CN113359438A - Two-axis engraving machine fault estimation method based on two-dimensional gain adjustment mechanism - Google Patents

Two-axis engraving machine fault estimation method based on two-dimensional gain adjustment mechanism Download PDF

Info

Publication number
CN113359438A
CN113359438A CN202110541053.4A CN202110541053A CN113359438A CN 113359438 A CN113359438 A CN 113359438A CN 202110541053 A CN202110541053 A CN 202110541053A CN 113359438 A CN113359438 A CN 113359438A
Authority
CN
China
Prior art keywords
estimation
fault
observer
gain adjustment
adjustment mechanism
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110541053.4A
Other languages
Chinese (zh)
Inventor
杨豫鹏
夏振浩
吴珺
朱俊威
顾曹源
周巧倩
张钧涵
王琪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University of Technology ZJUT
Original Assignee
Zhejiang University of Technology ZJUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang University of Technology ZJUT filed Critical Zhejiang University of Technology ZJUT
Priority to CN202110541053.4A priority Critical patent/CN113359438A/en
Publication of CN113359438A publication Critical patent/CN113359438A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

A two-axis engraving machine fault estimation method based on a two-dimensional gain adjustment mechanism comprises the following steps: step 1), carrying out model identification on the two-axis servo motion control system, and determining a mathematical model of the two-axis servo motion control system; step 2), designing an intermediate observer based on a two-dimensional gain adjustment mechanism; and 3), initializing observer parameters, and calculating observer gains L (k, t) and F (k, t). According to the invention, some key parameters are adjusted on line through a two-dimensional gain adjustment mechanism, so that the on-line fault estimation performance is improved, the reliability of the estimation effect is improved, and the precision of fault estimation is improved.

Description

Two-axis engraving machine fault estimation method based on two-dimensional gain adjustment mechanism
Technical Field
The invention belongs to the field of fault detection and estimation, and particularly provides an IE-based online fault estimation method for simultaneously estimating process faults and sensor faults. A dynamic two-dimensional gain adjustment mechanism is provided, and the mechanism improves the online fault estimation performance by adjusting some key parameters online.
Background
In some complex industrial scenarios requiring real-time supervision, due to possible system component aging or improper operation, a working machine body process failure or a numerical control system sensor failure is inevitably caused, so that a predetermined control performance cannot be timely realized in an emergency. To solve this problem, it is necessary to first identify and estimate the fault and to use online real-time identification, which will be more accurate than looking at the operation of the device through historical data. Fault estimation techniques are powerful tools for ensuring efficient operation of a system because, in addition to being able to capture the time at which a fault occurred, the equipment operator can also know the waveform, magnitude, and extent of damage to the system.
The fault estimation method based on the analytical model at the present stage comprises the following steps: sliding mode observers, adaptive observers, unknown input observers, robust observers, and the like. Among them, robust Extended State Observers (ESOs) based on the worst case performance index are the most common methods in the field of fault diagnosis. However, most of the fault estimation observers are designed off-line, and it is difficult to flexibly improve the fault estimation effect when dealing with some complex and variable systems. In particular, a sliding mode observer and an adaptive observer Intermediate observer (IE) may obtain an upper bound of the fault estimation error, but the upper bound is usually a very large number, which may hardly reflect the effective estimation performance. Since the design of robust ESOs is based on an optimization strategy where the performance indicators are worst-case, the same problem occurs in this type of observer. On the other hand, it should be emphasized that the existing fault estimation methods are still strict in conservatism, for example, robust ESOs usually use convex hull theory to solve some linear matrix inequalities, but the actual system parameters only use a small part of vertices formed by convex set. In addition, the adaptive observer needs to set several equality constraints, which are difficult to satisfy when system parameters change. Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a two-axis engraving machine fault estimation method based on a two-dimensional gain adjustment mechanism, and the provided dynamic two-dimensional gain adjustment mechanism can adjust some key parameters on line and improve the on-line fault estimation performance.
The invention provides the following technical scheme for solving the technical problems:
a two-axis engraving machine fault estimation method based on a two-dimensional gain adjustment mechanism comprises the following steps:
step 1), carrying out model identification on the two-axis servo motion control system, determining a mathematical model of the two-axis servo motion control system, and obtaining the mathematical model of the motion control platform after system identification as follows
x(k+1)＝Ax(k)+Bu(k)+Eufu(k) (1)
y(k)＝Cx(k)+Eyfy(k) (2)
Wherein
Figure BDA0003071575740000021
Respectively representing system status, control inputs, measurement outputs, process faults and sensor faults,
Figure BDA0003071575740000022
each representing a matrix of system parameters of appropriate dimensions,
Figure BDA0003071575740000023
a parameter distribution matrix representing the corresponding fault, wherein Eu is assumed to be a column full rank matrix, if EuB, then the process fault f at this timeu(k) Can be considered as an actuator failure, will
Figure BDA0003071575740000024
Rates of change, denoted as process fault and sensor fault, respectively, are assumed to be bounded, i.e., both are bounded
Figure BDA0003071575740000025
Wherein eta is more than 0 and gamma is more than 0;
step 2), designing an intermediate observer based on a two-dimensional gain adjustment mechanism, wherein the process is as follows:
first, the following intermediate variables are defined
Figure BDA0003071575740000026
Where ω is a manipulated variable to be determined, from which the value of the intermediate variable τ (k) is ultimately determined, the following system model is derived from (2) and (4)
Figure BDA0003071575740000027
The dynamic equation of the system is only related to the time axis parameter k, and in the novel IE to be mentioned later, besides the updating of the parameter along the time axis and the operation of the system, the improvement of the fault estimation result in another dimension is considered, namely, a set of T-step updating processes are additionally added in the interval of each sampling period; before the form of the observer is given, it is necessary to first explain the estimation update strategy of the two dimensions as follows: in order to ensure the estimation performance of the fault, an online estimation performance index with a threshold value is introduced, IE usually carries out parameter updating along a time axis, when the performance index falls into a range which does not meet the threshold value condition, a longitudinal gain adjustment process in a sampling period interval is activated, IE operates under the two-dimensional gain adjustment mechanism until the performance index meets the threshold value condition again, and then IE continues to operate along the time axis, namely returns to a common one-dimensional updating mode;
step 3), initializing observer parameters, and calculating observer gains L (k, t) and F (k, t), wherein the process is as follows:
initializing observer parametersNumber: k: -0, kn:＝0，t＝0，ω(k,t):＝ω(kn,tn)，ts:＝0，tmaxAnd a threshold δ; starting iteration IE, calculating observer gains L (k, t), F (k, t) through (6);
given a positive scalar α ∈ (0,1) and a parameter ω > 0, if a matrix exists
Figure BDA0003071575740000031
Figure BDA0003071575740000032
And a scalar ε > 0, satisfy
Figure BDA0003071575740000033
Then the estimated error of the IE is guaranteed to be consistent with the system state and eventually bounded, and satisfied
Figure BDA0003071575740000034
Wherein the content of the first and second substances,
Figure BDA0003071575740000035
Figure BDA0003071575740000036
Figure BDA0003071575740000037
Figure BDA0003071575740000038
Figure BDA0003071575740000039
Π′11＝Π11-αP1(k,t)，Π′22＝Π22-αP2(k,t)
meanwhile, the observer gain matrix is obtained by the formula:
Figure BDA00030715757400000310
the iteration IE is started and the following estimates are obtained:
Figure BDA00030715757400000311
Figure BDA00030715757400000312
Figure BDA0003071575740000041
Figure BDA0003071575740000042
further, the method comprises the following steps:
step 4), defining an estimation performance evaluation function and judging whether to perform longitudinal updating according to the evaluation function, wherein the process is as follows:
computing
Figure BDA0003071575740000043
And defining an estimated performance evaluation function
Figure BDA0003071575740000044
If the value of the performance evaluation function J ≧ δ, a longitudinal update, ω (k), will be maden,tn):＝ω(k,t)+ξ1When the maximum vertical update step t is exceededmaxWhen the estimated performance evaluation function is still larger than the threshold value, then ω (k)n,tn):＝ω(k,t)+ξ2，ξ1And xi2Respectively representing the step size of ω (k, t) updating in two dimensions;
step 5), judging whether to perform transverse updating according to the evaluation function, wherein the process is as follows:
if the value J of the performance evaluation function is less than or equal to delta, then the performance evaluation function is transversely updated, and the estimation value obtained by IE is used as the final estimation value at the current moment, namely
Figure BDA0003071575740000045
Figure BDA0003071575740000046
Output of
Figure BDA0003071575740000047
In the above step, the observer gains L (k, t) and F (k, t) are obtained by solving the stability condition,
Figure BDA0003071575740000048
Figure BDA0003071575740000049
respectively representing the estimated values obtained after the t-th iteration at the sampling time k.
Figure BDA00030715757400000410
Figure BDA00030715757400000411
Is the final estimate at time k, and, in addition,
Figure BDA00030715757400000412
the moving average value indicating the output estimation error is a performance index for evaluating the estimation effect of the design. q. q.sdThe window time representing the sliding average, δ is a threshold value pre-selected according to the particular system.
The invention considers the application of the two-axis servo motion control platform. The platform adopts a small two-axis engraving machine, the movement of an actuator on an x axis and a y axis is controlled by an alternating current servo motor reaching the ECMA-C10604SS model, the rated power of the platform is 400w, the CANopen protocol is integrated in the interior of the platform with the highest rotating speed of 1500(r/min), and the bus communication speed is about 1 Mb/s; meanwhile, in order to realize high-precision positioning, a Taida ASDA-A2 series servo driver is adopted to realize communication conversion; the control panel selects an STM32F407 development board, and carries an STM32F407ZGT6 chip and a CAN/485 communication interface; the upper computer runs the algorithm and sends corresponding processing results and control instructions to the servo system through the single chip microcomputer. The invention has the beneficial effects that: some key parameters are adjusted on line through a two-dimensional gain adjustment mechanism, so that the on-line fault estimation performance is improved, the reliability of the estimation effect is improved, and the fault estimation precision is improved.
Drawings
FIG. 1 is a flow chart of a two-dimensional gain adjustment mechanism algorithm;
FIG. 2 is a fault estimation performance indicator response curve;
FIG. 3 is a process fault and estimate;
FIG. 4 is a sensor fault and estimate.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention are further described below with reference to the accompanying drawings and simulation experiments.
Referring to fig. 1 to 4, a two-axis engraver fault estimation method based on a two-dimensional gain adjustment mechanism includes the steps of firstly, performing model identification on a two-axis servo motion control system to determine a mathematical model of the two-axis servo motion control system; and then designing an intermediate observer based on a two-dimensional gain adjustment mechanism, and finally judging to perform longitudinal updating or transverse updating according to a performance cost function.
The invention discloses a two-axis engraving machine fault estimation method based on a two-dimensional gain adjustment mechanism, which comprises the following steps of:
1) carrying out model identification on the two-axis servo motion control system, and determining a mathematical model of the two-axis servo motion control system;
2) designing an intermediate observer based on a two-dimensional gain adjustment mechanism;
3) initializing observer parameters and calculating observer gain;
4) performing longitudinal updating according to the performance evaluation function;
5) and performing horizontal updating according to the performance evaluation function.
Further, in the step 1), model identification is performed on the two-axis servo motion control system, a mathematical model of the two-axis servo motion control system is determined, and the mathematical model of the motion control platform obtained after system identification is as follows
x(k+1)＝Ax(k)+Bu(k)+Eufu(k) (1)
y(k)＝Cx(k)+Eyfy(k) (2)
Wherein
Figure BDA0003071575740000051
Respectively representing system status, control inputs, measurement outputs, process faults and sensor faults,
Figure BDA0003071575740000052
B＝[0 3.4414]T，C＝[0 1]respectively representing a system parameter matrix, Eu＝[0.1 0]T，Ey＝[0 1]TDenotes a parameter distribution matrix corresponding to the fault, where E is assumeduIs a column full rank matrix if EuB, then the process fault f at this timeu(k) Can be considered as an actuator failure with process failure set to fu(k)＝0.3sin(0.4k)，
Figure BDA0003071575740000061
In addition to that, will
Figure BDA0003071575740000062
Rates of change, denoted as process fault and sensor fault, respectively, are assumed to be bounded, i.e., both are bounded
Figure BDA0003071575740000063
Wherein eta is more than 0 and gamma is more than 0.
In the step 2), an intermediate observer is designed based on a two-dimensional gain adjustment mechanism, and the process is as follows:
first, the following intermediate variables are defined
Figure BDA0003071575740000064
Where ω is a manipulated variable to be determined, from which the value of the intermediate variable τ (k) is ultimately determined, the following system model is derived from (2) and (4)
Figure BDA0003071575740000065
The dynamic equation of the system is only related to a time axis parameter k, and in a novel IE to be mentioned later, besides updating the parameter along the time axis and running the system, the improvement of the fault estimation result in the other dimension is considered, namely, a group of T-step updating processes are additionally added in the interval of each sampling period, before the form of an observer is given, the estimation updating strategies of the two dimensions are necessarily explained as follows, in order to ensure the estimation performance of the fault, an online estimation performance index with a threshold value is introduced, the IE usually updates the parameter along the time axis, when the performance index falls into the range which does not meet the threshold value condition, a longitudinal gain adjusting process in the interval of the sampling period is activated, the IE runs under the gain adjusting mechanism of the two dimensions until the performance index meets the threshold value condition again, the IE will then continue to run along the time axis, i.e. return to the normal one-dimensional update mode.
In the step 3), observer parameters are initialized, and observer gains L (k, t), F (k, t) are calculated, wherein the process is as follows:
initializing observer parameters: k: -0, kn:＝0，t＝0，ω(k,t):＝ω(kn,tn)，ts:＝0，tmax5 and a threshold δ; the start-up of the iteration IE,and (6) calculating observer gains L (k, t), F (k, t).
Given a positive scalar α ∈ (0,1) and a parameter ω > 0, if a matrix exists
Figure BDA0003071575740000066
Figure BDA0003071575740000071
And a scalar ε > 0, satisfy
Figure BDA0003071575740000072
Then the estimated error of the IE is guaranteed to be consistent with the system state and eventually bounded, and satisfied
Figure BDA0003071575740000073
Setting the threshold delta to 0.22, wherein,
Figure BDA0003071575740000074
Figure BDA0003071575740000075
Figure BDA0003071575740000076
Figure BDA0003071575740000077
Figure BDA0003071575740000078
Π′11＝Π11-αP1(k,t)，Π′22＝П22-αP2(k,t)
simultaneously viewed from the above formulaThe detector gain matrix is:
Figure BDA0003071575740000079
the iteration IE is started and the following estimates are obtained:
Figure BDA00030715757400000710
Figure BDA00030715757400000711
Figure BDA00030715757400000712
Figure BDA00030715757400000713
in the step 4), an estimation performance evaluation function is defined and whether longitudinal updating is performed is judged according to the evaluation function, and the process is as follows:
computing
Figure BDA00030715757400000714
And defining an estimated performance evaluation function
Figure BDA00030715757400000715
If the value of the performance evaluation function J ≧ δ, a longitudinal update, ω (k), will be maden,tn):＝ω(k,t)+ξ1When the maximum vertical update step t is exceededmaxWhen the estimated performance evaluation function is still larger than the threshold value, then ω (k)n,tn):＝ω(k,t)+ξ2，ξ1And xi2Representing the step size of ω (k, t) updates in two dimensions, respectively.
In the step 5), whether to perform transverse updating is judged according to the evaluation function, and the process is as follows:
if the value J of the performance evaluation function is less than or equal to delta, then the performance evaluation function is transversely updated, and the estimation value obtained by IE is used as the final estimation value at the current moment, namely
Figure BDA0003071575740000081
Figure BDA0003071575740000082
Output of
Figure BDA0003071575740000083
In the above step, the observer gains L (k, t) and F (k, t) are obtained by solving the stability condition,
Figure BDA0003071575740000084
Figure BDA0003071575740000085
respectively representing the estimated values obtained after the t-th iteration at the sampling time k,
Figure BDA0003071575740000086
Figure BDA0003071575740000087
is the final estimate at time k, and, in addition,
Figure BDA0003071575740000088
a sliding average value representing an output estimation error is a performance index for evaluating the estimation effect of the design, qdRepresents the window time of the sliding average. δ is a threshold value pre-selected according to the particular system.
The invention provides a novel IE-based fault estimation method, which can limit fault estimation errors in a smaller energy range by introducing a TDGRM algorithm to update observer gains and various estimation values in real time on line. Compared with a common robust control method, the fault estimation method has higher reliability.
The embodiments of the present invention have been described and illustrated in detail above with reference to the accompanying drawings, but are not limited thereto. Many variations and modifications are possible which remain within the knowledge of a person skilled in the art, given the concept underlying the invention.

Claims (2)

1. A two-axis engraving machine fault estimation method based on a two-dimensional gain adjustment mechanism is characterized by comprising the following steps:
step 1), carrying out model identification on the two-axis servo motion control system, determining a mathematical model of the two-axis servo motion control system, and obtaining the mathematical model of the motion control platform after system identification as follows
x(k+1)＝Ax(k)+Bu(k)+Eufu(k) (1)
y(k)＝Cx(k)+Eyfy(k) (2)
Wherein
Figure FDA0003071575730000011
Respectively representing system status, control inputs, measurement outputs, process faults and sensor faults,
Figure FDA0003071575730000012
each representing a matrix of system parameters of appropriate dimensions,
Figure FDA0003071575730000013
a parameter distribution matrix representing the corresponding fault, wherein Eu is assumed to be a column full rank matrix, if EuB, then the process fault f at this timeu(k) Can be considered as an actuator failure, will
Figure FDA0003071575730000014
Rates of change, denoted as process fault and sensor fault, respectively, are assumed to be bounded, i.e., both are bounded
Figure FDA0003071575730000015
Wherein eta is more than 0 and gamma is more than 0;
step 2), designing an intermediate observer based on a two-dimensional gain adjustment mechanism, wherein the process is as follows:
first, the following intermediate variables are defined
Figure FDA0003071575730000016
Where ω is a manipulated variable to be determined, from which the value of the intermediate variable τ (k) is ultimately determined, the following system model is derived from (2) and (4)
Figure FDA0003071575730000017
The dynamic equation of the system is only related to the time axis parameter k, and in the novel IE to be mentioned later, besides the updating of the parameter along the time axis and the operation of the system, the improvement of the fault estimation result in another dimension is considered, namely, a set of T-step updating processes are additionally added in the interval of each sampling period; before the form of the observer is given, it is necessary to first explain the estimation update strategy of the two dimensions as follows: in order to ensure the estimation performance of the fault, an online estimation performance index with a threshold value is introduced, IE usually carries out parameter updating along a time axis, when the performance index falls into a range which does not meet the threshold value condition, a longitudinal gain adjustment process in a sampling period interval is activated, IE operates under the two-dimensional gain adjustment mechanism until the performance index meets the threshold value condition again, and then IE continues to operate along the time axis, namely returns to a common one-dimensional updating mode;
step 3), initializing observer parameters, and calculating observer gains L (k, t) and F (k, t), wherein the process is as follows:
initializing observer parameters: k: -0, kn:＝0，t＝0，ω(k,t):＝ω(kn,tn)，ts:＝0，tmaxAnd a threshold δ; starting iteration IE, calculating observer gains L (k, t), F (k, t) through (6);
given a positive scalar α ∈ (0,1) and a parameter ω > 0, if a matrix exists
Figure FDA0003071575730000021
Figure FDA0003071575730000022
Figure FDA0003071575730000023
And a scalar ε > 0, satisfy
Figure FDA0003071575730000024
Then the estimated error of the IE is guaranteed to be consistent with the system state and eventually bounded, and satisfied
Figure FDA0003071575730000025
Wherein the content of the first and second substances,
Figure FDA0003071575730000026
Figure FDA0003071575730000027
Figure FDA0003071575730000028
Figure FDA0003071575730000029
Figure FDA00030715757300000210
Π′11＝Π11-αP1(k,t)，Π′22＝Π22-αP2(k,t)
meanwhile, the observer gain matrix is obtained by the formula:
Figure FDA00030715757300000211
the iteration IE is started and the following estimates are obtained:
Figure FDA0003071575730000031
Figure FDA0003071575730000032
Figure FDA0003071575730000033
Figure FDA0003071575730000034
2. the method of claim 1 for estimating a fault in a two-axis engraver based on a two-dimensional gain adjustment mechanism, the method further comprising the steps of:
step 4), defining an estimation performance evaluation function and judging whether to perform longitudinal updating according to the evaluation function, wherein the process is as follows:
computing
Figure FDA0003071575730000035
And defining estimatorsCan evaluate the function
Figure FDA0003071575730000036
If the value of the performance evaluation function J ≧ δ, a longitudinal update, ω (k), will be maden,tn):＝ω(k,t)+ξ1When the maximum vertical update step t is exceededmaxWhen the estimated performance evaluation function is still larger than the threshold value, then ω (k)n,tn):＝ω(k,t)+ξ2，ξ1And xi2Respectively representing the step size of ω (k, t) updating in two dimensions;
step 5), judging whether to perform transverse updating according to the evaluation function, wherein the process is as follows:
if the value J of the performance evaluation function is less than or equal to delta, then the performance evaluation function is transversely updated, and the estimation value obtained by IE is used as the final estimation value at the current moment, namely
Figure FDA0003071575730000037
Figure FDA0003071575730000038
Output of
Figure FDA0003071575730000039
In the above step, the observer gains L (k, t) and F (k, t) are obtained by solving the stability condition,
Figure FDA00030715757300000310
Figure FDA00030715757300000311
respectively representing the estimated values obtained after the t-th iteration at the sampling time k,
Figure FDA00030715757300000312
is the final estimate at time k, and, in addition,
Figure FDA00030715757300000313
a sliding average value representing an output estimation error is a performance index for evaluating the estimation effect of the design, qdThe window time representing the sliding average, δ is a threshold value pre-selected according to the particular system.
CN202110541053.4A 2021-05-18 2021-05-18 Two-axis engraving machine fault estimation method based on two-dimensional gain adjustment mechanism Pending CN113359438A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110541053.4A CN113359438A (en) 2021-05-18 2021-05-18 Two-axis engraving machine fault estimation method based on two-dimensional gain adjustment mechanism

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110541053.4A CN113359438A (en) 2021-05-18 2021-05-18 Two-axis engraving machine fault estimation method based on two-dimensional gain adjustment mechanism

Publications (1)

Publication Number Publication Date
CN113359438A true CN113359438A (en) 2021-09-07

Family

ID=77526518

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110541053.4A Pending CN113359438A (en) 2021-05-18 2021-05-18 Two-axis engraving machine fault estimation method based on two-dimensional gain adjustment mechanism

Country Status (1)

Country Link
CN (1) CN113359438A (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107861383A (en) * 2017-10-23 2018-03-30 天津大学 Satellite failure diagnosis and fault tolerant control method based on Adaptive Observer
CN110209148A (en) * 2019-06-18 2019-09-06 江南大学 A kind of Fault Estimation method of the networked system based on description systematic observation device
CN110543162A (en) * 2019-07-24 2019-12-06 浙江工业大学 multiple fault identification method for motion control system under strong noise
CN110580035A (en) * 2019-09-02 2019-12-17 浙江工业大学 motion control system fault identification method under sensor saturation constraint
CN111090945A (en) * 2019-12-20 2020-05-01 淮阴工学院 Actuator and sensor fault estimation design method for switching system
CN111679658A (en) * 2020-06-29 2020-09-18 哈尔滨工业大学 Self-adaptive fault detection and isolation method for uncertain nonlinear control system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107861383A (en) * 2017-10-23 2018-03-30 天津大学 Satellite failure diagnosis and fault tolerant control method based on Adaptive Observer
CN110209148A (en) * 2019-06-18 2019-09-06 江南大学 A kind of Fault Estimation method of the networked system based on description systematic observation device
CN110543162A (en) * 2019-07-24 2019-12-06 浙江工业大学 multiple fault identification method for motion control system under strong noise
CN110580035A (en) * 2019-09-02 2019-12-17 浙江工业大学 motion control system fault identification method under sensor saturation constraint
CN111090945A (en) * 2019-12-20 2020-05-01 淮阴工学院 Actuator and sensor fault estimation design method for switching system
CN111679658A (en) * 2020-06-29 2020-09-18 哈尔滨工业大学 Self-adaptive fault detection and isolation method for uncertain nonlinear control system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
JUNWEI ZHU ETC: ""Performance-Guaranteed Fault Reconstruction for Mobile Robots via a Two-Dimensional Gain-Regulation Mechanism"", 《IEEE/ASME TRANSACTIONS ON MECHATRONICS》 *

Similar Documents

Publication Publication Date Title
CN108920863B (en) Method for establishing energy consumption estimation model of robot servo system
CN109597315B (en) Method, equipment and system for identifying health degradation state of mechanical equipment
CN111913803A (en) Service load fine granularity prediction method based on AKX hybrid model
CN111881574A (en) Wind turbine generator key component reliability modeling method based on distribution function optimization
CN103760827A (en) Saltus constrained off-line planning method for numerical control machining feed rate
CN108763250B (en) Photovoltaic power station monitoring data restoration method
CN114297912A (en) Tool wear prediction method based on deep learning
CN113359438A (en) Two-axis engraving machine fault estimation method based on two-dimensional gain adjustment mechanism
CN104678989A (en) State perception optimization method for improving fault diagnosability in noise environment
CN111859625A (en) Energy-saving control method and device based on big data and storage medium
CN116292057A (en) Control method, device, equipment and medium for variable-speed pumped storage unit
CN116560301A (en) Machine tool feeding system mathematical model parameter identification method based on gradient optimization
CN114093433B (en) Observer-based method and system for evaluating prediction precision of single-ton energy consumption in rectification process
CN115169721A (en) Single-ton energy consumption prediction method and system in rectification process based on migration identification
CN114814707A (en) Intelligent ammeter stress error analysis method, equipment, terminal and readable medium
CN111241749B (en) Permanent magnet synchronous motor chaos prediction method based on reserve pool calculation
CN111428345B (en) Performance evaluation system and method of random load disturbance control system
CN114337427A (en) Rotational inertia identification method of recursive least square method with forgetting factor
CN113569502A (en) Method for predicting air flow velocity of machining workshop of automobile industry
CN116595883B (en) Real-time online system state correction method for numerical reactor
CN116176737B (en) Vehicle control method and device, vehicle and storage medium
Hongzhou et al. Design of testing period for reliability assessment of numerical control machine tools considering working conditions
CN117922683A (en) Steering wheel angle control method, device, equipment and medium based on EPS
CN116384230A (en) Wind turbine generator system component load online estimation method, system and device
US11982996B2 (en) Method and apparatus for configuring processing parameters of production equipment, and computer-readable medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20210907