Method, device and medium for identifying parameters of lift motor of distributed electric propulsion system

Abstract

The present disclosure provides a method, device and medium for identifying parameters of a boost motor of a distributed electric propulsion system, which belongs to the field of computing technology and specifically includes: Step 1, obtaining the stator current i of the boost motor d ,i q , stator voltage u d ,u q and electrical angular velocity ω e ; Step 2, the stator current i d and i q and the stator voltage u d and u q Input the improved Newton-Raphson optimizer to obtain the process noise matrix Q and measurement noise matrix R of the extended Kalman filter of the lift motor parameter identification model; Step 3, the stator current i d ,i q , stator voltage u d ,u q , electrical angular velocity ω e The process noise matrix Q and the measurement noise matrix R are input into the extended Kalman filter to obtain the identification values ​​of the permanent magnet flux, stator resistance, and stator inductance of the boost motor. The disclosed solution improves the identification efficiency, accuracy, and adaptability.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computing technology, and more particularly to a method, device, and medium for identifying parameters of a boost motor in a distributed electric propulsion system. Background Art

[0002] Distributed electric propulsion systems are replacing traditional centralized turbine engine propulsion systems, becoming an inevitable trend in aircraft electrification. This presents enormous development opportunities and promising prospects for the aviation electrical field. Permanent magnet synchronous motors (PMSMs), with their advantages of small size, high power density, high integration, and excellent control performance, have been demonstrated and tested for use in distributed electric propulsion aircraft lift systems and hold great potential for further development. However, the parameters of PSMs are susceptible to temperature fluctuations and magnetic flux saturation, leading to electrical parameter variations that reduce the accuracy and reliability of motor control.

[0003] It can be seen that there is an urgent need for a parameter identification method for the distributed electric propulsion system lift motor that can improve the identification efficiency, accuracy and adaptability. Summary of the Invention

[0004] In view of this, the embodiments of the present disclosure provide a method, device and medium for identifying parameters of a boost motor in a distributed electric propulsion system, which at least partially solve the problems of poor identification efficiency, accuracy and adaptability in the prior art.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for identifying parameters of a boost motor in a distributed electric propulsion system, comprising:

[0006] Step 1: Get the stator current i of the boost motor d ,i q , stator voltage u d ,u q and electrical angular velocity ω e , wherein the stator current i d and i q According to the real-time sampling of the three-phase current of the boost motor and the coordinate transformation, the stator voltage u d and u q The electric angular velocity ω is obtained by outputting the current regulator according to the d-axis current difference and the q-axis current difference of the boost motor. e Obtained by sampling from the speed sensor;

[0007] Step 2: Set the stator current i d and i q and the stator voltage u d and u qInput the improved Newton-Raphson optimizer to obtain the process noise matrix Q and measurement noise matrix R of the extended Kalman filter of the lift motor parameter identification model;

[0008] Step 3: Set the stator current i d ,i q , stator voltage u d ,u q , electrical angular velocity ω e , process noise matrix Q and measurement noise matrix R are input into the extended Kalman filter to obtain the identification values ​​of the permanent magnet flux, stator resistance and stator inductance of the boost motor.

[0009] According to a specific implementation of the embodiment of the present disclosure, before step 2, the method further includes:

[0010] Construct the stator voltage equation of the boost motor in the dq coordinate system;

[0011] The stator current equation of the boost motor is obtained based on the stator voltage equation;

[0012] According to the stator current equation, the permanent magnet flux identification equation, stator inductance identification equation and stator resistance identification equation of the boost motor are obtained;

[0013] Based on the extended Kalman filter theory, the parameter identification model of the boost motor is obtained by combining the permanent magnet flux identification equation, the stator inductance identification equation and the stator resistance identification equation.

[0014] According to a specific implementation of the embodiment of the present disclosure, step 2 specifically includes:

[0015] Step 2.1, initialize the parameters and randomly generate the first population consisting of N individuals;

[0016] Step 2.2, generating a second population including N individuals based on the first population and the dynamic reverse learning strategy;

[0017] Step 2.3, merge the first population and the second population, and select the first N individuals in the merged population as the initial population based on the greedy strategy;

[0018] Step 2.4, calculate the fitness value of each individual in the initial population;

[0019] Step 2.5: For each individual, generate a new individual using the Newton-Raphson search rule and determine whether the random number generated in the current round is less than the first decision factor. If so, continue to use the new individual generated by the Newton-Raphson search rule to update the current individual. If not, generate a new individual based on the trap avoidance operation and update the current individual accordingly.

[0020] Step 2.6: Determine whether the random number generated in the current round is less than the second decision factor. If so, use the composite exploration strategy to generate a new individual. If not, use the dynamic reverse learning strategy to generate a new individual.

[0021] Step 2.7, by comparing the fitness values ​​of the new individual generated by the composite exploration strategy, the new individual generated based on the dynamic reverse learning strategy, and the current individual, decide whether to update the current individual;

[0022] Step 2.8: Repeat steps 2.5 to 2.7 until all individuals in the initial population are traversed, ending the iteration process and adding one to the number of iterations.

[0023] Step 2.9, determine whether the current number of iterations is greater than the preset number of iterations. If so, end the iteration and output the optimal value within the population as the process noise matrix Q and measurement noise matrix R of the extended Kalman filter. If not, return to step 2.5.

[0024] According to a specific implementation of the embodiment of the present disclosure, the expression for judging whether the random number generated in the current round is less than the first decision factor is:

[0025]

[0026] Among them, NRSR + represents the new search direction specified by the Newton-Raphson rule, X iT represents the current individual in the current iteration number, represents the individual with the best fitness value in the current iteration number, X1 and X2 are two random individuals in the current population, and They represent three different scale extensions of the Newton-Raphson search rule, a, b, r5 and r6 represent random numbers between 0 and 1, δ represents the discount factor, η represents the position search weighting factor, DF represents the decision factor, which is a value between 0 and 1, iT and iT max Represents the current iteration number and the maximum iteration number, Represents a new individual calculated using the trap avoidance operator.

[0027] According to a specific implementation of the embodiment of the present disclosure, the expression of the composite exploration strategy is:

[0028]

[0029] Among them, C, ζ, γ and L are adjustable parameters. and Represent the exploration results of the Skyhawk optimization algorithm and the exponential distribution optimizer, r9, r10 、r 11 、r 12 and r 13 are random numbers between 0 and 1, d is the dimension of individuals in the population, is the average value of all individuals in the population, represents a randomly selected individual in the population, and x, y, ω, f, Z1, Z2, D1, and D2 are auxiliary calculation factors.

[0030] According to a specific implementation of the embodiment of the present disclosure, the expression of the dynamic reverse learning strategy is:

[0031]

[0032] Among them, τ represents the weight factor, r7 and r8 are random numbers between 0 and 1, and u and l represent the upper and lower limits of the individual, respectively.

[0033] According to a specific implementation of the embodiment of the present disclosure, the expression of the fitness value is:

[0034] F fitness =∑ i (y act (j)-y est (j)) 2

[0035] Among them, y act (j) and y est (j) represents the measured value and estimated value of the jth parameter, respectively.

[0036] In a second aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0037] at least one processor; and,

[0038] a memory communicatively connected to the at least one processor; wherein,

[0039] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the distributed electric propulsion system lift motor parameter identification method in the aforementioned first aspect or any implementation of the first aspect.

[0040] In a third aspect, an embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the method for identifying parameters of a distributed electric propulsion system lift motor in the aforementioned first aspect or any implementation of the first aspect.

[0041] In a fourth aspect, an embodiment of the present disclosure further provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the method for identifying parameters of a distributed electric propulsion system lift motor in the aforementioned first aspect or any implementation of the first aspect.

[0042] The distributed electric propulsion system boost motor parameter identification scheme in the embodiment of the present disclosure includes: Step 1, obtaining the stator current i of the boost motor d ,i q , stator voltage u d ,u q and electrical angular velocity ω e , wherein the stator current i d and i q According to the real-time sampling of the three-phase current of the boost motor and the coordinate transformation, the stator voltage u d and u q The electric angular velocity ω is obtained by outputting the current regulator according to the d-axis current difference and the q-axis current difference of the boost motor. e According to the speed sensor sampling; Step 2, the stator current i d and i q and the stator voltage u d and u q Input the improved Newton-Raphson optimizer to obtain the process noise matrix Q and measurement noise matrix R of the extended Kalman filter of the lift motor parameter identification model; Step 3, the stator current i d ,i q , stator voltage u d ,u q , electrical angular velocity ω e , process noise matrix Q and measurement noise matrix R are input into the extended Kalman filter to obtain the identification values ​​of the permanent magnet flux, stator resistance and stator inductance of the boost motor.

[0043] The beneficial effects of the embodiments of the present disclosure are as follows: Through the scheme of the present disclosure, a parameter identification model suitable for the lift-boosting motor is first constructed, and two extended Kalman filters are used to identify the stator resistance, stator inductance, and permanent magnet flux, respectively, to reduce the coupling between the various parameters of the lift-boosting motor and improve the identification efficiency and stability; on this basis, the noise covariance matrices Q and R in the extended Kalman filter are adaptively optimized by improving the Newton-Raphson optimizer, and an adaptive function suitable for the extended Kalman filter parameter identification model is designed. By combining the global search capability of the improved Newton-Raphson optimizer with the recursive estimation characteristics of the extended Kalman filter, the present invention makes the parameter identification of the lift-boosting motor in a noisy environment more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 A flow chart of a method for identifying parameters of a boost motor in a distributed electric propulsion system provided by an embodiment of the present disclosure;

[0046] Figure 2 A schematic block diagram of a Newton-Raphson optimizer optimized extended state observer provided in an embodiment of the present disclosure;

[0047] Figure 3 A stator inductance identification waveform diagram observed in a simulation provided by an embodiment of the present disclosure;

[0048] Figure 4 A permanent magnet flux identification waveform diagram observed in a simulation provided by an embodiment of the present disclosure;

[0049] Figure 5 A stator resistance identification waveform diagram observed in a simulation provided by an embodiment of the present disclosure;

[0050] Figure 6 A system block diagram of a lift motor parameter identification system provided by an embodiment of the present disclosure;

[0051] Figure 7 A schematic diagram of a processing flow of an improved Newton-Raphson optimizer provided in an embodiment of the present disclosure;

[0052] Figure 8 A schematic diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0053] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0054] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0055] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this disclosure, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

[0056] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The illustrations only show components related to the present disclosure and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0057] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0058] Distributed electric propulsion systems, replacing traditional centralized turbine engine propulsion systems, are an inevitable trend in aircraft electrification, bringing tremendous development opportunities and promising prospects to the aviation electrical field. Permanent magnet synchronous motors, with their advantages of small size, high power density, high integration, and excellent control performance, have been demonstrated and tested for use in distributed electric propulsion aircraft lift systems and hold great potential for further development. However, the parameters of permanent magnet synchronous motors are susceptible to factors such as temperature fluctuations and magnetic flux saturation, leading to electrical parameter variations that reduce the accuracy and reliability of motor control.

[0059] To improve the stability of motor control systems, researchers have proposed a variety of parameter identification methods, including recursive least squares, model reference adaptive algorithms, and genetic algorithms. Recursive least squares offers the advantages of moderate computational complexity and simple implementation, but suffers from poor robustness and is prone to data saturation. Model reference adaptive algorithms offer a concise structure and are easy to implement, but they place high demands on initial parameter values ​​and adaptive rate design. While genetic algorithms can achieve high identification accuracy, they are prone to premature convergence and exhibit high computational complexity.

[0060] The extended Kalman filter algorithm is an optimized recursive estimation method for nonlinear systems. It can simultaneously estimate the parameters and states of the motor, has a certain anti-noise capability, and has achieved good results in parameter identification. However, the selection of the system noise covariance matrix Q and the measurement noise covariance matrix R in the extended Kalman filter algorithm still poses great challenges. Improper selection will affect the accuracy and efficiency of parameter identification. Therefore, how to effectively select appropriate Q and R matrices becomes a key issue in the application of the extended Kalman filter algorithm. The disclosed embodiment provides a method for identifying the parameters of a distributed electric propulsion system lift motor, which can be applied to the permanent magnet synchronous motor control process in aviation electrical scenarios.

[0061] See also Figure 1 , is a flow chart of a method for identifying parameters of a distributed electric propulsion system boost motor provided by an embodiment of the present disclosure. Figure 1 As shown, the method mainly includes the following steps:

[0062] Step 1: Get the stator current i of the boost motor d ,i q , stator voltage u d ,u q and electrical angular velocity ω e , wherein the stator current i d and i q According to the real-time sampling of the three-phase current of the boost motor and the coordinate transformation, the stator voltage u d and u qThe electric angular velocity ω is obtained by outputting the current regulator according to the d-axis current difference and the q-axis current difference of the boost motor. e Obtained by sampling from the speed sensor;

[0063] In specific implementation, the i of the lift motor is obtained. d 、i q 、u d 、u q and ω e , wherein the i of the lift motor d and i q According to the real-time sampling of the three-phase current of the lifting motor and the coordinate transformation, the u of the lifting motor is obtained. d and u q The ω of the lift-boosting motor is obtained by outputting the current regulator according to the d-axis current difference and the q-axis current difference of the lift-boosting motor. e Obtained by sampling from the speed sensor

[0064] Step 2: Set the stator current i d and i q and the stator voltage u d and u q Input the improved Newton-Raphson optimizer to obtain the process noise matrix Q and measurement noise matrix R of the extended Kalman filter of the lift motor parameter identification model;

[0065] Based on the above embodiment, before step 2, the method further includes:

[0066] Construct the stator voltage equation of the boost motor in the dq coordinate system;

[0067] The stator current equation of the boost motor is obtained based on the stator voltage equation;

[0068] According to the stator current equation, the permanent magnet flux identification equation, stator inductance identification equation and stator resistance identification equation of the boost motor are obtained;

[0069] Based on the extended Kalman filter theory, the parameter identification model of the boost motor is obtained by combining the permanent magnet flux identification equation, the stator inductance identification equation and the stator resistance identification equation.

[0070] Furthermore, the step 2 specifically includes:

[0071] Step 2.1, initialize the parameters and randomly generate the first population consisting of N individuals;

[0072] Step 2.2, generating a second population including N individuals based on the first population and the dynamic reverse learning strategy;

[0073] Step 2.3, merge the first population and the second population, and select the first N individuals in the merged population as the initial population based on the greedy strategy;

[0074] Step 2.4, calculate the fitness value of each individual in the initial population;

[0075] Step 2.5: For each individual, generate a new individual using the Newton-Raphson search rule and determine whether the random number generated in the current round is less than the first decision factor. If so, continue to use the new individual generated by the Newton-Raphson search rule to update the current individual. If not, generate a new individual based on the trap avoidance operation and update the current individual accordingly.

[0076] Step 2.6: Determine whether the random number generated in the current round is less than the second decision factor. If so, use the composite exploration strategy to generate a new individual. If not, use the dynamic reverse learning strategy to generate a new individual.

[0077] Step 2.7, by comparing the fitness values ​​of the new individual generated by the composite exploration strategy, the new individual generated based on the dynamic reverse learning strategy, and the current individual, decide whether to update the current individual;

[0078] Step 2.8: Repeat steps 2.5 to 2.7 until all individuals in the initial population are traversed, ending the iteration process and adding one to the number of iterations.

[0079] Step 2.9, determine whether the current number of iterations is greater than the preset number of iterations. If so, end the iteration and output the optimal value within the population as the process noise matrix Q and measurement noise matrix R of the extended Kalman filter. If not, return to step 2.5.

[0080] Furthermore, the expression for judging whether the random number generated in the current round is less than the first decision factor is:

[0081]

[0082] Among them, NRSR + represents the new search direction specified by the Newton-Raphson rule, X iT represents the current individual in the current iteration number, represents the individual with the best fitness value in the current iteration number, X1 and X2 are two random individuals in the current population, and They represent three different scale extensions of the Newton-Raphson search rule, a, b, r5 and r6 represent random numbers between 0 and 1, δ represents the discount factor, η represents the position search weighting factor, DF represents the decision factor, which is a value between 0 and 1, iT and iTmax Represents the current iteration number and the maximum iteration number, Represents a new individual calculated using the trap avoidance operator.

[0083] Furthermore, the expression of the composite exploration strategy is:

[0084]

[0085]

[0086] Among them, C, ζ, γ and L are adjustable parameters. and Represent the exploration results of the Skyhawk optimization algorithm and the exponential distribution optimizer, r9, r 10 、r 11 、r 12 and r 13 are random numbers between 0 and 1, d is the dimension of individuals in the population, is the average value of all individuals in the population, represents a randomly selected individual in the population, and x, y, ω, f, Z1, Z2, D1, and D2 are auxiliary calculation factors.

[0087] Furthermore, the expression of the dynamic reverse learning strategy is

[0088]

[0089] Among them, τ represents the weight factor, r7 and r8 are random numbers between 0 and 1, and u and l represent the upper and lower limits of the individual, respectively.

[0090] Furthermore, the expression of the fitness value is

[0091] F fitness =∑ i (y act (j)-y est (j)) 2

[0092] Among them, y act (j) and y est (j) represents the measured value and estimated value of the jth parameter, respectively.

[0093] During implementation, it should be noted that, in the embodiments of the present invention, "EKF" refers to the Extended Kalman Filter, and "NRBO" refers to the Newton-Raphson Optimizer. Unless otherwise specified, in the embodiments of the present invention, the q-axis current, d-axis current, α-axis voltage, β-axis voltage, α-axis current, and β-axis current refer to the current or voltage obtained by performing a dq transformation or a Clark transformation on the three-phase current or voltage of the boost motor.

[0094] In the embodiment of the present disclosure, Figure 2 and Figure 7 As shown, the control system increases the speed of the motor according to the reference signal and speed feedback signal ω m , after the speed regulator, outputs a signal indicating the q-axis current of the boost motor. This signal is used as the q-axis current reference value (q-axis current reference signal) of the first regulator and the q-axis current i of the boost motor. q are sent to the first regulator together, and the output signal of the regulator is the q-axis voltage reference signal u q At the same time, the preset d-axis current reference value (preset d-axis current reference signal) and the d-axis current i d is sent to the second regulator, and the output signal of the second regulator is the d-axis voltage reference signal u d The above two signals are transformed by inverse Park to generate α-axis voltage and β-axis voltage, which are used to adjust the speed of the boost motor through the SVPWM system and the switch tube loop.

[0095] In terms of obtaining the q-axis current and the d-axis current, the embodiment of the present invention is based on sampling the current and obtaining the α-axis current i through Clark transformation. α and β-axis current i β , and then through Park transformation, we can get the q-axis current i q and d-axis current i d In other words, the α-axis current, β-axis current, q-axis current and d-axis current are all based on real sampling values ​​and obtained through coordinate system transformation.

[0096] To obtain the lift-boost motor parameters, the q-axis voltage, d-axis voltage, q-axis current, d-axis current, and motor speed are input into the EKF module in the embodiment of the present invention. The EKF module optimizes the EKF module and calculates the estimated stator inductance, permanent magnet flux linkage, and stator resistance.

[0097] In the embodiments of the present disclosure, the technical solution adopted to solve the technical problem includes the following steps:

[0098] Step a) constructing a lift motor parameter identification model based on an extended Kalman filter;

[0099] Step b) adaptively optimizes the noise covariance matrices Q and R in the extended Kalman filter by improving the Newton-Raphson optimizer, and designs an adaptive function suitable for the extended Kalman filter parameter identification model.

[0100] Step a specifically includes:

[0101] Step a1) In the design of the extended Kalman filter parameter identifier of the boost motor, the stator voltage equation of the boost motor can be expressed in the dq coordinate system as

[0102]

[0103] Among them, i d 、i q 、u d 、u q are the stator current and voltage, R s is the stator resistance, L d and L q are stator inductance, ω e is the electrical angular velocity, ψ f is the permanent magnet flux.

[0104] Furthermore, according to the stator voltage equation, the stator current equation of the boost motor is:

[0105]

[0106] Step a2) According to the stator current equation, the identification equations of the permanent magnet flux, stator inductance and stator resistance of the boost motor are respectively:

[0107]

[0108] Step a3) The state variable vectors for identifying the permanent magnet flux, stator inductance and stator resistance of the electric propulsion motor are x1=[i d1 i q1 ψ f L s ] T and x2=[i d2 i q2 R s ] T Let L = 1 / L s , from formula (3) and formula (4) we can get

[0109]

[0110] Step a4) Further, by taking the derivative of x1 and x2 and discretizing them, the corresponding Jacobian matrix can be obtained as

[0111]

[0112]

[0113] Furthermore, the measurement equation is

[0114] z1=H1[i d1 i q1 ψ f L s ] T (9)

[0115] z2=H2[i d2 i q2 R s ] T (10)

[0116]

[0117] Step a5) The present invention estimates the permanent magnet flux, stator inductance and stator resistance based on the extended Kalman filter theory. In the prediction phase at time k, the present invention estimates the permanent magnet flux, stator inductance and stator resistance based on the input vector u(k) and the previous state Estimate to predict the next state It is calculated as follows:

[0118]

[0119] Additionally, the a priori error covariance matrix is ​​calculated:

[0120] P i (k+1|k)=φ i (k)P i (k|k)φ i T (k)+Q (13)

[0121] In the correction phase at time k, the Kalman gain is updated as follows:

[0122] K i (k+1)=P i (k+1|k)H i T [H i P i (k+1|k)H i T +R] -1 (14)

[0123] Then the calculation expression of the next state estimate is:

[0124]

[0125] For the next estimation, the posterior error covariance matrix is ​​updated as follows:

[0126] P i (k+1|k+1)=[IK i (k+1)H i ]P i (k+1|k) (16)

[0127] At this point, the present invention completes the construction of the entire lift motor parameter identification model based on the extended Kalman filter.

[0128] Step b specifically includes:

[0129] Step b1) The Newton-Raphson optimizer is a swarm intelligence meta-heuristic algorithm that primarily implements the entire optimization process using two operators: the Newton-Raphson search rule and the trap avoidance operator. The detailed calculation process is as follows:

[0130] Step 1: Randomly generate an initial population. Each individual in the population represents a possible solution;

[0131] Step 2: Calculate the fitness value of each individual to measure its performance in the optimization problem;

[0132] Step 3: For each individual, use the Newton-Raphson rule to calculate the new search direction. The Newton-Raphson search rule is as follows:

[0133]

[0134] Among them, iT represents the number of iterations; X iT is the individual selected from the current population; and They represent the two individuals with the lowest and highest fitness values ​​in the current population respectively; mean() represents the averaging function; r1, r2 and r3 represent random numbers between 0 and 1 respectively; ΔX iT It can be calculated as follows:

[0135]

[0136] Where r4 represents a random number between 0 and 1. After determining the search direction, the individual positions are updated to explore a better solution. The specific calculation method is as follows:

[0137]

[0138]

[0139] Among them, X1 and X2 are two random individuals in the current population; a, b and r5 are all random numbers between 0 and 1, iT max is the maximum number of iterations;

[0140] Step 4: Introduce trap avoidance operations to help the algorithm escape from local optimality. The calculation formula is as follows:

[0141]

[0142] Among them, α represents a random number between -1 and 1; β represents a random number between -0.5 and 0.5; μ1 and μ2 represent random numbers between 0 and 3 respectively; Represents all individuals in the population;

[0143] Step 5: When the random number r5 is less than the decision factor DF that controls the performance of the algorithm, the Newton-Raphson search rule is used to update the individual, otherwise the trap avoidance operation is used to update the individual. The specific calculation method is as follows:

[0144]

[0145] Step 6: If the maximum number of iterations is reached or a certain convergence criterion is met, the algorithm stops. Otherwise, return to Step 2.

[0146] Step b2) is to further improve the global search capability of the algorithm. The present invention introduces a dynamic reverse learning strategy and a composite exploration strategy on the standard Newton-Raphson optimizer. The dynamic reverse learning strategy is an adaptive optimization method that adjusts the search direction and step size by introducing a reverse learning mechanism. Specifically, it dynamically adjusts the learning rate according to the performance of the current solution, allowing the optimization algorithm to automatically reversely correct the search path that deviates excessively from the optimal solution during the search process, while improving the diversity of the population. Its calculation formula is as follows:

[0147]

[0148] Among them, τ represents the weight factor; r7 and r8 are random numbers between 0 and 1; u and l represent the upper and lower limits of the individual, respectively.

[0149] The composite exploration strategy uses a combination of the Skyhawk optimization algorithm and the exponential distribution optimizer to compensate for the shortcomings of the Newton-Raphson optimizer in global exploration capabilities, thereby better balancing global and local search and avoiding falling into local minima. Its calculation formula is as follows:

[0150]

[0151] Among them, C, ζ, γ and L are adjustable parameters; r9, r 10 、r 11 、r 12 and r 13 are random numbers between 0 and 1; d is the dimension of individuals in the population; is the average value of all individuals in the population; Represents a randomly selected individual in the population. The combination of these two strategies enables the improved Newton-Raphson optimizer to find better solutions to complex optimization problems.

[0152] In order to use the improved Newton-Raphson optimizer to dynamically adjust the weight matrices Q and R of the Kalman filter to minimize the error between the state estimation value and the measurement value of the extended Kalman filter algorithm, the present invention uses the mean square error between the actual value and the estimated value as the fitness function to construct the improved Newton-Raphson optimizer, that is,

[0153] F fitness =∑ i (y act (j)-y est (j)) 2 (33)

[0154] Among them, y act (j) and y est (j) denote the measured value and estimated value of the j-th parameter, respectively.

[0155] Step 3: Set the stator current i d ,i q , stator voltage u d ,u q , electrical angular velocity ω e , process noise matrix Q and measurement noise matrix R are input into the extended Kalman filter to obtain the identification values ​​of the permanent magnet flux, stator resistance and stator inductance of the boost motor.

[0156] The method for identifying parameters of a boost motor in a distributed electric propulsion system provided in this embodiment first constructs a parameter identification model suitable for the boost motor, and then uses two extended Kalman filters to identify the stator resistance, stator inductance, and permanent magnet flux, respectively, to reduce the coupling between the parameters of the boost motor and improve the identification efficiency and stability. On this basis, the noise covariance matrices Q and R in the extended Kalman filter are adaptively optimized by an improved Newton-Raphson optimizer, and an adaptive function suitable for the extended Kalman filter parameter identification model is designed. By combining the global search capability of the improved Newton-Raphson optimizer with the recursive estimation characteristics of the extended Kalman filter, the present invention makes the parameter identification of the boost motor in a noisy environment more accurate and efficient.

[0157] The method of the present invention will be further described below with reference to a specific embodiment. Figure 6 As shown in the figure, the DSP320F28335 is used as the core processor to build a lift motor parameter identification system experimental platform. The system structure includes:

[0158] Extended state observer 601: used to use the stator voltage and current of the boost motor in the stationary coordinate system as input variables of the EKF to obtain the stator inductance, permanent magnet flux linkage and stator resistance parameter values ​​of the motor;

[0159] Improved Newton-Raphson optimizer 602: used to take the estimated value and the measured value of the stator current of the motor as input variables to obtain the estimated value of the process noise matrix Q and the measurement noise matrix R of the EKF;

[0160] Observe the changes of various variables in the simulation model to obtain the performance results of the method proposed in the embodiment of the present invention. Figure 3 、 Figure 4 and Figure 5 shown.

[0161] The embodiment of the method for identifying parameters of the lift-boosting motor of a distributed electric propulsion aircraft of the present invention first obtains the i of the lift-boosting motor. d 、i q 、u d 、u q and ω e , wherein the i of the lift motor d and i q According to the real-time sampling of the three-phase current of the lifting motor and the coordinate transformation, the u of the lifting motor is obtained. d and u q The ω of the lift-boosting motor is obtained by outputting the current regulator according to the d-axis current difference and the q-axis current difference of the lift-boosting motor. e According to the speed sensor sampling, the i of the boost motor is obtained;d and i q And the i of the lift motor d and i q The estimated value is sent to the improved Newton-Raphson optimizer to obtain the process noise matrix Q and measurement noise matrix R of the extended Kalman filter; finally, the i of the boost motor is d 、i q 、u d 、u q and ω e The optimized process noise matrix Q and measurement noise matrix R of the extended Kalman filter are then fed into the extended Kalman filter model to obtain the identification values ​​of the permanent magnet flux, stator resistance, and stator inductance of the boost motor. By combining the global search capabilities of the Newton-Raphson optimizer with the recursive estimation characteristics of the extended Kalman filter, this invention enables more accurate and efficient parameter identification of boost motors in noisy environments, providing a new approach for application in distributed electric propulsion systems and promising broad application prospects.

[0162] See also Figure 8 The present disclosure also provides an electronic device 80, comprising at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the method for identifying parameters of a distributed electric propulsion system lift motor in the aforementioned method embodiment.

[0163] An embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the distributed electric propulsion system lift motor parameter identification method in the aforementioned method embodiment.

[0164] An embodiment of the present disclosure also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the distributed electric propulsion system lift motor parameter identification method in the aforementioned method embodiment.

[0165] Reference below Figure 8, which shows a schematic structural diagram of an electronic device 80 suitable for implementing the embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0166] like Figure 8 As shown, the electronic device 80 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage device 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the electronic device 80 are also stored in the RAM 803. The processing device 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0167] Typically, the following devices may be connected to the I / O interface 805: an input device 806 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 808 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 809. The communication device 809 may allow the electronic device 80 to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows the electronic device 80 with various devices, it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0168] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 809, or installed from the storage device 808, or installed from the ROM 802. When the computer program is executed by the processing device 801, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0169] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0170] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0171] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can perform the relevant steps of the method embodiment.

[0172] Alternatively, the computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device can perform the relevant steps of the above method embodiment.

[0173] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0174] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0175] The units involved in the embodiments described in this disclosure may be implemented by software or hardware.

[0176] It should be understood that various parts of the present disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0177] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A method for identifying parameters of a distributed electric propulsion system boost motor, characterized in that: include: Step 1: Get the stator current i of the boost motor d ,i q , stator voltage u d ,u q and electrical angular velocity ω e , wherein the stator current i d and i q According to the real-time sampling of the three-phase current of the boost motor and the coordinate transformation, the stator voltage u d and u q The electric angular velocity ω is obtained by outputting the current regulator according to the d-axis current difference and the q-axis current difference of the boost motor. e Obtained by sampling from the speed sensor; Step 2: Set the stator current i d and i q and the stator voltage u d and u q Input the improved Newton-Raphson optimizer to obtain the process noise matrix Q and measurement noise matrix R of the extended Kalman filter of the lift motor parameter identification model; Step 3: Set the stator current i d ,i q , stator voltage u d ,u q , electrical angular velocity ω e , process noise matrix Q and measurement noise matrix R are input into the extended Kalman filter to obtain the identification values ​​of the permanent magnet flux, stator resistance and stator inductance of the boost motor.

2. The method according to claim 1, characterized in that Before step 2, the method further includes: Construct the stator voltage equation of the boost motor in the dq coordinate system; The stator current equation of the boost motor is obtained based on the stator voltage equation; According to the stator current equation, the permanent magnet flux linkage identification equation, stator inductance identification equation and stator resistance identification equation of the boost motor are obtained; Based on the extended Kalman filter theory, the parameter identification model of the boost motor is obtained by combining the permanent magnet flux identification equation, the stator inductance identification equation and the stator resistance identification equation.

3. The method according to claim 1, characterized in that The step 2 specifically includes: Step 2.1, initialize the parameters and randomly generate the first population consisting of N individuals; Step 2.2, generating a second population including N individuals based on the first population and the dynamic reverse learning strategy; Step 2.3, merge the first population and the second population, and select the first N individuals in the merged population as the initial population based on the greedy strategy; Step 2.4, calculate the fitness value of each individual in the initial population; Step 2.5: For each individual, generate a new individual using the Newton-Raphson search rule and determine whether the random number generated in the current round is less than the first decision factor. If so, continue to use the new individual generated by the Newton-Raphson search rule to update the current individual. If not, generate a new individual based on the trap avoidance operation and update the current individual accordingly. Step 2.6: Determine whether the random number generated in the current round is less than the second decision factor. If so, use the composite exploration strategy to generate a new individual. If not, use the dynamic reverse learning strategy to generate a new individual. Step 2.7, by comparing the fitness values ​​of the new individual generated by the composite exploration strategy, the new individual generated based on the dynamic reverse learning strategy, and the current individual, decide whether to update the current individual; Step 2.8: Repeat steps 2.5 to 2.7 until all individuals in the initial population are traversed, ending the iteration process and adding one to the number of iterations. Step 2.9, determine whether the current number of iterations is greater than the preset number of iterations. If so, end the iteration and output the optimal value within the population as the process noise matrix Q and measurement noise matrix R of the extended Kalman filter. If not, return to step 2.

5.

4. The method according to claim 3, characterized in that The expression for judging whether the random number generated in the current round is less than the first decision factor is: Among them, NRSR + represents the new search direction specified by the Newton-Raphson rule, X iT represents the current individual in the current iteration number, represents the individual with the best fitness value in the current iteration number, X1 and X2 are two random individuals in the current population, and They represent three different scale extensions of the Newton-Raphson search rule, a, b, r5 and r6 represent random numbers between 0 and 1, δ represents the discount factor, η represents the position search weighting factor, DF represents the decision factor, which is a value between 0 and 1, iT and iT max Represents the current iteration number and the maximum iteration number, Represents a new individual calculated using the trap avoidance operator.

5. The method according to claim 4, characterized in that The expression of the composite exploration strategy is: Among them, C, ζ, γ and L are adjustable parameters. and Represent the exploration results of the Skyhawk optimization algorithm and the exponential distribution optimizer, r9, r 10 、r 11 、r 12 and r 13 are random numbers between 0 and 1, d is the dimension of individuals in the population, is the average value of all individuals in the population, represents a randomly selected individual in the population, and x, y, ω, f, Z1, Z2, D1, and D2 are auxiliary calculation factors.

6. The method according to claim 5, characterized in that The expression of the dynamic reverse learning strategy is: Among them, τ represents the weight factor, r7 and r8 are random numbers between 0 and 1, and u and l represent the upper and lower limits of the individual, respectively.

7. The method according to claim 6, characterized in that The expression of the fitness value is: F fitness =∑ i (y act (j)-y est (j)) 2 Among them, y act (j) and y est (j) represents the measured value and estimated value of the jth parameter, respectively.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for identifying parameters of a distributed electric propulsion system lift motor according to any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method for identifying parameters of a distributed electric propulsion system lift motor according to any one of claims 1 to 7.

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