Motor rotor position observation method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a motor rotor position observation method and device, equipment and a storage medium. The method comprises the following steps: setting observer gain; according to the two regular T-S fuzzy models and observer gains, a T-S fuzzy observer is constructed, and the T-S fuzzy observer has a corresponding observation error system; according to the observation error system, a cost function is constructed, and the cost function has corresponding cost parameters; under the condition that the observation error system is stable, a target cost parameter is determined, and the target cost parameter is a corresponding cost parameter when the cost function reaches a preset condition; determining a target gain matrix based on a cost function corresponding to the target cost parameter; and determining the position of the motor rotor according to a target T-S fuzzy observer, wherein the target T-S fuzzy observer is obtained by substituting the target gain matrix into the T-S fuzzy observer. The target T-S fuzzy observer in the embodiment of the invention is helpful for improving the observation precision in the full-speed domain range, so that the motor operation has high precision, high reliability and strong robustness.

Description

Technical Field

[0001] The present application relates to the field of motor control technology, and in particular to a motor rotor position observation method and device, a vehicle, an electronic device and a storage medium. Background Art

[0002] The key to permanent magnet synchronous motor control is to accurately obtain the motor rotor position information. Traditional position sensors not only increase the complexity of the system structure and the overall manufacturing cost, but also the reliability of position sensors is easily affected in the complex and changeable driving environment of new energy vehicles, such as high temperature, high humidity, and dust, making subsequent maintenance difficult. Therefore, it is particularly necessary to use a position-free observer to replace the position sensor.

[0003] However, the permanent magnet synchronous motor position-free observer in the prior art has significant problems in observation accuracy and dynamic response speed. The observation accuracy is limited by the inaccuracy of the motor model, the interference of noise and disturbance during operation, the time-varying nature of electrical and mechanical parameters, and the selection and implementation of the observer algorithm. The dynamic response speed is limited by the observer bandwidth, the influence of calculation delay, the matching degree of controller design, and the characteristics of the motor itself. These problems make it difficult for the observer to achieve high accuracy and fast response at the same time in practical applications, limiting the performance of position sensorless control under complex working conditions.

[0004] Therefore, how to improve the observation accuracy of the permanent magnet synchronous motor without a position observer and solve the problem of dynamic response speed in the prior art is a key issue to be solved urgently in this field. Summary of the invention

[0005] An embodiment of the present application provides a method for observing the position of a motor rotor to solve or at least partially solve the above-mentioned technical problem.

[0006] Correspondingly, an embodiment of the present application also provides a motor rotor position observation device, an electronic device and a storage medium to ensure the implementation and application of the above method.

[0007] In order to solve the above problems, an embodiment of the present application discloses a method for observing the position of a motor rotor, the method comprising:

[0008] Setting an observer gain, wherein the observer gain includes a gain matrix;

[0009] According to the two-rule TS fuzzy model and the observer gain, a TS fuzzy observer is constructed, and the TS fuzzy observer has a corresponding observation error system;

[0010] According to the observation error system, a cost function is constructed, wherein the cost function has corresponding cost parameters;

[0011] When the observation error system is stable, determining a target cost parameter, wherein the target cost parameter is the corresponding cost parameter when the cost function meets a preset condition;

[0012] Determining a target gain matrix based on the cost function corresponding to the target cost parameter;

[0013] The motor rotor position is determined according to a target TS fuzzy observer, where the target TS fuzzy observer is obtained by substituting the target gain matrix into the TS fuzzy observer.

[0014] In an optional embodiment of the present application, constructing a TS fuzzy observer according to the two-rule TS fuzzy model and the observer gain includes:

[0015] Set the upper and lower limits of the electrical angular velocity of the motor rotor;

[0016] Determining a membership function according to an upper limit and a lower limit corresponding to the electrical angular velocity of the motor rotor;

[0017] Based on the membership function, establishing the two-rule TS fuzzy model;

[0018] The TS fuzzy observer is constructed according to the two-rule TS fuzzy model and the observer gain.

[0019] In an optional embodiment of the present application, setting the observer gain includes:

[0020] Setting a gain constructor, wherein the gain constructor is a constructor for the electrical angular velocity of the motor rotor;

[0021] The observer gain is set according to the gain constructor and the gain matrix.

[0022] In an optional embodiment of the present application, before determining the target cost parameter when the observation error system is stable, the method further includes:

[0023] According to Lyapunov stability theory, a stability criterion function is constructed, wherein the stability criterion function is used to describe the stability of the observation error system;

[0024] According to the stability criterion function and the cost function, a first relationship between the cost function and the cost parameter is determined when the observation error system is stable.

[0025] In an optional embodiment of the present application, determining, according to the stability criterion function and the cost function, a first relationship between the cost function and the cost parameter when the observation error system is stable includes:

[0026] determining a second relationship between the stability criterion function and the cost function, wherein the second relationship includes the cost parameter when the observation error system is stable;

[0027] According to the second relationship between the stability criterion function and the cost function, the first relationship between the cost function and the cost parameter is determined when the observation error system is stable.

[0028] In an optional embodiment of the present application, when the stability criterion function satisfies a stability condition, the observation error system is stable, and when the observation error system is stable, determining the target cost parameter includes:

[0029] Set the preset precision, upper and lower limits of the search interval;

[0030] If the upper limit and the lower limit of the search interval do not meet the preset accuracy, calculating the preset parameters according to the upper limit and the lower limit of the search interval;

[0031] Determining whether a linear matrix inequality has a feasible solution according to an upper limit and a lower limit of the search interval, wherein the linear matrix inequality is obtained according to the second relationship between the stability criterion function and the cost function;

[0032] If the linear matrix inequality has a feasible solution, the stability criterion function satisfies the stability condition, and the initial cost parameter and the upper limit of the search interval are updated according to the preset parameters;

[0033] Determine whether the upper limit and the lower limit of the current search interval meet the preset accuracy;

[0034] If the upper limit and the lower limit of the current search interval meet the preset accuracy, the initial cost parameter is determined as the target cost parameter.

[0035] In an optional embodiment of the present application, after determining whether the linear matrix inequality has a feasible solution, the method further includes:

[0036] If the linear matrix inequality has no feasible solution, updating the upper limit of the search interval according to the preset parameters;

[0037] According to the upper limit and the lower limit of the current search interval, it is determined whether the upper limit and the lower limit of the current search interval meet the preset accuracy.

[0038] In an optional embodiment of the present application, after determining whether the upper limit and the lower limit of the current search interval meet the preset accuracy, the method further includes:

[0039] If the upper limit and the lower limit of the current search interval do not meet the preset accuracy, updating the preset parameters according to the upper limit and the lower limit of the current search interval;

[0040] According to the upper limit and the lower limit of the current search interval, it is determined whether the current linear matrix inequality has a feasible solution.

[0041] In an optional embodiment of the present application, determining the motor rotor position according to the target TS fuzzy observer includes:

[0042] The motor current and voltage are input into the target TS fuzzy observer to obtain the motor rotor position.

[0043] In an optional embodiment of the present application, inputting the motor current and voltage into the target TS fuzzy observer to obtain the motor rotor position includes:

[0044] Inputting the motor current and voltage into the target TS fuzzy observer to obtain the back electromotive force corresponding to the motor current and voltage;

[0045] The motor rotor position and the motor rotor speed are obtained according to the back electromotive force.

[0046] The present application also discloses a motor rotor position observation device, the device comprising:

[0047] A gain setting module, used for setting an observer gain, wherein the observer gain includes a gain matrix;

[0048] An observer construction module, used for constructing a TS fuzzy observer according to a two-rule TS fuzzy model and the observer gain, wherein the TS fuzzy observer has a corresponding observation error system;

[0049] A cost function construction module, used to construct a cost function according to the observation error system, wherein the cost function has corresponding cost parameters;

[0050] A target cost parameter determination module, used to determine a target cost parameter when the observation error system is stable, wherein the target cost parameter is the corresponding cost parameter when the cost function meets a preset condition;

[0051] A target gain matrix determination module, used to determine a target gain matrix based on the cost function corresponding to the target cost parameter;

[0052] The rotor position observation module is used to determine the motor rotor position according to a target TS fuzzy observer, wherein the target TS fuzzy observer is obtained by substituting the target gain matrix into the TS fuzzy observer.

[0053] An embodiment of the present application also discloses a vehicle, which includes: a motor; and a motor controller. When the motor controller controls the motor, the motor controller executes the motor rotor position observation method as described in one or more of the embodiments of the present application.

[0054] An embodiment of the present application also discloses an electronic device, including: a processor; and a memory, on which executable code is stored. When the executable code is executed, the processor executes the motor rotor position observation method as described in one or more of the embodiments of the present application.

[0055] The embodiments of the present application also disclose one or more machine-readable media on which executable codes are stored. When the executable codes are executed, the processor executes the motor rotor position observation method as described in one or more of the embodiments of the present application.

[0056] Compared with the prior art, the embodiments of the present application have the following advantages:

[0057] In the embodiment of the present application, the observer gain is set, and the observer gain includes a gain matrix; according to the two-rule TS fuzzy model and the observer gain, a TS fuzzy observer is constructed, and the TS fuzzy observer has a corresponding observation error system; according to the observation error system, a cost function is constructed, and the cost function has a corresponding cost parameter; when the observation error system is stable, the target cost parameter is determined, and the target cost parameter is the corresponding cost parameter when the cost function reaches the preset condition; the target gain matrix is ​​determined based on the cost function corresponding to the target cost parameter; the motor rotor position is determined according to the target TS fuzzy observer, and the target TS fuzzy observer is obtained by substituting the target gain matrix into the TS fuzzy observer. The target TS fuzzy observer in the embodiment of the present application can dynamically adjust the size of the target gain matrix according to the change of the motor rotor speed, thereby effectively solving the problem of decreased rotor position observation accuracy caused by internal parameter uncertainty, and the target TS fuzzy observer not only helps to improve the observation accuracy within the full speed range, but also can significantly accelerate the error convergence speed, so that the motor operation has high precision, high reliability and strong robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flowchart of a motor rotor position observation method embodiment of the present application;

[0059] Figure 2It is a two-rule TS fuzzy model establishment flow chart of an embodiment of a motor rotor position observation method of the present application;

[0060] Figure 3 is a cost parameter determination flow chart of an embodiment of a motor rotor position observation method of the present application;

[0061] Figure 4 It is an algorithm flow chart of an embodiment of a motor rotor position observation method of the present application;

[0062] Figure 5 This is a motor control schematic diagram of an embodiment of a motor rotor position observation method of the present application;

[0063] Figure 6 It is a structural block diagram of an embodiment of a motor rotor position observation device of the present application;

[0064] Figure 7 It is a schematic diagram of the structure of a device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0066] Reference Figure 1 , is a flowchart of a motor rotor position observation method embodiment of the present application, comprising the following steps:

[0067] Step 101: Set observer gain, where the observer gain includes a gain matrix.

[0068] In the embodiment of the present application, the state equation of the permanent magnet synchronous motor (PMSM) can be firstly written in the form of the following formula (1):

[0069]

[0070]

[0071] Where x = [i α i β e α e β ] T ,

[0072] Where x is the motor system state vector, is the state vector derivative of the motor system, y is the output, L s is the stator inductance, R sis the stator winding resistance, ω e is the electrical angular velocity of the motor rotor, θ e is the motor rotor position, i α ,i β ,u α ,u β is the motor current and voltage, e α ,e β is the back electromotive force, ψ f is the flux amplitude of the permanent magnet.

[0073] In one embodiment, according to the sector nonlinear method, the upper and lower limits corresponding to the electrical angular velocity of the motor rotor are set, that is, ω is set e ∈[ω e1 ,ω e2 ]. Then in ω e ∈[ω e1 ,ω e2 ] Within the electrical angular velocity range of the motor rotor, the dynamics of the motor system can be equivalently described by the two-rule TS fuzzy model shown in formula (2):

[0074]

[0075] in,

[0076]

[0077]

[0078]

[0079] Where x is the motor system state vector, is the state vector derivative of the motor system, t is time, ω e is the electrical angular velocity of the motor rotor, ω e1 is the lower limit of the electrical angular velocity of the motor rotor, ω e2 is the upper limit of the electrical angular velocity of the motor rotor, L s is the stator inductance, R s is the stator winding resistance, m i (ω e ) is the membership function, and u is the motor voltage.

[0080] In step 101 , observer gains are set for the TS fuzzy observer, where the observer gains include a gain matrix.

[0081] In this embodiment, the observer gain can be set as follows:

[0082] K(ω e )=c1(ω e)K1+c2(ω e )K2

[0083] in, c2=1-c1(3)

[0084] Among them, K(ω e ) is the observer gain, K1 and K2 are the gain matrices to be determined, ω e is the electrical angular velocity of the motor rotor, C1 and C2 are about ω e The constructor of .

[0085] In this embodiment, since the constructor of the observer gain is related to the electrical angular velocity of the motor rotor, the gain matrix can continuously adjust the gain size according to the change of the electrical angular velocity of the motor rotor, which is beneficial to improve the observation accuracy in the full speed range.

[0086] Step 102: construct a TS fuzzy observer according to the two-rule TS fuzzy model and the observer gain, wherein the TS fuzzy observer has a corresponding observation error system.

[0087] In step 102, a TS fuzzy observer is constructed according to the observer gain shown in formula (3) obtained in step 101 and the two-rule TS fuzzy model shown in formula (2). The TS fuzzy observer has a corresponding observation error system.

[0088] In this embodiment, the TS fuzzy observer finally constructed is shown in the following formula (4):

[0089]

[0090] in,

[0091]

[0092] x=[i α i β e α e β ] T ,

[0093]

[0094] c2=1-c1 (4)

[0095] Where x is the motor system state vector, is the state vector derivative of the motor system, t is time, ω e is the electrical angular velocity of the motor rotor, ωe1 is the lower limit of the electrical angular velocity of the motor rotor, ω e2 is the upper limit of the electrical angular velocity of the motor rotor, m i (ω e ) is the membership function, c j (ω e ) is about ω e The constructor of K j is the gain matrix, y is the output, L s is the stator inductance, R s is the stator winding resistance, θ e is the motor rotor position, i α ,i β ,u α ,u β is the motor current and voltage, e α ,e β is the back electromotive force, ψ f is the flux amplitude of the permanent magnet.

[0096] When the TS fuzzy observer corresponding to formula (4) is valid, it can be approximately considered that is the estimated value of the electrical angular velocity, which can be obtained by extracting information from the back electromotive force observed by the TS fuzzy observer using a phase-locked loop. Furthermore, it can be obtained that the corresponding observation error system when the TS fuzzy observer is effective can be expressed as formula (5):

[0097]

[0098] in,

[0099]

[0100] Where x is the motor system state vector, is the motor system state vector derivative, t is time, is the estimated value of electrical angular velocity, is the membership function, About The constructor of K j is the gain matrix, L s is the stator inductance, R s is the stator winding resistance.

[0101] Step 103: construct a cost function based on the observation error system, wherein the cost function has corresponding cost parameters.

[0102] In step 103, a cost function is constructed based on the observation error system, and the cost function has corresponding cost parameters.

[0103] In this embodiment, according to the observation error system shown in formula (5), a quadratic cost function J can be defined. The smaller the function value of J, the smaller the integral of the system error and the time axis, the faster the error convergence speed, and the smaller the fluctuation. Therefore, the optimal value of the observer gain in the global range can be found by minimizing J.

[0104] In one embodiment, the cost function J can be defined by formula (6):

[0105]

[0106] Among them, J is a quadratic cost function, x is the motor system state vector, t is time, T is a given function of transposing J, Q is an optional weight matrix and Q>0, and the value of Q is the weight of each motor system state quantity in the cost function J.

[0107] In one embodiment, a stability criterion function can be constructed as formula (7) by using the Lyapunov stability criterion (i.e., Lyapunov stability theory). The stability criterion function is used to describe the stability of the observation error system:

[0108] V(t)=x T (t)Px(t) (7)

[0109] Among them, V is the stability criterion function, x is the state vector of the motor system, t is the time, T represents the transpose, and P is a symmetric matrix.

[0110] By taking the derivative of formula (7) and integrating both sides simultaneously, we can obtain formula (8):

[0111] V(x(∞))-V(x(0))+J≤0 (8)

[0112] Among them, V is the stability criterion function, x is the state vector of the motor system, and J is the quadratic cost function.

[0113] When the stability criterion function satisfies the stability condition, the observation error system is stable. At this time, V(x(∞))=0. Further, formula (9) can be obtained. Formula (9) represents the first relationship between the cost function J and the cost parameter h when the observation error system is stable:

[0114] J≤x T (0)Px(0)≤hx T (0)x(0) (9)

[0115] Among them, J is the quadratic cost function, x is the motor system state vector, T represents the transpose, P is the symmetric matrix, and h is the cost parameter.

[0116] According to formula (9) and the definition formula (6) of the cost function J, the convergence speed of the observation error can be adjusted by the cost parameter h. Therefore, by determining the cost parameter h corresponding to when J is minimized when the observation error system is stable, the error convergence speed of the system can be accelerated, so that the motor operation has high precision, high reliability and strong robustness.

[0117] Step 104: when the observation error system is stable, determine a target cost parameter, where the target cost parameter is the corresponding cost parameter when the cost function meets a preset condition.

[0118] In step 104, when the observation error system is stable, a target cost parameter is determined, which is a corresponding cost parameter when the cost function meets a preset condition. In one embodiment, the preset condition is that the cost function reaches a minimum value when the observation error system is stable.

[0119] In an optional embodiment, the cost parameter h corresponding to the cost function reaching the minimum value when the observation error system is stable can be solved by LMI toolbox (Linear Matrix Inequality Toolbox, a set of software tools for processing linear matrix inequality (LMI) problems).

[0120] Step 105: Determine a target gain matrix based on the cost function corresponding to the target cost parameter.

[0121] In step 105, a target gain matrix is ​​determined based on a cost function corresponding to the target cost parameter.

[0122] In this embodiment, the corresponding cost function can be determined according to the cost parameter h corresponding to the minimum value of the cost function. Since the quadratic cost function J can be defined as formula (6) according to the observation error system shown in formula (5), the gain matrix in the observation error system equation at this time can be determined according to the cost function corresponding to the determined cost parameter h, and the target gain matrices K1 and K2 can be obtained.

[0123] Step 106: determining the motor rotor position according to a target TS fuzzy observer, wherein the target TS fuzzy observer is obtained by substituting the target gain matrix into the TS fuzzy observer.

[0124] In step 106, since the target gain matrices K1 and K2 have been determined, substituting them into the TS fuzzy observer can obtain the target TS fuzzy observer at this time. According to the target TS fuzzy observer at this time, the motor rotor position can be determined, and then the motor operation can be controlled.

[0125] In the embodiment of the present application, the observer gain is set, and the observer gain includes a gain matrix; according to the two-rule TS fuzzy model and the observer gain, a TS fuzzy observer is constructed, and the TS fuzzy observer has a corresponding observation error system; according to the observation error system, a cost function is constructed, and the cost function has a corresponding cost parameter; when the observation error system is stable, the target cost parameter is determined, and the target cost parameter is the corresponding cost parameter when the cost function reaches the preset condition; the target gain matrix is ​​determined based on the cost function corresponding to the target cost parameter; the motor rotor position is determined according to the target TS fuzzy observer, and the target TS fuzzy observer is obtained by substituting the target gain matrix into the TS fuzzy observer. The target TS fuzzy observer in the embodiment of the present application can dynamically adjust the size of the target gain matrix according to the change of the motor rotor speed, thereby effectively solving the problem of decreased rotor position observation accuracy caused by internal parameter uncertainty, and the target TS fuzzy observer not only helps to improve the observation accuracy within the full speed range, but also can significantly accelerate the error convergence speed, so that the motor operation has high precision, high reliability and strong robustness.

[0126] In an optional embodiment of the present application, constructing a TS fuzzy observer according to the two-rule TS fuzzy model and the observer gain includes:

[0127] Set the upper and lower limits of the electrical angular velocity of the motor rotor;

[0128] Determining a membership function according to an upper limit and a lower limit corresponding to the electrical angular velocity of the motor rotor;

[0129] Based on the membership function, establishing the two-rule TS fuzzy model;

[0130] The TS fuzzy observer is constructed according to the two-rule TS fuzzy model and the observer gain.

[0131] In this embodiment, a two-rule TS fuzzy model may be established according to a sector nonlinear method.

[0132] Reference Figure 2 , is a two-rule TS fuzzy model establishment flow chart of an embodiment of a motor rotor position observation method of the present application.

[0133] First, determine the nonlinear term as the electrical angular velocity ω of the motor rotor e , so as to further determine the upper and lower limits of the nonlinear term, that is, to set the upper and lower limits ω corresponding to the electrical angular velocity of the motor rotor e ∈[ω e1 ,ω e2 ]. According to the upper and lower limits of the electrical angular velocity of the motor rotor, the relevant membership function is determined. Based on the membership function, in ωe ∈[ω e1 ,ω e2 ] A two-rule TS fuzzy model is established within the electrical angular velocity range of the motor rotor. In one embodiment, the established two-rule TS fuzzy model can be expressed as the above formula (2).

[0134] Furthermore, a TS fuzzy observer is constructed based on the two-rule TS fuzzy model and the set observer gain. In one embodiment, if the set observer gain is the above formula (3), the constructed TS fuzzy observer can be expressed as the above formula (4).

[0135] The embodiment of the present application establishes a two-rule TS fuzzy model based on the sector nonlinear method, and then constructs a TS fuzzy observer. The target TS fuzzy observer that subsequently determines the target gain matrix not only helps to improve the observation accuracy within the full speed range, but also significantly accelerates the error convergence speed, making the motor operation have high precision, high reliability and strong robustness.

[0136] In an optional embodiment of the present application, setting the observer gain includes:

[0137] Setting a gain constructor, wherein the gain constructor is a constructor for the electrical angular velocity of the motor rotor;

[0138] The observer gain is set according to the gain constructor and the gain matrix.

[0139] In this embodiment, when setting the observer gain, it is necessary to first set a gain constructor, which is a constructor for the electrical angular velocity of the motor rotor, and then set the observer gain according to the gain constructor and the gain matrix.

[0140] In one embodiment, the gain constructor, gain matrix and observer gain may be set as shown in the above formula (3).

[0141] The gain constructor of the embodiment of the present application is a constructor for the electrical angular velocity of the motor rotor. Therefore, the TS fuzzy observer can dynamically adjust the size of the gain matrix according to the change of the motor rotor speed, thereby effectively solving the problem of decreased rotor position observation accuracy caused by internal parameter uncertainty.

[0142] In an optional embodiment of the present application, before determining the target cost parameter when the observation error system is stable, the method further includes:

[0143] According to Lyapunov stability theory, a stability criterion function is constructed, wherein the stability criterion function is used to describe the stability of the observation error system;

[0144] According to the stability criterion function and the cost function, a first relationship between the cost function and the cost parameter is determined when the observation error system is stable.

[0145] In this embodiment, a stability criterion function may be constructed according to Lyapunov's stability theory, and the stability criterion function is used to describe the stability of the observation error system.

[0146] Then, based on the stability criterion function and the cost function, the first relationship between the cost function and the cost parameter when the observed error system is stable is determined, and thus based on the first relationship between the cost function and the cost parameter, the target cost parameter when the observed error system is stable is determined.

[0147] In one embodiment, the cost function may be defined by the above formula (6). According to Lyapunov stability theory and formula (6), a stability criterion function may be constructed as the above formula (7).

[0148] When the stability criterion function formula (7) satisfies the stability condition, the observation error system is stable. At this time, the above formula (9) can be further obtained. Formula (9) can represent the first relationship between the cost function and the cost parameter when the observation error system is stable.

[0149] According to the first relationship between the cost function and the cost parameter when the observation error system is stable, as expressed in the above formula (9), and the definition formula (6) of the cost function, the convergence speed of the observation error can be adjusted by the cost parameter. Therefore, by determining the cost parameter corresponding to the minimization of the cost function when the observation error system is stable, the error convergence speed of the system can be accelerated, thereby further determining the target gain matrix at this time.

[0150] The embodiment of the present application constructs a stability criterion function according to Lyapunov stability theory, and determines the first relationship between the cost function and the cost parameter when the observation error system is stable, thereby determining the target cost parameter when the observation error system is stable, which can accelerate the error convergence speed of the system.

[0151] In an optional embodiment of the present application, determining, according to the stability criterion function and the cost function, a first relationship between the cost function and the cost parameter when the observation error system is stable includes:

[0152] determining a second relationship between the stability criterion function and the cost function, wherein the second relationship includes the cost parameter when the observation error system is stable;

[0153] According to the second relationship between the stability criterion function and the cost function, the first relationship between the cost function and the cost parameter is determined when the observation error system is stable.

[0154] In this embodiment, the second relationship between the stability criterion function and the cost function is first determined. When the observation error system is stable, the second relationship at this time includes a cost parameter.

[0155] Furthermore, the first relationship between the cost function and the cost parameter when the observation error system is stable may be determined according to the second relationship between the stability criterion function and the cost function.

[0156] In one embodiment, the cost function may be defined by the above formula (6), and the stability criterion function constructed by Lyapunov stability theory may be expressed as formula (7).

[0157] Further, the stability criterion function represented by the above formula (7) is differentiated and integrated on both sides at the same time, and the above formula (8) can be obtained. Formula (8) represents the second relationship between the stability criterion function and the cost function. When the stability criterion function satisfies the stability condition, the observation error system is stable. At this time, the second relationship includes the cost parameter. Therefore, the above formula (9) can be further obtained according to formula (8). Formula (9) represents the first relationship between the cost function and the cost parameter when the observation error system is stable.

[0158] The embodiment of the present application determines the first relationship between the cost function and the cost parameter when the observed error system is stable based on the second relationship between the stability criterion function and the cost function, thereby determining the target cost parameter when the observed error system is stable, which can accelerate the error convergence speed of the system.

[0159] In an optional embodiment of the present application, when the stability criterion function satisfies a stability condition, the observation error system is stable, and when the observation error system is stable, determining the target cost parameter includes:

[0160] Set the preset precision, upper and lower limits of the search interval;

[0161] If the upper limit and the lower limit of the search interval do not meet the preset accuracy, calculating the preset parameters according to the upper limit and the lower limit of the search interval;

[0162] Determining whether a linear matrix inequality has a feasible solution according to an upper limit and a lower limit of the search interval, wherein the linear matrix inequality is obtained according to the second relationship between the stability criterion function and the cost function;

[0163] If the linear matrix inequality has a feasible solution, the stability criterion function satisfies the stability condition, and the initial cost parameter and the upper limit of the search interval are updated according to the preset parameters;

[0164] Determine whether the upper and lower limits of the current search interval satisfy the preset accuracy;

[0165] If the upper and lower limits of the current search interval satisfy the preset accuracy, the initial cost parameter is determined as the target cost parameter.

[0166] In this embodiment, when the stability criterion function satisfies the stability condition, the observation error system is stable, and thus there is a target cost parameter in the case where the observation error system is stable. Specifically, the LMI toolbox can be used to solve the target cost parameter corresponding to the minimum value of the cost function when the observation error system is stable.

[0167] First, set the preset accuracy h, and the upper and lower limits of the search interval [h start , h end . In one embodiment, the upper and lower limits of the search interval can be set to the upper and lower limits corresponding to the electrical angular velocity of the motor rotor. If the upper and lower limits of the search interval do not satisfy the preset accuracy, that is, |h start - h end | < h, then calculate the preset parameter according to the upper and lower limits of the search interval. The preset parameter can be calculated according to the following formula (10):

[0168] h test = (h start + h end ) / 2 (10)

[0169] where h test is the preset parameter, h start is the lower limit of the search interval, and h end is the upper limit of the search interval.

[0170] After that, according to the upper and lower limits of the search interval, determine whether the linear matrix inequality has a feasible solution. The linear matrix inequality is obtained according to the second relationship between the stability criterion function and the cost function.

[0171] If the linear matrix inequality has a feasible solution, the stability criterion function satisfies the stability condition, and the initial cost parameter and the upper limit of the search interval are updated using the preset parameter. Determine whether the upper and lower limits of the current search interval satisfy the preset accuracy. If the upper and lower limits of the current search interval satisfy the preset accuracy, the initial cost parameter is determined as the target cost parameter.

[0172] In one embodiment, if the upper and lower limits of the current search interval do not meet the preset accuracy, the current preset parameters are continued to be calculated, and the linear matrix inequality is re-determined whether there is a feasible solution and the relevant parameters are updated until the upper and lower limits of the current search interval meet the preset accuracy.

[0173] In one embodiment, if the linear matrix inequality has no feasible solution, after updating the upper limit of the search interval according to the preset parameters, it is determined whether the upper limit and lower limit of the current search interval meet the preset precision according to the upper limit and lower limit of the current search interval. If not, the calculation of the current preset parameters continues, and it is determined again whether the linear matrix inequality has a feasible solution and the relevant parameters are updated until the upper limit and lower limit of the current search interval meet the preset precision.

[0174] In an optional embodiment, the weight matrix Q and the sub-interval partition parameter n also need to be set in advance. Then, judging whether the linear matrix inequality has a feasible solution can be specifically expressed as the following steps.

[0175] First, the cost function J is defined according to the above formula (6), and J includes the weight matrix Q. The stability criterion function is constructed through Lyapunov stability theory as formula (7):

[0176] After that, the formula (7) is derived and integrated on both sides, and the weight matrix Q is added to obtain the following formula (11):

[0177]

[0178] in,

[0179]

[0180] Where x is the motor system state vector, is the derivative of the stability criterion function, t is the time, is the estimated value of electrical angular velocity, is the membership function, About Constructor of, T means transpose, K j is the gain matrix, Q is an optional weight matrix and Q>0, the value of Q is the weight of each motor system state quantity in the cost function J, P is a symmetric matrix, L s is the stator inductance, R s is the stator winding resistance.

[0181] According to Schur's lemma, The equivalent condition can be expressed as the following formula (12):

[0182]

[0183] in,

[0184]

[0185]

[0186]

[0187] Among them, h is the cost parameter, is the estimated value of electrical angular velocity, is the membership function, About Constructor, P is a symmetric matrix, K j is the gain matrix, Q is an optional weight matrix and Q>0, the value of Q is the weight of each motor system state in the cost function J, * is a symmetric term, I is the unit matrix, L s is the stator inductance, R s is the stator winding resistance.

[0188] because Medium nonlinear term The LMI toolbox cannot directly solve formula (12) because of the existence of . Therefore, the parameter n is divided into sub-intervals and the electrical angular velocity ω of the motor rotor is e ∈[ω e1 ,ω e2 ] is divided into n uniform subintervals, and the upper limit of the kth subinterval satisfies the following formula (12):

[0189]

[0190] Among them, μ k is the upper limit of the kth subinterval, k represents the kth subinterval, n is the subinterval division parameter, ω e1 is the lower limit of the electrical angular velocity of the motor rotor, ω e2 It is the upper limit of the electrical angular velocity of the motor rotor.

[0191] At this time, in the kth subinterval, the electrical angular velocity of the motor rotor satisfies ω ek ∈[μ k-1 ,μ k ]. According to the upper and lower limits of the electrical angular velocity of the motor rotor in the kth subinterval, the membership function in the kth subinterval is obtained, which is expressed as the following formula (13):

[0192]

[0193]

[0194] in, and represents the membership function in the kth subinterval, μ k is the upper limit of the kth subinterval, μ k-1 is the lower limit of the kth subinterval, k represents the kth subinterval, is the estimated value of electrical angular velocity, ω ek is the kth sub-interval of the electrical angular velocity of the motor rotor.

[0195] In each subinterval ω ek superior, It can be approximated by the following formula (14):

[0196]

[0197] in, is a piecewise function of the cost parameter, is the estimated value of electrical angular velocity, With v 2k represents the membership function in the kth subinterval, μ k is the upper limit of the kth subinterval, μ k-1 is the lower limit of the kth subinterval, k represents the kth subinterval, h is the cost parameter, ω ek is the kth sub-interval of the electrical angular velocity of the motor rotor.

[0198] And in the kth subinterval, the function of the cost parameter is Satisfies the following formula (15):

[0199]

[0200] in, is a piecewise function of the cost parameter, is the estimated value of electrical angular velocity, k represents the kth subinterval, represents the membership function in the kth subinterval, h is the cost parameter, μ k+1-2 is the boundary of the kth subinterval.

[0201] Piecewise function of the cost parameter The original function of the cost parameter We can further obtain formula (16):

[0202]

[0203] in, are the upper and lower bounds of the approximation error, is the original function of the cost parameter, is a piecewise function of the cost parameter.

[0204] Therefore, according to formula (14) to formula (16), we can get formula (17):

[0205]

[0206] in,

[0207]

[0208]

[0209] in, are the upper and lower bounds of the approximation error, is the original function of the cost parameter, is a piecewise function of the cost parameter, is the estimated value of electrical angular velocity, k represents the kth subinterval, represents the membership function in the kth subinterval, H ij is a symmetric matrix, μ k+1-2 is the boundary of the kth subinterval, P is a symmetric matrix, K j is the gain matrix, Q is an optional weight matrix and Q>0, the value of Q is the weight of each motor system state in the cost function J, * is a symmetric term, I is the unit matrix, L s is the stator inductance, R s is the stator winding resistance.

[0210] When the linear matrix inequality shown in formula (18) has a feasible solution, formula (12) holds and the stability criterion function satisfies the stability condition. Formula (18) is as follows:

[0211]

[0212] Among them, H ij >0,H ij ≤M ij , P>0, P-aI≤0,

[0213]

[0214]

[0215]

[0216] Among them, k represents the kth subinterval, represents the membership function in the kth subinterval, h is the cost parameter, μ k+1-2 is the kth subinterval boundary, are the upper and lower bounds of the approximation error, H ij is a symmetric matrix, P is a symmetric matrix, Kj is the gain matrix, a is the preset calculation parameter, Q is the optional weight matrix and Q>0, the value of Q is the weight of each motor system state quantity in the cost function J, * is a symmetric term, I is the unit matrix, L s is the stator inductance, R s is the stator winding resistance.

[0217] The embodiment of the present application uses the LMI toolbox to solve the target cost parameters corresponding to when the cost function reaches the minimum value when the observation error system is stable, and then the corresponding target gain matrix can be obtained, thereby effectively solving the problem of decreased rotor position observation accuracy caused by internal parameter uncertainty.

[0218] In an optional embodiment of the present application, after determining whether the linear matrix inequality has a feasible solution, the method further includes:

[0219] If the linear matrix inequality has no feasible solution, updating the upper limit of the search interval according to the preset parameters;

[0220] According to the upper limit and the lower limit of the current search interval, it is determined whether the upper limit and the lower limit of the current search interval meet the preset accuracy.

[0221] In this embodiment, if the linear matrix inequality has no feasible solution, the upper limit of the search interval is updated according to preset parameters, and based on the upper and lower limits of the current search interval, it is determined whether the upper and lower limits of the current search interval meet the preset accuracy.

[0222] If the linear matrix inequality has no feasible solution, after updating the upper limit of the search interval according to the preset parameters, determine whether the upper limit and lower limit of the current search interval meet the preset accuracy according to the upper and lower limits of the current search interval. If not, continue to calculate the current preset parameters, and re-determine whether the linear matrix inequality has a feasible solution and update the relevant parameters until the upper and lower limits of the current search interval meet the preset accuracy.

[0223] The embodiment of the present application uses the LMI toolbox to determine whether a feasible solution exists for the linear matrix inequality, so as to solve the target cost parameter corresponding to when the cost function reaches the minimum value when the observation error system is stable.

[0224] In an optional embodiment of the present application, after determining whether the upper limit and the lower limit of the current search interval meet the preset accuracy, the method further includes:

[0225] If the upper limit and the lower limit of the current search interval do not meet the preset accuracy, updating the preset parameters according to the upper limit and the lower limit of the current search interval;

[0226] Judge whether there is a feasible solution to the current linear matrix inequality according to the upper and lower limits of the current search interval.

[0227] In this embodiment, if the upper and lower limits of the current search interval do not meet the preset accuracy, update the preset parameters according to the upper and lower limits of the current search interval, and re-judge whether there is a feasible solution to the linear matrix inequality and update the relevant parameters according to the upper and lower limits of the current search interval until the upper and lower limits of the current search interval meet the preset accuracy.

[0228] The embodiment of the present application judges whether the upper and lower limits of the current search interval meet the preset accuracy by using the LMI toolbox, so as to solve the target cost parameter corresponding to the minimum value of the cost function when the observation error system is stable.

[0229] Refer to Figure 3 , which is the flowchart for determining the cost parameter in an embodiment of the method for observing the position of the motor rotor of the present application.

[0230] Figure 3 Illustrates the specific process of using the LMI toolbox to solve the target cost parameter corresponding to the minimum value of the cost function when the observation error system is stable.

[0231] Step 1: Input the system parameters required for calculation, such as the weight matrix Q and the sub-interval division parameter n, and set the preset accuracy h, the upper and lower limits of the search interval [h start , h end . Where h start is the lower limit ω e1 of the electrical angular velocity of the motor rotor, and h end is the upper limit ω e2 of the electrical angular velocity of the motor rotor.

[0232] Step 2: Judge whether |h start - h end | < h is satisfied. If satisfied, execute Step 7; if not, execute Step 3.

[0233] Step 3: Calculate the preset parameter h test according to the upper and lower limits of the search interval according to the above formula (10).

[0234] Step 4: Judge whether there is a feasible solution to the linear matrix inequality LMI according to the upper and lower limits of the search interval. The linear matrix inequality is obtained according to the second relationship between the stability criterion function and the cost function. If the condition is satisfied, execute Step 5; if not, execute Step 6.

[0235] Step 5: Execute the initial cost parameter h min = h test , hend = htest , skip to step 2.

[0236] Step 6: Execute h end =h test , skip to step 2.

[0237] Step 7: Set the initial cost parameter h min As the target cost parameter, it is output together with the corresponding target gain matrix K1, K2.

[0238] Reference Figure 4 , is an algorithm flow chart of an embodiment of a motor rotor position observation method of the present application.

[0239] Figure 4 The specific algorithm flow of the embodiment of the present application using the TS fuzzy observer to observe the rotor position is shown.

[0240] Firstly, the state equation of permanent magnet synchronous motor is established, and the corresponding two-rule TS fuzzy model is established using the sector nonlinear method.

[0241] Afterwards, the TS fuzzy observer can be obtained according to the permanent magnet synchronous motor state equation and the two-rule TS fuzzy model, and the dynamic equation of the observation error system corresponding to the TS fuzzy observer can be obtained.

[0242] Then, according to the Lyapunov theory, the quadratic Lyapunov function corresponding to the dynamic equation of the observation error system, namely the cost function, is constructed.

[0243] Finally, according to the Schur lemma and the LMI toolbox, the target cost parameter h and the corresponding target gain matrices K1, K2 are solved, and the corresponding target TS fuzzy observer is obtained to determine the motor rotor position.

[0244] In an optional embodiment of the present application, determining the motor rotor position according to the target TS fuzzy observer includes:

[0245] The motor current and voltage are input into the target TS fuzzy observer to obtain the motor rotor position.

[0246] In this embodiment, the motor rotor position can be obtained by inputting the current and the corresponding voltage of the two-phase stationary coordinate system of the motor into the target TS fuzzy observer.

[0247] In the embodiment of the present application, the current and the corresponding voltage in the two-phase stationary coordinate system of the motor are input into the target TS fuzzy observer to observe the motor rotor position. Since the target TS fuzzy observer can dynamically adjust the size of the target gain matrix according to the change of the motor rotor speed, it effectively solves the problem of decreased rotor position observation accuracy caused by internal parameter uncertainty.

[0248] In an optional embodiment of the present application, inputting the motor current and voltage into the target TS fuzzy observer to obtain the motor rotor position includes:

[0249] Inputting the motor current and voltage into the target TS fuzzy observer to obtain the back electromotive force corresponding to the motor current and voltage;

[0250] The motor rotor position and the electrical angular velocity of the motor rotor are obtained according to the back electromotive force.

[0251] In this embodiment, the current and voltage of the motor in the two-phase stationary coordinate system are input into the target TS fuzzy observer to obtain the corresponding back electromotive force. The back electromotive force is further input into the phase-locked loop to obtain the motor rotor position and the electrical angular velocity of the motor rotor according to the back electromotive force.

[0252] In the embodiment of the present application, the current and the corresponding voltage in the two-phase stationary coordinate system of the motor are input into the target TS fuzzy observer to obtain the corresponding back electromotive force, and further obtain the observed motor rotor position. Since the target TS fuzzy observer can dynamically adjust the size of the target gain matrix according to the change of the motor rotor speed, it effectively solves the problem of decreased rotor position observation accuracy caused by internal parameter uncertainty.

[0253] Reference Figure 5 , is a motor control schematic diagram of an embodiment of a motor rotor position observation method of the present application.

[0254] Figure 5 The system schematic diagram of using the TS fuzzy observer of the embodiment of the present application to control the PMSM motor is shown. The specific steps of the motor control are as follows.

[0255] 1. Measure the three-phase stator voltage u generated by the three-phase bridge when the motor is running a 、u b 、u c , the corresponding three-phase current (I a ,I b ,I c ) is converted into the current (I α ,I β ).

[0256] 2. Through I α ,I β And the corresponding voltage u α and u β (is the corresponding voltage) is passed through the TS fuzzy observer provided in the embodiment of the present application to obtain the back electromotive force e α ,eβ ; Back electromotive force e α ,e β The motor rotor position θ is obtained through the phase-locked loop e and the actual rotor speed ω m .

[0257] 3.I α ,I β The current (I d ,I q ), the rotation angle θ of the rotation coordinate system e The motor rotor position θ calculated by the phase-locked loop e supply

[0258] 4.ω ref is the reference speed of the motor. The main task of the speed control loop (PI) is to adjust the speed according to the reference speed ω ref and the actual speed ω m The error between them generates the appropriate q-axis current reference value i q *, to drive the motor to reach the target speed.

[0259] 5. Calculate the reference value i of the current in the two-phase rotating coordinate system according to the control target d * and i q *. According to the error between the actual value and the reference value, input the controller to get u d * and u q *, that is, the voltage reference value of the d-axis and q-axis.

[0260] 6. will u d * and u q *Converted back to the voltage u in the two-phase stationary coordinate system through Park inverse transformation α * and u β *.

[0261] 7. will u α * and u β *Converted into a three-phase voltage signal through SVPWM (Space Vector Pulse Width Modulation), which is used to control the switching state of the inverter and drive the inverter to generate the required voltage vector.

[0262] 8. The inverter controls the three-phase voltage of the motor according to the PWM (Pulse Width Modulation) signal generated by SVPWM, thereby driving the PMSM permanent magnet synchronous motor to operate.

[0263] In the embodiment of the present application, the observer gain is set, and the observer gain includes a gain matrix; according to the two-rule TS fuzzy model and the observer gain, a TS fuzzy observer is constructed, and the TS fuzzy observer has a corresponding observation error system; according to the observation error system, a cost function is constructed, and the cost function has a corresponding cost parameter; when the observation error system is stable, the target cost parameter is determined, and the target cost parameter is the corresponding cost parameter when the cost function reaches the preset condition; the target gain matrix is ​​determined based on the cost function corresponding to the target cost parameter; the motor rotor position is determined according to the target TS fuzzy observer, and the target TS fuzzy observer is obtained by substituting the target gain matrix into the TS fuzzy observer. The target TS fuzzy observer in the embodiment of the present application can dynamically adjust the size of the target gain matrix according to the change of the motor rotor speed, thereby effectively solving the problem of decreased rotor position observation accuracy caused by internal parameter uncertainty, and the target TS fuzzy observer not only helps to improve the observation accuracy within the full speed range, but also can significantly accelerate the error convergence speed, so that the motor operation has high precision, high reliability and strong robustness.

[0264] It should be noted that, for the method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0265] On the basis of the above embodiments, this embodiment further provides a motor rotor position observation device, which is applied to electronic devices such as terminal devices and servers.

[0266] Reference Figure 6 , shows a structural block diagram of an embodiment of a motor rotor position observation device of the present application, which may specifically include the following modules:

[0267] A gain setting module 601, used to set an observer gain, wherein the observer gain includes a gain matrix;

[0268] An observer construction module 602 is used to construct a TS fuzzy observer according to the two-rule TS fuzzy model and the observer gain, wherein the TS fuzzy observer has a corresponding observation error system;

[0269] A cost function construction module 603 is used to construct a cost function according to the observation error system, and the cost function has corresponding cost parameters;

[0270] A target cost parameter determination module 604 is used to determine a target cost parameter when the observation error system is stable, wherein the target cost parameter is the corresponding cost parameter when the cost function meets a preset condition;

[0271] A target gain matrix determination module 605 is used to determine a target gain matrix based on the cost function corresponding to the target cost parameter;

[0272] The rotor position observation module 606 is used to determine the motor rotor position according to a target TS fuzzy observer, where the target TS fuzzy observer is obtained by substituting the target gain matrix into the TS fuzzy observer.

[0273] In an optional embodiment of the present application, the observer construction module 602 includes:

[0274] The electrical angular velocity threshold setting submodule is used to set the upper and lower limits corresponding to the electrical angular velocity of the motor rotor;

[0275] A membership function setting submodule, used to determine the membership function according to the upper limit and the lower limit corresponding to the electrical angular velocity of the motor rotor;

[0276] A two-rule TS fuzzy model establishment submodule, used for establishing the two-rule TS fuzzy model based on the membership function;

[0277] The TS fuzzy observer construction submodule is used to construct the TS fuzzy observer according to the two-rule TS fuzzy model and the observer gain.

[0278] In an optional embodiment of the present application, the gain setting module 601 is further used to:

[0279] Setting a gain constructor, wherein the gain constructor is a constructor for the electrical angular velocity of the motor rotor;

[0280] The observer gain is set according to the gain constructor and the gain matrix.

[0281] In an optional embodiment of the present application, the device further includes:

[0282] A stability criterion function construction module, used for constructing a stability criterion function according to Lyapunov stability theory, wherein the stability criterion function is used for describing the stability of the observation error system;

[0283] The first relationship determination module is used to determine, based on the stability criterion function and the cost function, a first relationship between the cost function and the cost parameter when the observation error system is stable.

[0284] In an optional embodiment of the present application, the first relationship determination module includes:

[0285] A second relationship determination submodule, used to determine a second relationship between the stability criterion function and the cost function, wherein the second relationship includes the cost parameter when the observation error system is stable;

[0286] The first relationship establishing submodule is used to determine the first relationship between the cost function and the cost parameter when the observation error system is stable according to the second relationship between the stability criterion function and the cost function.

[0287] In an optional embodiment of the present application, when the stability criterion function satisfies the stability condition, the observation error system is stable, and the target cost parameter determination module 604 includes:

[0288] The precision determination submodule is used to set the preset precision and the upper and lower limits of the search interval;

[0289] A parameter calculation submodule, configured to calculate a preset parameter according to the upper limit and the lower limit of the search interval if the upper limit and the lower limit of the search interval do not meet the preset accuracy;

[0290] a first feasible solution judgment submodule, used to judge whether a linear matrix inequality has a feasible solution according to an upper limit and a lower limit of the search interval, wherein the linear matrix inequality is obtained according to the second relationship between the stability criterion function and the cost function;

[0291] A first updating submodule, configured to update an initial cost parameter and an upper limit of the search interval according to the preset parameters if the linear matrix inequality has a feasible solution and the stability criterion function satisfies the stability condition;

[0292] A first precision determination submodule, used to determine whether the upper limit and the lower limit of the current search interval meet the preset precision;

[0293] The target cost parameter acquisition submodule is used to determine the initial cost parameter as the target cost parameter if the upper limit and the lower limit of the current search interval meet the preset accuracy.

[0294] In an optional embodiment of the present application, the target cost parameter determination module 604 further includes:

[0295] A second updating submodule, configured to update the upper limit of the search interval according to the preset parameters if the linear matrix inequality has no feasible solution;

[0296] The second precision judgment submodule is used to judge whether the upper limit and the lower limit of the current search interval meet the preset precision according to the upper limit and the lower limit of the current search interval.

[0297] In an optional embodiment of the present application, the target cost parameter determination module 604 further includes:

[0298] A third updating submodule, configured to update the preset parameters according to the upper limit and the lower limit of the current search interval if the upper limit and the lower limit of the current search interval do not meet the preset accuracy;

[0299] The second feasible solution judgment submodule is used to judge whether the current linear matrix inequality has a feasible solution according to the upper limit and the lower limit of the current search interval.

[0300] In an optional embodiment of the present application, the rotor position observation module 606 includes:

[0301] The input receiving submodule is used to input the motor current and voltage into the target TS fuzzy observer to obtain the motor rotor position.

[0302] In an optional embodiment of the present application, the input receiving submodule is further used for:

[0303] Inputting the motor current and voltage into the target TS fuzzy observer to obtain the back electromotive force corresponding to the motor current and voltage;

[0304] The motor rotor position and the electrical angular velocity of the motor rotor are obtained according to the back electromotive force.

[0305] In the embodiment of the present application, the observer gain is set, and the observer gain includes a gain matrix; according to the two-rule TS fuzzy model and the observer gain, a TS fuzzy observer is constructed, and the TS fuzzy observer has a corresponding observation error system; according to the observation error system, a cost function is constructed, and the cost function has a corresponding cost parameter; when the observation error system is stable, the target cost parameter is determined, and the target cost parameter is the corresponding cost parameter when the cost function reaches the preset condition; the target gain matrix is ​​determined based on the cost function corresponding to the target cost parameter; the motor rotor position is determined according to the target TS fuzzy observer, and the target TS fuzzy observer is obtained by substituting the target gain matrix into the TS fuzzy observer. The target TS fuzzy observer in the embodiment of the present application can dynamically adjust the size of the target gain matrix according to the change of the motor rotor speed, thereby effectively solving the problem of decreased rotor position observation accuracy caused by internal parameter uncertainty, and the target TS fuzzy observer not only helps to improve the observation accuracy within the full speed range, but also can significantly accelerate the error convergence speed, so that the motor operation has high precision, high reliability and strong robustness.

[0306] The embodiment of the present application also discloses a vehicle, which includes: a motor; and a motor controller. When the motor controller controls the motor, the motor controller executes the motor rotor position observation method shown in the embodiment of the present application.

[0307] The embodiment of the present application also provides a non-volatile readable storage medium, which stores one or more modules (programs). When the one or more modules are applied to a device, the device can execute instructions (instructions) of each method step in the embodiment of the present application.

[0308] The present application embodiment provides one or more machine-readable media on which instructions are stored, and when executed by one or more processors, an electronic device executes one or more of the methods described in the above embodiments. In the present application embodiment, the electronic device includes various types of devices such as terminal devices and servers (clusters).

[0309] The embodiments of the present disclosure may be implemented as a device configured as desired using any appropriate hardware, firmware, software, or any combination thereof, and the device may include electronic devices such as terminal devices and servers (clusters). Figure 7 An exemplary apparatus 700 that can be used to implement various embodiments described in this application is schematically illustrated.

[0310] For one embodiment, Figure 7 An exemplary apparatus 700 is shown having one or more processors 702, a control module (chip set) 704 coupled to at least one of the processor(s) 702, a memory 706 coupled to the control module 704, a non-volatile memory (NVM) / storage device 708 coupled to the control module 704, one or more input / output devices 710 coupled to the control module 704, and a network interface 712 coupled to the control module 704.

[0311] The processor 702 may include one or more single-core or multi-core processors, and the processor 702 may include any combination of general-purpose processors or special-purpose processors (such as graphics processors, application processors, baseband processors, etc.). In some embodiments, the device 700 can be used as a terminal device, server (cluster), etc. described in the embodiments of the present application.

[0312] In some embodiments, the apparatus 700 may include one or more computer-readable media (e.g., memory 706 or NVM / storage device 708) having instructions 714 and one or more processors 702 combined with the one or more computer-readable media and configured to execute the instructions 714 to implement a module to perform the actions described in the present disclosure.

[0313] For one embodiment, the control module 704 may include any suitable interface controller to provide any suitable interface to at least one of the processor(s) 702 and / or any suitable device or component in communication with the control module 704 .

[0314] The control module 704 may include a memory controller module to provide an interface to the memory 706. The memory controller module may be a hardware module, a software module, and / or a firmware module.

[0315] The memory 706 may be used, for example, to load and store data and / or instructions 714 for the device 700. For one embodiment, the memory 706 may include any suitable volatile memory, such as a suitable DRAM. In some embodiments, the memory 706 may include double data rate type four synchronous dynamic random access memory (DDR4 SDRAM).

[0316] For one embodiment, control module 704 may include one or more input / output controllers to provide an interface to NVM / storage device 708 and input / output device(s) 710 .

[0317] For example, NVM / storage 708 may be used to store data and / or instructions 714. NVM / storage 708 may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable non-volatile storage device(s) (e.g., one or more hard disk drives (HDDs), one or more compact disk (CD) drives, and / or one or more digital versatile disk (DVD) drives).

[0318] NVM / storage device 708 may include storage resources that are physically part of the device on which apparatus 700 is installed, or it may be accessible to the device without being part of the device. For example, NVM / storage device 708 may be accessed via input / output device(s) 710 over a network.

[0319] (One or more) input / output devices 710 may provide an interface for the apparatus 700 to communicate with any other appropriate device, and the input / output device 710 may include a communication component, an audio component, a sensor component, etc. The network interface 712 may provide an interface for the apparatus 700 to communicate through one or more networks, and the apparatus 700 may wirelessly communicate with one or more components of a wireless network according to any of one or more wireless network standards and / or protocols, for example, accessing a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, 5G, etc., or a combination thereof for wireless communication.

[0320] For one embodiment, at least one of the processor(s) 702 may be packaged together with the logic of one or more controllers (e.g., a memory controller module) of the control module 704. For one embodiment, at least one of the processor(s) 702 may be packaged together with the logic of one or more controllers of the control module 704 to form a system-in-package (SiP). For one embodiment, at least one of the processor(s) 702 may be integrated on the same die with the logic of one or more controllers of the control module 704. For one embodiment, at least one of the processor(s) 702 may be integrated on the same die with the logic of one or more controllers of the control module 704 to form a system-on-chip (SoC).

[0321] In various embodiments, the apparatus 700 may be, but is not limited to, a terminal device such as a server, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.). In various embodiments, the apparatus 700 may have more or fewer components and / or a different architecture. For example, in some embodiments, the apparatus 700 includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an application-specific integrated circuit (ASIC), and a speaker.

[0322] Among them, the main control chip can be used as a processor or control module in the detection device, sensor data, location information, etc. are stored in a memory or NVM / storage device, the sensor group can be used as an input / output device, and the communication interface may include a network interface.

[0323] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0324] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0325] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or 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 motor rotor position observation terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable motor rotor position observation terminal device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0326] These computer program instructions can also be stored in a computer readable memory that can guide a computer or other programmable motor rotor position observation terminal device to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including an instruction device, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0327] These computer program instructions can also be loaded into a computer or other programmable motor rotor position observation terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0328] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0329] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0330] The above is a detailed introduction to a motor rotor position observation method and device, an electronic device and a storage medium provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for observing the position of a motor rotor, characterized in that: The method comprises: Setting an observer gain, wherein the observer gain includes a gain matrix; According to the two-rule TS fuzzy model and the observer gain, a TS fuzzy observer is constructed, and the TS fuzzy observer has a corresponding observation error system; According to the observation error system, a cost function is constructed, wherein the cost function has corresponding cost parameters; When the observation error system is stable, determining a target cost parameter, wherein the target cost parameter is the corresponding cost parameter when the cost function meets a preset condition; Determining a target gain matrix based on the cost function corresponding to the target cost parameter; The motor rotor position is determined according to a target TS fuzzy observer, where the target TS fuzzy observer is obtained by substituting the target gain matrix into the TS fuzzy observer.

2. The method according to claim 1, characterized in that The method of constructing a TS fuzzy observer according to the two-rule TS fuzzy model and the observer gain includes: Set the upper and lower limits of the electrical angular velocity of the motor rotor; Determining a membership function according to an upper limit and a lower limit corresponding to the electrical angular velocity of the motor rotor; Based on the membership function, establishing the two-rule TS fuzzy model; The TS fuzzy observer is constructed according to the two-rule TS fuzzy model and the observer gain.

3. The method according to claim 1, characterized in that The setting of the observer gain comprises: Setting a gain constructor, wherein the gain constructor is a constructor for the electrical angular velocity of the motor rotor; The observer gain is set according to the gain constructor and the gain matrix.

4. The method according to claim 1, characterized in that: In the case where the observation error system is stable, before determining the target cost parameter, the method further includes: According to Lyapunov stability theory, a stability criterion function is constructed, wherein the stability criterion function is used to describe the stability of the observation error system; According to the stability criterion function and the cost function, a first relationship between the cost function and the cost parameter is determined when the observation error system is stable.

5. The method according to claim 4, characterized in that The determining, based on the stability criterion function and the cost function, a first relationship between the cost function and the cost parameter when the observation error system is stable comprises: determining a second relationship between the stability criterion function and the cost function, wherein the second relationship includes the cost parameter when the observation error system is stable; According to the second relationship between the stability criterion function and the cost function, the first relationship between the cost function and the cost parameter is determined when the observation error system is stable.

6. The method according to claim 5, characterized in that When the stability criterion function satisfies a stability condition, the observation error system is stable, and when the observation error system is stable, determining a target cost parameter includes: Set the preset precision, upper and lower limits of the search interval; If the upper limit and the lower limit of the search interval do not meet the preset accuracy, calculating the preset parameters according to the upper limit and the lower limit of the search interval; Determining whether a linear matrix inequality has a feasible solution according to an upper limit and a lower limit of the search interval, wherein the linear matrix inequality is obtained according to the second relationship between the stability criterion function and the cost function; If the linear matrix inequality has a feasible solution, the stability criterion function satisfies the stability condition, and the initial cost parameter and the upper limit of the search interval are updated according to the preset parameters; Determine whether the upper limit and the lower limit of the current search interval meet the preset accuracy; If the upper limit and the lower limit of the current search interval meet the preset accuracy, the initial cost parameter is determined as the target cost parameter.

7. The method according to claim 6, characterized in that After determining whether the linear matrix inequality has a feasible solution, the method further includes: If the linear matrix inequality has no feasible solution, updating the upper limit of the search interval according to the preset parameters; According to the upper limit and the lower limit of the current search interval, it is determined whether the upper limit and the lower limit of the current search interval meet the preset accuracy.

8. The method according to claim 6, characterized in that After determining whether the upper limit and the lower limit of the current search interval meet the preset accuracy, the method further includes: If the upper limit and the lower limit of the current search interval do not meet the preset accuracy, updating the preset parameters according to the upper limit and the lower limit of the current search interval; According to the upper limit and the lower limit of the current search interval, it is determined whether the current linear matrix inequality has a feasible solution.

9. The method according to claim 1, characterized in that: Determining the motor rotor position according to the target TS fuzzy observer includes: The motor current and voltage are input into the target TS fuzzy observer to obtain the motor rotor position.

10. The method according to claim 9, characterized in that The step of inputting the motor current and voltage into the target TS fuzzy observer to obtain the motor rotor position includes: Inputting the motor current and voltage into the target TS fuzzy observer to obtain the back electromotive force corresponding to the motor current and voltage; The motor rotor position and the electrical angular velocity of the motor rotor are obtained according to the back electromotive force.

11. A motor rotor position observation device, characterized in that: The device comprises: A gain setting module, used for setting an observer gain, wherein the observer gain includes a gain matrix; An observer construction module, used for constructing a TS fuzzy observer according to a two-rule TS fuzzy model and the observer gain, wherein the TS fuzzy observer has a corresponding observation error system; A cost function construction module, used to construct a cost function according to the observation error system, wherein the cost function has corresponding cost parameters; A target cost parameter determination module, used to determine a target cost parameter when the observation error system is stable, wherein the target cost parameter is the corresponding cost parameter when the cost function meets a preset condition; A target gain matrix determination module, used to determine a target gain matrix based on the cost function corresponding to the target cost parameter; The rotor position observation module is used to determine the motor rotor position according to a target TS fuzzy observer, wherein the target TS fuzzy observer is obtained by substituting the target gain matrix into the TS fuzzy observer.

12. A vehicle, characterized in that: The vehicle comprises: a motor; and A motor controller, when the motor controller controls the motor, causes the motor controller to execute the motor rotor position observation method as described in one or more of claims 1-10.

13. An electronic device, characterized in that: include: processor; and A memory having executable codes stored thereon, which, when executed, enables the processor to execute the motor rotor position observation method as described in one or more of claims 1-10.

14. One or more machine-readable media having executable codes stored thereon, which, when executed, enable a processor to execute the motor rotor position observation method according to one or more of claims 1-10.

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