CN116436354A - Permanent magnet synchronous motor control method based on improved whale algorithm and BP-PID - Google Patents

Permanent magnet synchronous motor control method based on improved whale algorithm and BP-PID Download PDF

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CN116436354A
CN116436354A CN202310384980.9A CN202310384980A CN116436354A CN 116436354 A CN116436354 A CN 116436354A CN 202310384980 A CN202310384980 A CN 202310384980A CN 116436354 A CN116436354 A CN 116436354A
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permanent magnet
magnet synchronous
synchronous motor
neural network
pid
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邱博之
李莹
唐嘉禄
张佰顺
华伟
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Xi'an Yellow River Electromechanical Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract

The disclosure relates to a permanent magnet synchronous motor control method based on an improved whale algorithm and BP-PID. The method comprises the following steps: establishing a permanent magnet synchronous motor control model of the electric automobile, and establishing a PID controller on the basis of the permanent magnet synchronous motor control model; establishing a BP neural network model and a setting model of a PID controller, and optimizing the BP neural network model by utilizing an improved whale algorithm to obtain an optimized BP neural network model; optimizing the setting model by utilizing the optimized BP neural network model to obtain the optimal PID control parameters; and controlling the permanent magnet synchronous motor control model through the optimal PID control parameters. The PID parameters are configured by using the BP neural network model, so that the problems that the algorithm robustness is poor and the application of the PID algorithm in occasions such as high speed, high precision and large disturbance is difficult to meet when the PID algorithm controls the PMSM motor of the electric automobile can be solved; in addition, the BP neural network model is optimized by utilizing the improved whale algorithm, and the convergence capacity of the BP neural network is improved.

Description

Permanent magnet synchronous motor control method based on improved whale algorithm and BP-PID
Technical Field
The disclosure relates to the technical field of direct current motors of new energy automobiles, in particular to a permanent magnet synchronous motor control method based on an improved whale algorithm and BP-PID.
Background
With the increasing world population and the increasingly demanding consumption of fossil energy, industry transformation due to environmental problems is necessary for the automotive industry. Meanwhile, due to the continuous development of intelligent control technology and the popularization of new energy concepts, new energy automobiles become mainstream goods in the future. Compared with the traditional automobile using fossil energy, the new energy automobile has no pollution in energy acquisition mode, is more intelligent in control mode, can realize power control by utilizing an integrated circuit and a special algorithm, can effectively avoid faults by a correct algorithm, and improves the driving safety. However, even in the field of new energy power with more advanced control modes, there are still some problems, most typically, a speed regulation mode, which directly affects the specific performance of the engine. The speed regulation is based on a control mode based on an algorithm, so that the problem is improved, and the speed regulation is essentially an optimization algorithm.
In the field of Permanent Magnet Synchronous Motor (PMSM) motor control of new energy automobiles at present, as the PMSM motor is a nonlinear and strong-coupling complex model, the problems of low rotating speed adjustment precision, low response speed, poor anti-interference capability and the like can occur in actual control, and the actual control requirement can not be met. The current mainstream control method is proportional, integral and derivative (proportion integration differentiation, PID) control, but the PID-based controller is a nonlinear model, which increases the computational cost in terms of parameters and subsequent modeling, so that there is a need in the art for an algorithm that can regulate PID parameters and can be traversed efficiently.
Accordingly, there is a need to provide a new solution to ameliorate one or more of the problems presented in the above solutions.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
It is an object of the present disclosure to provide a permanent magnet synchronous motor control method based on an improved whale algorithm and BP-PID, which overcomes, at least in part, one or more of the problems due to the limitations and disadvantages of the related art.
According to the embodiment of the disclosure, the permanent magnet synchronous motor control method based on the improved whale algorithm and BP-PID comprises the following steps:
establishing a permanent magnet synchronous motor control model of the electric automobile, and establishing a PID controller on the basis of the permanent magnet synchronous motor control model;
establishing a BP neural network model and a tuning model of a PID controller, wherein the tuning model comprises a PID control parameter K P 、K I And K D ；
Optimizing the BP neural network model by utilizing an improved whale algorithm to obtain the optimized BP neural network model;
optimizing the setting model by utilizing the optimized BP neural network model to obtain an optimal PID control parameter K P * 、K I * And K D * ；
And controlling the permanent magnet synchronous motor control model through the optimal PID control parameters.
In an embodiment of the disclosure, the step of establishing a BP neural network model and a tuning model of a PID controller includes:
establishing the BP neural network model; the BP neural network model consists of an input layer, two hidden layers and an output layer;
setting the number of nodes of an input layer of the BP neural network model to be equal to 3, wherein the input data of a first input node is a randomly initialized control parameter K P The input data of the second input node is randomly initialized control parameter K I The input data of the third input node is a randomly initialized control parameter K D ；
Setting the number of nodes of an output layer of the BP neural network model to be equal to 1, and outputting data which is the error of the permanent magnet synchronous motor control model;
the input data is transmitted from the input layer to the output layer, forward transmission is completed, errors are calculated, error signals are transmitted from the output layer along the original connection and returned to the output layer, reverse transmission is completed, and the input data of the input layer is updated;
repeating the previous step until the maximum iteration times of the BP neural network model are met, completing the training of the BP neural network model, and outputting PID control parameters K corresponding to the BP neural network model after training P 、K I And K D 。
In an embodiment of the disclosure, the step of optimizing the BP neural network model by using an improved whale algorithm to obtain the optimized BP neural network model includes:
determining an fitness function, wherein the fitness function of the whale algorithm is a steady-state error of the permanent magnet synchronous motor control model; wherein, the formula of the fitness function is:
Figure BDA0004173558790000031
in the formula e ss R (t) is a given input signal of the permanent magnet synchronous motor control model, and c (t) is an output signal of the permanent magnet synchronous motor control model;
initializing improved whale algorithm parameters: setting a population scale N, a maximum iteration number M, a logarithmic spiral shape constant coefficient b, a population individual coding length L and a current iteration number t;
initializing a population: randomly initializing N individuals p= [ x ] with code length 4 in search space 1 ，x 2 ，x 3 ，x 4 ]Wherein x is 1 ∈[1，20]Representing the hidden layer node number, x of the BP neural network model 2 ∈[0.01，0.2]Representing the learning rate, x of the BP neural network model 3 ∈[0.01，0.1]Minimum error, x, representing training target of the BP neural network model 4 ∈[0.05，0.09]Representing the initial weight variation of the BP neural network model.
In an embodiment of the disclosure, the step after the population initialization further includes:
calculating the fitness value of each individual X (t) by using the formula (1), and taking the individual with the minimum fitness value as the optimal individual X of the generation * (t) preserving X * (t); let G best ＝X * (t)；
If t is less than or equal to M, a, A and C are calculated according to the following calculation formula:
Figure BDA0004173558790000032
A＝2a·r 1 -a (3)；
C＝2r 2 (4)；
wherein: a is a convergence factor gradually linearly decreasing from 2 to 0 in the iterative process; a and C are coefficients; r is (r) 1 And r 2 Is [0,1]Random numbers in between.
In an embodiment of the disclosure, if t is less than or equal to M, the step of calculating a, C includes:
calculating l and p, wherein l is a random number between [ -1,1] and p is a random number between [0,1 ];
the adaptive inertia weight omega based on sine and cosine change is calculated, and the formula is as follows:
Figure BDA0004173558790000041
wherein: k is a constant coefficient;
the spatial position of each individual X (t) is updated separately using the following formula:
Figure BDA0004173558790000042
wherein: x is X * (t) represents the optimal individual for the t-th generation; x is X rand (t) represents an individual other than the optimal individual at the t-th generation; x (t+1) represents an individual whose spatial position is updated; the calculation formulas of D and D' are as follows:
D＝|C·X rand (t)-X(t)| (7)；
D′＝|X * (t)-X(t)| (8)；
wherein D represents the searching direction of the t generation, and D' represents the optimal searching direction of the t generation;
judging whether each dimension gene of X (t+1) exceeds the value range corresponding to the gene, if so, randomly generating a random number to replace the dimension gene in the value range corresponding to the gene;
calculating the fitness value of each individual X (t+1) by using the formula (1), and taking the individual with the smallest fitness value as the best individual X of the generation * (t+1) preserving X * (t+1)；
If X * (t+1) ratio G best ＝X * (t) is small, G best ＝X * (t+1)；
If t < M, t=t+1, then execution continues: if t is less than or equal to M, calculating a, A and C and subsequent steps;if t is greater than or equal to M, output G best ；G best The optimal BP neural network parameter is obtained.
In an embodiment of the disclosure, the optimizing the tuning model by using the optimized BP neural network model obtains an optimal PID control parameter K P * 、K I * And K D * The steps of (a) include:
setting the node number of the output layer of the optimized BP neural network model, wherein output data is the error of the permanent magnet synchronous motor control model;
the input data is transmitted from the input layer to the output layer, the forward transmission is completed, the error is calculated, the error signal is transmitted from the output layer along the original connection and returns to the output layer, the reverse transmission is completed, and the input data of the input layer is updated;
repeating the previous step until the maximum iteration times of the optimized BP neural network model are met, and outputting the optimal PID control parameter K corresponding to the optimized BP neural network model P * 、K I * And K D * 。
In an embodiment of the disclosure, the step of establishing a permanent magnet synchronous motor control model of the electric automobile includes:
the permanent magnet synchronous motor control model is a double closed-loop control model, the outer ring is closed-loop control of the rotor rotating speed, and the inner ring is closed-loop control of the stator in the sum of d and q axis current components.
In an embodiment of the disclosure, the permanent magnet synchronous motor control model is a dual closed-loop control model, the outer ring is closed-loop control of rotor rotation speed, and the inner ring is closed-loop control of the stator in d and q axis current components, and the steps include:
in the inner ring, ABC three-phase current is measured by a current sensor, and the actual current component i of the stator on the d and q axes is obtained after Clarke-Park conversion d And i q Then respectively with reference currents i of the stator in d and q axes dref =0 and i qref Compared with the reference number of the permanent magnet synchronous motor, the control of the current of the permanent magnet synchronous motor is realized through a current controller;
and (3) inputting the adjustment result of the reference currents of the stator on the d and q axes into a space vector pulse width modulation module for modulation after inverse Park conversion, and outputting 6 paths of pulse width modulation driving signals for controlling the driving voltage of the permanent magnet synchronous motor.
In an embodiment of the disclosure, the permanent magnet synchronous motor control model includes:
under the condition of no damping winding, the rotor of the permanent magnet synchronous motor ignores the influences of temperature, hysteresis loss and eddy current on the motor, and in a d-q coordinate system, the mathematical model of the voltage, the flux linkage and the torque of the permanent magnet synchronous motor is as follows:
permanent magnet synchronous motor voltage equation:
Figure BDA0004173558790000061
permanent magnet synchronous motor flux linkage equation:
Figure BDA0004173558790000062
torque equation of permanent magnet synchronous motor:
Figure BDA0004173558790000063
wherein u is d 、u q The voltage components of the permanent magnet synchronous motor stator on the d axis and the q axis are respectively; r is the resistance of the motor stator; i.e d 、i q The direct current components of the motor stator on d and q axes are respectively; psi phi type d 、ψ q Flux linkage components of two axes of the motor stator d and the motor stator q respectively; omega is the rotation speed of the motor rotor; l (L) d 、L q Inductance components of the motor on d and q axes respectively; psi phi type m Is the permanent magnet flux linkage of the motor; t (T) e Is motor torque; p (P) m Is the pole pair number of the motor.
In an embodiment of the disclosure, the step of controlling the permanent magnet synchronous motor control model through the optimal PID control parameter includes:
in the outer loop of the control model, the PID optimal control parameter K is adjusted P * 、K I * And K D * Controlling the rotating speed n of the motor;
Figure BDA0004173558790000064
e(h)＝n h -n ref (13)；
wherein n is ref Is given rotational speed, n h Represents the rotational speed error at the h moment, h E [0, k]E (h) represents a rotational speed error at the h-th time, e (k) represents a rotational speed error at the k-th time, and e (k-1) represents a rotational speed error at the k-1-th time; mu (mu) 1 (k) A control input amount indicating the rotation speed at the kth time;
in the inner ring of the permanent magnet synchronous motor control model, the actual current component i of the stator on the d axis is obtained after Clarke-Park conversion d ，i d With reference current i dref Difference of =0 to obtain current error by adjusting PID optimal control parameter K P * 、K I * And K D * Control i d ；
Figure BDA0004173558790000071
e 1 (h)＝(i d ) h -i dref (15)；
Wherein (i) d ) h Representing the actual current component of the stator on the d-axis at the h moment, e 1 (h) Representing the actual current component i at time h d With reference current i dref Current error of =0, e 1 (k) Representing the actual current component i at the kth time d With reference current i dref Current error of =0, e 1 (k-1) represents the actual current component i at the k-1 th time d With reference current i dref Current error of =0, μ 2 (k) A control input representing a current component of the stator on the d-axis at a kth time;
in the inner ring of the permanent magnet synchronous motor control model, the actual current component i of the stator on the q axis is obtained after Clarke-Park conversion q ，i q With reference current i qref Difference of =0 to obtain current error by adjusting PID optimal control parameter K P 、K I And K D Control i q ；
Figure BDA0004173558790000072
e 2 (h)＝(i q ) h -i qref (17)；
Wherein (i) q ) h Representing the actual current component at time h, e 2 (h) Representing the actual current component i at time h q With reference current i qref Current error of =0, e 1 (k) Representing the actual current component i at the kth time d With reference current i qref Current error of =0, e 2 (k-1) represents the k-1 time i q With reference current i qref Current error of =0, μ 3 (k) The control input of the current component of the stator on the q-axis at the kth time is represented.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
in one embodiment of the disclosure, through the method, on one hand, the PID parameters are configured by using the BP neural network model, so that the problems that the algorithm robustness is poor and the application of the PID algorithm in occasions such as high speed, high precision and large disturbance is difficult to meet when the PID algorithm controls the PMSM motor of the electric automobile can be solved; on the other hand, the BP neural network model is optimized by utilizing the improved whale algorithm, and the convergence capacity of the BP neural network is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 schematically illustrates a flow chart of steps of a method of controlling a permanent magnet synchronous motor based on a modified whale algorithm and BP-PID in an exemplary embodiment of the disclosure;
FIG. 2 schematically illustrates a control model block diagram based on the modified whale algorithm and BP-PID in an exemplary embodiment of the present disclosure;
FIG. 3 schematically illustrates a block diagram of an electric vehicle PMSM control system in an exemplary embodiment of the present disclosure;
FIG. 4 schematically illustrates a motor speed change step graph of an electric vehicle PMSM control system output with a BP-PID controller in an exemplary embodiment of the present disclosure;
FIG. 5 schematically illustrates a motor speed change step graph for controlling the output of an electric vehicle PMSM control system using a modified whale algorithm and a BP-PID controller in an exemplary embodiment of the present disclosure;
FIG. 6 schematically illustrates a graph of a parameter tuning process for parameter tuning a PID controller without using a BP neural network model in an exemplary embodiment of the disclosure;
fig. 7 schematically illustrates a graph of a parameter tuning process for parameter tuning a PID controller using a BP neural network model in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In the present exemplary embodiment, a permanent magnet synchronous motor control method based on improved whale algorithm and BP-PID is provided first. Referring to what is shown in fig. 1, the method may include: step S101 to step S105.
Step S101: and establishing a permanent magnet synchronous motor control model of the electric automobile, and establishing a PID controller on the basis of the permanent magnet synchronous motor control model.
Step S102: establishing a BP neural network model and a tuning model of a PID controller, wherein the tuning model comprises a PID control parameter K P 、K I And K D 。
Step S103: and optimizing the BP neural network model by using an improved whale algorithm to obtain the optimized BP neural network model.
Step S104: optimizing the setting model by utilizing the optimized BP neural network model to obtain an optimal PID control parameter K P * 、K I * And K D * 。
Step S105: and controlling the permanent magnet synchronous motor control model through the optimal PID control parameters.
By the method, on one hand, the PID parameters are configured by using the BP neural network model, so that the problems that the algorithm robustness is poor and the application of the PID algorithm in occasions with high speed, high precision, large disturbance and the like is difficult to be satisfied when the PMSM motor of the electric automobile is controlled by the PID algorithm can be solved; on the other hand, the BP neural network model is optimized by utilizing the improved whale algorithm, and the convergence capacity of the BP neural network is improved.
Next, the respective steps of the above-described method in the present exemplary embodiment will be described in more detail with reference to fig. 1.
In step S101, a permanent magnet synchronous motor control model of the electric vehicle is built, and a PID controller is built based on the permanent magnet synchronous motor control model.
Specifically, a specific process of establishing the permanent magnet synchronous motor control model of the electric vehicle will be described in the following embodiments, which are not described herein.
In one embodiment, the permanent magnet synchronous motor control model is a double closed-loop control model, the outer ring is closed-loop control of the rotating speed of the rotor, and the inner ring is closed-loop control of the sum of current components of the stator on d and q axes.
In one embodiment, in the inner ring, ABC three-phase current is measured by a current sensor, and the actual current component i of the stator on the d and q axes is obtained after Clarke-Park conversion d And i q Then respectively with reference currents i of the stator in d and q axes dref =0 and i qref Compared with the reference number of the permanent magnet synchronous motor, the control of the current of the permanent magnet synchronous motor is realized through a current controller;
and (3) inputting the adjustment result of the reference currents of the stator on the d and q axes into a space vector pulse width modulation module for modulation after inverse Park conversion, and outputting 6 paths of pulse width modulation driving signals for controlling the driving voltage of the permanent magnet synchronous motor.
In step S102, a BP neural network model and a tuning model of a PID controller are established, wherein the tuning model comprises a PID control parameter K P 、K I And K D . Specifically, in the process of optimizing and configuring PID control parameters in a setting model by utilizing a BP neural network model, the BP neural network model is built first, then the BP neural network model is subjected to iterative training to obtain a trained BP neural network model, and the PID control parameters corresponding to the trained BP neural network model are output. The PID control parameters respectively comprise K P 、K I And K D . Furthermore, PID control parameters are configured by using the BP neural network model, so that the robustness of a PID controller can be effectively improved, and the control performance of the PMSM motor of the electric automobile is improved.
In one embodiment, the BP neural network model is established; the BP neural network model consists of an input layer, two hidden layers and an output layer;
setting the number of nodes of an input layer of the BP neural network model to be equal to 3, wherein the input data of a first input node is a randomly initialized control parameter K P The input data of the second input node is randomly initialized control parameter K I The input data of the third input node is a control parameter which is randomly initializedNumber K D ；
Setting the number of nodes of an output layer of the BP neural network model to be equal to 1, and outputting data which is the error of the permanent magnet synchronous motor control model;
the input data is transmitted from the input layer to the output layer, forward transmission is completed, errors are calculated, error signals are transmitted from the output layer along the original connection and returned to the output layer, reverse transmission is completed, and the input data of the input layer is updated;
repeating the previous step until the maximum iteration times of the BP neural network model are met, completing the training of the BP neural network model, and outputting PID control parameters K corresponding to the BP neural network model after training P 、K I And K D 。
In step S103, the BP neural network model is optimized by using the improved whale algorithm, and the BP neural network model after optimization is obtained.
Specifically, an improved whale algorithm (Whale Optimization Algorithm, WOA) is adopted to set the hidden layer node number, the learning rate, the minimum training target error and the initial weight variable quantity of the BP neural network model, so that the convergence capacity of the BP neural network model is improved, and the control performance of the control algorithm on the DC motor of the new energy automobile is indirectly improved. The specific process of optimizing the BP neural network model using the improved whale algorithm is described in the following examples, and is not repeated here.
In one embodiment, the step of optimizing the BP neural network model using the modified whale algorithm to obtain the optimized BP neural network model includes:
determining an fitness function, wherein the fitness function of the whale algorithm is a steady-state error of the permanent magnet synchronous motor control model; wherein, the formula of the fitness function is:
Figure BDA0004173558790000111
in the formula e ss For the steady state error of the permanent magnet synchronous motor control model, r (t) isA given input signal of the permanent magnet synchronous motor control model, c (t) is an output signal of the permanent magnet synchronous motor control model;
initializing improved whale algorithm parameters: setting a population scale N, a maximum iteration number M, a logarithmic spiral shape constant coefficient b, a population individual coding length L and a current iteration number t;
initializing a population: randomly initializing N individuals p= [ x ] with code length 4 in search space 1 ，x 2 ，x 3 ，x 4 ]Wherein x is 1 ∈[1，20]Representing the hidden layer node number, x of the BP neural network model 2 ∈[0.01，0.2]Representing the learning rate, x of the BP neural network model 3 ∈[0.01，0.1]Minimum error, x, representing training target of the BP neural network model 4 ∈[0.05，0.09]Representing the initial weight variation of the BP neural network model.
In one embodiment, the step after the population initialization further comprises:
calculating the fitness value of each individual X (t) by using the formula (1), and taking the individual with the minimum fitness value as the optimal individual X of the generation * (t) preserving X * (t); let G best ＝X * (t)；
If t is less than or equal to M, a, A and C are calculated according to the following calculation formula:
Figure BDA0004173558790000112
A＝2a·r 1 -a (3)；
C＝2r 2 (4)；
wherein: a is a convergence factor gradually linearly decreasing from 2 to 0 in the iterative process; a and C are coefficients; r is (r) 1 And r 2 Is [0,1]Random numbers in between.
In one embodiment, if t is less than or equal to M, the step of calculating a, A, C comprises:
calculating l and p, wherein l is a random number between [ -1,1] and p is a random number between [0,1 ];
the adaptive inertia weight omega based on sine and cosine change is calculated, and the formula is as follows:
Figure BDA0004173558790000121
wherein: k is a constant coefficient;
the spatial position of each individual X (t) is updated separately using the following formula:
Figure BDA0004173558790000122
wherein: x is X * (t) represents the optimal individual for the t-th generation; x is X rand (t) represents an individual other than the optimal individual at the t-th generation; x (t+1) represents an individual whose spatial position is updated; the calculation formulas of D and D' are as follows:
D＝|C·X rand (t)-X(t)| (7)；
D′＝|X * (t)-X(t)| (8)；
wherein D represents the searching direction of the t generation, and D' represents the optimal searching direction of the t generation;
judging whether each dimension gene of X (t+1) exceeds the value range corresponding to the gene, if so, randomly generating a random number to replace the dimension gene in the value range corresponding to the gene;
calculating the fitness value of each individual X (t+1) by using the formula (1), and taking the individual with the smallest fitness value as the best individual X of the generation * (t+1) preserving X * (t+1)；
If X * (t+1) ratio G best ＝X * (t) is small, G best ＝X * (t+1)；
If t < M, t=t+1, then execution continues: if t is less than or equal to M, calculating a, A and C and subsequent steps; if t is greater than or equal to M, output G best ；G best The optimal BP neural network parameter is obtained.
Specifically, a self-adaptive inertia weight strategy based on sine and cosine changes is introduced into a traditional whale optimization algorithm, so that the global searching capability of the whale algorithm is improved. Optimizing the BP neural network model by utilizing an improved whale algorithm, namely optimizing parameters of the BP neural network model to obtain optimal BP neural network parameters, namely obtaining the optimized BP neural network model. The BP neural network parameters comprise hidden layer node number, learning rate, training target minimum error, initial weight change amount and the like.
In step S104, the tuning model is optimized by using the optimized BP neural network model to obtain an optimal PID control parameter K P * 、K I * And K D * 。
Specifically, the optimized BP neural network model is adopted to optimize the correction model, so that the optimal PID control parameters can be obtained, and the permanent magnet synchronous motor can be controlled through the optimal control parameters. Wherein the optimal PID control parameters comprise optimal control parameters K p * Optimum control parameter K I * And an optimal control parameter K D * . The process of optimizing the correction model by using the optimized BP neural network model is described in the following embodiments, which will not be described herein.
In one embodiment, setting the optimized output layer node number of the BP neural network model, wherein the output data is the error of the permanent magnet synchronous motor control model;
the input data is transmitted from the input layer to the output layer, the forward transmission is completed, the error is calculated, the error signal is transmitted from the output layer along the original connection and returns to the output layer, the reverse transmission is completed, and the input data of the input layer is updated;
repeating the previous step until the maximum iteration times of the optimized BP neural network model are met, and outputting the optimal PID control parameter K corresponding to the optimized BP neural network model P * 、K I * And K D * 。
In one embodiment, the permanent magnet synchronous motor control model includes:
under the condition of no damping winding, the rotor of the permanent magnet synchronous motor ignores the influences of temperature, hysteresis loss and eddy current on the motor, and in a d-q coordinate system, the mathematical model of the voltage, the flux linkage and the torque of the permanent magnet synchronous motor is as follows:
permanent magnet synchronous motor voltage equation:
Figure BDA0004173558790000131
permanent magnet synchronous motor flux linkage equation:
Figure BDA0004173558790000132
torque equation of permanent magnet synchronous motor:
Figure BDA0004173558790000133
wherein u is d 、u q The voltage components of the permanent magnet synchronous motor stator on the d axis and the q axis are respectively; r is the resistance of the motor stator; i.e d 、i q The direct current components of the motor stator on d and q axes are respectively; psi phi type d 、ψ q Flux linkage components of two axes of the motor stator d and the motor stator q respectively; omega is the rotation speed of the motor rotor; l (L) d 、L q Inductance components of the motor on d and q axes respectively; psi phi type m Is the permanent magnet flux linkage of the motor; t (T) e Is motor torque; p (P) m Is the pole pair number of the motor.
In step S105, the permanent magnet synchronous motor control model is controlled by the optimal PID control parameters.
Specifically, the process of controlling the permanent magnet synchronous motor control model by the optimal PID control parameters is described in the following embodiments, which are not repeated here.
In one embodiment, the step of controlling the permanent magnet synchronous motor control model by the optimal PID control parameters includes:
in the outer loop of the control model, the PID optimal control parameter K is adjusted P * 、K I * And K D * Controlling the rotating speed n of the motor;
Figure BDA0004173558790000141
e(h)＝n h -n ref (13)；
wherein n is ref Is given rotational speed, n h Represents the rotational speed error at the h moment, h E [0, k]E (h) represents a rotational speed error at the h-th time, e (k) represents a rotational speed error at the k-th time, and e (k-1) represents a rotational speed error at the k-1-th time; μx (k) represents a control input amount of the rotation speed at the kth time;
in the inner ring of the permanent magnet synchronous motor control model, the actual current component i of the stator on the d axis is obtained after Clarke-Park conversion d ，i d With reference current i dref Difference of =0 to obtain current error by adjusting PID optimal control parameter K P * 、K I * And K D * Control i d ；
Figure BDA0004173558790000142
e 1 (h)＝(i d ) h -i dref (15)；
Wherein (i) d ) h Representing the actual current component of the stator on the d-axis at the h moment, e 1 (h) Representing the actual current component i at time h d With reference current i dref Current error of =0, e 1 (k) Representing the actual current component i at the kth time d With reference current i dref Current error of =0, e 1 (k-1) represents the actual current component i at the k-1 th time d With reference current i dref Current error of =0, μ 2 (k) A control input representing a current component of the stator on the d-axis at a kth time;
in the inner ring of the permanent magnet synchronous motor control model, the actual current component i of the stator on the q axis is obtained after Clarke-Park conversion q ，i q With reference current i qref Difference of =0 to obtain current error by adjusting PID optimal control parameter K P * 、K I * And K D * Control i q ；
Figure BDA0004173558790000151
e 2 (h)＝(i q ) h -i qref (17)；
Wherein (i) q ) h Representing the actual current component at time h, e 2 (h) Representing the actual current component i at time h q With reference current i qref Current error of =0, e 1 (k) Representing the actual current component i at the kth time d With reference current i qref Current error of =0, e 2 (k-1) represents the k-1 time i q With reference current i qref Current error of =0, μ 3 (k) The control input of the current component of the stator on the q-axis at the kth time is represented.
By the method, on one hand, the PID parameters are configured by using the BP neural network model, so that the problems that the algorithm robustness is poor and the application of the PID algorithm in occasions with high speed, high precision, large disturbance and the like is difficult to be satisfied when the PMSM motor of the electric automobile is controlled by the PID algorithm can be solved; on the other hand, the BP neural network model is optimized by utilizing the improved whale algorithm, and the convergence capacity of the BP neural network is improved.
The invention is further illustrated by example 1.
Example 1
Based on the simulation of the invention, the existing model is compared, and the experimental result and the rest model result of the invention are as follows:
as can be seen from fig. 4 and 5, the control method of the permanent magnet synchronous motor of the electric automobile based on the improved whale algorithm and BP-PID provided by the invention has stronger capability of tracking given input signals and better control performance compared with the traditional BP-PID controller.
The overshoot can effectively show the convergence accuracy of the control method, and the overshoot sigma calculation formula is as follows:
σ＝[y max -y(∞)]/y(∞)×100％ (18)；
in which y max For the instantaneous maximum deviation value, y (+) is the steady state value, overshoot is shown in FIGS. 4 and 5.
The invention realizes the regulation and control of the speed of a new energy automobile through setting three control parameters of PID, and the PID parameter setting process of the permanent magnet synchronous motor control method based on the improved whale algorithm and BP-PID and the traditional BP-PID controller is shown in figures 6 and 7.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims (10)

1. A permanent magnet synchronous motor control method based on an improved whale algorithm and BP-PID is characterized by comprising the following steps:
establishing a permanent magnet synchronous motor control model of the electric automobile, and establishing a PID controller on the basis of the permanent magnet synchronous motor control model;
establishing a BP neural network model and a tuning model of a PID controller, wherein the tuning model comprises a PID control parameter K P 、K I And K D ；
Optimizing the BP neural network model by utilizing an improved whale algorithm to obtain the optimized BP neural network model;
after the utilization and optimizationThe BP neural network model of the (B) is optimized to the tuning model to obtain the optimal PID control parameter K P * 、K I * And K D * ；
And controlling the permanent magnet synchronous motor control model through the optimal PID control parameters.
2. The method for controlling a permanent magnet synchronous motor based on the improved whale algorithm and the BP-PID according to claim 1, wherein the step of establishing the BP neural network model and the tuning model of the PID controller comprises the steps of:
establishing the BP neural network model; the BP neural network model consists of an input layer, two hidden layers and an output layer;
setting the number of nodes of an input layer of the BP neural network model to be equal to 3, wherein the input data of a first input node is a randomly initialized control parameter K P The input data of the second input node is randomly initialized control parameter K I The input data of the third input node is a randomly initialized control parameter K D ；
Setting the number of nodes of an output layer of the BP neural network model to be equal to 1, and outputting data which is the error of the permanent magnet synchronous motor control model;
the input data is transmitted from the input layer to the output layer, forward transmission is completed, errors are calculated, error signals are transmitted from the output layer along the original connection and returned to the output layer, reverse transmission is completed, and the input data of the input layer is updated;
repeating the previous step until the maximum iteration times of the BP neural network model are met, completing the training of the BP neural network model, and outputting PID control parameters K corresponding to the BP neural network model after training P 、K I And K D 。
3. The method for controlling a permanent magnet synchronous motor based on an improved whale algorithm and BP-PID according to claim 2, wherein the step of optimizing the BP neural network model using the improved whale algorithm to obtain the optimized BP neural network model comprises:
determining an fitness function, wherein the fitness function of the whale algorithm is a steady-state error of the permanent magnet synchronous motor control model; wherein, the formula of the fitness function is:
Figure FDA0004173558770000021
in the formula e ss R (t) is a given input signal of the permanent magnet synchronous motor control model, and c (t) is an output signal of the permanent magnet synchronous motor control model;
initializing improved whale algorithm parameters: setting a population scale N, a maximum iteration number M, a logarithmic spiral shape constant coefficient b, a population individual coding length L and a current iteration number;
initializing a population: randomly initializing N individuals with coding length 4 in search space, = [ x ] 1 ，x 2 ，x 3 ，x 4 ]Wherein x is 1 ∈[1，20]Representing the hidden layer node number, x of the BP neural network model 2 ∈[0.01，0.2]Representing the learning rate, x of the BP neural network model 3 ∈[0.01，0.1]Minimum error, x, representing training target of the BP neural network model 4 ∈[0.05，0.09]Representing the initial weight variation of the BP neural network model.
4. The method for controlling a permanent magnet synchronous motor based on the improved whale algorithm and BP-PID according to claim 3, wherein the step after population initialization further comprises:
calculating the fitness value of each individual X (t) by using the formula (1), and taking the individual with the minimum fitness value as the optimal individual X of the generation * (t) preserving X * (r); let G best ＝X * (t)；
If t is less than or equal to M, a, A and C are calculated according to the following calculation formula:
Figure FDA0004173558770000022
A＝2a·r 1 -a (3)；
C＝2r 2 (4)；
wherein: a is a convergence factor gradually linearly decreasing from 2 to 0 in the iterative process; a and C are coefficients; r is (r) 1 And r 2 Is [0,1]Random numbers in between.
5. The method for controlling a permanent magnet synchronous motor based on the improved whale algorithm and BP-PID according to claim 4, wherein said step of calculating a, A, C if t.ltoreq.M, comprises, after:
calculating l and p, wherein l is a random number between [ -1,1] and p is a random number between [0,1 ];
the adaptive inertia weight omega based on sine and cosine change is calculated, and the formula is as follows:
Figure FDA0004173558770000031
wherein: k is a constant coefficient;
the spatial position of each individual X (t) is updated separately using the following formula:
Figure FDA0004173558770000032
wherein: x is X * (t) represents the optimal individual for the t-th generation; x is X and (t) represents an individual other than the optimal individual at the t-th generation; x (t+1) represents an individual whose spatial position is updated; the calculation formulas of D and D' are as follows:
D＝|C·Xr a n d (t)-X(t)| (7)；
D′＝|X * (t)-X(t)| (8)；
wherein D represents the searching direction of the t generation, and D' represents the optimal searching direction of the t generation;
judging whether each dimension gene of X (t+1) exceeds the value range corresponding to the gene, if so, randomly generating a random number to replace the dimension gene in the value range corresponding to the gene;
calculating the fitness value of each individual X (t+1) by using the formula (1), and taking the individual with the smallest fitness value as the best individual X of the generation * (t+1) preserving X * (t+1)；
If X * (t+1) ratio G best ＝X * (t) is small, G best ＝X * (t+1)；
If t < M, t=t+1, then execution continues: if t is less than or equal to M, calculating a, A and C and subsequent steps; if t is greater than or equal to M, output G best ；G best The optimal BP neural network parameter is obtained.
6. The method for controlling a permanent magnet synchronous motor based on the improved whale algorithm and BP-PID as claimed in claim 5, wherein said optimizing said tuning model using an optimized BP neural network model results in an optimal PID control parameter K P * 、K I * And K D * The steps of (a) include:
setting the node number of the output layer of the optimized BP neural network model, wherein output data is the error of the permanent magnet synchronous motor control model;
the input data is transmitted from the input layer to the output layer, the forward transmission is completed, the error is calculated, the error signal is transmitted from the output layer along the original connection and returns to the output layer, the reverse transmission is completed, and the input data of the input layer is updated;
repeating the previous step until the maximum iteration times of the optimized BP neural network model are met, and outputting the optimal PID control parameter K corresponding to the optimized BP neural network model P * 、K I * And K D * 。
7. The method for controlling a permanent magnet synchronous motor based on the improved whale algorithm and BP-PID according to claim 6, wherein the step of establishing a permanent magnet synchronous motor control model of an electric vehicle comprises:
the permanent magnet synchronous motor control model is a double closed-loop control model, the outer ring is closed-loop control of the rotor rotating speed, and the inner ring is closed-loop control of the stator in the sum of d and q axis current components.
8. The method for controlling a permanent magnet synchronous motor based on the improved whale algorithm and BP-PID according to claim 7, wherein the permanent magnet synchronous motor control model is a double closed-loop control model, the outer ring is a closed-loop control of the rotor speed, and the inner ring is a closed-loop control of the stator in the d, q-axis current components and includes:
in the inner ring, ABC three-phase current is measured by a current sensor, and the actual current component i of the stator on the d and q axes is obtained after Clarke-Park conversion d And i q Then respectively with reference currents i of the stator in d and q axes dref =0 and i qref Compared with the reference number of the permanent magnet synchronous motor, the control of the current of the permanent magnet synchronous motor is realized through a current controller;
and (3) inputting the adjustment result of the reference currents of the stator on the d and q axes into a space vector pulse width modulation module for modulation after inverse Park conversion, and outputting 6 paths of pulse width modulation driving signals for controlling the driving voltage of the permanent magnet synchronous motor.
9. The method for controlling a permanent magnet synchronous motor based on the improved whale algorithm and BP-PID according to claim 8, wherein the permanent magnet synchronous motor control model comprises:
under the condition of no damping winding, the rotor of the permanent magnet synchronous motor ignores the influences of temperature, hysteresis loss and eddy current on the motor, and in a d-q coordinate system, the mathematical model of the voltage, the flux linkage and the torque of the permanent magnet synchronous motor is as follows:
permanent magnet synchronous motor voltage equation:
Figure FDA0004173558770000051
permanent magnet synchronous motor flux linkage equation:
Figure FDA0004173558770000052
torque equation of permanent magnet synchronous motor:
Figure FDA0004173558770000053
wherein u is d 、u q The voltage components of the permanent magnet synchronous motor stator on the d axis and the q axis are respectively; r is the resistance of the motor stator; i.e d 、i q The direct current components of the motor stator on d and q axes are respectively; psi phi type d 、ψ q Flux linkage components of two axes of the motor stator d and the motor stator q respectively; omega is the rotation speed of the motor rotor; l (L) d 、L q Inductance components of the motor on d and q axes respectively; psi phi type m Is the permanent magnet flux linkage of the motor; t (T) e Is motor torque; p (P) m Is the pole pair number of the motor.
10. The method for controlling a permanent magnet synchronous motor based on the improved whale algorithm and BP-PID according to claim 9, wherein the step of controlling the permanent magnet synchronous motor control model by the optimal PID control parameters comprises:
in the outer loop of the control model, the PID optimal control parameter K is adjusted P * 、K I * And K D * Controlling the rotating speed n of the motor;
Figure FDA0004173558770000054
e(h)＝n h -n ref (13)；
wherein n is ref Is given rotational speed, n h Represents the rotational speed error at the h moment, h E [0, k]E (h) represents a rotational speed error at the h-th time, e (k) represents a rotational speed error at the k-th time, and e (k-1) represents a rotational speed error at the k-1-th time; mu (mu) 1 (k) A control input amount indicating the rotation speed at the kth time;
in the inner ring of the permanent magnet synchronous motor control model, the actual current component i of the stator on the d axis is obtained after Clarke-Park conversion d ，i d With reference current i dref Difference of =0 to obtain current error by adjusting PID optimal control parameter K P * 、K I * And K D * Control i d ；
Figure FDA0004173558770000061
e 1 (h)＝(i d ) h -i dref (15)；
Wherein (i) d ) h Representing the actual current component of the stator on the d-axis at the h moment, e 1 (h) Representing the actual current component i at time h d With reference current i dref Current error of =0, e 1 (k) Representing the actual current component i at the kth time d With reference current i dref Current error of =0, e 1 (k-1) represents the actual current component i at the k-1 th time d With reference current i dref Current error of =0, μ 2 (k) A control input representing a current component of the stator on the d-axis at a kth time;
in the inner ring of the permanent magnet synchronous motor control model, the actual current component i of the stator on the q axis is obtained after Clarke-Park conversion d ，i d With reference current i qref Difference of =0 to obtain current error by adjusting PID optimal control parameter K P * 、K I * And K D * Control i q ；
Figure FDA0004173558770000062
e 2 (h)＝(i q ) h -i qref (17)；
Wherein (i) q ) h Representing the actual current component at time h, e 2 (h) Representing the actual current component i at time h q With reference current i qref Current error of =0, e 1 (k) Representing the actual current component i at the kth time d With reference current i qref Current error of =0, e 2 (k-1) represents the k-1 time i q With reference current i qref Current error of =0, μ 3 (k) The control input of the current component of the stator on the q-axis at the kth time is represented.
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