Permanent magnet synchronous motor resistance on-line identification method and system

Abstract

The invention discloses a permanent magnet synchronous motor resistance online identification method and system, and belongs to the technical field of permanent magnet synchronous motor parameter identification. According to the on-line identification method for the resistance of the permanent magnet synchronous motor, on-line identification of the resistance is realized under the condition that the resistance of the motor is changed by constructing a Clark transformation model, a Park transformation model, a current estimation model, a current error calculation model, a resistance estimation model and a sliding mode gain adaptive control model; therefore, the technical problems of complexity, high implementation difficulty, long response time and the like of the existing identification algorithm can be solved. Furthermore, the sliding mode gain adaptive model can effectively improve the resistance identification precision. When the temperature of the motor of the dust collector rapidly rises, the internal resistance of the dust collector is changed, the resistance change can be identified in real time, the control performance of the motor is improved, and the scheme is detailed, scientific and reasonable.

Description

Technical Field

[0001] The invention relates to a method and system for online identification of resistance of a permanent magnet synchronous motor, belonging to the technical field of parameter identification of permanent magnet synchronous motors. Background Art

[0002] When a permanent magnet synchronous motor is running, the stator resistance will change due to factors such as temperature, which will affect the motor control performance. Therefore, online identification of stator resistance is particularly important for motor control performance. Currently, the commonly used identification methods are mainly divided into two categories: offline identification and online identification.

[0003] Among them, the commonly used methods for offline identification include the pulse voltage method. For example, patent CN202011577429.9 proposes that when the motor is stationary, high-frequency voltages with different duty cycles are injected into the motor, the motor current is collected and the resistance value is calculated. The calculations are accumulated and the average value is obtained to obtain the resistance identification value.

[0004] This method is relatively simple to implement, but it cannot identify the resistance in real time during the operation of the motor, and cannot track parameter changes in real time. Online identification can identify the resistance while the motor is running. Currently, the commonly used online identification methods include the least squares method and the model reference adaptive method. For example, patent CN202210649037.1 proposes an identification method based on the least squares method, and patent CN202310975922.3 proposes an identification method based on model reference adaptive. These methods can realize the identification of resistance, but their calculations are relatively complex and will be limited by chip computing power, hardware, etc. in practical applications.

[0005] Patent CN201610633002.3 proposes an online identification method based on a sliding mode observer. This method constructs a sliding mode observer according to the q-axis voltage equation in a synchronous rotating coordinate system to equivalently calculate the resistance value of the stator resistor. It has the advantages of small calculation amount and simple algorithm, but it still has problems such as slow identification response and poor accuracy. It does not meet the high-precision and high-response positioning requirements of robots, CNC machine tools, etc. for servo systems.

[0006] The information disclosed in this Background Art is only for understanding the background of the inventive concept and therefore it may include information that does not constitute the prior art. Summary of the invention

[0007] In response to the above problem or one of the above problems, an object of the present invention is to provide a method and system for online identification of resistance of a permanent magnet synchronous motor. By constructing a Clark transformation model, a Park transformation model, a current estimation model, a current error calculation model, a resistance estimation model, and a sliding mode gain adaptive control model, online identification of resistance is achieved when the motor resistance changes, thereby solving technical problems such as the complexity of existing identification algorithms, the difficulty of implementation, and the long response time.

[0008] In view of the above problem or one of the above problems, the second object of the present invention is to provide a method and system for online identification of resistance of a permanent magnet synchronous motor, which can effectively improve the resistance identification accuracy and can quickly complete the identification of resistance changes.

[0009] To achieve one of the above purposes, the first technical solution of the present invention is:

[0010] A method for online identification of resistance of a permanent magnet synchronous motor comprises the following steps:

[0011] Step 1: transform the sampled three-phase current of the permanent magnet synchronous motor through the pre-built Clark transformation model to obtain the actual two-phase current in the static two-phase coordinate system;

[0012] Step 2: Using the pre-built Park transformation model, the actual two-phase current and the electrical angle information of the permanent magnet synchronous motor are processed to calculate the actual direct-axis current and the actual quadrature-axis current;

[0013] Step 3, using a pre-built current estimation model, obtaining an estimated quadrature-axis current according to the actual direct-axis current and the actual quadrature-axis current, and based on a feedback gain and feedback of an estimation resistor;

[0014] Step 4, using a pre-built current error calculation model to process the estimated quadrature-axis current and the actual quadrature-axis current, and outputting the current error;

[0015] Step 5: Using the pre-built resistance estimation model, based on the sliding surface, the current error is calculated to obtain the resistance estimation value;

[0016] Step six, based on the pre-built sliding mode gain adaptive control model and based on the stability condition, feedback of the resistance estimation value is obtained to achieve real-time update of the sliding mode gain when the resistance changes.

[0017] After continuous exploration and experiments, the present invention constructs a Clark transformation model, a Park transformation model, a current estimation model, a current error calculation model, a resistance estimation model, and a sliding mode gain adaptive control model to realize online identification of resistance when the motor resistance changes, thereby solving technical problems such as the complexity of existing identification algorithms, the difficulty of implementation, and the long response time.

[0018] Furthermore, the sliding mode gain adaptive model of the present invention can effectively improve the resistance identification accuracy, and thus can quickly complete the identification of resistance changes.

[0019] Furthermore, the present invention is applied in high-speed occasions such as vacuum cleaners. When the temperature of the vacuum cleaner motor rises rapidly, it will cause the internal resistance to change. Then, the present invention can identify the resistance change in real time and improve the motor control performance. The solution is detailed, scientific and reasonable.

[0020] As the preferred technical measures:

[0021] Step 1: The method of transforming the sampled three-phase current of the permanent magnet synchronous motor through the pre-built Clark transformation model to obtain the actual two-phase current in the two-phase coordinate system is as follows:

[0022] The three-phase current i of the permanent magnet synchronous motor is obtained by current sampling a 、i b 、i c ;

[0023] The three-phase current i of the permanent magnet synchronous motor a 、i b 、i c Input into Clark transformation, output the actual two-phase current i in the stationary two-phase coordinate system α and i β ;

[0024] The transformation formula of Clark transformation is:

[0025]

[0026] As the preferred technical measures:

[0027] Step 2: Use the pre-built Park transformation model to process the actual two-phase current and the electrical angle information of the permanent magnet synchronous motor. The method for calculating the actual direct-axis current and the actual quadrature-axis current is as follows:

[0028] The actual two-phase current i under the stationary coordinate α and i β The actual electrical angle information θ of the permanent magnet synchronous motor e Together as the input of Park transformation, after Park transformation, the actual direct axis current i of the permanent magnet synchronous motor in synchronous rotating coordinates is obtained. d and the actual quadrature axis current i q , the formula of Park transformation is:

[0029]

[0030] where θe is the actual electrical angle, sampled by an encoder or sensorless control method.

[0031] As the preferred technical measures:

[0032] Step 3: Using the pre-built current estimation model, according to the actual direct-axis current and the actual quadrature-axis current, and based on the feedback gain and the feedback of the estimation resistor, the method for obtaining the estimated quadrature-axis current is as follows:

[0033] The actual speed ω of the permanent magnet synchronous motor is acquired by using an encoder or a sensorless control method;

[0034] The actual direct-axis current i d , actual quadrature axis current i q , actual speed ω, and the quadrature axis voltage u in synchronous rotating coordinates output by the permanent magnet synchronous motor vector control system q , feedback value Z q +lZ eq As input, after current estimation, the output is the estimated quadrature axis current

[0035] As the preferred technical measures:

[0036] After current estimation, the estimated quadrature-axis current is output The implementation steps are as follows:

[0037] First, construct the voltage equation of the permanent magnet synchronous motor in the dq axis coordinate system, and its expression is as follows:

[0038]

[0039] In the formula, i d 、i q are the dq axis stator currents, u d 、u q are the dq axis stator voltages respectively, ω is the actual speed, ψ f is the permanent magnet flux, R is the stator resistance, L s is the stator inductance;

[0040] The estimated resistance is closed-loop fed back and the estimated resistance feedback is used as input to construct a new current observer calculation formula as follows:

[0041]

[0042] Among them, Z eq For Z q The switching function is obtained after low-pass filtering; the calculation formula of the switching function is as follows:

[0043] Z eq =Zq ω c1 / (s+ω c1 );

[0044] In the formula, ω c1 is the cut-off frequency, l is the feedback gain of the control function;

[0045] The calculated current estimate is in differential form, which is integrated to obtain the estimated quadrature axis current

[0046] As the preferred technical measures:

[0047] Step 4: Use the pre-built current error calculation model to process the estimated quadrature-axis current and the actual quadrature-axis current. The method for outputting the current error is as follows:

[0048] According to the estimated quadrature-axis current Calculate the current error with the actual quadrature axis current iq The current error calculation formula is as follows:

[0049]

[0050] As the preferred technical measures:

[0051] Step 5: Use the pre-built resistance estimation model to calculate the current error based on the sliding surface to obtain the resistance estimation value as follows:

[0052] The current error As the input of the symbolic function, the output after the symbolic function operation is and the sliding mode gain k R As the input of the multiplier, the multiplication output Z q ;

[0053] Z q As the input of the first low-pass filter, the output Z after low-pass filtering eq ;

[0054] Z eq Multiplying the feedback factor l outputs lZ eq ; Z q With lZ eq As the input of the adder, the output is Z after addition. q +lZ eq ;

[0055] Z q +lZ eq The feedback current estimation model is used as input and also as the input value of the second low-pass filter. Based on the sliding surface, the resistance estimation value is output after low-pass filtering. The calculation formula is as follows:

[0056] R=(Z q +lZ eq )·(ω c2 / (s+ω c2 )).

[0057] As the preferred technical measures:

[0058] Based on the sliding surface, the method for estimating the output resistance after low-pass filtering is as follows:

[0059] Set the sliding surface, its expression is as follows:

[0060]

[0061] When the sliding mode dynamically moves to the sliding surface, S=0 is satisfied, that is, Substituting it into the current error calculation formula, we get the approximate equivalent formula of resistance R, which is as follows:

[0062] R=Z q +lZ dq ,

[0063] Among them, Z q +lZ eq is a high frequency signal,

[0064] In order to filter the high-frequency signal, a low-pass filter is used to filter it, and the final resistance identification formula is obtained as follows:

[0065] R=(Z q +lZ eq )·(ω c2 / s+ω c2 )

[0066] In the formula, ω c2 is the cut-off frequency.

[0067] As the preferred technical measures:

[0068] Step 6: Based on the pre-built sliding mode gain adaptive control model and the stability condition, the method of obtaining feedback of the resistance estimation value to achieve real-time updating of the sliding mode gain when the resistance changes is as follows:

[0069] The calculated resistance estimate is fed back and multiplied by the gain n as the input of the multiplier. The actual current i q As the input of the sign function, after the sign function operation, the output is sign(i q ) is used as the input of the multiplier, and the sliding mode gain is output after the multiplier operation. The specific implementation steps are as follows:

[0070] Based on the stability condition Lyapunov, the Lyapunov function is constructed, and its expression is as follows:

[0071] V=S

[0072] in

[0073] According to the Lyapunov stability law, the system stability expression is established:

[0074]

[0075] Right now

[0076] Substituting the current error calculation formula into the system stability expression, we get the system stability formula:

[0077]

[0078] When iq>0, When , the system stability equation is as follows:

[0079]

[0080] At this time, it is deduced The conditions to be met are

[0081] When iq>0, When , the system stability equation is as follows:

[0082]

[0083] At this time, it is deduced The conditions to be met are

[0084] In summary, when iq>0, The conditions to be met are:

[0085]

[0086] Similarly, when iq<0, we can infer The conditions to be met are:

[0087]

[0088] In summary, The conditions that need to be met can be simplified to:

[0089]

[0090] When l = m, Substituting into the above formula, we can deduce that the condition for system stability is:

[0091]

[0092] When m=5 and n=2, the above conditions can be met. At this time, l=5.

[0093] To achieve one of the above purposes, the second technical solution of the present invention is:

[0094] A permanent magnet synchronous motor resistance online identification system, comprising:

[0095] one or more processors;

[0096] A storage device for storing one or more programs;

[0097] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method for online identification of resistance of a permanent magnet synchronous motor.

[0098] To achieve one of the above purposes, the third technical solution of the present invention is:

[0099] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for online identification of resistance of a permanent magnet synchronous motor.

[0100] Compared with the prior art solutions, the present invention has the following beneficial effects:

[0101] After continuous exploration and experiments, the present invention constructs a Clark transformation model, a Park transformation model, a current estimation model, a current error calculation model, a resistance estimation model, and a sliding mode gain adaptive control model to realize online identification of resistance when the motor resistance changes, thereby solving technical problems such as the complexity of existing identification algorithms, the difficulty of implementation, and the long response time.

[0102] Furthermore, the sliding mode gain adaptive model of the present invention can effectively improve the resistance identification accuracy, and thus can quickly complete the identification of resistance changes.

[0103] Furthermore, the present invention is applied in high-speed occasions such as vacuum cleaners. When the temperature of the vacuum cleaner motor rises rapidly, it will cause the internal resistance to change. Then, the present invention can identify the resistance change in real time and improve the motor control performance. The solution is detailed, scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] Figure 1 A schematic diagram of a flow chart of a method for online identification of resistance of a permanent magnet synchronous motor according to the present invention;

[0105] Figure 2 An online identification block diagram of the permanent magnet synchronous motor resistance online identification method of the present invention;

[0106] Figure 3 Another online identification block diagram of the online identification method for the resistance of a permanent magnet synchronous motor of the present invention. DETAILED DESCRIPTION

[0107] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0108] On the contrary, the present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the following detailed description of the present invention. Those skilled in the art can fully understand the present invention without the description of these details.

[0109] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0110] like Figure 1 As shown, the first specific embodiment of the method for online identification of resistance of a permanent magnet synchronous motor of the present invention is as follows:

[0111] A method for online identification of resistance of a permanent magnet synchronous motor comprises the following steps:

[0112] Step 1: transform the sampled three-phase current of the permanent magnet synchronous motor through the pre-built Clark transformation model to obtain the actual two-phase current in the two-phase coordinate system;

[0113] Step 2: Using the pre-built Park transformation model, the actual two-phase current and the electrical angle information of the permanent magnet synchronous motor are processed to calculate the actual direct-axis current and the actual quadrature-axis current;

[0114] Step 3, using a pre-built current estimation model, obtaining an estimated quadrature-axis current according to the actual direct-axis current and the actual quadrature-axis current, and based on a feedback gain and feedback of an estimation resistor;

[0115] Step 4, using a pre-built current error calculation model to process the estimated quadrature-axis current and the actual quadrature-axis current, and outputting the current error;

[0116] Step 5: Using the pre-built resistance estimation model, based on the sliding surface, the current error is calculated to obtain the resistance estimation value;

[0117] Step six, based on the pre-built sliding mode gain adaptive control model and based on the stability condition, feedback of the resistance estimation value is obtained to achieve real-time update of the sliding mode gain when the resistance changes.

[0118] like Figure 2 As shown, the second specific embodiment of the method for online identification of resistance of a permanent magnet synchronous motor of the present invention is as follows:

[0119] A method for online identification of resistance of a permanent magnet synchronous motor includes the following contents:

[0120] Adopt i d =0 method is used to control the PMSM. Since there is no resistance component in the d-axis voltage equation, it is impossible to construct an observer to estimate it. The q-axis voltage equation contains resistance variables. In this regard, some relevant scholars have constructed a traditional sliding mode observer based on the q-axis voltage equation to estimate the resistance. The principle is explained below.

[0121] The three-phase current of the permanent magnet synchronous motor is sampled to obtain i a 、i b 、i c ; Use encoder or sensorless control method to collect permanent magnet synchronous motor position and speed information (angle θ e and speed ω), the sampled three-phase current (i a 、i b 、i c ) is used as the input of Clark transformation, and the output is the current i in the stationary coordinate system. α and i β ; Current i in the stationary coordinate system α and i β As the input of Park transformation, the angle information θ collected by encoder or sensorless method is e It is also used as input, and the output is the current i in the synchronous rotating coordinate system. d and i q . The synchronous rotating coordinate system current i d 、i q As input, the synchronous rotating coordinate system voltage u of the motor vector control system q As input, the current estimation module outputs the current estimation value.

[0122] Estimated q-axis current As the input of the subtractor, the actual current i is obtained by current sampling and Clark and Park transformation. qIt also serves as the input of the subtractor, and the output of the subtractor is connected to the input of the sign function module.

[0123] Current error As the input of the symbolic function module, the symbolic function output Z q . An output Z q As the input of the low-pass filter module, the output after low-pass filtering is the identification value of the resistor Another output Z q Connected to the current estimation module, it feeds back the current estimation as input.

[0124] The method for identifying the resistance of a permanent magnet synchronous motor using the resistance identification method based on the traditional sliding mode observer is as follows: The three-phase current is obtained according to the current sampling, and after the Clark and Park transformation, the i d 、i q As the input of the current estimation module, the motor speed information ω is also the input of the current estimation module, and the u of the motor control system vector control q It is also used as the input of the current estimation module, and outputs the current estimation value after current estimation. Estimated current And the actual sampled current i q As the input of the subtractor, the current error between the estimated current and the actual current is output after the subtractor. The input of the sign function module is the output current error of the subtractor Output is a symbolic function One of the outputs is the input of the low-pass filter, and the other is fed back to the current estimation module as one of the inputs of the current estimation; the symbol function Z q As the input of the low-pass filter, the output resistance estimation after low-pass filtering The specific process is:

[0125] Step 1: Construct a Clark transform module and perform Clark transform, which includes the following:

[0126] The three-phase current i of the permanent magnet synchronous motor is obtained by current sampling a 、i b 、i c , and input into Clark transform, the transformation formula of Clark transform is:

[0127]

[0128] The output of the Clark transformation is the actual current i in the stationary two-phase coordinate system. α and i β , as the input of Park transform.

[0129] Step 2: Construct a Park transformation module and perform Park transformation, which includes the following contents:

[0130] The current i in the stationary coordinate obtained in step 1 α and i β The actual electrical angle information θ of the permanent magnet synchronous motor e Together as the input of the Park transformation module, after Park transformation, the actual direct axis current i of the permanent magnet synchronous motor in the synchronous rotating coordinate is obtained. d And the actual quadrature axis current iq, the formula for Park transformation is:

[0131]

[0132] where θ e is the actual electrical angle, sampled by an encoder or sensorless control method.

[0133] Step 3: Construct a current estimation module to perform current estimation, which includes the following:

[0134] The actual speed ω of the permanent magnet synchronous motor is acquired by using an encoder or a sensorless control method, and then the actual speed is output to the current estimation module;

[0135] The actual direct-axis current i calculated in step 2 is d and the actual quadrature axis current iq, the actual speed ω of the permanent magnet synchronous motor acquired, and the quadrature axis voltage u in the synchronous rotating coordinate output by the permanent magnet synchronous motor vector control system q As the input of the current estimation module, the output is the estimated quadrature axis current after current estimation The specific steps to achieve this are as follows:

[0136] First, the voltage equation of the permanent magnet synchronous motor in the dq axis coordinate system is as follows:

[0137]

[0138] In the formula, i d 、i q are the dq axis stator currents, u d 、u q are the dq axis stator voltages respectively, ω is the estimated angular velocity, ψ f is the permanent magnet flux, R is the stator resistance, L s is the stator inductance.

[0139] This method uses i d= 0 control method to control the PMSM. Substituting it into formula (4), it can be seen that there is no resistance component in the d-axis voltage equation at this time, and it is impossible to construct an observer to estimate it. The q-axis voltage equation contains a resistance component, so a sliding mode observer is constructed based on the q-axis current to estimate the q-axis current. According to formula (4), the current observer model constructed by the traditional sliding mode observer is as follows:

[0140]

[0141] In the formula, Z q is a symbolic function where k R is the sliding mode gain.

[0142] The value calculated by equation (5) is the differential value of the estimated q-axis current, and integrating it can obtain the estimated value of the q-axis current.

[0143] Step 4: Construct a current error calculation module to perform current error calculation, which includes the following contents:

[0144] The estimated current obtained in step 3 The actual q-axis current i is obtained by current sampling q As the input of the subtractor, the output current error Subtract equation (2) from equation (1), and the state equation of the q-axis current error is:

[0145]

[0146] in, is the current error.

[0147] Step 4: Construct a resistance estimation module to perform resistance estimation, which includes the following:

[0148] Define the sliding surface

[0149]

[0150] According to the sliding mode variable structure theory, when the sliding mode dynamically moves to the sliding surface, s = 0, that is, Substituting it into formula (6), we can see that the resistance R is approximately equivalent to R = Z q .

[0151] Current error at step 3 output As input, after the symbolic function operation, the output is Z q , as the input of the low-pass filter module, and fed back to the current estimation module as input.

[0152] Step 5: Construct a low-pass filter module to perform low-pass filtering, which includes the following:

[0153] Step 4 output Z q As the input of the low-pass filter module, Z q It is a high-frequency signal. In order to filter the high-frequency signal, a low-pass filter is used to filter it. The final resistance identification formula is as follows:

[0154]

[0155] where ω c is the cutoff frequency.

[0156] Final output resistance estimate

[0157] like Figure 2 As shown, the third specific embodiment of the method for online identification of resistance of a permanent magnet synchronous motor of the present invention is as follows:

[0158] A method for online identification of resistance of a permanent magnet synchronous motor includes the following contents:

[0159] The three-phase current of the permanent magnet synchronous motor is sampled to obtain i a 、i b 、i c ; Use encoder or sensorless control method to collect permanent magnet synchronous motor position and speed information (angle θ e and speed ω), the sampled three-phase current (i a 、i b 、i c ) is used as the input of Clark transformation, and the output is the current i in the stationary coordinate system. α and i β ; Current i in the stationary coordinate system α and i β As the input of Park transformation, the angle information θ collected by encoder or sensorless method is e It is also used as input, and the output is the current i in the synchronous rotating coordinate system. d and i q . The synchronous rotating coordinate system current i d 、i q As input, the synchronous rotating coordinate system voltage u of the motor vector control system q As input, the q-axis current estimation value is obtained through the current estimation module output

[0160] Estimated current The actual current i q As the input of the subtractor, the current error is obtained after the difference operation. The output is connected to the sign function module. Current Error As the input of the sign function module, after the sign function operation, it is multiplied by the sliding mode gain and the output Z q ; Z q As the input of the first low-pass filter, the output Z after low-pass filtering eq ; Z eq The feedback coefficient l is used as the input of the multiplier, and the output after multiplication is lZ eq ; Z q With lZ eq As the input of the adder, after addition operation, the output is Z q +lZ eq ; Z q +lZ eq As the input of the second low-pass filter, it is also fed back to the current estimation module as the input of the current estimation module, Z q +lZ eq Estimated output resistance after low-pass filtering Estimated resistance value As input to the sliding mode gain adaptation block, the resistance estimate Multiplied by the gain n as one of the inputs to the multiplier, the actual current i q As the input of the symbol function module, after the symbol function operation, the output is sign(i q ) is used as another input of the multiplier, and after the multiplier operation, the sliding mode gain is output. Realize sliding mode gain adaptive control.

[0161] The online identification method of PMSM resistance using the improved sliding mode observer is as follows: the three-phase current is obtained according to the current sampling, and after Clark and Park transformation, i is obtained. d 、i q As the input of the current estimation module, the motor speed information ω is also the input of the current estimation module, and the u of the motor control system vector control q It is also used as the input of the current estimation module, and outputs the current estimation value after current estimation. Estimated current And the actual sampled current i q As the input of the subtractor, the current error between the estimated current and the actual current is output after the subtractor. Current error As the input of the sign function, the output is about the current error The symbolic function and the sliding mode gain k R Together as the input of the multiplier, the output Z q As the input of the low-pass filter, the output Z after low-pass filtering eq=Z q ω c1 / (s+ω c1 ), Z eq and the feedback coefficient l as the input of the multiplier, and the output lZ eq ; lZ eq and Z q As the input of the adder, after addition operation, the output is Z q +lZ eq ; Z q +lZ eq The current estimation module is fed back as the input value and also as the input of the second low-pass filter. The output resistance estimation value after filtering The resistance estimate is fed back to the sliding mode gain adaptation module as the input value, and is multiplied by the gain n as the input of the multiplier. The actual current i q After the symbol function operation, the output is sign(i q ) is used as the input of the multiplier, and the sliding mode gain is output after the multiplier operation. Realize sliding mode gain adaptive control. The specific process is:

[0162] Step 1: Construct the Clark transform module and perform Clark transform, which includes the following:

[0163] The three-phase current i of the permanent magnet synchronous motor is obtained by current sampling a 、i b 、i c , and input into Clark transform, the transformation formula of Clark transform is:

[0164]

[0165] The output of the Clark transformation is the actual current i in the stationary two-phase coordinate system. α and i β , as the input of Park transform.

[0166] Step 2: Construct the Park transformation module and perform Park transformation, which includes the following contents:

[0167] The current i in the stationary coordinate obtained in step 1 α and i β The actual electrical angle information θ of the permanent magnet synchronous motor e Together as the input of the Park transformation module, after Park transformation, the actual direct axis current i of the permanent magnet synchronous motor in the synchronous rotating coordinate is obtained. d and the actual quadrature axis current i q , the formula of Park transformation is:

[0168]

[0169] where θ e is the actual electrical angle, sampled by an encoder or sensorless control method.

[0170] Step 3: Construct a current estimation module to perform current estimation, which includes the following:

[0171] The actual speed ω of the permanent magnet synchronous motor is acquired by using an encoder or a sensorless control method, and then the actual speed is output to the current estimation module;

[0172] The actual direct-axis current i calculated in step 2 is d and the actual quadrature axis current i q , and the actual speed ω of the permanent magnet synchronous motor obtained, and the quadrature axis voltage u in the synchronous rotating coordinate output by the permanent magnet synchronous motor vector control system q As the input of the current estimation module, and the feedback value Z q +lZ eq As input, after current estimation, the output is the estimated quadrature axis current The implementation steps are as follows:

[0173] First, the voltage equation of the permanent magnet synchronous motor in the dq axis coordinate system is as follows:

[0174]

[0175] In the formula, i d 、i q are the dq axis stator currents, u d 、u q are the dq axis stator voltages respectively, ω is the estimated angular velocity, ψ f is the permanent magnet flux, R is the stator resistance, L s is the stator inductance.

[0176] The present invention performs closed-loop feedback on the estimated resistance, takes the estimated resistance feedback as input, and constructs a new current observer calculation formula as follows:

[0177]

[0178] Among them, Z eq For Z q The switching function obtained after low-pass filtering, Z eq =Z q ω c1 / (s+ω c1 ).

[0179] In the formula, ω c1 is the cut-off frequency, and l is the feedback gain of the control function.

[0180] The calculated current estimate is in differential form, and integrating it gives the current estimate

[0181] Step 4: Calculate the current error, which includes the following:

[0182] The estimated quadrature-axis current output from step 3 is The actual quadrature axis current i q As input, the output current error Subtracting equation (11) from equation (1), we get the current error state equation as follows:

[0183]

[0184] Step 5: Construct a resistance estimation module to perform resistance estimation, which includes the following:

[0185] Step 4 Calculate the current error obtained As the input of the symbolic function, the output after the symbolic function operation is and the sliding mode gain k R As the input of the multiplier, the multiplication output Z q ; Z q As the input of the first low-pass filter, the output Z after low-pass filtering eq , Z eq Multiplying the feedback factor l outputs lZ eq ; Z q With Z eq As the input of the adder, the output is Z after addition. q +lZ eq ; Z q +lZ eq The feedback is used as input to the current estimation module and also as the input value of the second low-pass filter. After low-pass filtering, the output resistance estimation value is calculated as follows:

[0186] R=(Z q +lZ eq )·(ω c2 / (s+ω c2 ))

[0187] The specific implementation steps are as follows:

[0188] Define the sliding surface

[0189]

[0190] When the sliding mode dynamically moves to the sliding surface, S=0 is satisfied, that is, Substituting it into formula (12), we can see that the resistance R is approximately equivalent to R = Z q +lZdq , but Z q +lZ eq It is a high-frequency signal. In order to filter the high-frequency signal, a low-pass filter is used to filter it. The final resistance identification formula is as follows:

[0191] R=(Z q +lZ eq )·(ω c2 / s+ω c2 ) (14)

[0192] In the formula, ω c2 is the cut-off frequency.

[0193] Step 6: Perform sliding mode gain adaptive control, which includes the following contents:

[0194] The resistance estimate calculated in step 5 is fed back and multiplied by the gain n as the input of the multiplier. The actual current i q As the input of the sign function, after the sign function operation, the output is sign(i q ) is used as the input of the multiplier, and the sliding mode gain is output after the multiplier operation. The specific implementation steps are as follows:

[0195] The proposed algorithm needs to satisfy the Lyapunov stability condition and construct the Lyapunov function as follows:

[0196] V=S(15)

[0197] According to Lyapunov stability law, the system must be stable if Right now

[0198]

[0199] Substituting formula (12) into the above formula, we can get the following formula:

[0200]

[0201] when i q >0, When , formula (17) can be rewritten as:

[0202]

[0203] At this time, it is deduced The conditions to be met are

[0204] when i q >0, When , formula (17) can be rewritten as:

[0205]

[0206] At this time, it is deduced The conditions to be met are

[0207] In summary, when i q >0 o'clock, The conditions to be met are:

[0208]

[0209] Similarly, when i q <0, it is deduced The conditions to be met are:

[0210]

[0211] According to formula (20) and formula (21), The conditions that need to be met can be simplified to:

[0212]

[0213] Assume l = m, and Substituting into the above formula, we can deduce that the condition for system stability is:

[0214]

[0215] When m=5 and n=2, the above conditions can be met. At this time, l=5.

[0216] Sliding mode gain adaptation can improve the resistance identification accuracy. This technology can identify the resistance in real time when the resistance changes, and quickly complete the identification of the resistance change. In high-speed occasions such as vacuum cleaners, the temperature rises rapidly, causing the resistance to change. The resistance change can be identified in real time to improve the motor control performance. This technology has certain practicality.

[0217] An embodiment of a device applying the method of the present invention:

[0218] An electronic device comprising:

[0219] one or more processors;

[0220] A storage device for storing one or more programs;

[0221] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method for online identification of resistance of a permanent magnet synchronous motor.

[0222] A computer medium embodiment using the method of the present invention:

[0223] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for online identification of resistance of a permanent magnet synchronous motor.

[0224] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, and computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program codes.

[0225] The present application is described by flowcharts or / and block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each process or / and block in the flowchart or / and block diagram and the combination of the processes or / and blocks in the flowchart or / and block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart or / and block diagram. Figure 1 Process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0226] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 Process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0227] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 Process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0228] The model in this application is an object that objectively describes the morphological structure with the help of physical or virtual representation. The object is not equal to the object and is not limited to physical and virtual. It can be a data processing function, software program, processing mode, usage method, operation method, workflow, application process, electronic hardware, circuit module, processing system, system imitation or simulation object.

[0229] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field can still modify or replace the specific implementation methods of the present invention with equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A method for online identification of resistance of a permanent magnet synchronous motor, characterized in that: The following steps are involved: Step 1: transform the sampled three-phase current of the permanent magnet synchronous motor through the pre-built Clark transformation model to obtain the actual two-phase current in the static two-phase coordinate system; Step 2: Using the pre-built Park transformation model, the actual two-phase current and the electrical angle information of the permanent magnet synchronous motor are processed to calculate the actual direct-axis current and the actual quadrature-axis current; Step 3, using a pre-built current estimation model, obtaining an estimated quadrature-axis current according to the actual direct-axis current and the actual quadrature-axis current, and based on a feedback gain and feedback of an estimation resistor; Step 4, using a pre-built current error calculation model to process the estimated quadrature-axis current and the actual quadrature-axis current, and outputting the current error; Step 5: Using the pre-built resistance estimation model, based on the sliding surface, the current error is calculated to obtain the resistance estimation value; Step six, based on the pre-built sliding mode gain adaptive control model and based on the stability condition, feedback of the resistance estimation value is obtained to achieve real-time update of the sliding mode gain when the resistance changes.

2. A method for online identification of resistance of a permanent magnet synchronous motor as claimed in claim 1, characterized in that: Step 1: The method of transforming the sampled three-phase current of the permanent magnet synchronous motor through the pre-built Clark transformation model to obtain the actual two-phase current in the stationary two-phase coordinate system is as follows: The three-phase current i of the permanent magnet synchronous motor is obtained by current sampling a 、i b 、i c ; The three-phase current i of the permanent magnet synchronous motor a 、i b 、i c Input into Clark transformation, output the actual two-phase current i in the stationary two-phase coordinate system α and i β ; The transformation formula of Clark transformation is:

3. The method for online identification of resistance of a permanent magnet synchronous motor according to claim 1, characterized in that: Step 2: Use the pre-built Park transformation model to process the actual two-phase current and the electrical angle information of the permanent magnet synchronous motor. The method for calculating the actual direct-axis current and the actual quadrature-axis current is as follows: The actual two-phase current i under the stationary coordinate α and i β The actual electrical angle information θ of the permanent magnet synchronous motor e Together as the input of Park transformation, after Park transformation, the actual direct axis current i of the permanent magnet synchronous motor in synchronous rotating coordinates is obtained. d and the actual quadrature axis current i q , the formula of Park transformation is: where θ e is the actual electrical angle, sampled by an encoder or sensorless control method.

4. The method for online identification of resistance of a permanent magnet synchronous motor according to claim 1, characterized in that: Step 3: Using the pre-built current estimation model, according to the actual direct-axis current and the actual quadrature-axis current, and based on the feedback gain and the feedback of the estimation resistor, the method for obtaining the estimated quadrature-axis current is as follows: The actual speed ω of the permanent magnet synchronous motor is acquired by using an encoder or a sensorless control method; The actual direct-axis current i d , actual quadrature axis current i q , actual speed ω, and the quadrature axis voltage u in synchronous rotating coordinates output by the permanent magnet synchronous motor vector control system q , feedback value Z q +lZ eq As input, after current estimation, the output is the estimated quadrature axis current 5. A method for online identification of resistance of a permanent magnet synchronous motor as claimed in claim 4, characterized in that: After current estimation, the estimated quadrature-axis current is output The implementation steps are as follows: First, construct the voltage equation of the permanent magnet synchronous motor in the dq axis coordinate system, and its expression is as follows: In the formula, i d 、i q are the dq axis stator currents, u d 、u q are the dq axis stator voltages respectively, ω is the actual speed, ψ f is the permanent magnet flux, R is the stator resistance, L s is the stator inductance; The estimated resistance is closed-loop fed back and the estimated resistance feedback is used as input to construct a new current observer calculation formula as follows: Among them, Z eq For Z q The switching function is obtained after low-pass filtering; the calculation formula of the switching function is as follows: Z eq =Z q ω c1 / (s+ω c1 ); In the formula, ω c1 is the cut-off frequency, l is the feedback gain of the control function; The calculated current estimate is in differential form, which is integrated to obtain the estimated quadrature axis current 6. A method for online identification of resistance of a permanent magnet synchronous motor as claimed in claim 1, characterized in that: Step 4: Use the pre-built current error calculation model to process the estimated quadrature-axis current and the actual quadrature-axis current. The method for outputting the current error is as follows: According to the estimated quadrature-axis current The actual quadrature axis current i q , calculate the current error The current error calculation formula is as follows:

7. A method for online identification of resistance of a permanent magnet synchronous motor as claimed in claim 1, characterized in that: Step 5: Use the pre-built resistance estimation model to calculate the current error based on the sliding surface to obtain the resistance estimation value as follows: The current error As the input of the symbolic function, the output after the symbolic function operation is and the sliding mode gain k R As the input of the multiplier, the multiplication output Z q ; Z q As the input of the first low-pass filter, the output Z after low-pass filtering eq ; Z eq Multiplying the feedback factor l outputs lZ eq ; Z q With Z eq As the input of the adder, the output is Z after addition. q +lZ eq ; Z q +lZ eq The feedback current estimation model is used as input and also as the input value of the second low-pass filter. Based on the sliding surface, the resistance estimation value is output after low-pass filtering. The calculation formula is as follows: R=(Z q +lZ eq )·(ω c2 / (s+ω c2 ))。 8. A method for online identification of resistance of a permanent magnet synchronous motor as claimed in claim 7, characterized in that: Based on the sliding surface, the method for estimating the output resistance after low-pass filtering is as follows: Set the sliding surface, its expression is as follows: When the sliding mode dynamically moves to the sliding surface, S=0 is satisfied, that is, Substituting it into the current error calculation formula, we get the approximate equivalent formula of resistance R, which is as follows: R=Z q +lZ dq , Among them, Z q +lZ eq is a high frequency signal, In order to filter the high-frequency signal, a low-pass filter is used to filter it, and the final resistance identification formula is obtained as follows: R=(Z q +lZ eq )·(ω c2 / s+ω c2 ) In the formula, ω c2 is the cut-off frequency.

9. A method for online identification of resistance of a permanent magnet synchronous motor as claimed in claim 1, characterized in that: Step 6: Based on the pre-built sliding mode gain adaptive control model and the stability condition, the method of obtaining feedback of the resistance estimation value to achieve real-time updating of the sliding mode gain when the resistance changes is as follows: The calculated resistance estimate is fed back and multiplied by the gain n as the input of the multiplier. The actual current i q As the input of the sign function, after the sign function operation, the output is sign(i q ) is used as the input of the multiplier, and the sliding mode gain is output after the multiplier operation. The specific implementation steps are as follows: Based on the Lyapunov stability condition, the Lyapunov function is constructed, and its expression is as follows: V=S in According to the Lyapunov stability law, the system stability expression is established: Right now Substituting the current error calculation formula into the system stability expression, we get the system stability formula: when i q >0, When , the system stability equation is as follows: At this time, it is deduced The conditions to be met are when i q >0, When , the system stability equation is as follows: At this time, it is deduced The conditions to be met are In summary, when i q >0 o'clock, The conditions to be met are: Similarly, when i q When <0, it is deduced The conditions to be met are: In summary, The conditions that need to be met are simplified to: When l = m, Substituting into the above formula, the conditions for satisfying the stability of the system are as follows: When the values ​​m=5 and n=2, the above conditions are met, and l=5.

10. A permanent magnet synchronous motor resistance online identification system, characterized in that: It includes: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a method for online identification of resistance of a permanent magnet synchronous motor as described in any one of claims 1-9.

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