Switched reluctance motor control method based on direct instantaneous torque control

Abstract

The invention discloses a switched reluctance motor control method based on direct instantaneous torque control, and relates to the field of motor control. The method comprises the following steps of: obtaining a torque difference value delta T according to the difference between a reference torque Tref and an actual torque T, and obtaining the state of a three-phase switch at the moment according to a conduction rule by the numerical value and the change trend of the torque difference value delta T so as to form a voltage vector; when the number of the obtained voltage vector is one, the voltage vector acts on a power converter of the switched reluctance motor in the next control period, when the number of the obtained voltage vector is two, a corresponding predicted torque is calculated according to the voltage vector, the predicted torque is compared with a reference torque, and the predicted torque is calculated according to the reference torque. And judging a vector needing to be acted according to a comparison result, and acting the vector on a power converter of the switched reluctance motor. According to the invention, the single-phase conduction region can be reduced, the torque ripple of the commutation region can also be reduced, and the effect is better under the dynamic condition.

Description

Technical Field

[0001] The present invention relates to the field of motor control, and in particular to a switched reluctance motor control method based on direct instantaneous torque control. Background Art

[0002] The switched reluctance motor (SRM) has a simple structure, low cost, wide speed regulation range, and can work normally in harsh environments. Therefore, it is widely used in home appliances, industrial manufacturing, new energy vehicles, aerospace and other fields. However, the double-convex structure of the SRM itself makes it very nonlinear, resulting in large torque pulsation during operation, especially during phase change. In order to solve this problem, the prior art has adopted a direct instantaneous torque control strategy, which reasonably arranges the conduction phase according to the rate of change of inductance with the rotor position angle, and divides the working cycle of each phase into a single-phase conduction area and a two-phase conduction area (phase change area) according to the number of conduction phases at a certain position, but fails to fully consider the working characteristics of the SRM; in the prior art, the speed loop usually adopts PI control, but PI control is difficult to solve the contradiction between rapidity and overshoot, and it is difficult to meet the requirements in some occasions with high speed regulation requirements. Summary of the invention

[0003] In view of the above-mentioned defects of the prior art, the present invention provides a control method of a switched reluctance motor based on direct instantaneous torque control, wherein the power converter of the switched reluctance motor includes a three-phase bridge arm, and comprises the following steps:

[0004] According to the reference torque T ref The torque difference ΔT is obtained by subtracting the actual torque T. The value and change trend of the torque difference ΔT are obtained according to the conduction rule to obtain the state of the three-phase switch at this time and combined into a voltage vector;

[0005] When the number of obtained voltage vectors is one, the voltage vector is applied to the power converter of the switched reluctance motor in the next control cycle. When the number of obtained voltage vectors is two, the corresponding predicted torque is calculated based on the voltage vector, the predicted torque is compared with the reference torque, and the vector to be applied is determined based on the comparison result and applied to the power converter of the switched reluctance motor.

[0006] Furthermore, in the conduction rule, the commutation area is divided into commutation area 1 and commutation area 2, and the inner limit value ΔT of the torque hysteresis control rule is set. max and outer limit ΔT min ;

[0007] In the commutation zone 1, the conduction rule of the open phase is: when ΔT<-ΔT min When the state is 0, when -ΔT minWhen ΔT < 0, the state is 0 or 1, and when ΔT > 0, the state is 1; the conduction rule of the shut-off phase is: when ΔT < -ΔT max When -ΔT max <ΔT<-ΔT min When -ΔT min <ΔT<0, the state is 0, when 0<ΔT<ΔT max When ΔT>ΔT max , state is 1;

[0008] In the commutation zone 2, the conduction rule of the open phase is: ΔT<-ΔT max When -ΔT max <ΔT<-ΔT min When -ΔT min <ΔT<0, the state is 0, when 0<ΔT<ΔT min When ΔT>ΔT min , the state is 1; the conduction rule of the shut-off phase is: when ΔT<-ΔT min When -ΔT min When ΔT<0, the state is 0 or -1. When 0<ΔT<ΔT min When ΔT min <ΔT<ΔT max When ΔT>ΔT max , the state is 1.

[0009] Furthermore, the predicted torque T k+1 for:

[0010]

[0011] Among them, i k+1 is the current value at time k+1; θ k+1 is the rotor position angle at time k+1; L dsat is the inductance after saturation; A and B are constants.

[0012] Furthermore, when the number of voltage vectors obtained is V1 and V2, the corresponding predicted torques are set to T max and T min , and T min Less than T max ;

[0013] When T ref <T min When , the voltage vector V2 is selected to continue to act in the next control cycle;

[0014] When Tref >T max When , the voltage vector V1 is selected to continue to act in the next control cycle;

[0015] When T min <T ref <T max When the two vectors are in effect, the action time is adjusted so that both voltage vectors continue to act in the next control cycle.

[0016] Furthermore, the action times T1 and T2 of the voltage vectors V1 and V2 are respectively:

[0017]

[0018] Furthermore, the reference torque T ref The methods for obtaining include:

[0019] Reference speed ω ref The tracking signal ω1 is obtained by the first-order differential tracker. The observed value z1 of the output signal of the second-order extended state observer is subtracted from the tracking signal ω1 and the torque T is obtained by the nonlinear state error feedback rate. The reference torque T is obtained by combining the estimated value z2 of the total disturbance of the system. ref .

[0020] Furthermore, the control equation of the nonlinear state error feedback rate is:

[0021]

[0022] Where: k1 is the tracking factor; δ is the filtering factor; α3 is the adjustable nonlinear factor.

[0023] Furthermore, the tracking factor k1 is adjusted by a fuzzy control method.

[0024] Compared with the prior art, the present invention has the following technical effects:

[0025] 1. The present invention subdivides the commutation area of ​​the traditional direct instantaneous torque control, which can significantly suppress the torque pulsation in the commutation area. The selection of multiple vector combination methods and the accurate calculation of their action time improve the control accuracy. By comparing with the simulation results of the traditional direct instantaneous torque control, it can be seen that this method can reduce the torque pulsation by more than 15% under different loads and different speeds, reduce the current peak by more than 0.5A while ensuring the torque, and can also suppress the generation of negative torque.

[0026] 2. The present invention solves the problems of slow response speed and large overshoot of the PI controller of the direct instantaneous torque speed loop by adopting a nonlinear anti-disturbance control controller to replace the traditional PI controller, thereby improving the accuracy of the reference torque output by the controller and reducing the influence of the speed loop on the torque inner loop. Since the ADRC parameter design is difficult to adapt to the nonlinear SRM drive system with complex and time-varying parameters, a fuzzy controller is used to adjust its parameters. The simulation results show that compared with PI control and anti-disturbance control systems, fuzzy anti-disturbance control has a shorter response time, smaller overshoot, and better anti-interference performance and robustness.

[0027] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the overall process of a specific embodiment of the present invention;

[0029] Figure 2 is a flowchart of a fuzzy auto-disturbance rejection control program according to a specific embodiment of the present invention;

[0030] Figure 3 is a structural diagram of a first-order active disturbance rejection control of a switched reluctance motor according to a specific embodiment of the present invention;

[0031] Figure 4 is a membership function of an input quantity of a specific embodiment of the present invention, Figure 4 a is the membership curve of e, Figure 4 b is the membership curve of ec;

[0032] Figure 5 is a membership function of the output quantity of a specific embodiment of the present invention, Figure 5 a is the membership function curve of Δk0 and Δk1, Figure 5 b is the membership function curve of Δk2;

[0033] Figure 6 It is a TPDITC workflow diagram of a specific embodiment of the present invention;

[0034] Figure 7 It is a block diagram of a SRM direct instantaneous torque control system based on fuzzy auto-disturbance rejection according to a specific embodiment of the present invention;

[0035] Figure 8 is a no-load speed waveform diagram of a specific embodiment of the present invention;

[0036] Fig. 9 is a load speed waveform diagram of a specific embodiment of the present invention;

[0037] Fig.10 This is a waveform diagram of a sudden change in speed when no-load according to a specific embodiment of the present invention;

[0038] Fig.11 This is a waveform diagram of a sudden change in speed under load according to a specific embodiment of the present invention;

[0039] Fig.12 This is a torque waveform diagram when the speed suddenly changes according to a specific embodiment of the present invention. Fig.12 a is the torque diagram under PI control, Fig.12 b is the torque waveform under ADRC mode, Fig.12 c is the torque waveform under fuzzy anti-disturbance control mode;

[0040] Fig.13 This is a torque waveform diagram when the torque suddenly changes according to a specific embodiment of the present invention;

[0041] Fig.14 is a simulation waveform of the change of the motor moment of inertia according to a specific embodiment of the present invention, Fig.14 a is the moment of inertia waveform under traditional PI control, Fig.14 b is the moment of inertia waveform under fuzzy ADRC;

[0042] Fig.15 is a simulation waveform of stator resistance change under unified PI control of a specific embodiment of the present invention, Fig.15 a is the speed waveform, Fig.15 b is the torque waveform;

[0043] Fig.16 is a simulation waveform of stator resistance change under fuzzy auto-disturbance rejection control according to a specific embodiment of the present invention. Fig.16 a is the speed waveform, Fig.16 b is the torque waveform;

[0044] Fig.17 This is a DITC simulation diagram of a specific embodiment of the present invention at a speed of 500 r / min and a load of 3 N·m. Fig.17 a is the total torque waveform, Fig.17 b is the three-phase current waveform. Fig.17 c is the three-phase torque waveform;

[0045] Fig.18 This is a TPDITC simulation diagram at a speed of 500 r / min and a load of 3 N·m in a specific embodiment of the present invention. Fig.18 a is the total torque waveform, Fig.18 b is the three-phase current waveform. Fig.18 c is the three-phase torque waveform;

[0046] Fig.19It is a DITC simulation diagram under the conditions of a speed of 500r / min and a load of 8N·m in a specific embodiment of the present invention. Fig.19 a is the total torque waveform, Fig.19 b is the three-phase current waveform. Fig.19 c is the three-phase torque waveform;

[0047] Fig. 20 It is a TPDITC simulation diagram under the conditions of a speed of 500r / min and a load of 8N·m in a specific embodiment of the present invention. Fig. 20 a is the total torque waveform, Fig. 20 b is the three-phase current waveform. Fig. 20 c is the three-phase torque waveform;

[0048] Fig.21 It is a DITC simulation diagram under the conditions of a speed of 2000r / min and a load of 3N·m in a specific embodiment of the present invention. Fig.21 a is the total torque waveform, Fig.21 b is the three-phase current waveform. Fig.21 c is the three-phase torque waveform;

[0049] Fig. 22 It is a TPDITC simulation diagram under the conditions of a speed of 2000r / min and a load of 3N·m in a specific embodiment of the present invention. Fig. 22 a is the total torque waveform, Fig. 22 b is the three-phase current waveform. Fig. 22 c is the three-phase torque waveform;

[0050] Fig.23 It is a DITC simulation diagram under the conditions of a speed of 2000r / min and a load of 8N·m in a specific embodiment of the present invention. Fig.23 a is the total torque waveform, Fig.23 b is the three-phase current waveform. Fig.23 c is the three-phase torque waveform;

[0051] Fig.24 It is a TPDITC simulation diagram under the conditions of a speed of 2000r / min and a load of 8N·m in a specific embodiment of the present invention. Fig.24 a is the total torque waveform, Fig.24 b is the three-phase current waveform. Fig.24 c is the three-phase torque waveform;

[0052] Fig.25 is a simulation waveform under a sudden change in speed in a specific embodiment of the present invention;

[0053] Fig.26 is a simulation waveform under a torque mutation in a specific embodiment of the present invention;

[0054] Fig. 27 It is a torque magnification diagram when the torque changes suddenly in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0056] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present invention in a schematic manner, and thus the illustrations only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0057] While some exemplary embodiments of the present invention have been described for the purpose of illustration, it should be understood that the present invention may be implemented in other ways not specifically shown in the drawings.

[0058] like Figure 1 As shown, in a specific embodiment, a switch reluctance motor control method based on direct instantaneous torque control is provided, wherein the power converter of the switch reluctance motor includes a three-phase bridge arm,

[0059] S1, speed outer loop control, the motor speed is controlled by fuzzy anti-disturbance control to obtain the reference torque T ref .

[0060] In this embodiment, the mechanical motion equation of the SRM is:

[0061]

[0062] Transform the above formula to get:

[0063]

[0064] Where: T L is the load torque, which can be regarded as an external disturbance because it changes at any time; D is the damping coefficient; J is the moment of inertia. These coefficients will affect the motor control system and can be regarded as interference terms. The above disturbances are recorded as the total disturbance of the system, called a(t), and its expression is: Let b = 1 / J, we get

[0065] The ADRC includes a Tracking Differentiator (TD), an Extended State Observer (ESO), and a Nonlinear State Error Feedback (NLSEF). Figure 2 As shown, the fuzzy ADRC process described in this embodiment is to first adjust the parameters of the TD module and the ESO module so that the calculated e and ec can meet the domain requirements, and then infer the parameter changes Δk0, Δk1, and Δk2 of the NLSEF module through fuzzy control, and finally substitute them into the NLSEF to adjust the parameters k0, k1, and k2. Specifically, it includes an ADRC step and a parameter adjustment step. The ADRC principle is as follows: Figure 3 As shown, the steps of the anti-disturbance control include: reference speed ω ref The tracking signal ω1 is obtained by the first-order differential tracker; the second-order extended state observer outputs the observation value z1 and the estimated value z2 of the total disturbance of the system; the observation value z1 is subtracted from the tracking signal ω1 and the torque T is obtained by the nonlinear state error feedback rate; the reference torque T is obtained by combining the estimated value z2 of the total disturbance of the system ref .

[0066] The equation after the first-order TD module is discretized is:

[0067]

[0068] Where: h is the sampling period; r is the speed tracking factor; h0 is the filtering factor; fhan function is a nonlinear function, which is:

[0069]

[0070] The equation of the second-order extended state observer is:

[0071]

[0072] Where: e is the error between the motor speed observation value z1 and the motor actual speed ω; T ref is the reference torque of the motor; β1 and β2 are feedback gains; α1, α2, δ are all adjustable parameters; b is the compensation gain; fal is a nonlinear function, which is:

[0073]

[0074] In this embodiment, the control equation of the nonlinear state error feedback rate is:

[0075]

[0076] Where: k1 is the tracking factor; k0, k1, k2 are all adjustable parameters; δ is the filtering factor; α3 is the adjustable nonlinear factor.

[0077] The parameter setting process includes:

[0078] Parameter adjustment of tracking differentiator: The main function of TD is to track the input, introduce transition and buffering processes, make the sudden signal smooth, and extract the differential of the signal, so as to reduce the overshoot while improving the response speed. The size of r affects the tracking speed. The larger r is, the shorter the time required for the transition process. However, r should not be too large, otherwise overshoot will occur. h is the sampling step of the discrete system, and h0 is the filtering factor. Generally, a value greater than h is selected to reduce overshoot and enhance the filtering effect.

[0079] Parameter tuning of the extended state observer: ESO provides a feedback loop for the system to observe and compensate for system disturbances and outputs. In the formula, α affects the adaptability of the ESO and the operating space of the system. According to experience, 0.5 or 0.25 is generally selected. δ is the width of the linear interval of the function fal. The larger the value, the better the filtering effect. Generally, the initial value is selected as 0.01, and then adjusted according to the system conditions. b represents the compensation gain factor. Selecting a suitable value can accurately compensate for system disturbances. However, in actual applications, if b is not accurate enough, the ESO can compensate for it, so the selection of b is not very demanding. β1 and β2 are the most critical parameters of the ESO module, which affect the response speed of the system. The larger the value, the shorter the time it takes for the system to return to a steady state after a change. However, if it is too large, it will cause overshoot and oscillation. For the first-order anti-disturbance controller of the speed loop of the SRM speed control system, in order to reduce the number of parameters that need to be tuned, the system bandwidth ω0 and the bandwidth ω of the ESO are introduced when taking the value. c Make your selection.

[0080] The equation of the second-order ESO module is assigned poles according to the characteristics of the linear system, and the expression of the characteristic equation is obtained as follows: p(s) = s 2 +β1s+β2; where β1=2ω0, ω0=3~5ω c .

[0081] Modify the parameters of the NLSEF module: first, take the error and error change rate as the input of fuzzy control, then calculate according to the established fuzzy rules, output the changes of k0, k1, k2 Δk0, Δk1, Δk2, and finally input them into the NLSEF module to adjust its parameters. The parameter fuzzification process is:

[0082] In this embodiment, the input of fuzzy control is the difference e between the speed signal ω1 followed by the TD module and the observed value of the ESO module and the differential of the error ec, and the output is the change Δk0, Δk1, Δk2 of the parameters of the NLSEF module. The domains of these variables are set as:

[0083] The domains of e and ec are both set to {-3, -2, -1, 0, 1, 2, 3};

[0084] The domains of Δk0 and Δk1 are both set to {-0.3, -0.2, -0.1, 0, 1, 2, 3};

[0085] The domain of Δk2 is set to {-0.06, -0.04, -0.02, 0, 0.02, 0.04, 0.06}.

[0086] The fuzzy subsets corresponding to the system input and output are defined as: positive large (PB), positive middle (PM), positive small (PS), zero (ZO), negative large (NB), negative middle (NM), and negative small (NS).

[0087] The membership function is related to which fuzzy subset the fuzzy value belongs to, which is also crucial to the control effect. Common membership functions include triangular, trapezoidal, and Gaussian. Through consulting data and past experience, this embodiment uses a triangular membership function in the middle and a normal distribution membership function on both sides. The membership curves corresponding to each parameter are shown in Figure 1. Figure 4 and Figure 5 shown.

[0088] The fuzzy rules in this embodiment are:

[0089] 1. When the absolute value of the input error |e| is large, it means that the output speed differs too much from the reference speed. At this time, k1 should be increased, even if Δk1 increases, to improve the response speed of the system and make the output speed quickly reach the reference speed. However, in order to avoid overshoot caused by excessive |e|, the value of k2 should be appropriately reduced to reduce Δk2, and k0 and Δk0 should be kept small or 0 to maintain system stability.

[0090] 2. When the absolute value of the input error |e| and its differential |ec| are both moderate, in order to avoid excessive overshoot, the value of k1 should be appropriately reduced. At this time, when the correction coefficients Δk0 and Δk2 are appropriately selected, the response speed can be guaranteed.

[0091] 3. When the absolute value of the input error |e| is small, in order to improve the anti-interference ability of the system, change Δk0 and Δk1 to increase the values ​​of k0 and k1 appropriately;

[0092] 4. When the differential absolute value of the error |ec| is large, it may be subject to greater interference and severe oscillation. This can be solved by reducing k2. At the same time, in order to keep the system stable, appropriately increase Δk0 and k0, and reduce Δk1 and k1.

[0093] According to the above control rules, e and ec are input to the fuzzy controller, and the Mamdani reasoning method is used to obtain the parameter correction rule table shown in Tables 1, 2, and 3.

[0094] Table 1 Fuzzy rule table of Δk0

[0095]

[0096] Table 2 Fuzzy rule table of Δk1

[0097]

[0098] Table 3 Fuzzy rule table of Δk2

[0099]

[0100] The value obtained after the above fuzzification and fuzzy reasoning is a fuzzy value. Before output, it must be clarified into an accurate value. This paper adopts the weighted average method. Taking Δβ0 as an example, the calculation formula is:

[0101]

[0102] Where: Δk j is the discrete domain fuzzy value of Δk0 corresponding to the case where e and ec are known; μ kj (Δk0) is Δk j The membership degree corresponding to the fuzzy linguistic variable.

[0103] After the correction parameters Δk0, Δk1, and Δk2 are obtained through defuzzification, the parameters of the NLSEF module are adjusted as follows:

[0104]

[0105] Where: k0′, k1′, k2′ are the original gain parameters of the NLSEF module.

[0106] S2, according to the reference torque T ref The torque difference ΔT is obtained by subtracting the actual torque T, and the power converter is controlled by the TPDITC control strategy. The control process is as follows: Figure 6 As shown in the figure, m represents the number of selected vectors, including:

[0107] S21. Select a voltage vector according to the conduction rule. When the number of the obtained voltage vectors is one, apply the voltage vector to the power converter of the switched reluctance motor in the next control cycle. When the number of the obtained voltage vectors is two, execute step S22.

[0108] In this embodiment, the division of the phase conduction area is the same as that of the traditional DITC, so the conduction rule is also the same, except that the commutation area is divided into two areas according to the characteristics of the motor, and different conduction rules are set.

[0109] In the single-phase conduction area, the conduction rule of the open phase is: when ΔT<-ΔT max When -ΔT max <ΔT<-ΔT min When -ΔT min <ΔT<ΔT min When ΔT>ΔT min , the state is 1; the state of the shut-off phase is -1.

[0110] In the commutation zone 1, the conduction rule of the open phase is: when ΔT<-ΔT min When the state is 0, when -ΔT min When ΔT < 0, the state is 0 or 1, and when ΔT > 0, the state is 1; the conduction rule of the shut-off phase is: when ΔT < -ΔT max When -ΔT max <ΔT<-ΔT min When -ΔT min <ΔT<0, the state is 0, when 0<ΔT<ΔT max When ΔT>ΔT max , state is 1;

[0111] In the commutation zone 2, the conduction rule of the open phase is: ΔT<-ΔT max When -ΔT max <ΔT<-ΔT min When -ΔT min <ΔT<0, the state is 0, when 0<ΔT<ΔT min When ΔT>ΔT min , the state is 1; the conduction rule of the shut-off phase is: when ΔT<-ΔT min When -ΔT min When ΔT<0, the state is 0 or -1. When 0<ΔT<ΔT min When ΔT min <ΔT<ΔT maxWhen ΔT>ΔT max When , the state is 1. In the above conduction rule, ΔT max and ΔT min They are the inner limit and outer limit of the torque hysteresis control rule respectively.

[0112] In this embodiment, the single-phase conduction area takes the B phase as an example, and the phase switching area takes the A and B phase switching as an example, and the vector switch tables are shown in Table 4 and Table 5.

[0113] Table 4 DITC vector switching table when ΔT<0

[0114]

[0115] Table 5 DITC vector switching table when ΔT>0

[0116]

[0117] S22. Calculate the corresponding predicted torque according to the voltage vector, compare the predicted torque with the reference torque, determine the vector to be acted on according to the comparison result, and act on the power converter of the switched reluctance motor.

[0118] When the obtained voltage vector quantity is V1 and V2, the corresponding predicted torque is set as T max and T min , and T min Less than T max ;

[0119] When T ref <T min When , the voltage vector V2 is selected to continue to act in the next control cycle;

[0120] When T ref >T max When , the voltage vector V1 is selected to continue to act in the next control cycle;

[0121] When T min <T ref <T max When the two vectors are in effect, the action time is adjusted so that both voltage vectors continue to act in the next control cycle.

[0122] Based on the detected current and rotor position angle θ of the motor at time k, the voltage vector U to be applied at the next moment is substituted k+1 , the current value i at time k+1 can be predicted k+1 and the rotor position angle θ k+1 , the predicted torque T k+1 for:

[0123]

[0124] Among them, L dsat is the inductance after saturation; A and B are constants.

[0125] i k+1 is the current value at time k+1, which is;

[0126] θ k+1 is the rotor position angle at time k+1, which is: θ k+1 =θ k +ωΔT;

[0127]

[0128] The action times T1 and T2 of the voltage vectors V1 and V2 are:

[0129]

[0130] Among them, T pwm is the time for one control cycle.

[0131] In order to further verify the effect of the above embodiment, in a specific embodiment, a SRM direct instantaneous torque control system based on fuzzy anti-disturbance is constructed. Figure 7 As shown in Figure 6, the parameters in the fuzzy ADRS are r=50; δ0=0.01; and the observer system bandwidth ω0=250. The simulation uses a three-phase 12 / 8-pole switched reluctance motor with a rated power of 5.5kw, a rated voltage of 310V, and a rated speed of 3000r / m. The set turn-on angle and turn-off angle are 1° and 19° respectively, the commutation area angle is set to 4°, the inner hysteresis limit is 0.3N·m, and the outer hysteresis limit is 0.5N·m. The specific simulation parameters of SRM are shown in Table 6. The switching frequency is 10kHz.

[0132] Table 6 SRM simulation parameters

[0133]

[0134] Under the condition of no-load and direct instantaneous torque control method based on torque prediction, the speed loop is simulated and analyzed by using PID, anti-disturbance control and fuzzy anti-disturbance control respectively, and the reference speed is 2000r / min. The waveform is as follows: Figure 8As shown. By comparing the waveforms under the three control methods, it can be seen that under the traditional PI control, the speed response takes the longest time and has the largest overshoot, requiring 86ms to stabilize at 2000r / min, the maximum speed reaches 2048r / min, and the overshoot is 2.4%. The speed stabilization time of ordinary ADRC is 0.03s, and the speed overshoot is 10r / min. The fuzzy ADRC has the shortest response time, reaching the given speed after 0.02s, and the maximum speed is only 2003r / min. By comparing the simulation results, it can be seen that compared with the other two methods, the fuzzy anti-disturbance control not only reduces the speed overshoot, but also saves the response time.

[0135] In order to verify the characteristics of the motor under load, the motor is started with a load of 8N·m and a given speed of 1000r / min. The simulation waveform is as follows: Fig. 9 As shown. It can be seen that under the control of PI, ADRC, and fuzzy ADRC, the time for the speed to reach stability is 79ms, 36ms, and 18ms respectively. The maximum speed overshoot under PI control is 30r / min, the maximum overshoot of ADRC is 8r / min, and there is no obvious overshoot of fuzzy ADRC. It can be seen that whether under no-load or load conditions, fuzzy ADRC has the best speed control effect compared with the other two methods.

[0136] The corresponding speed waveform under the sudden change of speed is as follows: Fig.10 As shown in the figure. Under no-load conditions, the motor speed increases from 2000r / min to 2500r / min in 0.2s. The overshoot of traditional PI control is 35r / min, and the stabilization time is 0.053s. The peak values ​​of ADRC and fuzzy ADRC are 2508r / min and 2502r / min respectively. After 22ms and 9ms of speed mutation, the speed returns to the stable value. This shows that when the speed mutation occurs, fuzzy ADRC still has the smallest overshoot and the fastest recovery speed.

[0137] When the load torque is 15N·m, the engine is started at a given speed of 1000r / min, and the speed is increased to 2000r / min after 0.2s. The simulation waveforms under the three conditions are as follows: Fig.11 As shown in the figure, it can be seen that the traditional PI control has the largest overshoot and the longest response time, followed by ADRC, and the fuzzy ADRC has the smallest overshoot and the shortest response time.

[0138] Fig.12The figure shows the torque change when the speed changes suddenly. It can be seen from the figure that after the speed changes suddenly, the torque will suddenly increase to about 35N·m in a short time. In the process of recovering to the load torque, the torque fluctuation is the largest under PI control, and the torque recovery process is the fastest, followed by ADRC, and the fuzzy automatic anti-disturbance control is the least affected. After the torque stabilizes, the torque pulsation corresponding to the fuzzy automatic anti-disturbance control is also the smallest, indicating that it has the fastest response and the strongest anti-disturbance ability.

[0139] In order to verify the followability of the SRM control system when the load torque suddenly changes, the motor is simulated under the condition of torque sudden change. The speed waveform is as follows: Fig.13 First, start the machine under no-load condition, set the reference speed to 1000r / min, and increase the load to 10N·m at 0.2s. The speed waveform is as follows: Fig.13 As shown. When the load increases, the speed will recover under the three control modes, but they all show different levels of decline. Among them, PI control declines the most, which is 38r / min, ADRC control declines to 990r / min, and fuzzy anti-disturbance control declines the least, which is only 5r / min. The speed recovery time is 142ms, 53ms, and 25ms respectively. It can be seen that when the torque changes, the fuzzy ADRC has the smallest speed fluctuation and the shortest time to recover to the stable speed, which shows that it has excellent dynamic performance and strong anti-interference ability.

[0140] In order to verify the effect of fuzzy anti-disturbance when the internal parameters of the motor change, the motor parameters are changed when the load torque is 0, the speed waveform is recorded and analyzed, and the reference speed is set to 1000r / min. When other parameters remain unchanged, the motor's rotational inertia changes from 0.0082kg·m 2 becomes 0.015kg·m 2 The obtained simulation waveform is as follows Fig.14 As shown in the figure, it can be seen that under the traditional PI control mode, as the moment of inertia increases, the response time and overshoot increase accordingly, and the speed curve moves backward as a whole; under the fuzzy anti-disturbance control strategy, the speed waveform does not change much with the moment of inertia, the response time and overshoot increase slightly, and the system performance does not change basically. By comparison, it can be seen that fuzzy anti-disturbance control has a strong ability to adapt to system changes.

[0141] In addition to the rotational inertia analyzed above, the stator winding resistance of the motor also affects the operation of the motor. Therefore, when running at no load, the resistance of the motor changes from 0.15Ω to 1Ω in 0.2s. Fig.15 It can be seen that under the traditional PI control method, when the stator resistance increases, the motor speed and torque fluctuations will also increase accordingly; however, under the fuzzy anti-disturbance control, the waveform obtained is as follows Fig.16As shown in the figure, the speed and torque waveforms do not change much with the stator resistance, and the speed recovers quickly, which shows that the system has strong anti-interference ability and good robustness.

[0142] This embodiment performs simulations under different speeds and torques, and uses the torque ripple coefficient to analyze the current and torque of the motor. The expressions are as follows:

[0143]

[0144] Where: T max Indicates the maximum torque, T min Indicates the minimum torque, T av Represents the average torque. Since the average torque of the motor is difficult to calculate, it is generally expressed as the reference torque T ref Instead of T av .

[0145] When the motor speed is 500r / min, the load is 3N·m and the load is 8N·m, the conventional DITC method and the TPDITC control method of this embodiment are used for simulation control, and the obtained simulation waveforms are as follows: Fig.17 and 18 As shown. The simulation waveforms are the total torque waveform, the three-phase current waveform, and the three-phase torque waveform from top to bottom. As can be seen from the above figure, the current amplitude of the traditional DITC is 7.8A, the upper and lower torque amplitudes are 3.41N·m and 2.36N·m respectively, the torque fluctuation is 1.05N·m, and the torque pulsation coefficient is 35%; the current amplitude of TPDITC is 7.2A, the upper and lower torque amplitudes are 3.21N·m and 2.77N·m respectively, the torque fluctuation is 0.44N·m, and the torque pulsation coefficient is 14.67%. Through comparative analysis, the torque pulsation coefficient of TPDITC is reduced by 20.33% compared with DITC, and the current peak value is also slightly reduced, and the negative torque of each phase is also suppressed.

[0146] When the motor speed is 500r / min and the load is 8N·m, the current amplitude of the traditional DITC is 11.8A, the upper and lower torque amplitudes are 8.99N·m and 6.85N·m respectively, the torque fluctuation is 2.14N·m, and the torque pulsation coefficient is 26.75%; the current amplitude of TPDITC is 10.5A, the upper and lower torque amplitudes are 8.65N·m and 7.81N·m respectively, the torque fluctuation is 0.84N·m, and the torque pulsation coefficient is 10.5%, which is 16.25% less than that of the traditional DITC. The results show that after the load of the system increases, TPDITC can still effectively suppress torque pulsation.

[0147] right Fig.17 , Fig.18 , Fig.19 , Fig. 20 Summarizing, we get Table 7. It can be seen that at low speed, TPDITC reduces the motor torque pulsation, and the effect is particularly obvious in the commutation interval. Moreover, under the premise of ensuring the total torque of the motor, the current peak is reduced, and there is almost no current in the inductance reduction area, which effectively suppresses the generation of negative torque and still has a good effect when the load increases.

[0148] Table 7 SRM operating conditions at low speed

[0149]

[0150] The simulation waveform of the motor when the speed is 2000r / min and the load is 3N·m is as follows Fig.21 and 22 As shown in the figure, the current amplitude of the traditional DITC is 8.2A, the upper and lower torque amplitudes are 3.73N·m and 2.58N·m respectively, the torque fluctuation is 1.15N·m, and the torque pulsation coefficient is 38.33%; the current amplitude of TPDITC is 7.4A, the upper and lower torque amplitudes are 3.24N·m and 2.72N·m respectively, the torque fluctuation is 0.52N·m, and the torque pulsation coefficient is 17.33%. Through comparative analysis, the torque pulsation of TPDITC is reduced by 21% compared with the traditional DITC, and the current peak is slightly reduced. By comparing the three-phase torque waveforms under the two control methods, it can be seen that TPDITC also suppresses the negative torque of each phase.

[0151] The simulation waveform when the speed is 2000r / min and the load is 8N·m is as follows Fig.23 and 24 As shown in the figure, the current amplitude of the traditional DITC is 12.1A, the upper and lower torque amplitudes are 9.11N·m and 6.79N·m, the torque fluctuation is 2.32N·m, and the torque ripple coefficient is 29%; the current amplitude of the TPDITC is 10.9A, the upper and lower torque amplitudes are 8.71N·m and 7.72N·m, the torque fluctuation is 0.99N·m, and the torque ripple is 12.38%, which is 16.62% less than that of the traditional DITC.

[0152] Table 8 SRM operating conditions at high speed

[0153]

[0154] By comparison, it can be seen that TPDITC can suppress the torque pulsation of the motor under different speeds and loads, and the current peak is also reduced to a certain extent. Moreover, because the unique inductance change law of SRM is taken into account when setting the conduction rule, the negative torque is reduced to varying degrees under these working conditions.

[0155] In order to verify the dynamic response capability of SRM under TPDITC control, the motor speed and torque mutation simulation was carried out. When the load was 10N·m, the motor speed was increased from 200r / min to 1000r / min at 0.25s. The simulation waveforms are as follows: Fig.25 As shown in the figure, the speed can quickly and smoothly rise to the specified speed, but there will be overshoot in the process, the maximum speed will reach 1036r / min, and the speed PI controller will be saturated, which will increase the reference torque generated and cause a sudden change in the motor output electromagnetic torque. Finally, after 0.05s, the torque will still be stable at about 10N·m.

[0156] Fig.26 This is a simulation diagram obtained when the torque suddenly changes from no-load to 10N·m at 0.2s, and then changes back to no-load at 0.4s. It can be seen from the figure that the torque increases sharply from 0N·m to 10N·m after 0.02s, and the speed drops from 500r / min to about 469r / min. After 0.05s, the speed recovers to 500r / min. When the load suddenly changes from 10N·m to no-load, the load changes to 0N·m after 0.03s, and the speed increases to 527r / min. After 0.05s, it recovers and stabilizes to 500r / min.

[0157] Fig. 27 This is the torque magnification diagram corresponding to the load mutation. It can be seen from the figure that when the load is unloaded, the load torque range is -0.15N·m~0.21N·m, and the torque fluctuation is 0.36N·m; when the load is 10N·m, the load torque range is 9.48N·m~10.61N·m, and the torque fluctuation is 1.13N·m. In the case of sudden changes in working conditions, compared with the given torque and given load, the torque fluctuation does not increase too much.

[0158] In summary, when the external working conditions and internal parameters of the motor change, the traditional PI system will be greatly affected and it is difficult to meet the control accuracy requirements. Fuzzy anti-disturbance control not only has the excellent dynamic and static characteristics of fuzzy control, reducing the torque pulsation of the motor, but also has the excellent following performance, anti-interference ability and response speed of anti-disturbance control, so that the control system still has good robustness when the parameters change and are subject to external disturbances. When the torque and speed are constant, the TPDITC strategy can effectively suppress the torque pulsation of the SRM motor and has good static characteristics. By analyzing the curves obtained by the sudden change of working conditions, it can be known that the speed and torque will fluctuate when the external conditions change suddenly, but they will eventually stabilize at the given speed and load torque. In order to optimize the response time and overshoot phenomenon under dynamic conditions, the PI controller in the speed loop can be further optimized.

[0159] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A switched reluctance motor control method based on direct instantaneous torque control, wherein the power converter of the switched reluctance motor comprises a three-phase bridge arm, characterized in that: The steps include: According to the reference torque T ref The torque difference ΔT is obtained by subtracting the actual torque T. The value and change trend of the torque difference ΔT are obtained according to the conduction rule to obtain the state of the three-phase switch at this time and combined into a voltage vector; When the number of obtained voltage vectors is one, the voltage vector is applied to the power converter of the switched reluctance motor in the next control cycle. When the number of obtained voltage vectors is two, the corresponding predicted torque is calculated based on the voltage vector, the predicted torque is compared with the reference torque, and the vector to be applied is determined based on the comparison result and applied to the power converter of the switched reluctance motor.

2. The switched reluctance motor control method based on direct instantaneous torque control according to claim 1, characterized in that: In the conduction rule, the commutation area is divided into commutation area 1 and commutation area 2, and the inner limit value ΔT of the torque hysteresis control rule is set. max and outer limit ΔT min ; In the commutation zone 1, the conduction rule of the open phase is: when ΔT<-ΔT min When the state is 0, when -ΔT min When ΔT < 0, the state is 0 or 1, and when ΔT > 0, the state is 1; the conduction rule of the shut-off phase is: when ΔT < -ΔT max When -ΔT max <ΔT<-ΔT min When -ΔT min <ΔT<0, the state is 0, when 0<ΔT<ΔT max When ΔT>ΔT max , state is 1; In the commutation zone 2, the conduction rule of the open phase is: ΔT<-ΔT max When -ΔT max <ΔT<-ΔT min When -ΔT min <ΔT<0, the state is 0, when 0<ΔT<ΔT min When ΔT>ΔT min , the state is 1; the conduction rule of the shut-off phase is: when ΔT<-ΔT min When -ΔT min When ΔT<0, the state is 0 or -1. When 0<ΔT<ΔT min When ΔT min <ΔT<ΔT max When ΔT>ΔT max , the state is 1.

3. The switched reluctance motor control method based on direct instantaneous torque control according to claim 1, characterized in that: The predicted torque T k+1 for: Among them, i k+1 is the current value at time k+1; θ k+1 is the rotor position angle at time k+1; L dsat is the inductance after saturation; A and B are constants.

4. The switched reluctance motor control method based on direct instantaneous torque control according to claim 1, characterized in that: When the obtained voltage vector quantity is V1 and V2, the corresponding predicted torque is set as T max and T min , and T min Less than T max ; When T ref <T min When , the voltage vector V2 is selected to continue to act in the next control cycle; when T ref >T max When , the voltage vector V1 is selected to continue to act in the next control cycle; when T min <T ref <T max When the two vectors are in effect, the action time is adjusted so that both voltage vectors continue to act in the next control cycle.

5. The switched reluctance motor control method based on direct instantaneous torque control according to claim 4, characterized in that: The action times T1 and T2 of the voltage vectors V1 and V2 are:

6. The switched reluctance motor control method based on direct instantaneous torque control according to claim 1, characterized in that: The reference torque T ref The methods for obtaining include: Reference speed ω ref The tracking signal ω1 is obtained by the first-order differential tracker. The observed value z1 of the output signal of the second-order extended state observer is subtracted from the tracking signal ω1 and the torque T is obtained by the nonlinear state error feedback rate. The reference torque T is obtained by combining the estimated value z2 of the total disturbance of the system. ref .

7. The switched reluctance motor control method based on direct instantaneous torque control according to claim 6, characterized in that: The control equation of the nonlinear state error feedback rate is: Where: k1 is the tracking factor; δ is the filtering factor; α3 is the adjustable nonlinear factor.

8. The switched reluctance motor control method based on direct instantaneous torque control according to claim 7, characterized in that: The tracking factor k1 is adjusted by a fuzzy control method.

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