor, current and torque are distributed to determine the reference current and reference torque of each conducting phase.

[0011] Predict the current of the switched reluctance motor at the next moment based on the current actual speed, actual current and rotor position of the switched reluctance motor.

[0012] The control current of each conducting phase is determined by performing relative error processing on the reference current of each conducting phase and the predicted current of the switched reluctance motor.

[0013] The fuzzy logic-based cost function optimizes the duty cycle of the conducting phase, and controls the turn-off and turn-on duty cycles of the power converter of the switched reluctance motor based on the current rotor position signal of the switched reluctance motor and the optimized duty cycle of the conducting phase, thereby achieving control of the switched reluctance motor current; wherein, the optimization of the duty cycle of the conducting phase based on the fuzzy logic-based cost function specifically includes:

[0014] Based on the membership degrees of current error, current change rate, and torque ripple weighting factor, a control rule table for adjusting the torque ripple weighting factor is determined according to different application scenarios. Based on this control rule table, the optimal torque ripple weighting factor is obtained through fuzzy logic optimization, which in turn yields the optimal current ripple weighting factor. Using the optimal torque ripple weighting factor and the optimal current ripple weighting factor, a fuzzy logic-based cost function is constructed. When the fuzzy logic-based cost function is minimized, the optimal duty cycle of the conducting phase is determined.

[0015] Furthermore, the step of obtaining the actual speed of the switched reluctance motor, determining the speed deviation based on a given reference speed, and performing preprocessing includes:

[0016] Collect rotor position signals for each phase of the switched reluctance motor;

[0017] The actual speed of the motor is calculated based on the rotor position signal;

[0018] The speed deviation is determined based on the given reference speed and the actual speed of the motor;

[0019] A nonlinear function is introduced to preprocess the speed deviation, resulting in the preprocessed speed deviation.

[0020] Furthermore, based on the current rotor position signal of the switched reluctance motor, current and torque distribution are performed to determine the reference current and reference torque for each conducting phase, specifically as follows:

[0021] The three-phase switching logic signals are determined based on the current rotor position signal of the switched reluctance motor.

[0022] The reference current and the actual current sampling value of each conducting phase are obtained by using the sine and cosine distribution of the current, and the voltage state of each conducting phase is determined.

[0023] The reference torque for each phase of the conducting phase is determined by using the sine and cosine distribution of torque.

[0024] Furthermore, based on the current actual speed, actual current, and rotor position of the switched reluctance motor, the current of the switched reluctance motor at the next moment is predicted, specifically as follows:

[0025] Determine the corresponding incremental inductance parameters based on the current actual current and rotor position of the switched reluctance motor;

[0026] The winding voltage of the switched reluctance motor in the current control cycle is determined based on the current actual speed, actual current, rotor position, and incremental inductance parameters of the switched reluctance motor.

[0027] Based on the current actual speed, actual current and rotor position of the switched reluctance motor, the back electromotive force is estimated online using the quasi-linear model of the switched reluctance motor.

[0028] The current slope is determined using the back electromotive force and the winding voltage of the switched reluctance motor during the current control cycle.

[0029] By using a determined current slope, accurate current tracking and prediction can be achieved, thus obtaining the current of the switched reluctance motor at the next moment.

[0030] Furthermore, by utilizing a determined current slope, accurate current tracking and prediction are achieved to obtain the switched reluctance motor current at the next moment, specifically:

[0031] When the actual current is less than the reference current, voltage, current, and position are sampled at the beginning of the current cycle. A voltage with a certain duty cycle is applied to the motor windings. Then, the reference current for the next cycle is the m-phase current at rotor position θ. m.est(k+1) The current distributed at the location;

[0032] When the actual current is greater than the reference current, the voltage, current, and position are sampled at the beginning of the current cycle. A voltage with a certain duty cycle is applied to the motor windings. Then, the reference current for the next cycle is the m-phase current at rotor position θ. m.est(k+1) The current distributed at the location.

[0033] Furthermore, the control current for each conducting phase is determined by performing relative error processing on the reference current of each conducting phase and the predicted current of the switched reluctance motor, specifically as follows:

[0034] The relative error is processed based on the reference current of each conducting phase and the predicted current of the switched reluctance motor to obtain the compensation error of each conducting phase.

[0035] The control current of each conducting phase is determined based on the sum of the compensation error of each conducting phase and the reference current of each conducting phase.

[0036] Furthermore, the membership degree of the current error, the membership degree of the current rate of change, and the membership degree of the weighting factor of the torque ripple are calculated as follows: the membership function of the fuzzy variable is determined based on the fuzzy set of the current error and its universe of discourse, and the membership degree of the element in the universe of discourse to the fuzzy linguistic variable is determined to obtain the membership degree of the current error.

[0037] The membership function of the fuzzy variable is determined based on the fuzzy set of the rate of change of current and its universe of discourse, and the membership degree of the elements in the universe of discourse to the fuzzy linguistic variable is determined to obtain the membership degree of the rate of change of current.

[0038] The membership function of the fuzzy variable is determined by the fuzzy set of the weight factors of torque pulsation and its universe of discourse. The membership degree of the elements in the universe of discourse to the fuzzy linguistic variable is then determined, and the membership degree of the weight factors of torque pulsation is obtained.

[0039] Furthermore, the cost function based on fuzzy logic is:

[0040]

[0041] In the formula: T ref Let m be the desired torque; m be the number of phases of the motor; α and β are the weighting coefficients for torque ripple and current ripple, respectively, and α + β = 1. This is the square of the error between the compensated control current and the actual current for each conducting phase.

[0042] Furthermore, by controlling the turn-off and turn-on duty cycles of the power converter of the switched reluctance motor based on the current rotor position signal of the switched reluctance motor and the optimized duty cycle of the conducting phase, the current of the switched reluctance motor is controlled, including:

[0043] Based on the cost function of fuzzy logic, the duty cycle that minimizes the cost function is selected to control the voltage as the final control quantity.

[0044] After modulation, the signal is transmitted to the power converter side. The smaller the cost function value, the smaller the torque ripple.

[0045] According to some embodiments, a second aspect of the present invention provides a fuzzy logic-based switched reluctance motor current control system, employing the following technical solution:

[0046] A fuzzy logic-based current control system for a switched reluctance motor includes:

[0047] The preprocessing unit is configured to acquire the actual speed of the switched reluctance motor, determine the speed deviation based on the given reference speed, and perform preprocessing.

[0048] The speed closed-loop control unit is configured to control the speed closed loop based on the pre-processed speed deviation to obtain the reference current and reference torque of the switched reluctance motor.

[0049] The current and torque distribution unit is configured to perform current and torque distribution based on the current rotor position signal of the switched reluctance motor, and to determine the reference current and reference torque for each conducting phase.

[0050] The current prediction unit is configured to predict the current of the switched reluctance motor at the next moment based on the current actual speed, actual current and rotor position of the switched reluctance motor.

[0051] The error processing unit is configured to perform relative error processing based on the reference current of each conducting phase and the predicted current of the switched reluctance motor to determine the control current of each conducting phase.

[0052] The fuzzy logic-based cost function optimization unit is configured to optimize the duty cycle of the conducting phase based on the fuzzy logic cost function, and control the turn-off and turn-on duty cycles of the power converter of the switched reluctance motor according to the current rotor position signal of the switched reluctance motor and the optimized duty cycle of the conducting phase, thereby controlling the current of the switched reluctance motor; wherein, the optimization of the duty cycle of the conducting phase based on the fuzzy logic cost function specifically includes:

[0053] Based on the membership degrees of current error, current change rate, and torque ripple weighting factor, a control rule table for adjusting the torque ripple weighting factor is determined according to different application scenarios. Based on this control rule table, the optimal torque ripple weighting factor is obtained through fuzzy logic optimization, which in turn yields the optimal current ripple weighting factor. Using the optimal torque ripple weighting factor and the optimal current ripple weighting factor, a fuzzy logic-based cost function is constructed. When the fuzzy logic-based cost function is minimized, the optimal duty cycle of the conducting phase is determined.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] This invention introduces a nonlinear function to preprocess the speed error, which can stabilize the output performance of the speed loop and optimize control performance. It employs current prediction based on current slope and feedforward compensation of the reference current to improve the predictive capability of the control system. Optimizing the control current indirectly and effectively controls the torque. By optimizing the weighting factor of the cost function through fuzzy logic, and then selecting the optimal duty cycle to track the reference torque, torque ripple can be optimized under low switching frequencies. Attached Figure Description

[0056] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0057] Figure 1 This is a flowchart of a current control method for a switched reluctance motor based on fuzzy logic in an embodiment of the present invention;

[0058] Figure 2 This is a flowchart of the duty cycle optimization based on the cost function of predicted current in an embodiment of the present invention;

[0059] Figure 3 This is a block diagram of a switched reluctance motor current control system based on a cost function of fuzzy logic in an embodiment of the present invention.

[0060] Figure 4 This is a voltage state block diagram of current and torque distribution in an embodiment of the present invention (taking phase B as an example);

[0061] Figure 5 The current error E in this embodiment of the invention i Membership curve;

[0062] Figure 6 The current change rate EC in this embodiment of the invention i Membership curve;

[0063] Figure 7 This is the membership curve of the weighting factor α for torque ripple in this embodiment of the invention. Detailed Implementation

[0064] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0065] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0066] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0067] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0068] Example 1

[0069] like Figure 1 As shown, this embodiment provides a current control method for a switched reluctance motor based on fuzzy logic. In this embodiment, the method includes the following steps:

[0070] Obtain the actual speed of the switched reluctance motor, determine the speed deviation based on the given reference speed, and perform preprocessing.

[0071] Based on the pre-processed speed deviation control speed closed loop, the reference current and reference torque of the switched reluctance motor are obtained;

[0072] Based on the current rotor position signal of the switched reluctance motor, current and torque are distributed to determine the reference current and reference torque of each conducting phase.

[0073] Predict the current of the switched reluctance motor at the next moment based on the current actual speed, actual current and rotor position of the switched reluctance motor.

[0074] The control current of each conducting phase is determined by performing relative error processing on the reference current of each conducting phase and the predicted current of the switched reluctance motor.

[0075] The duty cycle of the conducting phase is optimized based on the cost function of fuzzy logic. The turn-off and conduction duty cycles of the power converter of the switched reluctance motor are controlled according to the current rotor position signal of the switched reluctance motor and the optimized duty cycle of the conducting phase, thereby realizing the control of the current of the switched reluctance motor.

[0076] The method described in this embodiment adopts... Figure 3The control block diagram shown is mainly divided into four parts: current distribution module, predicted current module, error preprocessing module, and current compensation module. The speed regulator outputs reference current and reference torque. The torque distribution module distributes the reference torque to two phases or a single phase of the motor according to the region where the motor rotor is located. The current distribution module distributes the reference current to two phases or a single phase of the motor according to the region where the motor rotor is located. At the same time, the predicted current module samples the motor current, position, and voltage information to predict the current of the next cycle. It performs error processing on the predicted current value and the current reference value output by the speed closed loop to form current compensation in the reference current. It uses fuzzy logic to optimize the coefficients of the cost function and selects the voltage formed by the duty cycle that minimizes the cost function value to be applied to the power converter side.

[0077] Specifically, the method described in this embodiment includes:

[0078] Step 1: The system obtains a given speed reference value through an external sampling circuit, and the controller converts it into a corresponding digital input controller; based on the position detection signal detected by the system, the rotor position is obtained, and the actual motor speed is calculated at the same time.

[0079] pass Figure 3 The position detection module acquires the rotor position signal θ of each phase, and then calculates the actual speed ω of the motor based on the rotor position; simultaneously, the system obtains a given speed reference value ω through an external sampling circuit. ref The controller is converted into a corresponding digital input controller.

[0080] Step 2: For the speed deviation e = ω ref -ω is preprocessed; the method for preprocessing the deviation is as follows:

[0081] A nonlinear function is introduced to preprocess the speed deviation, enabling the controller to achieve an ideal control mode, namely "small error, large gain; large error, small gain". The specific calculation formula is as follows:

[0082]

[0083] In the formula: sign(e) is the sign function of the error, α is the nonlinear factor, and δ is the length of the linear interval. Based on practical testing experience, we take α = 0.3 and δ = 10.

[0084] Step 3: Input the pre-processed deviation into the speed closed loop, such as... Figure 3 The medium-speed outer loop module uses incremental PID control, and its output values ​​are the desired current and torque, thus obtaining the motor's reference current I. ref and reference torque parameter T ref .

[0085] Step 4: Determine the current and torque distribution of the conducting phases based on their positional relationships;

[0086] Based on the rotor position signal obtained in step one, the three-phase switch logic signals SA, SB, and SC are determined. Then, the reference current for each conducting phase is obtained using current distribution and torque distribution. and reference torque and the actual current sampling value I of the corresponding phase act(A) I act(B) I act(C) And determine the voltage state of each phase so that the duty cycle can be determined subsequently based on different voltage states, such as Figure 4 As shown;

[0087] It should be noted that the formulas for torque distribution and current distribution are the same, both based on sine and cosine distribution. The distribution here is the reference torque for each conducting phase, while the actual torque is obtained by looking up the table. The first term of the cost function is the difference between the required torque and the second term is the square of the error between the compensated control current and the actual current for each conducting phase.

[0088] Furthermore, the determination of the voltage state of each phase:

[0089] Taking phase B control as an example, the distribution function of the current in phase j is f. j If (θ), then the current allocated to this phase is:

[0090]

[0091] Taking phase B control as an example, the torque distribution function of phase j is f. j If (θ), then the torque allocated to that phase is:

[0092]

[0093] Where m represents the number of phases of the motor, taking phase B as an example, when phase B works alone, the S state is selected in {-1, 0, 1} to maintain constant torque, and phases A and C are in the off state. When BC commutates, the reference torque of phase B decreases, and the torque in the power circuit in {0, -1} states decreases, but the torque in state 0 decreases more slowly. In order to better track the rapidly decreasing reference torque, state -1 is selected as the control state for reducing the torque of phase B when BC commutates. To prevent the torque of phase B from decreasing too quickly and deviating from the reference torque, state 1 should also be retained at the same time. Therefore, phase B selects state {-1, 1} to track the desired reference torque curve. When the torque of phase C increases, state {-1, 0, 1} is selected to track the reference torque of phase C.

[0094] Step 5: Based on the current rotational speed, current, position, and other information, derive the reference current for the next cycle using formulas. Then, the desired torque for each phase is obtained by looking up a table based on the position and current.

[0095] In step five, the current prediction uses incremental inductance parameters to estimate the back electromotive force and current slope, thereby achieving accurate current tracking and reducing torque ripple. This method is simple and easy to implement.

[0096] a. Phase current slope prediction

[0097] Assuming that interphase coupling effects and magnetic circuit nonlinearity are neglected, the phase voltage balance equation of the switched reluctance motor is:

[0098]

[0099] In the formula: u is the winding voltage, u = p * U dc p represents the DC bus voltage U. dc The duty cycle; i is the winding phase current; R is the winding internal resistance and other losses equivalent resistance; incremental inductance L inc (i,θ) is found by searching L inc The -i-θ table is used to obtain the value; θ is the rotor mechanical angle; ω is the rotor speed. The current predictive controller needs to utilize L... inc The -i-θ table is used to estimate the back electromotive force and current slope. The incremental inductance is not only related to the position but also to the current. Under the condition that the motor is stationary, the current is introduced into any phase by current chopping. The upper and lower switches of the phase bridge arm need to be turned on or off at the same time. The incremental inductance is obtained by the difference between the current rise slope when the switch is turned on and the current fall slope when the switch is turned off, as shown in equation (3).

[0100]

[0101] By using equation (3) to change the current chopping value and the motor position, the incremental inductance L of the motor when it is saturated and unsaturated can be obtained. inc -i-θ. The incremental inductance of the switched reluctance prototype can be obtained through motor body simulation. When the current is less than 12A, the motor is in a non-saturated state, and the incremental inductance value remains unchanged at the same position.

[0102] In practical control systems, the voltage required across the winding is usually calculated using digital control. From equation (2), the voltage required in the k-th control cycle can be approximated as...

[0103]

[0104] In the formula: i k and θ k Let i be the current and angle in the k-th period, respectively; k+1 T is the current in the (k+1)th period; sThe PWM control cycle is given. Equation (4) shows that the back EMF acts as a feedforward, compensating for the phase voltage under both saturated and unsaturated conditions, thus improving the controller's performance. Since a deadbeat predictive control strategy is adopted, the reference value can be reached within one control cycle, therefore a particularly precise phase inductance model is not required to estimate the back EMF. This method utilizes a quasi-linear model of a switched reluctance motor to estimate the back EMF online. The back EMF is calculated as follows:

[0105]

[0106] In the formula: m1 represents the phase inductance value when the motor is unsaturated, which depends only on the position. Since the incremental inductance value is approximately equal to the phase inductance value when the motor is unsaturated, the unsaturated phase inductance value m1 can be replaced by the incremental inductance value, reducing data storage; a represents the maximum current value when the motor is unsaturated, which can be obtained through motor body simulation, a = 12A. θ is the rotor mechanical angle; ω is the rotor speed. k and θ k These represent the current and angle in the k-th period, respectively.

[0107] Combining equations (4) and (5), we get

[0108]

[0109] As can be seen from equation (6), under the premise of a fixed PWM period, phase current predictive control is achieved by estimating the back electromotive force in the basic electromagnetic relationship. If the winding voltage, instantaneous inductance, back electromotive force, and resistance during motor operation are known, the real-time change of winding current in each control cycle can be estimated, thereby achieving accurate tracking of the reference current by the phase current. The current value of the current control cycle can be initially set to i. k The slope is K slope The straight line represents the winding voltage u. k Within a control cycle, there are three possible voltage conditions: positive, negative, and zero voltage. Therefore, the winding current slope also has three possible values. Rising slope l up(k) Absolute value of the descending slope l down(k) and zero pressure descent slope l zero(k) The absolute values ​​are respectively

[0110]

[0111]

[0112]

[0113] b. Current prediction control strategy based on current slope

[0114] The current slope method is based on the incremental inductance of the motor and requires L...inc The -i-θ table is used to find the incremental inductance corresponding to the actual current and position in the current cycle, and then the back electromotive force is calculated by equation (5); the phase current is predicted by the current slope. Zero voltage can reduce peak flux linkage and iron loss while maintaining the average torque. Therefore, positive voltage + zero voltage and negative voltage + zero voltage are used to track the current.

[0115] At the start of the current cycle, voltage, current, and position are sampled. When the actual current is less than the reference current, a voltage with a certain duty cycle is applied to the motor windings, and the reference current for the next cycle is then determined. The m phase (m = A, B, CL, representing the number of phases of the motor) is at rotor position θ. m.est(k+1) The current distributed at θ m,est(k+1) =θ m,k +ω m,k T s It can be expressed as a formula.

[0116]

[0117] When the actual current is greater than the reference current, the voltage, current, and position are sampled at the beginning of the current cycle. A voltage with a certain duty cycle is applied to the motor windings, and the reference current for the next cycle is then... The m phase (m = A, B, CL, representing the number of phases of the motor) is at rotor position θ. m.est(k+1) The current distributed at θ m,est(k+1) =θ m,k +ω m,k T s It can be expressed as a formula.

[0118]

[0119] Since the torque T is a nonlinear function of the current i and the rotor angle θ, it is difficult to express it precisely using a mathematical expression, because obtaining the reference current for the next cycle is crucial. and location information θ m.est(k+1) Then, the torque value T for the corresponding state can be obtained by looking up a table. A T B T C .

[0120] Step 6: Perform error processing on the reference current and the predicted current to obtain the error to be compensated for in the current, thereby forming a new reference current for the conducting phase.

[0121] In step six, the reference current output by the speed loop is processed by the three-phase reference current distributed in step four. Compared with the reference current predicted in step five By performing relative error processing, the current error to be compensated can be obtained. Feedforward compensation is applied to the reference current to obtain the control current i. A i B i C Control the actual output current I of the SRM act(A) I act(B) I act(C) Rapidly approximate the reference current This indirectly results in a smooth output torque.

[0122] The process for handling relative errors is as follows:

[0123]

[0124] The three-phase control current i is obtained after feedforward compensation. A i B and i C for

[0125]

[0126] The three-phase control current is obtained by using relative error processing in order to quickly approximate the reference current. In addition, this way, the second term of the cost function is obtained by subtracting the actual current from the control current, and the error between the two is relatively small, resulting in a smaller sum of squares, which is beneficial for the cost function to quickly approach the minimum value.

[0127] Step 7: A control strategy based on a cost function of fuzzy logic to optimize the duty cycle of the conducting phase is proposed.

[0128] In step seven, different switching states result in different voltages applied across the motor windings, leading to varying motor operating states and consequently different torques. To better track the desired torque, it is necessary to define the selection criteria for the optimal control quantity and use a cost function to evaluate a series of predicted values.

[0129] The cost function can be expressed as

[0130]

[0131] In the formula: T ref denoted as the desired torque; m is the number of phases of the motor; α and β are the weighting coefficients for torque ripple and current ripple, respectively. This is the square of the error between the compensated control current and the actual current for each conducting phase.

[0132] This embodiment proposes a control strategy for optimizing the duty cycle of the conducting phase based on fuzzy logic optimization of the cost function weight factor. Specifically, the control strategy is as follows:

[0133] In step four, different switching states result in different voltages applied across the motor windings, leading to varying motor operating states and consequently different torques. To better track the desired torque, it is necessary to define a criterion for selecting the optimal control quantity and use a cost function to evaluate a series of predicted values. The cost function can be expressed as follows:

[0134]

[0135] In the formula: T ref Let m be the desired torque; m be the number of phases of the motor; α and β are the weighting coefficients for torque ripple and current ripple, respectively, and α + β = 1. This is the square of the error between the compensated control current and the actual current for each conducting phase.

[0136] The cost function based on fuzzy logic is used to obtain the duty cycle of voltage. However, the weighting factors of torque ripple and current ripple are different in this cost function, resulting in different duty cycles.

[0137] Since α + β = 1, the weighting factor β = 1 - α for current ripple can be determined by optimizing the value of α according to the fuzzy logic rules. The steps of the fuzzy logic-based current controller algorithm for α are as follows:

[0138] Figure 5 For current error E i Membership function For current error E i The fuzzy set and its universe of discourse are defined as follows:

[0139] E i The fuzzy set is: {NB, NM, NS, ZO, PS, PM, PB};

[0140] E i The domain of discourse is: {-0.6,-0.5,-0.4,-0.3,-0.2,-0.1,0,0.1,0.2,0.3,0.4,0.5,0.6};

[0141] Based on the current error E i The fuzzy set and its universe of discourse determine the membership function of the fuzzy variable, and thus determine the membership degree of the elements within the universe of discourse to the fuzzy linguistic variable, thereby obtaining... Figure 5 Medium current error E i The degree of membership.

[0142] In step seven, Figure 6 The rate of change of current EC i Membership function For the rate of change of current EC iThe fuzzy set and its universe of discourse are defined as follows (where the rate of change of current is set to 100% at rated value, and the number 10 in the universe of discourse below is actually 10% of the rated value, and the other numbers are the same):

[0143] EC i The fuzzy set is: {ZO,PS,PM,PB};

[0144] EC i The domain of discourse is: {0,10,30,50,70,90,100};

[0145] According to the change in current EC i The fuzzy set and its universe of discourse determine the membership function of the fuzzy variable, and thus determine the membership degree of the elements within the universe of discourse to the fuzzy linguistic variable, thereby obtaining... Figure 6 Medium current change rate EC i The degree of membership.

[0146] In step seven, for Figure 7 The membership function μ of the weighting factor α for torque ripple. α The fuzzy set and its universe of discourse for the weighting factor α of torque ripple are defined as follows:

[0147] μ α The fuzzy set is: {NB, NM, NS, MI, PS, PM, PB};

[0148] μ α The domain of discourse is: {0,0.1,0.3,0.5,0.7,0.9,1};

[0149] Based on the weighting factor μ of torque ripple α The fuzzy set and its universe of discourse determine the membership function of the fuzzy variable, and thus determine the membership degree of the elements within the universe of discourse to the fuzzy linguistic variable, thereby obtaining... Figure 7 Weighting factor μ for torque ripple α The degree of membership.

[0150] The above applies to the current error E i Current change rate EC i The definitions of the fuzzy set, universe of discourse, and membership degree of the torque ripple weighting factor α are not optimal and can be adjusted based on experience in practical applications. Based on the above settings, a control rule table for the torque ripple weighting factor α is determined through actual debugging experience, as shown in Table 1. The contents of the control rule table can also be adjusted according to different application scenarios. This rule table determines the coefficients of the cost function; different coefficients result in different cost functions. Ultimately, the duty cycle that minimizes the cost function is selected.

[0151] Table 1 Control rules for the weighting factor α of torque ripple.

[0152]

[0153] According to Table 1, the following are examples of the values ​​for the weighting factor α for torque ripple:

[0154] 1) If the current error E i If E > 0.6, then E i =PB; Rate of change of current EC i >100, then EC i =PB; Looking up the table, we get α = PB. The weighting factor α for torque ripple is 1, so the weighting factor β for current ripple is 0.

[0155] 2) If the current error E i If E < -0.6, then E i =NB; Rate of change of current EC i <10, then EC i =PS; Looking up the table, we get α = NB. The weighting factor α for torque ripple is 0, so the weighting factor β for current ripple is 1.

[0156] 3) If the current error E i If E < 0.1, then E i =PS; Rate of change of current EC i <70, then EC i =PM; from the table, we find α = MI. The weighting factor α for torque ripple is 0.5, so the weighting factor β for current ripple is 0.5.

[0157] 4) If the current error E i =0, then E i =ZO; Current change rate EC i <30, then EC i =PS; From the table, we get α = NM. The weighting factor α for torque ripple is 0.1, so the weighting factor β for current ripple is 0.9.

[0158] Meanwhile, the duration of the input voltage also affects the actual output torque of the motor, especially at low switching frequencies. Applying the same control voltage within the same cycle can result in significant torque deviation at the end of that cycle, causing unnecessary torque pulsation. A cost function is used to adjust the duty cycle of the control voltage vector. The sum of the squares of the errors between the compensated control current and the actual current for each phase, combined with the reference torque and actual torque for each phase, is used as the input to the cost function. The duty cycle that minimizes the cost function is selected as the duty cycle for the regulator's output voltage.

[0159] The predictive current control algorithm flow is as follows: Figure 2 As shown. The specific steps are as follows:

[0160] In the k-th control cycle, the motor state is first sampled to obtain information such as current i(k) and rotor position θ(k). Then, the current i(k+1) and position θ(k+1) in the (k+1)-th cycle are predicted under different candidate control voltages u. The torque is obtained by looking up the table based on this information. Finally, based on the cost function, the duty cycle that minimizes the cost function is selected to control the voltage u as the final control quantity, and it is transmitted to the power converter side after modulation. The smaller the cost function value, the smaller the torque ripple.

[0161] Step 8: Based on the positional relationship and the duty cycle in Step 7, control the turn-off and turn-on duty cycles of the power devices in real time to achieve real-time control of the motor current.

[0162] Example 2

[0163] This embodiment provides a fuzzy logic-based current control system for a switched reluctance motor, including:

[0164] The preprocessing unit is configured to acquire the actual speed of the switched reluctance motor, determine the speed deviation based on the given reference speed, and perform preprocessing.

[0165] The speed closed-loop control unit is configured to control the speed closed loop based on the pre-processed speed deviation to obtain the reference current and reference torque of the switched reluctance motor.

[0166] The current and torque distribution unit is configured to perform current and torque distribution based on the current rotor position signal of the switched reluctance motor, and to determine the reference current and reference torque for each conducting phase.

[0167] The current prediction unit is configured to predict the current of the switched reluctance motor at the next moment based on the current actual speed, actual current and rotor position of the switched reluctance motor.

[0168] The error processing unit is configured to perform relative error processing based on the reference current of each conducting phase and the predicted current of the switched reluctance motor to determine the control current of each conducting phase.

[0169] The fuzzy logic-based cost function optimization unit is configured to optimize the duty cycle of the conducting phase based on the fuzzy logic cost function, and control the turn-off and turn-on duty cycles of the power converter of the switched reluctance motor according to the current rotor position signal of the switched reluctance motor and the optimized duty cycle of the conducting phase, thereby controlling the current of the switched reluctance motor; wherein, the optimization of the duty cycle of the conducting phase based on the fuzzy logic cost function specifically includes:

[0170] Based on the membership degrees of current error, current change rate, and torque ripple weighting factor, a control rule table for adjusting the torque ripple weighting factor is determined according to different application scenarios. Based on this control rule table, the optimal torque ripple weighting factor is obtained through fuzzy logic optimization, which in turn yields the optimal current ripple weighting factor. Using the optimal torque ripple weighting factor and the optimal current ripple weighting factor, a fuzzy logic-based cost function is constructed. When the fuzzy logic-based cost function is minimized, the optimal duty cycle of the conducting phase is determined.

[0171] Based on a fuzzy logic-based cost function optimization unit, a control strategy is proposed to optimize the duty cycle of the conducting phase using a cost function based on fuzzy logic. The difference between the compensated control current of each phase and the sampled actual current is calculated, and the sum of the squares of the difference, combined with the reference torque and actual torque of each phase, is used as the input of the cost function. The control voltage of the motor is optimized by minimizing the cost function, thereby optimizing the actual output torque of the motor and reducing torque ripple. Switched reluctance motor and position detection: This part includes the motor body and the position sensor.

[0172] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A current control method for a switched reluctance motor based on fuzzy logic, characterized in that, include: Obtain the actual speed of the switched reluctance motor, determine the speed deviation based on the given reference speed, and perform preprocessing. Based on the pre-processed speed deviation control speed closed loop, the reference current and reference torque of the switched reluctance motor are obtained; Based on the current rotor position signal of the switched reluctance motor, current and torque are distributed to determine the reference current and reference torque of each conducting phase. Predict the current of the switched reluctance motor at the next moment based on the current actual speed, actual current and rotor position of the switched reluctance motor. The control current of each conducting phase is determined by performing relative error processing on the reference current of each conducting phase and the predicted current of the switched reluctance motor. The duty cycle of the conducting phase is optimized based on the cost function of fuzzy logic, and the turn-off and conduction duty cycles of the power converter of the switched reluctance motor are controlled according to the current rotor position signal of the switched reluctance motor and the optimized duty cycle of the conducting phase, so as to realize the control of the current of the switched reluctance motor. Specifically, the optimization of the duty cycle of the conducting phase based on the cost function of fuzzy logic includes: Based on the membership degrees of current error, current change rate, and torque ripple weighting factor, a control rule table for adjusting the torque ripple weighting factor is determined according to different application scenarios. Based on this control rule table, the optimal torque ripple weighting factor is obtained through fuzzy logic optimization, which in turn yields the optimal current ripple weighting factor. Using the optimal torque ripple weighting factor and the optimal current ripple weighting factor, a fuzzy logic-based cost function is constructed. When the fuzzy logic-based cost function is minimized, the optimal duty cycle of the conducting phase is determined.

2. The current control method for a switched reluctance motor based on fuzzy logic as described in claim 1, characterized in that, The process of obtaining the actual speed of the switched reluctance motor, determining the speed deviation based on a given reference speed, and performing preprocessing includes: Collect rotor position signals for each phase of the switched reluctance motor; The actual speed of the motor is calculated based on the rotor position signal; The speed deviation is determined based on the given reference speed and the actual speed of the motor; A nonlinear function is introduced to preprocess the speed deviation, resulting in the preprocessed speed deviation.

3. The current control method for a switched reluctance motor based on fuzzy logic as described in claim 1, characterized in that, Based on the current rotor position signal of the switched reluctance motor, current and torque are distributed to determine the reference current and reference torque for each conducting phase, specifically as follows: The three-phase switching logic signals are determined based on the current rotor position signal of the switched reluctance motor. The reference current and the actual current sampling value of each conducting phase are obtained by using the sine and cosine distribution of the current, and the voltage state of each conducting phase is determined. The reference torque for each phase of the conducting phase is determined by using the sine and cosine distribution of torque.

4. The current control method for a switched reluctance motor based on fuzzy logic as described in claim 1, characterized in that, The current of the switched reluctance motor at the next moment is predicted based on the current actual speed, actual current, and rotor position of the switched reluctance motor. Specifically: Determine the corresponding incremental inductance parameters based on the current actual current and rotor position of the switched reluctance motor; The winding voltage of the switched reluctance motor in the current control cycle is determined based on the current actual speed, actual current, rotor position, and incremental inductance parameters of the switched reluctance motor. Based on the current actual speed, actual current and rotor position of the switched reluctance motor, the back electromotive force is estimated online using the quasi-linear model of the switched reluctance motor. The current slope is determined using the back electromotive force and the winding voltage of the switched reluctance motor during the current control cycle. By using a determined current slope, accurate current tracking and prediction can be achieved, thus obtaining the current of the switched reluctance motor at the next moment.

5. The current control method for a switched reluctance motor based on fuzzy logic as described in claim 4, characterized in that, Accurate current tracking and prediction are achieved by using a determined current slope to obtain the switched reluctance motor current at the next moment, specifically: When the actual current is less than the reference current, voltage, current, and position are sampled at the beginning of the current cycle. A voltage with a certain duty cycle is applied to the motor windings. Then, the reference current for the next cycle is the m-phase current at rotor position θ. m.est(k+1) The current distributed at the location; When the actual current is greater than the reference current, the voltage, current, and position are sampled at the beginning of the current cycle. A voltage with a certain duty cycle is applied to the motor windings. Then, the reference current for the next cycle is the m-phase current at rotor position θ. m.est(k+1) The current distributed at the location.

6. The current control method for a switched reluctance motor based on fuzzy logic as described in claim 1, characterized in that, The control current for each conducting phase is determined by performing relative error processing on the reference current of each conducting phase and the predicted current of the switched reluctance motor, specifically as follows: The relative error is processed based on the reference current of each conducting phase and the predicted current of the switched reluctance motor to obtain the compensation error of each conducting phase. The control current of each conducting phase is determined based on the sum of the compensation error of each conducting phase and the reference current of each conducting phase.

7. The current control method for a switched reluctance motor based on fuzzy logic as described in claim 1, characterized in that, The methods for calculating the membership degree of the current error, the membership degree of the current change rate, and the membership degree of the weighting factor of the torque ripple are as follows: The membership function of the fuzzy variable is determined based on the fuzzy set of the current error and its universe of discourse, and then the membership degree of the elements in the universe of discourse to the fuzzy linguistic variable is determined to obtain the membership degree of the current error. The membership function of the fuzzy variable is determined based on the fuzzy set of the rate of change of current and its universe of discourse, and the membership degree of the elements in the universe of discourse to the fuzzy linguistic variable is determined to obtain the membership degree of the rate of change of current. The membership function of the fuzzy variable is determined by the fuzzy set of the weight factors of torque pulsation and its universe of discourse. The membership degree of the elements in the universe of discourse to the fuzzy linguistic variable is then determined, and the membership degree of the weight factors of torque pulsation is obtained.

8. The current control method for a switched reluctance motor based on fuzzy logic as described in claim 1, characterized in that, The cost function based on fuzzy logic is: In the formula: T ref Let m be the desired torque; m be the number of phases of the motor; α and β are the weighting coefficients for torque ripple and current ripple, respectively, and α + β = 1. Let be the square of the error between the compensated control current and the actual current for each conducting phase.

9. The current control method for a switched reluctance motor based on fuzzy logic as described in claim 1, characterized in that, Based on the current rotor position signal of the switched reluctance motor and the optimized duty cycle of the conducting phase, the power converter of the switched reluctance motor is controlled to switch the turn-off and conduction duty cycles, thereby controlling the current of the switched reluctance motor, including: Based on the cost function of fuzzy logic, the duty cycle that minimizes the cost function is selected to control the voltage as the final control quantity. After modulation, the signal is transmitted to the power converter side. The smaller the cost function value, the smaller the torque ripple.

10. A current control system for a switched reluctance motor based on fuzzy logic, characterized in that, include: The preprocessing unit is configured to acquire the actual speed of the switched reluctance motor, determine the speed deviation based on the given reference speed, and perform preprocessing. The speed closed-loop control unit is configured to control the speed closed loop based on the pre-processed speed deviation to obtain the reference current and reference torque of the switched reluctance motor. The current and torque distribution unit is configured to perform current and torque distribution based on the current rotor position signal of the switched reluctance motor, and to determine the reference current and reference torque for each conducting phase. The current prediction unit is configured to predict the current of the switched reluctance motor at the next moment based on the current actual speed, actual current and rotor position of the switched reluctance motor. The error processing unit is configured to perform relative error processing based on the reference current of each conducting phase and the predicted current of the switched reluctance motor to determine the control current of each conducting phase. The fuzzy logic-based cost function optimization unit is configured to optimize the duty cycle of the conducting phase based on a fuzzy logic cost function, and control the turn-off and turn-on duty cycles of the power converter of the switched reluctance motor according to the current rotor position signal of the switched reluctance motor and the optimized duty cycle of the conducting phase, thereby controlling the current of the switched reluctance motor; wherein, the optimization of the duty cycle of the conducting phase based on the fuzzy logic cost function specifically includes: Based on the membership degrees of current error, current change rate, and torque ripple weighting factor, a control rule table for adjusting the torque ripple weighting factor is determined according to different application scenarios. Based on this control rule table, the optimal torque ripple weighting factor is obtained through fuzzy logic optimization, which in turn yields the optimal current ripple weighting factor. Using the optimal torque ripple weighting factor and the optimal current ripple weighting factor, a fuzzy logic-based cost function is constructed. When the fuzzy logic-based cost function is minimized, the optimal duty cycle of the conducting phase is determined.

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