CN109491251B - AC servo system model identification method and equipment considering data disturbance compensation - Google Patents

AC servo system model identification method and equipment considering data disturbance compensation Download PDF

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CN109491251B
CN109491251B CN201811550131.1A CN201811550131A CN109491251B CN 109491251 B CN109491251 B CN 109491251B CN 201811550131 A CN201811550131 A CN 201811550131A CN 109491251 B CN109491251 B CN 109491251B
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谢远龙
王书亭
刘伦洪
罗年猛
张捷
蒋立泉
孟杰
赵伟
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Huazhong University of Science and Technology
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Abstract

The invention discloses an alternating current servo system model identification method and equipment considering data disturbance compensation, and belongs to the technical field of motion control of alternating current servo systems. The method comprises the following steps: the method comprises the steps of constructing a model structure of an alternating current servo system, designing a model identification criterion function, carrying out an excitation experiment on the system, obtaining corresponding input and output feedback data, further calculating data disturbance compensation quantity by using an expected value of process data, and superposing the data disturbance compensation quantity to self-adaptive updating correction of model parameters, so that the influence of data disturbance is eliminated, and the accuracy of a model identification algorithm is improved. The model identification method considering data disturbance compensation provided by the invention simultaneously considers the situations of data noise interference and data frame loss, can correct the frame loss rate of the controlled system in real time, has wider applicability, can iteratively correct data disturbance information estimated unbiased to the adaptive updating of model parameters of the system, reduces the identification error of the model, and further improves the precision and reliability of the model identification algorithm.

Description

AC servo system model identification method and equipment considering data disturbance compensation
Technical Field
The invention belongs to the technical field of motion control of an alternating current servo system, and particularly relates to an alternating current servo system model identification method considering data disturbance compensation.
Background
In the field of industrial automation, a bus type alternating current servo system forms a closed-loop control system by using an advanced Ethernet field bus, thereby avoiding a traditional point-to-point connection mode. Compared with the traditional pulse type alternating current servo system, the bus type alternating current servo system has the advantages of high speed, large data packet capacity, long transmission distance, low cost, flexible support of physical topological structure and the like.
However, when the relevant signal is acquired, noise interference may be generated due to the actual condition of the operating environment or the defects of the sensor itself, which affects the accuracy of data acquisition, so that the real signal of the system acquired by the upper computer is actually a signal with measurement disturbance or measurement noise. Meanwhile, in the process of transmitting data to the upper computer, due to the actual connection condition of the link, problems such as data collision, node contention failure and the like will cause the loss of the transmitted data frame. On the other hand, in consideration of link congestion or damage in the data transmission process, etc., the master device and the slave device receive data frames which are inconsistent with the transmitted data frames, and invalid field bus data packets are generated, so that data is incomplete.
When a control decision is made for the bus type alternating current servo system, under the conditions of noise interference and data frame loss, the data disturbance information of the system is utilized to determine the model structure of a controlled object and identify the model parameters. The existing compensation method for data disturbance has two problems:
1) unbiased estimation of disturbance information is not simultaneously performed for two conditions of noise interference and data frame loss, so that the practicability is limited, and the complexity of a model identification algorithm is increased if compensation strategies are respectively studied for the noise interference and the data frame loss;
2) the compensation method for data frame loss needs to predict the frame loss probability of the system or the system time axis of the frame loss period in advance, and the frame loss rate of the data driving system at different moments is time-varying in consideration of the change of external environment, such as abrupt communication link state, sensor failure and the like.
Disclosure of Invention
Aiming at the defects or improvement requirements in the prior art, the invention provides an alternating current servo system model identification method and equipment considering data disturbance compensation, and aims to carry out unbiased estimation on data disturbance information by utilizing input and output data actually acquired by a system, so that the data disturbance information is superposed into adaptive updating of system model parameters, model identification errors are finally reduced, and the precision and reliability of a model identification algorithm are improved.
To achieve the above object, according to an aspect of the present invention, there is provided an ac servo system model identification method considering data disturbance compensation, including the following steps:
step 1: determining a model structure of the alternating current servo system:
Figure BDA0001910429120000021
wherein q is a time shift factor, iq(t) and y (t) are input current signal and output speed signal respectively,
Figure BDA0001910429120000022
and
Figure BDA0001910429120000023
for the system model parameters to be identified, nbAnd naThe order of the input signal and the output signal, respectively, t representing a time sequence;
setting up
Figure BDA0001910429120000024
For the system model parameter matrix to be optimized, then y (t) is simplified to:
Figure BDA0001910429120000025
Figure BDA0001910429120000026
wherein,
Figure BDA0001910429120000027
representing a data set collected by the system for an information matrix;
step 2: and (3) carrying out an excitation experiment to obtain corresponding feedback data:
considering the random data frame loss and the measurement noise of the input end and the output end in the model structure of the step 1, exchanging the input data i actually received by the servo systemqm(t) and output data ym(t) is expressed as:
iqm(t)＝iqr(t)iq(t)+iqd(t)
ym(t)＝yr(t)y(t)+yd(t)
wherein iqd(t) and yd(t) noise interference for input data and output data, iqr(t) and yr(t) is a random binary variable characterizing whether actual data is successfully transmitted, a value of 1 indicates that actual data is successfully transmitted, a value of 0 indicates that a data frame is lost, and i is present considering that input and output data always exist in the same linkqr(t)＝yr(t)；
And step 3: designing a model identification optimization criterion function:
with the actual collected process data, the design model identification optimization criteria function is as follows:
Figure BDA0001910429120000031
Figure BDA0001910429120000032
Figure BDA0001910429120000033
wherein,
Figure BDA0001910429120000034
for identifying the optimal model parameters of the AC servo system, which are obtained, the estimated value of theta for minimizing J (theta) is represented, N represents the data capacity,
Figure BDA0001910429120000035
representing an information matrix constructed using the actual feedback signal;
and 4, step 4: unbiased estimation data disturbance compensation quantity:
defining the real-time compensation amount of the disturbance information as
Figure BDA0001910429120000036
Then the real-time compensation amount of the disturbance information can be calculated by the following formula:
Figure BDA0001910429120000037
wherein,
Figure BDA0001910429120000038
identifying a model parameter matrix obtained at the moment (t-1) according to the model identification optimization criterion function in the step (3);
and 5: model parameter adaptive update taking data disturbance compensation into account:
obtained according to step 4
Figure BDA0001910429120000041
The model parameter adaptive update is represented by the following formula:
Figure BDA0001910429120000042
Figure BDA0001910429120000043
wherein, grad represents calculating falling gradient information,
Figure BDA0001910429120000044
r (t) is a convergence factor, and satisfies the condition that r (0) is r0Not less than 0, when satisfying
Figure BDA0001910429120000045
And (4) finishing the iteration, wherein a is a preset threshold value.
Further, in step 5, r (t) is calculated by the following formula:
Figure BDA0001910429120000046
Figure BDA0001910429120000047
wherein λ (t) and λ (t-1) are intermediate variables, and ζ (t) represents a forgetting factor, which is specifically defined as:
Figure BDA0001910429120000048
wherein, mu1And mu2To adjust the coefficients, to balance the stability and convergence of the model identification algorithm.
Further, in step 4, definition E represents the expected value,
Pr＝E[iqr(t)]＝E[yr(t)]for the expected value of the probability of successful data transmission, the following expected value relationship can be obtained:
Figure BDA0001910429120000049
Figure BDA00019104291200000410
wherein s represents an arbitrary time;
thus, a desired correlation of no data perturbation to an information matrix with data perturbation can be established:
Figure BDA0001910429120000051
Figure BDA0001910429120000052
wherein diag represents a diagonal matrix, Λ (t) is a data disturbance compensation quantity to be estimated,
Figure BDA0001910429120000053
is dimension nθAnd a unit matrix of
Figure BDA0001910429120000054
And
Figure BDA0001910429120000055
are intermediate variables, respectively representing:
Figure BDA0001910429120000056
Figure BDA0001910429120000057
thus, there are:
Figure BDA0001910429120000058
Figure BDA0001910429120000059
is Λ (t).
According to another aspect of the present invention, there is provided an ac servo system model identification apparatus considering data disturbance compensation, including a processor and an ac servo system model identification program module; the ac servo system model identification program module executes any one of the above-mentioned ac servo system model identification methods when being called by the processor.
In general, compared with the prior art, the above technical solution contemplated by the present invention can obtain the following beneficial effects:
1. compared with the prior art, the model identification algorithm considering data disturbance compensation simultaneously considers the conditions of data noise interference and data frame loss, and can correct the frame loss rate of the controlled system in real time, so the method has wider applicability
2. The method of the invention does not need to predict the expected information and the frame loss rate of the data noise in advance, can adaptively adjust the model identification parameters according to the real-time running state of the system, and can iteratively correct the estimated data disturbance information into the adaptive updating of the model parameters of the system, thereby reducing the model identification error and further improving the precision and the reliability of the model identification algorithm.
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FIG. 1 is a control structure diagram of an AC servo system according to an embodiment of the present invention;
FIG. 2 is a block diagram of a model identification method considering data disturbance compensation according to an embodiment of the present invention;
FIG. 3 is a flowchart of a model identification method considering disturbance compensation according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and 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. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Fig. 1 shows a speed loop control structure of an ac servo system based on a vector control principle. The speed loop is the basis for realizing the fast tracking and accurate positioning of the servo system and provides a current reference value for the current inner loop. In the actual current closed-loop control, the reference value of the motor current component for generating flux linkage is set toZero is id0. A, B two-phase currents can be obtained through a current sensor, and direct-axis currents and quadrature-axis currents are obtained through Clark and Park conversion and are respectively used as feedback values of flux linkage closed-loop control current components and torque closed-loop control current components. The position and the rotating speed of the rotor can be obtained through the feedback of the encoder, the rotor position information is used for Park and inverse transformation thereof, and the calculated rotating speed of the motor is used for a feedback value of motor speed closed-loop control.
For an alternating current servo system, a speed controller generally adopts a PI controller structure, and the dynamic response performance and stability of the system are ensured. Since the adjustment frequency of the current inner loop is much higher than that of the speed loop, for the control structure of the speed loop, the current inner loop also belongs to a part of the traditional control model of the motor.
FIG. 2 is a block diagram of an AC servo system model identification method considering data disturbance compensation according to the present invention, which is implemented by the following steps as shown in FIG. 3:
step 1: determining a model structure of the alternating current servo system:
Figure BDA0001910429120000061
wherein q is a time shift factor, iq(t) and y (t) are input current signal and output speed signal respectively,
Figure BDA0001910429120000071
and
Figure BDA0001910429120000072
for the system model parameters to be identified, nbAnd naRespectively, order of the input signal and the output signal, n in this embodimentb＝na＝3。
Setting up
Figure BDA0001910429120000073
For the system model parameter matrix to be optimized, then y (t) is simplified to:
Figure BDA0001910429120000074
Figure BDA0001910429120000075
wherein,
Figure BDA0001910429120000076
representing a data set collected by the system for an information matrix;
step 2: and (3) carrying out an excitation experiment to obtain corresponding feedback data:
setting input and output data information of a model identification optimization criterion acquisition system, considering random data frame loss and measurement noise of an input end and an output end, and exchanging input data i received by a servoqm(t) and output data ym(t) can be expressed as:
iqm(t)＝iqr(t)iq(t)+iqd(t)
ym(t)＝yr(t)y(t)+yd(t)
wherein iqd(t) and yd(t) is noise interference, iqr(t) and yr(t) is a random binary variable for representing whether the actual data is successfully transmitted, the value of 1 represents the successful transmission of the actual data, the value of 0 represents the data loss frame, and considering the communication principle and the protocol mechanism of the bus type alternating current servo system data transmission, the input data and the output data are transmitted through the same link, so iqr(t)＝yr(t)。
And step 3: designing a model identification optimization criterion function:
thus, the model identification optimization criteria are:
Figure BDA0001910429120000077
wherein N-100 represents the size of the data capacity,
Figure BDA0001910429120000078
an information matrix constructed using the actual feedback signal is represented as:
Figure BDA0001910429120000081
the optimal model parameters are then:
Figure BDA0001910429120000082
Figure BDA0001910429120000083
representing system model parameters identified using an optimization algorithm
And 4, step 4: unbiased estimation data disturbance compensation quantity:
from the concept of the information matrix, the following expected values can be derived:
Figure BDA0001910429120000084
Figure BDA0001910429120000085
wherein E represents an expectation value, Pr＝E[iqr(t)]＝E[yr(t)]The expected value of smooth data transmission is obtained.
Thus, a desired correlation of no data perturbation to an information matrix with data perturbation can be established:
Figure BDA0001910429120000086
Figure BDA0001910429120000087
wherein Λ (t) is the data disturbance compensation quantity to be estimated,
Figure BDA0001910429120000088
is dimension nθAnd a unit matrix of
Figure BDA0001910429120000089
And
Figure BDA00019104291200000810
are intermediate variables, respectively representing:
Figure BDA00019104291200000811
Figure BDA00019104291200000812
thus, there are:
Figure BDA00019104291200000813
the compensation amount of disturbance information can be derived
Figure BDA00019104291200000814
Represented by the formula:
Figure BDA00019104291200000815
and 5: model parameter adaptive update taking data disturbance compensation into account
Calculating the descending gradient information of the model parameter identification optimization criterion:
Figure BDA0001910429120000091
where grad represents calculating falling gradient information.
After the data disturbance compensation quantity is superposed, the model parameter self-adaptive updating is represented by the following formula:
Figure BDA0001910429120000092
wherein,
Figure BDA0001910429120000093
r (t) is a convergence factor, and when the variation of the model parameter satisfies that r (0) is 1 ≧ 0
Figure BDA0001910429120000094
When the iteration is finished, a is 0.01 and is a preset threshold value.
Further, the convergence factor of the present invention is calculated by the following formula:
Figure BDA0001910429120000095
Figure BDA0001910429120000096
wherein λ (t) and λ (t-1) are intermediate variables, and ζ (t) represents a forgetting factor, which is specifically defined as:
Figure BDA0001910429120000097
wherein, mu11 and μ22 is an adjustment coefficient, which can balance the stability and convergence of the model identification algorithm.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (4)

1. An alternating current servo system model identification method considering data disturbance compensation is characterized by comprising the following steps:
step 1: determining a model structure of the alternating current servo system:
Figure FDA0001910429110000011
wherein q is a time shift factor, iq(t) and y (t) are input current signal and output speed signal respectively,
Figure FDA0001910429110000012
and
Figure FDA0001910429110000013
for the system model parameters to be identified, nbAnd naThe order of the input signal and the output signal, respectively, t representing a time sequence;
setting up
Figure FDA0001910429110000014
For the system model parameter matrix to be optimized, then y (t) is simplified to:
Figure FDA0001910429110000015
Figure FDA0001910429110000016
wherein,
Figure FDA0001910429110000017
representing a data set collected by the system for an information matrix;
step 2: and (3) carrying out an excitation experiment to obtain corresponding feedback data:
considering the random data frame loss and the measurement noise of the input end and the output end in the model structure of the step 1, exchanging the input data i actually received by the servo systemqm(t) and output data ym(t) is expressed as:
iqm(t)＝iqr(t)iq(t)+iqd(t)
ym(t)＝yr(t)y(t)+yd(t)
wherein iqd(t) and yd(t) noise interference for input data and output data, iqr(t) and yr(t) is a random binary variable characterizing whether actual data is successfully transmitted, a value of 1 indicates that actual data is successfully transmitted, a value of 0 indicates that a data frame is lost, and i is present considering that input and output data always exist in the same linkqr(t)＝yr(t)；
And step 3: designing a model identification optimization criterion function:
with the actual collected process data, the design model identification optimization criteria function is as follows:
Figure FDA0001910429110000021
Figure FDA0001910429110000022
Figure FDA0001910429110000023
wherein,
Figure FDA0001910429110000024
for identifying the optimal model parameters of the AC servo system, which are obtained, the estimated value of theta for minimizing J (theta) is represented, N represents the data capacity,
Figure FDA0001910429110000025
representing an information matrix constructed using the actual feedback signal;
and 4, step 4: unbiased estimation data disturbance compensation quantity:
defining the real-time compensation amount of the disturbance information as
Figure FDA0001910429110000026
Then the real-time compensation amount of the disturbance information can be calculated by the following formula:
Figure FDA0001910429110000027
wherein,
Figure FDA0001910429110000028
identifying a model parameter matrix obtained at the moment (t-1) according to the model identification optimization criterion function in the step (3);
and 5: model parameter adaptive update taking data disturbance compensation into account:
obtained according to step 4
Figure FDA0001910429110000029
The model parameter adaptive update is represented by the following formula:
Figure FDA00019104291100000210
Figure FDA00019104291100000211
wherein, grad represents calculating falling gradient information,
Figure FDA00019104291100000212
r (t) is a convergence factor, and satisfies the condition that r (0) is r0Not less than 0, when satisfying
Figure FDA00019104291100000213
And (4) finishing the iteration, wherein a is a preset threshold value.
2. The method as claimed in claim 1, wherein r (t) in step 5 is calculated by the following formula:
Figure FDA0001910429110000031
Figure FDA0001910429110000032
wherein λ (t) and λ (t-1) are intermediate variables, and ζ (t) represents a forgetting factor, which is specifically defined as:
Figure FDA0001910429110000033
wherein, mu1And mu2To adjust the coefficients, to balance the stability and convergence of the model identification algorithm.
3. The method as claimed in claim 1 or 2, wherein in step 4, defining E represents an expected value,
Pr＝E[iqr(t)]＝E[yr(t)]for the expected value of the probability of successful data transmission, the following expected value relationship can be obtained:
Figure FDA0001910429110000034
Figure FDA0001910429110000035
wherein s represents an arbitrary time;
thus, a desired correlation of no data perturbation to an information matrix with data perturbation can be established:
Figure FDA0001910429110000036
Figure FDA0001910429110000037
wherein diag represents a diagonal matrix, Λ (t) is a data disturbance compensation quantity to be estimated,
Figure FDA0001910429110000038
is dimension nθAnd a unit matrix of
Figure FDA0001910429110000039
And
Figure FDA00019104291100000310
are intermediate variables, respectively representing:
Figure FDA0001910429110000041
Figure FDA0001910429110000042
thus, there are:
Figure FDA0001910429110000043
Figure FDA0001910429110000044
is Λ (t).
4. An alternating current servo system model identification device considering data disturbance compensation is characterized by comprising a processor and an alternating current servo system model identification program module; the AC servo system model identification program module, when called by the processor, executes the AC servo system model identification method according to any one of claims 1 to 3.
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CN115805590B (en) * 2022-11-17 2024-09-24 武汉纺织大学 Disturbance compensation control method and device for mobile mechanical arm

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5304905A (en) * 1991-04-25 1994-04-19 Mitsubishi Denki Kabushiki Kaisha Motor servo-system controller having comparison of detected current with model current integrations
CN101615023A (en) * 2008-06-27 2009-12-30 深圳市大族激光科技股份有限公司 A kind of method, Apparatus and system of measuring the control performance of lathe servo-drive system
CN102497156A (en) * 2011-12-27 2012-06-13 东南大学 Neural-network self-correcting control method of permanent magnet synchronous motor speed loop
CN103338003A (en) * 2013-06-28 2013-10-02 西安交通大学 Online simultaneous identifying method for load torque and inertia of motor
CN104953913A (en) * 2015-07-03 2015-09-30 兰州交通大学 Networked AC (alternating current) motor LS-SVM (least squares support vector machine) generalized inverse decoupling control method based on active-disturbance rejection
CN105739310A (en) * 2016-02-16 2016-07-06 北京理工大学 Multi-model-based servo system adaptive control system
CN106325073A (en) * 2016-10-31 2017-01-11 华中科技大学 Servo system position IP controller model-free self-correcting method based on fractional order
CN108279571A (en) * 2018-02-02 2018-07-13 哈尔滨工业大学 A kind of model parameter identification method of finite angle electromechanical servo system

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5304905A (en) * 1991-04-25 1994-04-19 Mitsubishi Denki Kabushiki Kaisha Motor servo-system controller having comparison of detected current with model current integrations
CN101615023A (en) * 2008-06-27 2009-12-30 深圳市大族激光科技股份有限公司 A kind of method, Apparatus and system of measuring the control performance of lathe servo-drive system
CN102497156A (en) * 2011-12-27 2012-06-13 东南大学 Neural-network self-correcting control method of permanent magnet synchronous motor speed loop
CN103338003A (en) * 2013-06-28 2013-10-02 西安交通大学 Online simultaneous identifying method for load torque and inertia of motor
CN104953913A (en) * 2015-07-03 2015-09-30 兰州交通大学 Networked AC (alternating current) motor LS-SVM (least squares support vector machine) generalized inverse decoupling control method based on active-disturbance rejection
CN105739310A (en) * 2016-02-16 2016-07-06 北京理工大学 Multi-model-based servo system adaptive control system
CN106325073A (en) * 2016-10-31 2017-01-11 华中科技大学 Servo system position IP controller model-free self-correcting method based on fractional order
CN108279571A (en) * 2018-02-02 2018-07-13 哈尔滨工业大学 A kind of model parameter identification method of finite angle electromechanical servo system

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Data-driven adaptive fractional order PI control for PMSM servo system with measurement noise and data dropouts;Yuanlong Xie等;《ISA Transactions》;20180430;第75卷;第172-188页 *
基于工业以太网的总线型交流伺服系统关键技术研究;丁信忠;《中国博士学位论文全文数据库信息科技辑》;20121231(第12(2012)期);第I140-24页 *
永磁同步电机伺服系统的自抗扰控制技术研究;张捷;《中国优秀硕士学位论文全文数据库工程科技II辑》;20160331(第03(2016)期);第C042-321页 *

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