CN116442250B - Self-adaptive intelligent control method, system and storage medium for linear motor - Google Patents
Self-adaptive intelligent control method, system and storage medium for linear motor Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1615—Programme controls characterised by special kind of manipulator, e.g. planar, scara, gantry, cantilever, space, closed chain, passive/active joints and tendon driven manipulators
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
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Abstract
The embodiment of the application provides a self-adaptive intelligent control method, a self-adaptive intelligent control system and a storage medium for a linear motor. The method comprises the following steps: the method comprises the steps of obtaining power posture data and gravity load data of a multi-axis manipulator in a preset time period and running data of a current linear motor, obtaining motor power load excitation data and combining motor load power factors to judge the working state of the motor, obtaining motor power interference factors according to a current motor working state threshold, processing the motor power load excitation data and combining the motor load power factors and the motor power interference factors in a motor disturbance self-adaptive model to obtain motor output efficiency data, adjusting the linear motor, obtaining motor correction aging change data and judging the validity of the working state of the motor after adjustment, and accordingly achieving the technology of self-adaptive intelligent control of the motor according to the motor power interference factors and obtaining motor output efficiency data in the motor disturbance self-adaptive model in combination with the motor power load excitation data.
Description
Technical Field
The application relates to the field of intelligent engineering in the field of intelligent motor control, in particular to a self-adaptive intelligent control method, a self-adaptive intelligent control system and a storage medium for a linear motor.
Background
The multi-axis manipulator is used for a robot arm, a linear motor used by the multi-axis manipulator is a kinetic energy driving device for providing driving force for the multi-axis manipulator, and the linear motor of the existing multi-axis manipulator at present mostly controls output torque and rotating speed according to an output instruction of a controller signal central system so as to control the output torque, the actuating stroke, the speed and the acceleration of the multi-axis manipulator, but does not have the function of performing self-adaptive intelligent regulation and control according to the operation gesture, the driving force, the load and the load change of the multi-axis manipulator.
In view of the above problems, an effective technical solution is currently needed.
Disclosure of Invention
The embodiment of the application aims to provide a self-adaptive intelligent control method, a self-adaptive intelligent control system and a storage medium for a linear motor, wherein the self-adaptive intelligent control method, the self-adaptive intelligent control system and the storage medium can be used for obtaining motor output efficacy data in a motor disturbance self-adaptive model according to motor power interference factors and motor power load excitation data.
The embodiment of the application also provides a self-adaptive intelligent control method of the linear motor, which comprises the following steps:
Acquiring power posture data and gravity load data of the multi-axis manipulator in a preset time period, wherein the power posture data and the gravity load data comprise instantaneous speed data, acceleration data, centroid line inclination data, load distribution data, load data and gravity center position data;
acquiring operation data of a current linear motor, including rotational speed, angular acceleration, dynamic output moment and power load data;
acquiring motor power load excitation data according to the power attitude data, the gravity load data and the operation data, and judging the working state of the motor by combining a preset motor load power factor;
according to the current motor working state threshold value obtained by comparison, combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data to calculate and obtain a motor power interference factor;
according to the motor power load excitation data, combining the motor load power factor and the motor power interference factor, estimating and processing the motor state to be adapted in a motor disturbance self-adaptive model to obtain motor output efficacy data;
adjusting the linear motor according to the motor output efficiency data and acquiring motor correction aging change data;
And judging the validity of the working state of the motor after the motor is regulated according to the motor correction aging change data.
Optionally, in the adaptive intelligent control method for a linear motor according to the embodiment of the present application, the acquiring power posture data and gravity load data of the multi-axis manipulator in a preset period of time includes instantaneous velocity data, acceleration data, centroid line inclination data, load distribution data, load data, and gravity center position data, including:
acquiring a power modulation period of a linear motor of the multi-axis manipulator;
acquiring a dynamic parameter set of the multi-axis manipulator in a preset time period according to the power modulation period, wherein the dynamic parameter set comprises a weight parameter, a load parameter, an inertia parameter, a speed parameter, an angle posture parameter and a gravity distribution parameter;
acquiring power posture data in the period of the preset time period according to the load parameter, the inertia parameter, the speed parameter and the angle posture parameter, wherein the power posture data comprises instantaneous speed data, acceleration data and centroid line inclination data;
and acquiring gravity load data in the preset time period according to the weight parameter, the load parameter and the gravity distribution parameter, wherein the gravity load data comprises load distribution data, load data and gravity center position data.
Optionally, in the adaptive intelligent control method for a linear motor according to the embodiment of the present application, the obtaining motor power load excitation data according to the power posture data, the gravity load data and the operation data and determining the working state of the motor by combining a preset motor load power factor includes:
calculating and obtaining motor power load excitation data according to the obtained acceleration data, centroid line inclination data, load data, centroid position data and angular acceleration and dynamic output moment of the linear motor in the preset time period;
obtaining a preset motor load power factor according to the configuration attribute information of the linear motor, wherein the motor load power factor comprises a motor load response factor and a motor power response factor;
acquiring motor running load data according to the motor power load excitation data and the motor load power factor;
threshold value comparison is carried out according to the motor operation load data and a motor operation load threshold value;
judging the working state of the motor according to the threshold comparison result;
the program calculation formula of the motor power load excitation data is as follows:
；
the calculation formula of the motor operation load data is as follows:
；
Wherein W is motor power load excitation data, S is acceleration data, L is centroid line inclination data, H is load data, J is centroid position data,for angular acceleration +.>For dynamic output torque->、/>、/>、/>、/>、/>Is a motor power constraint coefficient; r is motor running load data, v is motor load response factor, and p is motor power response factor.
Optionally, in the adaptive intelligent control method for a linear motor according to the embodiment of the present application, the calculating, according to the compared current motor working state threshold, the motor power interference factor by combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data includes:
calculating to obtain a motor power interference factor according to the threshold comparison result of the motor working state and combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data;
the calculation program of the motor power interference factor is as follows:
;
wherein n is the motor power interference factor,s is acceleration data, L is centroid line inclination data, H is load data, J is centroid position data, and +_is for threshold comparison result>、/>、/>、/>Is a motor power constraint coefficient.
Optionally, in the adaptive intelligent control method for a linear motor according to the embodiment of the present application, the estimating the to-be-adapted state of the motor in the motor disturbance adaptive model according to the motor power load excitation data in combination with the motor load power factor and the motor power interference factor to obtain motor output efficacy data includes:
inquiring in a multi-axis manipulator intelligent management system according to multi-axis manipulator type information and configuration attribute information of the linear motor to obtain a corresponding motor disturbance self-adaptive model;
the motor disturbance self-adaptive model is obtained through training according to a motor disturbance data set collected according to the type of the multi-axis manipulator and the linear motor attribute history;
the motor disturbance data set comprises sample motor power load excitation data, sample motor load power factors, sample motor power interference factors and sample motor output efficacy data which are acquired in a historical manner;
and according to the obtained motor power load excitation data, combining the motor load power factor and the motor power interference factor, performing estimation processing in the motor disturbance self-adaptive model to obtain motor output efficacy data.
Optionally, in the adaptive intelligent control method for a linear motor according to the embodiment of the present application, the adjusting the linear motor according to the motor output efficiency data and obtaining motor correction aging change data includes:
adjusting the output power of the linear motor according to the obtained motor output efficacy data;
collecting second motor operation load data of the motor in a time period after adjustment;
performing difference comparison according to the second motor operation load data and the motor operation load data before adjustment to obtain difference data;
and correcting the aging change data as motor according to the difference data.
Optionally, in the adaptive intelligent control method for a linear motor according to the embodiment of the present application, the performing validity judgment on the adjusted working state of the motor according to the motor correction aging data includes:
setting a motor correction efficiency threshold according to the motor load power factor;
comparing the motor correction aging change data with the motor correction efficiency threshold value according to the motor correction aging change data;
and if the motor correction aging change data is larger than the motor correction efficiency threshold, the motor adjustment efficiency effectiveness is effective, otherwise, the motor adjustment efficiency is ineffective.
In a second aspect, an embodiment of the present application provides a linear motor adaptive intelligent control system, including: the linear motor self-adaptive intelligent control system comprises a memory and a processor, wherein the memory comprises a linear motor self-adaptive intelligent control method program, and the linear motor self-adaptive intelligent control method program is executed by the processor to realize the following steps:
acquiring power posture data and gravity load data of the multi-axis manipulator in a preset time period, wherein the power posture data and the gravity load data comprise instantaneous speed data, acceleration data, centroid line inclination data, load distribution data, load data and gravity center position data;
acquiring operation data of a current linear motor, including rotational speed, angular acceleration, dynamic output moment and power load data;
acquiring motor power load excitation data according to the power attitude data, the gravity load data and the operation data, and judging the working state of the motor by combining a preset motor load power factor;
according to the current motor working state threshold value obtained by comparison, combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data to calculate and obtain a motor power interference factor;
according to the motor power load excitation data, combining the motor load power factor and the motor power interference factor, estimating and processing the motor state to be adapted in a motor disturbance self-adaptive model to obtain motor output efficacy data;
Adjusting the linear motor according to the motor output efficiency data and acquiring motor correction aging change data;
and judging the validity of the working state of the motor after the motor is regulated according to the motor correction aging change data.
Optionally, in the adaptive intelligent control system for a linear motor according to the embodiment of the present application, the acquiring power posture data and gravity load data of the multi-axis manipulator in a preset period of time includes instantaneous velocity data, acceleration data, centroid line inclination data, load distribution data, load data, and gravity center position data, including:
acquiring a power modulation period of a linear motor of the multi-axis manipulator;
acquiring a dynamic parameter set of the multi-axis manipulator in a preset time period according to the power modulation period, wherein the dynamic parameter set comprises a weight parameter, a load parameter, an inertia parameter, a speed parameter, an angle posture parameter and a gravity distribution parameter;
acquiring power posture data in the period of the preset time period according to the load parameter, the inertia parameter, the speed parameter and the angle posture parameter, wherein the power posture data comprises instantaneous speed data, acceleration data and centroid line inclination data;
and acquiring gravity load data in the preset time period according to the weight parameter, the load parameter and the gravity distribution parameter, wherein the gravity load data comprises load distribution data, load data and gravity center position data.
In a third aspect, an embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium includes a linear motor adaptive intelligent control method program, where the linear motor adaptive intelligent control method program, when executed by a processor, implements the steps of the linear motor adaptive intelligent control method according to any one of the foregoing embodiments.
As can be seen from the foregoing, according to the self-adaptive intelligent control method, system and storage medium for a linear motor provided by the embodiments of the present application, by acquiring power posture data and gravity load data of a multi-axis manipulator in a preset time period and operation data of a current linear motor, acquiring motor power load excitation data and motor load power factor, judging a motor working state, acquiring a motor power interference factor according to a current motor working state threshold, processing in a motor disturbance self-adaptive model according to the motor power load excitation data and the motor load power factor and the motor power interference factor to acquire motor output efficiency data, adjusting the linear motor, acquiring motor correction aging data and judging the validity of the working state after motor adjustment, a technology for performing self-adaptive intelligent control on the motor according to the motor power interference factor and acquiring motor output efficiency data in the motor disturbance self-adaptive model according to the motor power load excitation data is realized.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a self-adaptive intelligent control method for a linear motor according to an embodiment of the present application;
fig. 2 is a flowchart of acquiring power posture data and gravity load data of a multi-axis manipulator in a preset time period in a self-adaptive intelligent control method of a linear motor according to an embodiment of the present application;
fig. 3 is a flowchart of obtaining motor power load excitation data and judging a motor working state by combining a preset motor load power factor in the self-adaptive intelligent control method of a linear motor according to an embodiment of the present application;
Fig. 4 is a schematic structural diagram of a self-adaptive intelligent control system for a linear motor according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart of a method for adaptively controlling a linear motor according to some embodiments of the application. The self-adaptive intelligent control method of the linear motor is used in terminal equipment, such as a computer terminal, a display terminal and the like. The self-adaptive intelligent control method of the linear motor comprises the following steps:
s101, acquiring power posture data and gravity load data of a multi-axis manipulator in a preset time period, wherein the power posture data and the gravity load data comprise instantaneous speed data, acceleration data, centroid line inclination data, load distribution data, load data and gravity center position data;
s102, acquiring operation data of a current linear motor, wherein the operation data comprise rotating speed, angular acceleration, dynamic output moment and power load data;
s103, acquiring motor power load excitation data according to the power attitude data, the gravity load data and the operation data, and judging the working state of the motor by combining a preset motor load power factor;
s104, calculating and obtaining a motor power interference factor according to the current motor working state threshold value obtained through comparison and combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data;
s105, according to the motor power load excitation data, combining the motor load power factor and the motor power interference factor, estimating and processing the motor to-be-adapted state in a motor disturbance self-adaptive model to obtain motor output efficacy data;
S106, adjusting the linear motor according to the motor output efficiency data and acquiring motor correction aging change data;
and S107, judging the validity of the working state of the motor after adjustment according to the motor correction aging change data.
It should be noted that, in order to achieve the purpose of obtaining the output power efficiency data of the motor according to the state parameter data of the multi-axis manipulator and the linear motor, adjusting the input working parameters of the linear motor, and obtaining the suitability good and bad effect judgment of the motor obtaining adjustment according to the state data of the motor in the period obtained after adjustment, that is, the motor correction aging change data, so as to achieve the self-adaptive intelligent adjustment function of the linear motor, specifically, obtain the power posture data and the gravity load data of the multi-axis manipulator in the period of the preset time period and the running data of the current linear motor, obtain the motor power load excitation data according to the power posture data and the gravity load data and the running data, combine the preset motor load power factor to judge the working state of the motor, then calculate the motor power interference factor according to the current motor working state threshold obtained by comparison and combine the acceleration data, the centroid line inclination data, the load distribution data and the center of gravity position data, estimate the motor to obtain the output power efficiency data of the motor according to the motor power excitation data and the motor power interference factor in the motor disturbance self-adaptive model, and finally obtain the power efficiency data of the linear motor, correct the motor and adjust the aging change data according to the motor aging change data.
Referring to fig. 2, fig. 2 is a flowchart of acquiring power posture data and gravity load data of a multi-axis manipulator in a preset time period in a linear motor adaptive intelligent control method according to some embodiments of the present application. According to the embodiment of the application, the power posture data and the gravity load data of the multi-axis manipulator in the preset time period are obtained, wherein the power posture data and the gravity load data comprise instantaneous speed data, acceleration data, centroid line inclination data, load distribution data, load data and centroid position data, and the method specifically comprises the following steps:
s201, acquiring a power modulation period of a linear motor of the multi-axis manipulator;
s202, acquiring a dynamic parameter set of the multi-axis manipulator in a preset time period according to the power modulation period, wherein the dynamic parameter set comprises a weight parameter, a load parameter, an inertia parameter, a speed parameter, an angle posture parameter and a gravity distribution parameter;
s203, power posture data in the preset time period is obtained according to the load parameters, the inertia parameters, the speed parameters and the angle posture parameters, wherein the power posture data comprise instantaneous speed data, acceleration data and centroid line inclination data;
s204, acquiring gravity load data in the period of the preset time period according to the weight parameter, the load parameter and the gravity distribution parameter, wherein the gravity load data comprises load distribution data, load data and gravity center position data.
The power modulation period is determined according to the adjustment period of the power of the linear motor of the multi-axis manipulator, a set of dynamic parameters of the multi-axis manipulator and the linear motor in a preset time period is acquired according to the modulation period, the preset time period is smaller than or equal to the modulation period, the preset time period for parameter acquisition and adjustment is set as a preset time period, and the power posture data and the gravity load data in the preset time period are respectively obtained according to the acquired dynamic parameter set.
Referring to fig. 3, fig. 3 is a flowchart of a method for adaptively controlling a linear motor according to some embodiments of the present application to obtain motor power load excitation data and determine a motor operating state by combining a preset motor load power factor. According to the embodiment of the application, the motor power load excitation data is obtained according to the power posture data, the gravity load data and the operation data, and the motor working state is judged by combining with a preset motor load power factor, specifically:
s301, calculating and obtaining motor power load excitation data according to the obtained acceleration data, centroid line inclination data, load data, centroid position data and the angular acceleration and dynamic output moment of the linear motor in the preset time period;
S302, obtaining a preset motor load power factor according to configuration attribute information of the linear motor, wherein the motor load power factor comprises a motor load response factor and a motor power response factor;
s303, acquiring motor running load data by combining the motor load power factor according to the motor power load excitation data;
s304, comparing the motor operation load data with a motor operation load threshold value according to the motor operation load data;
s305, judging the working state of the motor according to a threshold comparison result;
the program calculation formula of the motor power load excitation data is as follows:
；
the calculation formula of the motor operation load data is as follows:
；
wherein W is motor power load excitation data, S is acceleration data, L is centroid line inclination data, H is load data, J is centroid position data,for angular acceleration +.>For dynamic output torque->、/>、/>、/>、/>、/>The power constraint coefficient of the motor (the coefficient is inquired by a multi-axis manipulator intelligent management system); r is motor running load data, v is motor load response factor, and p is motor power response factor.
It should be noted that, according to the configuration attribute information of the linear motor, a preset motor load power factor is obtained, where the motor load power factor is an attribute factor of the performance of the linear motor, reflects the inherent attribute of the load power of the linear motor, and includes a motor load response factor and a motor power response factor, and according to the obtained motor operation load data and a preset motor operation load threshold, a threshold comparison is performed to determine an operation state of the motor in a period of a preset time period, where a period of the threshold comparison is [0,0.75 ], [0.75,0.95 ]), [0.95,1.0], which respectively correspond to a light load, a normal load and an overload of the motor operation load, and if the motor operation state threshold is 0.92, the motor operation load is the normal load.
According to the embodiment of the invention, the motor power interference factor is obtained by calculating the current motor working state threshold value obtained by comparison and combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data, and is specifically as follows:
calculating to obtain a motor power interference factor according to the threshold comparison result of the motor working state and combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data;
the calculation program of the motor power interference factor is as follows:
;
wherein n is the motor power interference factor,the motor working state threshold value is that S is acceleration data, L is centroid line inclination data, H is load data and J is a centroid positionPut data, ->、/>、/>、/>Is a motor power constraint coefficient.
It should be noted that, in order to evaluate the interference condition of the linear motor under the manual force load of the multi-axis machine in the preset period of time, a calculation program is set to obtain a motor power interference factor, and the interference factor can reflect the dynamic interference under the power load excitation of the motor.
According to the embodiment of the invention, the motor output efficacy data is obtained by estimating and processing the motor to-be-adapted state in a motor disturbance self-adaptive model according to the motor power load excitation data in combination with the motor load power factor and the motor power interference factor, specifically:
Inquiring in a multi-axis manipulator intelligent management system according to multi-axis manipulator type information and configuration attribute information of the linear motor to obtain a corresponding motor disturbance self-adaptive model;
the motor disturbance self-adaptive model is obtained through training according to a motor disturbance data set collected according to the type of the multi-axis manipulator and the linear motor attribute history;
the motor disturbance data set comprises sample motor power load excitation data, sample motor load power factors, sample motor power interference factors and sample motor output efficacy data which are acquired in a historical manner;
and according to the obtained motor power load excitation data, combining the motor load power factor and the motor power interference factor, performing estimation processing in the motor disturbance self-adaptive model to obtain motor output efficacy data.
It should be noted that, in order to obtain parameters of motor adaptive output power, torque and kinetic energy under adaptive linear motor adaptive property and interference and motor power load excitation, according to motor power load excitation data and combining motor load power factor and motor power interference factor, estimation processing is performed in a motor disturbance adaptive model to obtain motor output efficacy data, the motor output efficacy data can reflect the motor adaptive output parameters, and the motor output efficacy data is adaptive output data under adaptive state excitation of the linear motor, where, in order to obtain a motor disturbance adaptive model adaptive to multi-axis manipulator type and linear motor configuration property, processing training is performed in an initial model according to obtained historical collected sample data to obtain a trained motor disturbance adaptive model, and the greater and closer the number of sample data is, the more accurate the obtained motor disturbance adaptive model is;
The calculation formula of the motor output efficiency data is as follows:
；
wherein K is motor output efficacy data, W is motor power load excitation data, v is motor load response factor, p is motor power response factor, n is motor power interference factor,and the power interference coefficient of the motor (the system is obtained by inquiring the configuration attribute information of the linear motor).
According to the embodiment of the invention, the linear motor is regulated according to the motor output efficacy data and motor correction aging change data is obtained, specifically:
adjusting the output power of the linear motor according to the obtained motor output efficacy data;
collecting second motor operation load data of the motor in a time period after adjustment;
performing difference comparison according to the second motor operation load data and the motor operation load data before adjustment to obtain difference data;
and correcting the aging change data as motor according to the difference data.
The output power of the linear motor is adjusted according to the obtained motor output efficiency data, second motor operation load data of the motor in a period of time is acquired and calculated after adjustment is completed, difference data is obtained by calculating a difference value between the second motor operation load data and motor operation load data before adjustment, and the difference data is used as motor correction failure change data and is used as test data for motor adjustment effect.
According to the embodiment of the invention, the validity judgment of the working state of the motor after adjustment is carried out according to the motor correction aging change data, specifically comprises the following steps:
setting a motor correction efficiency threshold according to the motor load power factor;
comparing the motor correction aging change data with the motor correction efficiency threshold value according to the motor correction aging change data;
and if the motor correction aging change data is larger than the motor correction efficiency threshold, the motor adjustment efficiency effectiveness is effective, otherwise, the motor adjustment efficiency is ineffective.
The method is characterized in that a correspondingly set motor correction efficiency threshold value is obtained according to a motor load power factor, the threshold value reflects self-adaptive correction capability of the linear motor, the self-adaptive correction capability is an inherent threshold value set according to configuration attributes of the linear motor, the self-adaptive correction capability is determined by the self-attributes of the linear motor, the threshold value is used as a comparison threshold value to measure the adjustment degree and the adjustment effect of the linear motor before and after self-adaptive adjustment, the threshold value is compared with the obtained motor correction aging change data, and the effectiveness of the motor adjustment effect is judged according to a threshold value comparison result.
As shown in fig. 4, the invention also discloses a linear motor self-adaptive intelligent control system 4, which comprises a memory 41 and a processor 42, wherein the memory 41 comprises a linear motor self-adaptive intelligent control method program, and the linear motor self-adaptive intelligent control method program when executed by the processor 42 realizes the following steps:
Acquiring power posture data and gravity load data of the multi-axis manipulator in a preset time period, wherein the power posture data and the gravity load data comprise instantaneous speed data, acceleration data, centroid line inclination data, load distribution data, load data and gravity center position data;
acquiring operation data of a current linear motor, including rotational speed, angular acceleration, dynamic output moment and power load data;
acquiring motor power load excitation data according to the power attitude data, the gravity load data and the operation data, and judging the working state of the motor by combining a preset motor load power factor;
according to the current motor working state threshold value obtained by comparison, combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data to calculate and obtain a motor power interference factor;
according to the motor power load excitation data, combining the motor load power factor and the motor power interference factor, estimating and processing the motor state to be adapted in a motor disturbance self-adaptive model to obtain motor output efficacy data;
adjusting the linear motor according to the motor output efficiency data and acquiring motor correction aging change data;
And judging the validity of the working state of the motor after the motor is regulated according to the motor correction aging change data.
It should be noted that, in order to achieve the purpose of obtaining the output power efficiency data of the motor according to the state parameter data of the multi-axis manipulator and the linear motor, adjusting the input working parameters of the linear motor, and obtaining the suitability good and bad effect judgment of the motor obtaining adjustment according to the state data of the motor in the period obtained after adjustment, that is, the motor correction aging change data, so as to achieve the self-adaptive intelligent adjustment function of the linear motor, specifically, obtain the power posture data and the gravity load data of the multi-axis manipulator in the period of the preset time period and the running data of the current linear motor, obtain the motor power load excitation data according to the power posture data and the gravity load data and the running data, combine the preset motor load power factor to judge the working state of the motor, then calculate the motor power interference factor according to the current motor working state threshold obtained by comparison and combine the acceleration data, the centroid line inclination data, the load distribution data and the center of gravity position data, estimate the motor to obtain the output power efficiency data of the motor according to the motor power excitation data and the motor power interference factor in the motor disturbance self-adaptive model, and finally obtain the power efficiency data of the linear motor, correct the motor and adjust the aging change data according to the motor aging change data.
According to the embodiment of the invention, the power posture data and the gravity load data of the multi-axis manipulator in the preset time period are obtained, wherein the power posture data and the gravity load data comprise instantaneous speed data, acceleration data, centroid line inclination data, load distribution data, load data and centroid position data, and the method specifically comprises the following steps:
acquiring a power modulation period of a linear motor of the multi-axis manipulator;
acquiring a dynamic parameter set of the multi-axis manipulator in a preset time period according to the power modulation period, wherein the dynamic parameter set comprises a weight parameter, a load parameter, an inertia parameter, a speed parameter, an angle posture parameter and a gravity distribution parameter;
acquiring power posture data in the period of the preset time period according to the load parameter, the inertia parameter, the speed parameter and the angle posture parameter, wherein the power posture data comprises instantaneous speed data, acceleration data and centroid line inclination data;
and acquiring gravity load data in the preset time period according to the weight parameter, the load parameter and the gravity distribution parameter, wherein the gravity load data comprises load distribution data, load data and gravity center position data.
The power modulation period is determined according to the adjustment period of the power of the linear motor of the multi-axis manipulator, a set of dynamic parameters of the multi-axis manipulator and the linear motor in a preset time period is acquired according to the modulation period, the preset time period is smaller than or equal to the modulation period, the preset time period for parameter acquisition and adjustment is set as a preset time period, and the power posture data and the gravity load data in the preset time period are respectively obtained according to the acquired dynamic parameter set.
According to the embodiment of the invention, the motor power load excitation data is obtained according to the power posture data, the gravity load data and the operation data, and the motor working state is judged by combining with a preset motor load power factor, specifically:
calculating and obtaining motor power load excitation data according to the obtained acceleration data, centroid line inclination data, load data, centroid position data and angular acceleration and dynamic output moment of the linear motor in the preset time period;
obtaining a preset motor load power factor according to the configuration attribute information of the linear motor, wherein the motor load power factor comprises a motor load response factor and a motor power response factor;
acquiring motor running load data according to the motor power load excitation data and the motor load power factor;
threshold value comparison is carried out according to the motor operation load data and a motor operation load threshold value;
judging the working state of the motor according to the threshold comparison result;
the program calculation formula of the motor power load excitation data is as follows:
；
the calculation formula of the motor operation load data is as follows:
；
wherein W is motor power load excitation data, S is acceleration data, L is centroid line inclination data, H is load data, J is centroid position data, Is angular acceleration，/>For dynamic output torque->、/>、/>、/>、/>、/>The power constraint coefficient of the motor (the coefficient is inquired by a multi-axis manipulator intelligent management system); r is motor running load data, v is motor load response factor, and p is motor power response factor.
It should be noted that, according to the configuration attribute information of the linear motor, a preset motor load power factor is obtained, where the motor load power factor is an attribute factor of the performance of the linear motor, reflects the inherent attribute of the load power of the linear motor, and includes a motor load response factor and a motor power response factor, and according to the obtained motor operation load data and a preset motor operation load threshold, a threshold comparison is performed to determine an operation state of the motor in a period of a preset time period, where a period of the threshold comparison is [0,0.75 ], [0.75,0.95 ]), [0.95,1.0], which respectively correspond to a light load, a normal load and an overload of the motor operation load, and if the motor operation state threshold is 0.92, the motor operation load is the normal load.
According to the embodiment of the invention, the motor power interference factor is obtained by calculating the current motor working state threshold value obtained by comparison and combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data, and is specifically as follows:
Calculating to obtain a motor power interference factor according to the threshold comparison result of the motor working state and combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data;
the calculation program of the motor power interference factor is as follows:
;
wherein n is the motor power interference factor,the motor working state threshold value is that S is acceleration data, L is centroid line inclination data, H is load data, J is centroid position data and +.>、/>、/>、/>Is a motor power constraint coefficient.
It should be noted that, in order to evaluate the interference condition of the linear motor under the manual force load of the multi-axis machine in the preset period of time, a calculation program is set to obtain a motor power interference factor, and the interference factor can reflect the dynamic interference under the power load excitation of the motor.
According to the embodiment of the invention, the motor output efficacy data is obtained by estimating and processing the motor to-be-adapted state in a motor disturbance self-adaptive model according to the motor power load excitation data in combination with the motor load power factor and the motor power interference factor, specifically:
inquiring in a multi-axis manipulator intelligent management system according to multi-axis manipulator type information and configuration attribute information of the linear motor to obtain a corresponding motor disturbance self-adaptive model;
The motor disturbance self-adaptive model is obtained through training according to a motor disturbance data set collected according to the type of the multi-axis manipulator and the linear motor attribute history;
the motor disturbance data set comprises sample motor power load excitation data, sample motor load power factors, sample motor power interference factors and sample motor output efficacy data which are acquired in a historical manner;
and according to the obtained motor power load excitation data, combining the motor load power factor and the motor power interference factor, performing estimation processing in the motor disturbance self-adaptive model to obtain motor output efficacy data.
It should be noted that, in order to obtain parameters of motor adaptive output power, torque and kinetic energy under adaptive linear motor adaptive property and interference and motor power load excitation, according to motor power load excitation data and combining motor load power factor and motor power interference factor, estimation processing is performed in a motor disturbance adaptive model to obtain motor output efficacy data, the motor output efficacy data can reflect the motor adaptive output parameters, and the motor output efficacy data is adaptive output data under adaptive state excitation of the linear motor, where, in order to obtain a motor disturbance adaptive model adaptive to multi-axis manipulator type and linear motor configuration property, processing training is performed in an initial model according to obtained historical collected sample data to obtain a trained motor disturbance adaptive model, and the greater and closer the number of sample data is, the more accurate the obtained motor disturbance adaptive model is;
The calculation formula of the motor output efficiency data is as follows:
；
wherein K is motor output efficacy data, W is motor power load excitation data, v is motor load response factor, p is motor power response factor, n is motor power interference factor,and the power interference coefficient of the motor (the system is obtained by inquiring the configuration attribute information of the linear motor).
According to the embodiment of the invention, the linear motor is regulated according to the motor output efficacy data and motor correction aging change data is obtained, specifically:
adjusting the output power of the linear motor according to the obtained motor output efficacy data;
collecting second motor operation load data of the motor in a time period after adjustment;
performing difference comparison according to the second motor operation load data and the motor operation load data before adjustment to obtain difference data;
and correcting the aging change data as motor according to the difference data.
The output power of the linear motor is adjusted according to the obtained motor output efficiency data, second motor operation load data of the motor in a period of time is acquired and calculated after adjustment is completed, difference data is obtained by calculating a difference value between the second motor operation load data and motor operation load data before adjustment, and the difference data is used as motor correction failure change data and is used as test data for motor adjustment effect.
According to the embodiment of the invention, the validity judgment of the working state of the motor after adjustment is carried out according to the motor correction aging change data, specifically comprises the following steps:
setting a motor correction efficiency threshold according to the motor load power factor;
comparing the motor correction aging change data with the motor correction efficiency threshold value according to the motor correction aging change data;
and if the motor correction aging change data is larger than the motor correction efficiency threshold, the motor adjustment efficiency effectiveness is effective, otherwise, the motor adjustment efficiency is ineffective.
The method is characterized in that a correspondingly set motor correction efficiency threshold value is obtained according to a motor load power factor, the threshold value reflects self-adaptive correction capability of the linear motor, the self-adaptive correction capability is an inherent threshold value set according to configuration attributes of the linear motor, the self-adaptive correction capability is determined by the self-attributes of the linear motor, the threshold value is used as a comparison threshold value to measure the adjustment degree and the adjustment effect of the linear motor before and after self-adaptive adjustment, the threshold value is compared with the obtained motor correction aging change data, and the effectiveness of the motor adjustment effect is judged according to a threshold value comparison result.
A third aspect of the present invention provides a computer readable storage medium, in which a linear motor adaptive intelligent control method program is included, which when executed by a processor, implements the steps of the linear motor adaptive intelligent control method according to any one of the above.
The application discloses a self-adaptive intelligent control method, a system and a storage medium for a linear motor, wherein the method comprises the steps of obtaining power posture data and gravity load data of a multi-axis manipulator in a preset time period and running data of a current linear motor, obtaining motor power excitation data and motor load power factors, judging a motor working state, obtaining motor power interference factors according to a current motor working state threshold value, processing and obtaining motor output efficacy data in a motor disturbance self-adaptive model according to the motor power excitation data and the motor load power factors and the motor power interference factors, adjusting the linear motor, obtaining motor correction time-varying data and judging the validity of the working state after motor adjustment, so that the technology of self-adaptive intelligent control of the motor is realized according to the motor power interference factors and obtaining motor output efficacy data in the motor disturbance self-adaptive model according to the motor power excitation data.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a removable storage device, a read-only memory, a random access memory, a magnetic or optical disk, or other various media capable of storing program code.
Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
Claims (10)
1. The self-adaptive intelligent control method for the linear motor is characterized by comprising the following steps of:
acquiring power posture data and gravity load data of the multi-axis manipulator in a preset time period, wherein the power posture data and the gravity load data comprise instantaneous speed data, acceleration data, centroid line inclination data, load distribution data, load data and gravity center position data;
acquiring operation data of a current linear motor, including rotational speed, angular acceleration, dynamic output moment and power load data;
Acquiring motor power load excitation data according to the power attitude data, the gravity load data and the operation data, and judging the working state of the motor by combining a preset motor load power factor;
according to the current motor working state threshold value obtained by comparison, combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data to calculate and obtain a motor power interference factor;
according to the motor power load excitation data, combining the motor load power factor and the motor power interference factor, estimating and processing the motor state to be adapted in a motor disturbance self-adaptive model to obtain motor output efficacy data;
adjusting the linear motor according to the motor output efficiency data and acquiring motor correction aging change data;
and judging the validity of the working state of the motor after the motor is regulated according to the motor correction aging change data.
2. The method for adaptively controlling a linear motor according to claim 1, wherein the acquiring power posture data and gravity load data of the multi-axis manipulator in a preset period of time includes instantaneous velocity data, acceleration data, centroid line inclination data, load distribution data, load data and centroid position data, and includes:
Acquiring a power modulation period of a linear motor of the multi-axis manipulator;
acquiring a dynamic parameter set of the multi-axis manipulator in a preset time period according to the power modulation period, wherein the dynamic parameter set comprises a weight parameter, a load parameter, an inertia parameter, a speed parameter, an angle posture parameter and a gravity distribution parameter;
acquiring power posture data in the period of the preset time period according to the load parameter, the inertia parameter, the speed parameter and the angle posture parameter, wherein the power posture data comprises instantaneous speed data, acceleration data and centroid line inclination data;
and acquiring gravity load data in the preset time period according to the weight parameter, the load parameter and the gravity distribution parameter, wherein the gravity load data comprises load distribution data, load data and gravity center position data.
3. The method for adaptively controlling a linear motor according to claim 2, wherein the step of obtaining motor power load excitation data according to the power posture data, the gravity load data and the operation data and determining the motor operating state by combining a preset motor load power factor comprises the steps of:
calculating and obtaining motor power load excitation data according to the obtained acceleration data, centroid line inclination data, load data, centroid position data and angular acceleration and dynamic output moment of the linear motor in the preset time period;
Obtaining a preset motor load power factor according to the configuration attribute information of the linear motor, wherein the motor load power factor comprises a motor load response factor and a motor power response factor;
acquiring motor running load data according to the motor power load excitation data and the motor load power factor;
threshold value comparison is carried out according to the motor operation load data and a motor operation load threshold value;
judging the working state of the motor according to the threshold comparison result;
the program calculation formula of the motor power load excitation data is as follows:
；
the calculation formula of the motor operation load data is as follows:
；
wherein W is motor power load excitation data, S is acceleration data, L is centroid line inclination data, H is load data, J is centroid position data,for angular acceleration +.>For dynamic output torque->、/>、/>、/>、/>、/>Is a motor power constraint coefficient; r is motor running load data, v is motor load response factor, and p is motor power response factor.
4. The method for adaptively controlling a linear motor according to claim 3, wherein the calculating the motor power interference factor according to the compared current motor operating state threshold value in combination with the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data comprises:
Calculating to obtain a motor power interference factor according to the threshold comparison result of the motor working state and combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data;
the calculation program of the motor power interference factor is as follows:
;
wherein n is the motor power interference factor,s is acceleration data, L is centroid line inclination data, H is load data, J is centroid position data, and +_is for threshold comparison result>、/>、/>、/>Is a motor power constraint coefficient.
5. The method for adaptively controlling a linear motor according to claim 4, wherein the estimating the state to be adapted of the motor in the motor disturbance adaptive model according to the motor power load excitation data in combination with the motor load power factor and the motor power interference factor to obtain motor output efficacy data includes:
inquiring in a multi-axis manipulator intelligent management system according to multi-axis manipulator type information and configuration attribute information of the linear motor to obtain a corresponding motor disturbance self-adaptive model;
the motor disturbance self-adaptive model is obtained through training according to a motor disturbance data set collected according to the type of the multi-axis manipulator and the linear motor attribute history;
The motor disturbance data set comprises sample motor power load excitation data, sample motor load power factors, sample motor power interference factors and sample motor output efficacy data which are acquired in a historical manner;
and according to the obtained motor power load excitation data, combining the motor load power factor and the motor power interference factor, performing estimation processing in the motor disturbance self-adaptive model to obtain motor output efficacy data.
6. The adaptive intelligent control method of a linear motor according to claim 5, wherein the adjusting the linear motor according to the motor output efficiency data and obtaining motor correction aging data comprises:
adjusting the output power of the linear motor according to the obtained motor output efficacy data;
collecting second motor operation load data of the motor in a time period after adjustment;
performing difference comparison according to the second motor operation load data and the motor operation load data before adjustment to obtain difference data;
and correcting the aging change data as motor according to the difference data.
7. The adaptive intelligent control method of a linear motor according to claim 6, wherein the determining the validity of the adjusted working state of the motor according to the motor correction aging data comprises:
Setting a motor correction efficiency threshold according to the motor load power factor;
comparing the motor correction aging change data with the motor correction efficiency threshold value according to the motor correction aging change data;
and if the motor correction aging change data is larger than the motor correction efficiency threshold, the motor adjustment efficiency effectiveness is effective, otherwise, the motor adjustment efficiency is ineffective.
8. A self-adaptive intelligent control system of a linear motor is characterized in that the system comprises: the linear motor self-adaptive intelligent control system comprises a memory and a processor, wherein the memory comprises a linear motor self-adaptive intelligent control method program, and the linear motor self-adaptive intelligent control method program is executed by the processor to realize the following steps:
acquiring power posture data and gravity load data of the multi-axis manipulator in a preset time period, wherein the power posture data and the gravity load data comprise instantaneous speed data, acceleration data, centroid line inclination data, load distribution data, load data and gravity center position data;
acquiring operation data of a current linear motor, including rotational speed, angular acceleration, dynamic output moment and power load data;
acquiring motor power load excitation data according to the power attitude data, the gravity load data and the operation data, and judging the working state of the motor by combining a preset motor load power factor;
According to the current motor working state threshold value obtained by comparison, combining the acceleration data, the centroid line inclination data, the load distribution data and the centroid position data to calculate and obtain a motor power interference factor;
according to the motor power load excitation data, combining the motor load power factor and the motor power interference factor, estimating and processing the motor state to be adapted in a motor disturbance self-adaptive model to obtain motor output efficacy data;
adjusting the linear motor according to the motor output efficiency data and acquiring motor correction aging change data;
and judging the validity of the working state of the motor after the motor is regulated according to the motor correction aging change data.
9. The adaptive intelligent control system of claim 8, wherein the acquiring power pose data and gravity load data of the multi-axis manipulator over a preset period of time, including instantaneous velocity data, acceleration data, centroid line tilt data, load distribution data, load data, and centroid position data, comprises:
acquiring a power modulation period of a linear motor of the multi-axis manipulator;
acquiring a dynamic parameter set of the multi-axis manipulator in a preset time period according to the power modulation period, wherein the dynamic parameter set comprises a weight parameter, a load parameter, an inertia parameter, a speed parameter, an angle posture parameter and a gravity distribution parameter;
Acquiring power posture data in the period of the preset time period according to the load parameter, the inertia parameter, the speed parameter and the angle posture parameter, wherein the power posture data comprises instantaneous speed data, acceleration data and centroid line inclination data;
and acquiring gravity load data in the preset time period according to the weight parameter, the load parameter and the gravity distribution parameter, wherein the gravity load data comprises load distribution data, load data and gravity center position data.
10. A computer readable storage medium, wherein the computer readable storage medium includes a linear motor adaptive intelligent control method program, and when the linear motor adaptive intelligent control method program is executed by a processor, the steps of the linear motor adaptive intelligent control method according to any one of claims 1 to 7 are implemented.
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