CN113179073B - Motor control method for improving position precision - Google Patents
Motor control method for improving position precision Download PDFInfo
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- CN113179073B CN113179073B CN202110665727.1A CN202110665727A CN113179073B CN 113179073 B CN113179073 B CN 113179073B CN 202110665727 A CN202110665727 A CN 202110665727A CN 113179073 B CN113179073 B CN 113179073B
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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
- 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
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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
- 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
- H02P23/0031—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control implementing a off line learning phase to determine and store useful data for on-line control
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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
- H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
- H02P23/0077—Characterised by the use of a particular software algorithm
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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
- H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
- H02P23/14—Estimation or adaptation of motor parameters, e.g. rotor time constant, flux, speed, current or voltage
Abstract
The invention relates to the technical field of motor control, in particular to a motor control method for improving position precision. The motor controller for improving the position accuracy includes: the system comprises a signal acquisition unit, a central processing unit, a motor control unit and a data storage unit; the circuit in the signal acquisition unit is connected with the central processing unit, the central processing unit is connected with the data storage unit, the data storage unit is connected with the motor control unit, and the central processing unit is connected with the motor control unit. This motor controller guarantees that terminal actuating mechanism can be according to the appointed position of accurate arrival of requirement, and control accuracy is higher, and is more intelligent, and a controller can be suitable for a plurality of application scenes, satisfies the requirement of research and development and product, has reduced the cost of research and development.
Description
Technical Field
The invention relates to the technical field of motor control, in particular to a motor control method for improving position precision.
Background
With the development of power electronic technology and the development of artificial intelligence technology, motors become execution mechanisms in which various motion mechanisms must participate. The motor control, especially the position control of the motor, becomes an important component of modern motion control, and directly influences the performance of the motion mechanism. The motor control is widely applied to the fields of injection molding machines, textile machinery, packaging machinery, numerical control machines, aerospace and the like. The motor control method is a main power source of various moving objects, the performance, characteristics and reliability of the moving objects determine the performance of the whole moving system, and the motor position needs to be accurately controlled in many fields.
In a traditional method for controlling the position of a motor, instruments such as an encoder or a rotary transformer are added on the motor, and the position is controlled through feedback of various instruments. However, the control accuracy of this control method is not high, so that the end effector cannot move to a desired position more accurately when the end effector is going to operate, and the control fails or the control accuracy is not satisfactory. The existing motor position control method is not intelligent enough, cannot meet the increasingly developed era, cannot learn autonomously, is not high in adaptability, and one motor cannot adapt to different application environments. Meanwhile, a plurality of feedbacks or position sensors with higher precision are required to be added to one precise position control, so that the research and development cost is increased.
Therefore, in view of the current situation, it is urgently needed to design and produce a motor control method for improving the position accuracy, so as to solve the problems that in the prior art, when a terminal actuating mechanism does an action, the terminal actuating mechanism cannot be accurately moved to a required position, one motor cannot adapt to different application environments, and the research and development cost is high.
Disclosure of Invention
The invention aims to provide a motor control method for improving position accuracy, which ensures that a tail end actuating mechanism can accurately reach a specified position according to requirements and has higher control accuracy.
The purpose of the invention is realized by the following technical scheme:
a motor control method for improving position accuracy is suitable for the motor controller for improving position accuracy, and comprises the following steps:
s1, a signal acquisition unit acquires and classifies related data of a motor;
s2, performing per-unit on the data, expressing the per-unit data through a formula (1),
wherein x is min For minimum values, x, of the same kind of data collected max Is the maximum value of the same kind of data collected, and x is any value of the same kind of data collectedMean value, y is the per unit value corresponding to x, α is the coefficient of the collected data of different kinds, e β Compensation values for different kinds of data collected;
s3, grouping the collected data of different types;
s4, establishing a neural network model;
s5, training a neural network model, performing adaptive learning, and storing the trained neural network model into a data storage unit;
s6, inputting information of different running states of the motor into the neural network model trained in the step S5, and predicting the corresponding motor running time;
s7, judging application scenes, and calling different self-adaptive learning data according to different scenes;
s8, sending the running time to a motor control unit, converting the running time into a motor control signal and controlling the motor to move;
s9, the motor control unit collects the current position and the real-time position;
s10, judging whether the position meets the requirement, if so, executing a step S11, and if not, executing a step S12;
s11, the motor control unit continues to control the motor to move according to the motor control signal;
step S12, further compensating the position of the motor, then returning to execute step S9, wherein the compensation formula is formula (3),
wherein, S is the position after the motor is controlled, PWM is the switching frequency converted according to the time sent by the central processing unit, and theta is a compensation coefficient.
Preferably, in step S3, after the collected different kinds of data are grouped, each group of data corresponds to one of the rotation speed of the motor, the real-time position of the motor, the current position of the motor, and the running time of the motor, and each group of data is mapped into the range of [0.1,0.85] through formula (1) and is used as a sample for neural network model and adaptive learning.
Preferably, in step S4, the input of the neural network model is the rotation speed of the motor, the current position of the motor, and the real-time position of the motor, and the output of the neural network model is the running time of the motor.
Preferably, in step S5, the training neural network model and the adaptive learning step are as follows:
setting an error m for the target of the neural network model, wherein the learning frequency is n, the initial value is in the period range of [ a, b ], and according to the learning mode, the system adjusts the learning frequency and the period of the initial value, and the learning expression formula is as follows:
ε(k)＝n*ε(k)*δ (2)
wherein n is the learning frequency, epsilon learning efficiency and delta is the learning rate multiple;
if the error after learning is within m, increasing the rate multiple or decreasing the number of learning times by the multiple; if the error after learning is outside m, the rate is reduced by a multiple or the number of learning is increased while the neural network model is built for learning within m.
The invention has the beneficial effects that:
the motor controller for improving the position precision adopts the dual modes of the central processing unit and the motor controller unit, controls the motion of the motor, ensures the stability of the motor, has higher control precision, ensures that the terminal actuating mechanism can accurately reach the appointed position according to the requirement, and ensures the motion performance of the motor. The motor control unit makes up for the tiny position loss caused by time delay in the motor control, and improves the motor position precision. Meanwhile, an intelligent neural network and a deep autonomous learning method are adopted, so that the intellectualization of the controller is improved, the applicability of the controller is improved, and the position precision of the motor is improved. The signal acquisition unit integrates various signal acquisition circuits, can be suitable for different motor types and different feedbacks, increases the application scenes of the controller, and reduces the research and development cost. Meanwhile, the stability, safety and reliability of the motor can be improved.
Drawings
Fig. 1 is a schematic structural diagram of a motor controller for improving position accuracy according to the present embodiment;
fig. 2 is a flowchart of a preferred method of controlling a motor to improve position accuracy according to this embodiment.
In the figure:
1. a signal acquisition unit; 2. a central processing unit; 3. a motor control unit; 4. and a data storage unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Device embodiment
As shown in fig. 1, the motor controller for improving the position accuracy provided in this embodiment includes a signal acquisition unit 1, a central processing unit 2, a motor control unit 3, and a data storage unit 4.
The circuit in the signal acquisition unit 1 is connected with the central processing unit 2, the central processing unit 2 is connected with the data storage unit 4, the data storage unit 4 is connected with the motor control unit 3, and the central processing unit 2 is connected with the motor control unit 3.
The signal acquisition unit 1 is used for acquiring control signals of the motor and transmitting the control signals to the central processing unit 2. The central processing unit 2 is used for analyzing the data sent by the signal acquisition unit 1, calculating an accurate control position through an intelligent neural network and an autonomous learning planning control method and algorithm, sending a corresponding control signal to the motor control unit 3, and storing the data in the data storage unit 4. The data storage unit 4 is mainly used for storing data of the central processing unit 2 and the motor control unit 3, and a control method and an algorithm formed by intelligent neural network and autonomous learning. The motor control unit 3 is used for receiving the position control signal of the central processing unit 2 and driving the motor to a corresponding position according to the position control signal.
Specifically, the signal acquisition unit 1 may acquire signals of motors such as position, speed, current, and the like, but is not limited to these information. And the signal acquisition unit 1 can carry out signal acquisition to different motors, has integrated the processing circuit of multiple collection signal, has improved the suitability in the hardware of motor controller.
As the preferred scheme, the central processing unit 2 is composed of a high-performance CPU or MCU, which is convenient for the calculation of various data and the establishment of a model. The function of the intelligent control system is to analyze data sent by the signal acquisition unit 1, further process the data, apply an intelligent neural network and autonomous learning, plan out a proper algorithm and a proper control method, calculate an accurate control position, send a corresponding control signal to the motor control unit 3, and simultaneously store the data in the data storage unit 4. The addition of the central processing unit 2 improves the position control precision of the motor, increases the stability, safety and reliability of the motor, ensures that the motor can accurately reach an appointed position according to requirements, has higher control precision and is more intelligent, and one controller can be suitable for a plurality of application scenes.
Preferably, the data storage unit 4 is mainly composed of storage media such as RAM and flash, and is mainly used for storing data of the central processing unit 2 and the motor control unit 3, and various control methods and algorithms formed through an intelligent neural network and autonomous learning. The data storage unit 4 is a data storage device for improving the control precision of the motor position, can well provide effective data, effectively stores and applies various data, shortens the control time and improves the control efficiency.
As a preferred scheme, the motor control unit 3 is divided into a motor control part and a motor driving part, the motor control part needs to receive a position control signal accurate to the central processing unit 2, convert the position control signal into a driving signal and send the driving signal to the motor driving part, and the motor is driven to rotate to a specific certain position. As a further preferred scheme, the signal acquisition unit 1 is further connected with the motor control unit 3, and meanwhile, the motor control unit 3 retrieves data in the signal acquisition unit 1 to further adjust the position of the motor, so that the accurate control of the position of the motor is ensured, and the stability of the motor is improved. The motor control unit 3 sends the relevant control data and further adjusted data to the data storage unit 4 for storage.
Method embodiment
As shown in fig. 2, the motor control method for improving the position accuracy according to this aspect is applied to the above-mentioned motor controller for improving the position accuracy, and includes the following steps:
s1, a signal acquisition unit acquires and classifies related data of the motor.
S2, in order to facilitate calling, performing per-unit on the data, wherein a data formula after per-unit is expressed by a general formula as follows:
wherein x is min For minimum values, x, of the same kind of data collected max Is the maximum value of the collected data of the same kind, x is any value of the collected data of the same kind, and y is the per unit value corresponding to x; alpha is the coefficient of the acquired data of different kinds, e β Compensation values for different kinds of data collected.
And S3, grouping the collected different types of data. Preferably, each set of data corresponds to a rotation speed of the motor, a real-time position of the motor, a current position of the motor, and a running time of the motor. Each set of data is mapped into the range of [0.1,0.85] by formula (1) and used as a sample for neural network model and adaptive learning.
And S4, establishing a neural network model (n can be selected according to different types of control motors and variables), wherein the input of the neural network model is the rotating speed of the motor, the current position of the motor and the real-time position of the motor, and the output of the neural network model is the running time of the motor, namely the number of neurons in an input layer of the network is 3, and the number of neurons in an output layer is 1.
And S5, training a neural network model, learning in a self-adaptive mode, and storing data.
Setting an error m for the target of the neural network model, (m can be adjusted according to the system), the learning frequency is n, the initial value can be in the period range of [ a, b ], according to the learning mode, the system adjusts the learning frequency by itself, the period of the initial value and the learning result of the learning rate, and the intelligent adjustment is carried out. The expression formula of the self-adaptive learning is as follows:
ε(k)＝n*ε(k)*δ (2)
wherein n is the learning frequency, epsilon learning efficiency, and delta is the learning rate multiple, and can be increased or decreased.
When the error after learning is within m, the rate multiple can be increased by multiple, and the learning frequency can be reduced at the same time, so that the learning efficiency is increased, and the learning time is shortened. And meanwhile, establishing a neural network model for learning within m, and storing the trained neural network model into a data storage unit.
And S6, judging the running time of the motor.
And (5) inputting the positions, the rotating speeds and the like of the motors in different running states into the neural network model trained in the step (S5), and predicting the corresponding motor running time.
And S7, judging application scenes, and calling different self-adaptive learning data according to different scenes.
And S8, sending the running time to a motor control unit, converting the running time into a motor control signal, and controlling the motor to move. In order to improve the position accuracy of the motor, step S9 is also performed.
And S9, the motor control unit collects the current position and the real-time position.
And S10, judging whether the position meets the requirement, if so, executing a step S11, and if not, executing a step S12.
And S11, the motor control unit continues to control the motor to move by the motor control signal.
And step S12, further compensating the position of the motor, and then returning to execute step S9, wherein the compensation formula is formula (3).
Wherein, S is the position after the motor is controlled, PWM is the switching frequency converted according to the time sent by the central processing unit, and theta is a compensation coefficient.
The above are only typical examples of the present invention, and besides, the present invention may have other embodiments, and all the technical solutions formed by equivalent substitutions or equivalent changes are within the scope of the present invention as claimed.
Claims (4)
1. A motor control method for improving position accuracy is characterized by comprising the following steps:
s1, a signal acquisition unit acquires and classifies related data of a motor;
s2, performing per-unit on the data, expressing the per-unit data through a formula (1),
wherein x is min For minimum values, x, of the same kind of data collected max Is the maximum value of the collected data of the same kind, x is any value of the collected data of the same kind, y is the per unit value corresponding to x, alpha is the coefficient of the collected data of different kinds, e β Compensation values for different types of data collected;
s3, grouping the collected data of different types;
s4, establishing a neural network model;
s5, training a neural network model, performing adaptive learning, and storing the trained neural network model into a data storage unit;
s6, inputting information of different running states of the motor into the neural network model trained in the step S5, and predicting the corresponding motor running time;
s7, judging application scenes, and calling different self-adaptive learning data according to different scenes;
s8, sending the running time to a motor control unit, converting the running time into a motor control signal and controlling the motor to move;
s9, the motor control unit collects the current position and the real-time position;
s10, judging whether the position meets the requirement, if so, executing a step S11, and if not, executing a step S12;
s11, the motor control unit continues to control the motor to move according to the motor control signal;
step S12, further compensating the position of the motor, then returning to execute step S9, wherein the compensation formula is formula (3),
wherein, S is the position after the motor is controlled, PWM is the switching frequency converted according to the time sent by the central processing unit, and theta is a compensation coefficient.
2. The motor control method for improving positional accuracy according to claim 1,
in step S3, after the collected different kinds of data are grouped, each group of data corresponds to one of the rotation speed of the motor, the real-time position of the motor, the current position of the motor, and the operation time of the motor, and each group of data is mapped into the range of [0.1,0.85] by formula (1) and used as a sample for neural network model and adaptive learning.
3. The method of claim 2, wherein in step S4, the input of the neural network model is a rotation speed of the motor, a current position of the motor, and a real-time position of the motor, and the output of the neural network model is a running time of the motor.
4. The method for controlling a motor to improve position accuracy as set forth in claim 1, wherein the step of training the neural network model and the step of adaptively learning in step S5 are as follows:
setting an error m for the target of the neural network model, wherein the learning times is n, the initial value is in the period range of [ a, b ], and according to the learning mode, the system adjusts the learning times and the period of the initial value, and the learning expression formula is as follows:
ε(k)＝n*ε(k)*δ (2)
wherein n is the learning frequency, epsilon learning efficiency and delta is the learning rate multiple;
if the error after learning is within m, increasing the rate multiple or decreasing the number of learning times by the multiple; if the error after learning is outside m, the rate is reduced by a multiple or the number of learning is increased while the neural network model is built for learning within m.
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