CN116232132A - Rotating speed control method of brushless direct current motor of electric automobile - Google Patents

Rotating speed control method of brushless direct current motor of electric automobile Download PDF

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CN116232132A
CN116232132A CN202310316551.8A CN202310316551A CN116232132A CN 116232132 A CN116232132 A CN 116232132A CN 202310316551 A CN202310316551 A CN 202310316551A CN 116232132 A CN116232132 A CN 116232132A
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fuzzy
pid
rule
rotating speed
control
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吕红明
郭大鹏
张瑞
秦彦彬
朱凯斌
丁福生
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Yancheng Institute of Technology
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Yancheng Institute of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/08—Arrangements for controlling the speed or torque of a single motor
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/0004—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/0004—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P23/0013—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using fuzzy control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/0004—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P23/0018—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using neural networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
    • H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
    • H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters
    • H02P27/08—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters with pulse width modulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/08—Arrangements for controlling the speed or torque of a single motor
    • H02P6/085—Arrangements for controlling the speed or torque of a single motor in a bridge configuration
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/34—Modelling or simulation for control purposes

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Fuzzy Systems (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Control Of Motors That Do Not Use Commutators (AREA)

Abstract

The invention relates to the technical field of brushless direct current motors, in particular to a rotating speed control method of an electric vehicle brushless direct current motor based on a self-adaptive neural network fuzzy inference system-improved genetic algorithm (ANFIS-IGA). The control method used by the method can overcome the defects that the response rate of the traditional PID control is not ideal enough, the initial parameters of the fuzzy PID controller and the like are seriously dependent on expert setting, and the method can carry out collaborative optimization on the initial parameters of the fuzzy PID and all fuzzy membership functions, and has better dynamic characteristics, control precision and robustness.

Description

Rotating speed control method of brushless direct current motor of electric automobile
Technical Field
The invention relates to the technical field of brushless direct current motors, in particular to a rotating speed control method of an electric vehicle brushless direct current motor based on a self-adaptive neural network fuzzy inference system-improved genetic algorithm (ANFIS-IGA).
Background
As the amount of maintenance of global automobiles increases rapidly, the demand for petroleum becomes larger and larger, and the problem of energy shortage has become a focus of global attention. The global petroleum resources are extremely deficient, the petroleum in China is seriously dependent on import, and the problem of energy shortage is more serious. In the face of such severe energy shortage situation, electric automobile technology is developed, and the gradual replacement of fuel automobiles with electric automobiles is an effective way to solve the global energy crisis and environmental pollution. The control performance of the motor driving system is related to the safety and reliability of the whole vehicle. In the driving process, road conditions are complex and changeable, and the automobile can frequently accelerate and decelerate, climb, brake and the like, so that the requirements of different rotating speeds can be satisfied sufficiently, and the automobile has good dynamic-static response capability.
In a driving system of an electric vehicle, a permanent magnet synchronous motor and a brushless dc motor are often used. The brushless DC motor has the advantages of simple maintenance, long service life, reliable operation and no reversing spark. In addition, the electric motor has the advantages of high efficiency, large starting torque, good control performance, stable operation, wide speed regulation range, good low-speed performance and the like, and is widely applied to electric automobiles.
When the electric automobile runs under different road conditions, the parameters of the electric automobile are always changed. Therefore, the controller of the electric automobile plays a vital role. At present, for a fuzzy PID controller of a brushless direct current motor, an online adjustment of initial parameters of the fuzzy PID is realized by adopting a fixed domain mode. The membership function and the fuzzy rule of the fuzzy logic controller are automatically generated and optimized by adopting summarized expert experience, so that the number of the membership function and the fuzzy rule is also a fixed value, and the adaptivity of the fuzzy logic controller cannot be better reflected. And the fuzzy rule of the membership function is required to be optimized, so that the complexity of operation is increased.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a brushless direct current motor fuzzy control method based on a self-adaptive neural network fuzzy inference system-improved genetic algorithm, which can improve the stability and anti-interference performance of the rotation speed control of the brushless direct current motor.
In order to achieve the above purpose, the technical scheme provided by the invention comprises the following steps:
s1, constructing a brushless direct current motor model and a fuzzy PID controller;
s2, building a motor speed regulation system of the electric automobile based on the brushless direct current motor and the fuzzy PID controller;
s3, optimizing fuzzy rules of the fuzzy controller by using an improved genetic algorithm, and realizing real-time adjustment of PID parameters by adopting a method for setting PID by using a self-adaptive fuzzy reasoning system;
s4, outputting a control variable by using the optimized fuzzy PID regulator, and optimally controlling the rotating speed of the motor;
further, the specific steps of the step S1 are as follows:
s1.1, constructing a simulation model of a brushless direct current motor based on a MATLAB simulation platform according to a mathematical model of the brushless direct current motor;
s1.2, constructing a classical fuzzy PID controller by combining a relevant principle of fuzzy control, and setting an initial value of PID parameters and a fuzzy rule according to expert experience;
further, the specific steps of the step S2 are as follows:
s2.1, a double closed-loop control system of the brushless direct current motor is built on the basis of an MATLAB simulation platform, a fuzzy PID controller is adopted by a rotating speed loop, and a classical PI controller is adopted by a current loop;
s2.2, the fuzzy controller adopts a two-input and three-output Mamdani controller, the input is a rotating speed error e and an error change rate ec, and the output is PID gain change delta K p 、ΔK i And DeltaK d ；
S2.3 PID gain variation ΔK p 、ΔK i And DeltaK d Combining the calculation to obtain a control signal U PID Is that
Figure BDA0004150269030000021
K p1 ＝ΔK p ×K p ,K i1 ＝ΔK i ×K i ,K d1 ＝ΔK d ×K d
Wherein: k (K) p Is a proportional gain; k (K) i Is an integral coefficient; k (K) d Is a differential coefficient; e is the regulator bias input signal;
s2.4, the control process of the brushless direct current motor speed regulating system of the electric automobile based on the fuzzy PID controller is as follows: comparing the actual rotating speed of the motor with a set target rotating speed, sampling to obtain a sampling error e, taking the sampling error e and the error change rate ec as input quantity of a fuzzy controller, mapping the input numerical quantity into a fuzzy quantity through a scale factor and a membership function, obtaining a corresponding output fuzzy quantity according to a fuzzy rule base, and obtaining an output numerical increment delta K through definition p 、ΔK i And DeltaK d In actual operation, the fuzzy controller performs on-line adjustment on the PID parameters once until the stable state is reached.
Further, the specific steps of the step S3 are as follows:
s3.1, the genetic algorithm provides an effective method for searching a large and complex solution space, which is close to an optimal solution and avoids local minima, based on the evolution theory, so that the method is widely applied to the parameter optimization of the fuzzy controller. Aiming at the problem that the fuzzy control rule of the fuzzy PID controller is too dependent on expert experience, an improved genetic optimization algorithm is adopted to iterate the fuzzy rule of the fuzzy controller on line until the optimal fuzzy rule is obtained;
the specific steps of optimizing fuzzy rules by the improved genetic algorithm in the step S3.1 are as follows:
s3.1-1, initializing a population, wherein the fuzzy control rule is used as a gene, the gene is encoded by adopting a binary method, and the number of bits of the gene is set to be 20; taking vectors obtained by arranging the genes as chromosomes to represent the optimized result of a group of fuzzy control rules; the number of individuals of the initial population is set to 100 and the corresponding matrix is randomly generated.
S3.1-2, calculating an adaptability value, wherein in the process of optimizing fuzzy rules, the adaptability function is closely related to an objective function of the optimization design. According to the model for optimizing the fuzzy control rule, the objective function is an optimization problem of minimum values, so that the objective function needs to be converted to meet the requirement of the fitness function. The mapping relation between the adaptive function F (x) and the fuzzy rule optimization function F (x) can be obtained according to the mapping relation:
Figure BDA0004150269030000031
wherein C is max There are various selection methods, and penalty function methods are employed in the present invention.
S3.1-3, improving a crossover operator and a mutation operator, and in order to reduce the probability of damage to good genes due to crossover mutation, introducing new genes when the genes fall into a local optimal solution, the invention provides a dynamic adjustment formula of crossover probability and mutation probability:
Figure BDA0004150269030000032
Figure BDA0004150269030000041
wherein f max Is the maximum fitness value in the contemporary population; f (f) avg Average fitness value for the current generation population; f (f) c The adaptability value is larger in two crossed bodies; f (f) m Fitness value for the individual to be mutated; p (P) c1 And P c2 Upper and lower limit values for crossover probability; p (P) m1 And P m2 The upper and lower limit values for the probability of variation.
S3.1-4, optimizing the fuzzy rule by using an NSGA-II genetic optimization algorithm based on the improved method, and obtaining the optimized fuzzy rule after repeated iterative computation. The NSGA-II algorithm is shown in the flow chart of FIG. 3, and the specific implementation process is as follows:
the first step: initial population and set the evolution algebra gen=1.
And a second step of: judging whether a first generation sub population is generated, if so, enabling an evolution algebra Gen=2, otherwise, performing non-dominant sorting and selection, gaussian intersection and mutation on the initial population to generate the first generation sub population, and enabling the evolution algebra Gen=2.
And a third step of: and combining the parent population and the offspring population into a new population.
Fourth step: judging whether a new parent population is generated, if not, calculating an objective function of an individual in the new population, and executing operations such as rapid non-dominant sorting, congestion degree calculation, elite strategy and the like to generate the new parent population; otherwise, the fifth step is entered.
Fifth step: and selecting, crossing and mutating the generated parent population to generate a child population.
Sixth step: judging whether Gen is equal to the maximum evolution algebra, if not, the evolution algebra Gen=Gen+1 and returning to the third step; otherwise, the algorithm operation is ended.
S3.3, an adaptive fuzzy inference system (ANFIS) is a fuzzy inference system based on a Takagi-Sugeno model, and the system combines a neural network with the fuzzy inference system;
s3.4, the ANFIS adopted by the invention has a 5-layer structure, and the structure is shown in figure 4. The structural input is the error of the rotation speed of the brushless DC motor and the change rate thereof, wherein x is used 1 ,x 2 And (3) representing. The first layer is a membership function generation layer. Each neuron node in the layer represents a logical language value. The membership function is a gaussian function:
Figure BDA0004150269030000051
wherein: i-the number of input variables; j-number of fuzzy variables; mu (mu) ij (x i ) -inputting a fuzzy variable value corresponding to the variable; m is m ij -a central value of a gaussian function; delta ij -Gao SihanThe width of the number.
S3.5, the second layer is a fuzzy reasoning layer, each node represents a fuzzy rule, and the triggering strength of each rule obtained by multiplying membership functions is as follows:
w k ＝μ 1j (x 1 )×μ 2j (x 2 )
s3.6, the third layer determines the ratio of the trigger intensity of each rule to the sum of the trigger intensities of all rules. The output is the trigger intensity for each rule normalized:
Figure BDA0004150269030000052
s3.7, fourth layer gives the output of each rule:
Figure BDA0004150269030000053
s3.8, fifth layer is the deblurring layer, which calculates the total output of the system:
Figure BDA0004150269030000054
s3.9, the ANFIS-PID control method combines the traditional PID control, fuzzy control and neural network control to realize real-time adjustment of PID parameters, thereby ensuring that the system is always in an optimal state. The structure of the ANFIS-PID used in the present invention is shown in FIG. 5. For a brushless direct current motor system, a control loop inputs the collected deviation e of a motor rotating speed value and a set motor rotating speed value and the deviation change rate de/dt of the motor rotating speed into fuzzy inference, and then outputs delta K in a self-adaptive way through a least square method of an ANFIS structure and a neural network module p 、ΔK i And DeltaK d Thus realizing the online adjustment of PID parameters;
further, the specific steps of the step S4 are as follows:
s4.1, assigning the optimized fuzzy rule to a fuzzy controller through a simulation platform, and optimizing and improving the fuzzy controller;
s4.2, based on the optimized fuzzy controller, the self-adaptive fuzzy inference system adjusts the PID regulator to realize real-time optimization adjustment of the PID parameters, and the PID regulator with the adjusted parameters outputs control variables;
and S4.3, controlling the rotating speed of the brushless direct current motor according to the output control variable.
The beneficial effects of the invention are as follows: in the traditional fuzzy PID control system of the brushless DC motor, although the performance is improved to a certain extent compared with the traditional PID control, the response speed is not ideal enough, membership functions, fuzzy rules, initial parameters of a fuzzy PID controller and the like are set by an expert, a great deal of experience is required by the expert setting method, experience rules of different models are different, and a long process is required for collecting a certain amount of data to form a knowledge base. The invention provides a brushless direct current motor control method of a self-adaptive neural network fuzzy reasoning system-improved genetic algorithm. The control method used is used for carrying out cooperative optimization on the initial parameters of the fuzzy PID and all fuzzy membership functions, and has better dynamic characteristics, control precision and robustness.
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In order that the invention may be more readily and clearly understood, a further detailed description of the invention will be rendered by reference to specific embodiments that are illustrated in the appended drawings, in which:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a general block diagram of the present invention;
FIG. 3 is a flow chart of an improved genetic algorithm implementation of the present invention;
FIG. 4 is a block diagram of an adaptive fuzzy inference system of the present invention;
FIG. 5 is a block diagram of the adaptive fuzzy inference system tuning PID of the present invention;
Detailed Description
The following will further describe the technical solution in the embodiment of the present invention with reference to the accompanying drawings in the embodiment of the present invention:
as shown in fig. 1, the technical scheme provided by the invention comprises the following steps:
s1, constructing a brushless direct current motor model and a fuzzy PID controller;
s2, building a motor speed regulation system of the electric automobile based on the brushless direct current motor and the fuzzy PID controller;
s3, optimizing fuzzy rules of the fuzzy controller by using an improved genetic algorithm, and realizing real-time adjustment of PID parameters by adopting a method for setting PID by using a self-adaptive fuzzy reasoning system;
s4, outputting a control variable by using the optimized fuzzy PID regulator, and optimally controlling the rotating speed of the motor;
the specific process of building the brushless direct current motor model and the fuzzy PID controller is as follows:
s1.1, constructing a simulation model of a brushless direct current motor based on a MATLAB simulation platform according to a mathematical model of the brushless direct current motor;
s1.2, constructing a classical fuzzy PID controller by combining a relevant principle of fuzzy control, and setting an initial value of PID parameters and a fuzzy rule according to expert experience;
step S2, a specific process for building a motor speed regulation system of the electric automobile based on the brushless direct current motor and the fuzzy PID controller is as follows:
s2.1, a double closed-loop control system of the brushless direct current motor is built on the basis of an MATLAB simulation platform, a fuzzy PID controller is adopted by a rotating speed loop, and a classical PI controller is adopted by a current loop;
s2.2, the fuzzy controller adopts a two-input and three-output Mamdani controller, the input is a rotating speed error e and an error change rate ec, and the output is PID gain change delta K p 、ΔK i And DeltaK d ；
S2.3 PID gain variation ΔK p 、ΔK i And DeltaK d Combining the calculation to obtain a control signal U PID Is that
Figure BDA0004150269030000071
K p1 ＝ΔK p ×K p ,K i1 ＝ΔK i ×K i ,K d1 ＝ΔK d ×K d
Wherein: k (K) p Is a proportional gain; k (K) i Is an integral coefficient; k (K) d Is a differential coefficient; e is the regulator bias input signal;
s2.4, the control process of the brushless direct current motor speed regulating system of the electric automobile based on the fuzzy PID controller is as follows: comparing the actual rotating speed of the motor with a set target rotating speed, sampling to obtain a sampling error e, taking the sampling error e and the error change rate ec as input quantity of a fuzzy controller, mapping the input numerical quantity into a fuzzy quantity through a scale factor and a membership function, obtaining a corresponding output fuzzy quantity according to a fuzzy rule base, and obtaining an output numerical increment delta K through definition p 、ΔK i And DeltaK d In actual operation, the fuzzy controller performs on-line adjustment on the PID parameters once until the stable state is reached.
Step S3, optimizing fuzzy rules of the fuzzy controller by using an improved genetic algorithm, and adopting a method for setting PID by adopting a self-adaptive fuzzy inference system to realize real-time adjustment of PID parameters, wherein the specific steps are as follows:
s3.1, the genetic algorithm provides an effective method for searching a large and complex solution space, which is close to an optimal solution and avoids local minima, based on the evolution theory, so that the method is widely applied to the parameter optimization of the fuzzy controller. Aiming at the problem that the fuzzy control rule of the fuzzy PID controller is too dependent on expert experience, an improved genetic optimization algorithm is adopted to iterate the fuzzy rule of the fuzzy controller on line until the optimal fuzzy rule is obtained;
referring to the algorithm flow chart shown in fig. 3, the specific steps for improving the optimization of the fuzzy rule by the genetic algorithm in the step S3.1 are as follows:
s3.1-1, initializing a population, wherein the fuzzy control rule is used as a gene, the gene is encoded by adopting a binary method, and the number of bits of the gene is set to be 20; taking vectors obtained by arranging the genes as chromosomes to represent the optimized result of a group of fuzzy control rules; the number of individuals of the initial population is set to 100 and the corresponding matrix is randomly generated.
S3.1-2, calculating an adaptability value, wherein in the process of optimizing fuzzy rules, the adaptability function is closely related to an objective function of the optimization design. According to the model for optimizing the fuzzy control rule, the objective function is an optimization problem of minimum values, so that the objective function needs to be converted to meet the requirement of the fitness function. The mapping relation between the adaptive function F (x) and the fuzzy rule optimization function F (x) can be obtained according to the mapping relation:
Figure BDA0004150269030000081
wherein C is max There are various selection methods, and penalty function methods are employed in the present invention.
S3.1-3, improving a crossover operator and a mutation operator, and in order to reduce the probability of damage to good genes due to crossover mutation, introducing new genes when the genes fall into a local optimal solution, the invention provides a dynamic adjustment formula of crossover probability and mutation probability:
Figure BDA0004150269030000082
Figure BDA0004150269030000083
wherein f max Is the maximum fitness value in the contemporary population; f (f) avg Average fitness value for the current generation population; f (f) c The adaptability value is larger in two crossed bodies; f (f) m Fitness value for the individual to be mutated; p (P) c1 And P c2 Upper and lower limit values for crossover probability; p (P) m1 And P m2 The upper and lower limit values for the probability of variation.
S3.1-4, optimizing the fuzzy rule by using an NSGA-II genetic optimization algorithm based on the improved method, and obtaining the optimized fuzzy rule after repeated iterative computation. The specific implementation process is as follows:
the first step: initial population and set the evolution algebra gen=1.
And a second step of: judging whether a first generation sub population is generated, if so, enabling an evolution algebra Gen=2, otherwise, performing non-dominant sorting and selection, gaussian intersection and mutation on the initial population to generate the first generation sub population, and enabling the evolution algebra Gen=2.
And a third step of: and combining the parent population and the offspring population into a new population.
Fourth step: judging whether a new parent population is generated, if not, calculating an objective function of an individual in the new population, and executing operations such as rapid non-dominant sorting, congestion degree calculation, elite strategy and the like to generate the new parent population; otherwise, the fifth step is entered.
Fifth step: and selecting, crossing and mutating the generated parent population to generate a child population.
Sixth step: judging whether Gen is equal to the maximum evolution algebra, if not, the evolution algebra Gen=Gen+1 and returning to the third step; otherwise, the algorithm operation is ended.
S3.3, an adaptive fuzzy inference system (ANFIS) is a fuzzy inference system based on a Takagi-Sugeno model, and the system combines a neural network with the fuzzy inference system;
s3.4 As shown in FIG. 4, the ANFIS used in the present invention has a 5-layer structure. The structural input is the error of the rotation speed of the brushless DC motor and the change rate thereof, wherein x is used 1 ,x 2 And (3) representing. The first layer is a membership function generation layer. Each neuron node in the layer represents a logical language value. The membership function is a gaussian function:
Figure BDA0004150269030000091
wherein: i-the number of input variables; j-number of fuzzy variables; mu (mu) ij (x i )—Inputting a fuzzy variable value corresponding to the variable; m is m ij -a central value of a gaussian function; delta ij -width of gaussian function.
S3.5, the second layer is a fuzzy reasoning layer, each node represents a fuzzy rule, and the triggering strength of each rule obtained by multiplying membership functions is as follows:
w k ＝μ 1j (x 1 )×μ 2j (x 2 )
s3.6, the third layer determines the ratio of the trigger intensity of each rule to the sum of the trigger intensities of all rules. The output is the trigger intensity for each rule normalized:
Figure BDA0004150269030000101
s3.7, fourth layer gives the output of each rule:
Figure BDA0004150269030000102
s3.8, fifth layer is the deblurring layer, which calculates the total output of the system:
Figure BDA0004150269030000103
s3.9, referring to FIG. 5, the ANFIS-PID control method combines the traditional PID control, fuzzy control and neural network control to realize real-time adjustment of PID parameters, thereby ensuring that the system is always in an optimal state. For a brushless direct current motor system, a control loop inputs the collected deviation e of a motor rotating speed value and a set motor rotating speed value and the deviation change rate de/dt of the motor rotating speed into fuzzy inference, and then outputs delta K in a self-adaptive way through a least square method of an ANFIS structure and a neural network module p 、ΔK i And DeltaK d Thus realizing the online adjustment of PID parameters;
and S4, outputting a control variable by using the optimized fuzzy PID regulator, wherein the specific process of optimizing and controlling the rotating speed of the motor is as follows:
s4.1, assigning the optimized fuzzy rule to a fuzzy controller through a simulation platform, and optimizing and improving the fuzzy controller;
s4.2, based on the optimized fuzzy controller, the self-adaptive fuzzy inference system adjusts the PID regulator to realize real-time optimization adjustment of the PID parameters, and the PID regulator with the adjusted parameters outputs control variables;
and S4.3, controlling the rotating speed of the brushless direct current motor according to the output control variable.
Referring to fig. 2, the specific process of controlling the rotation speed of the brushless dc motor according to the output control variable in step S4.3 is as follows: the controller based on the self-adaptive neural network fuzzy reasoning system-improved genetic algorithm is used for a speed regulating system of the motor, the deviation of the running speed of the motor and the target speed and the change rate of the deviation are calculated through the comparison of the rotating speed of the input motor and the given target speed, the speed regulating system can adaptively output corresponding current values, the output current values output voltage values required by the motor through a current loop PI regulator, and a PID voltage module obtains and outputs a required duty ratio through PID regulation on the target voltage values output by the PID current module and the actual acquired bus voltage values, and PWM waves are generated through a PWM module to control the conduction of a three-phase inverter bridge power device to be cut off and the three-phase voltage of the motor. In this embodiment, six complementary PWM waves are output, so as to control the on and off of six power devices of the three-phase inverter bridge, and the calculated duty ratio is used to adjust the magnitude of the three-phase voltage of the motor, so as to control the rotation speed of the motor to approach the expected value.
The foregoing embodiments have been provided to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the foregoing embodiments are merely illustrative of the present invention and are not intended to limit the scope of the present invention, therefore, all changes that may be made according to the shape and principles of the present invention are included in the scope of the present invention.

Claims (3)

1. The rotating speed control method of the brushless direct current motor of the electric automobile is characterized by comprising the following steps of:
s1, building a brushless direct current motor model and a fuzzy PID controller, and building an electric vehicle motor speed regulation system based on the brushless direct current motor and the fuzzy PID controller;
s2, optimizing a fuzzy rule of the fuzzy controller by using an improved genetic algorithm;
s3, adopting a method for adjusting the PID by a self-adaptive fuzzy inference system to realize real-time adjustment of PID parameters;
and S4, outputting a control variable by using the optimized fuzzy PID regulator, and optimally controlling the rotating speed of the motor.
2. The method for controlling the rotational speed of a brushless dc motor of an electric vehicle according to claim 1, wherein the specific steps of step S2 are as follows:
s2.1, generating a fuzzy controller: the fuzzy controller adopts a two-input and three-output Mamdani controller, and the input is a rotating speed error e and an error change rate e c The output is PID gain variation ΔK p 、ΔK i And DeltaK d The method comprises the steps of carrying out a first treatment on the surface of the Obtaining corresponding output fuzzy quantity according to a fuzzy rule base optimized by a genetic algorithm, and obtaining an output numerical increment delta K through definition p 、ΔK i And DeltaK d Accumulating the initial parameters of the online PID, and finally outputting PID parameters which enable the system to have better steady state and dynamic performance;
s2.2, the genetic algorithm provides an effective method for searching a large and complex solution space, which is close to an optimal solution and avoids local minima, based on an evolution theory, so that the method is widely applied to the parameter optimization of the fuzzy controller. Aiming at the problem that the fuzzy control rule of the fuzzy PID controller is too dependent on expert experience, an improved genetic optimization algorithm is adopted to iterate the fuzzy rule of the fuzzy controller on line until the optimal fuzzy rule is obtained;
s2.3, on the basis of a traditional genetic algorithm, in order to reduce the probability of damage to excellent genes due to cross variation, and simultaneously introduce new genes when the excellent genes fall into a local optimal solution, a cross operator and a mutation operator are improved, and a dynamic adjustment formula of the cross probability and the mutation probability is provided:
Figure QLYQS_1
Figure QLYQS_2
wherein f max Is the maximum fitness value in the contemporary population; f (f) avg Average fitness value for the current generation population; f (f) c The adaptability value is larger in two crossed bodies; f (f) m Fitness value for the individual to be mutated; p (P) c1 And P c2 Upper and lower limit values for crossover probability; p (P) m1 And P m2 The upper and lower limit values for the probability of variation.
S2.4, optimizing the fuzzy rule by using an NSGA-II genetic optimization algorithm based on the improved method, and obtaining the optimized fuzzy rule after repeated iterative computation.
3. The method for fuzzy control of a brushless dc motor of an electric vehicle based on an adaptive neural network fuzzy inference system-improved genetic algorithm (ANFIS-IGA) of claim 1, wherein the specific steps of step S3 are as follows:
s3.1, an adaptive fuzzy inference system (ANFIS) is a fuzzy inference system based on a Takagi-Sugeno model, the system combines a neural network with the fuzzy inference system, and the ANFIS used in the invention has a 5-layer structure, and the structure is shown in figure 4. The input of the structure is the error of the rotating speed of the brushless direct current motor and the change rate thereof. The first layer is a membership function generation layer. Each neuron node in the layer represents a logical language value. The membership function is a gaussian function:
Figure QLYQS_3
wherein: i-the number of input variables; j-number of fuzzy variables; mu (mu) ij (x i ) -inputting a fuzzy variable value corresponding to the variable; m is m ij -a central value of a gaussian function; delta ij -width of gaussian function.
S3.2, the second layer is a fuzzy reasoning layer, each node represents a fuzzy rule, and the triggering strength of each rule obtained by multiplying membership functions is as follows:
w k ＝μ 1j (x 1 )×μ 2j (x 2 )
s3.3, the third layer determines the ratio of the trigger intensity of each rule to the sum of the trigger intensities of all rules. The output is the trigger intensity for each rule normalized:
Figure QLYQS_4
s3.4, the fourth layer gives the output of each rule:
Figure QLYQS_5
s3.5, fifth layer is the deblurring layer, which calculates the total output of the system:
Figure QLYQS_6
s3.6, the ANFIS-PID control method combines the traditional PID control, fuzzy control and neural network control to realize real-time adjustment of PID parameters, thereby ensuring that the system is always in an optimal state. For a brushless direct current motor system, a control loop inputs the collected deviation e of a motor rotating speed value and a set motor rotating speed value and the deviation change rate de/dt of the motor rotating speed into fuzzy inference, and then outputs delta K in a self-adaptive way through a least square method of an ANFIS structure and a neural network module p 、ΔK i And DeltaK d Thereby realizing the online adjustment of PID parameters and the output of the PID regulator with adjusted parametersAnd outputting a control variable, and controlling the rotating speed of the brushless direct current motor according to the output control variable.
CN202310316551.8A 2023-03-29 2023-03-29 Rotating speed control method of brushless direct current motor of electric automobile Pending CN116232132A (en)

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CN117691920A (en) * 2024-02-01 2024-03-12 成都航空职业技术学院 Automatic control method for servo motor

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117691920A (en) * 2024-02-01 2024-03-12 成都航空职业技术学院 Automatic control method for servo motor
CN117691920B (en) * 2024-02-01 2024-04-12 成都航空职业技术学院 Automatic control method for servo motor

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