CN113467236B - Method for time lag compensation of error signal - Google Patents
Method for time lag compensation of error signal Download PDFInfo
- Publication number
- CN113467236B CN113467236B CN202110671903.2A CN202110671903A CN113467236B CN 113467236 B CN113467236 B CN 113467236B CN 202110671903 A CN202110671903 A CN 202110671903A CN 113467236 B CN113467236 B CN 113467236B
- Authority
- CN
- China
- Prior art keywords
- error signal
- time
- lag
- channel
- estimation model
- 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.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000001914 filtration Methods 0.000 claims abstract description 10
- 238000012546 transfer Methods 0.000 claims abstract description 6
- 238000004364 calculation method Methods 0.000 claims abstract description 5
- 230000014509 gene expression Effects 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/12—Timing analysis or timing optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a method for time lag compensation of error signals, which comprises the following steps: 1) Identifying a time-lag channel in a control system; identifying a primary channel and a secondary channel in a control system to obtain a primary channel estimation model P (z) and a secondary channel estimation model
2) Collecting controller output signal y (n) and error signal e in control process t (n) combining the channel estimation model to identify the time-lag channel filter on line to obtain a time-lag transfer function estimation model
3) According to
Calculating an inverse system B (z) and using it on the acquired error signal e t (n) inverse filtering to obtain the estimated value of the real error signal
4) According to the collected error signal e t (n) and true error signal estimate
Establishing a moving average model, and correcting the estimation error signal to obtain
And the error signal is used as a real error signal after time lag compensation and is put into a control system to control the iterative calculation of filter parameters. The method can improve the convergence speed and the control precision of the main control link.
Description
Technical Field
The invention belongs to the technical field of digital signal processing, and particularly relates to a time-lag compensation method for an error signal.
Background
The real-time performance of the transform domain block least mean square algorithm is poor, the convergence and the stability of the algorithm are influenced by the existence of time lag, and even if the self-adaptive algorithm has certain adaptive capacity to the time lag, the system time lag still needs to be compensated. The source of skew is mainly three-fold: 1. the acquisition and transmission process of signals between hardware; 2. carrying out operation processes of preprocessing and parameter iterative updating on the acquired signals; 3. and outputting the signal to an error signal sensor to acquire the signal. The first part is determined by the system hardware performance; the second part is related to the operation amount and the process by controlling the performance of the processor and the specific realization of an algorithm; the time lag caused by the third part is contained in the phase of the secondary channel, and the model is estimated
Filtering the reference signal may be considered to compensate for this time lag.
In a more complex control algorithm, the influence of time lag caused by complex filtering operation between a reference signal and an output signal on a control effect is large, even the situation of control divergence occurs, and online identification and compensation of the time lag are very necessary. In previous research, the influence of compensation time lag on a control system is roughly divided into two types: 1) Converting random time lag into fixed time delay, and designing a controller; 2) And (4) modeling and analyzing the random time delay, and compensating in an algorithm. Although the former has a simple structure, the problem of low precision exists when random time lag is converted into fixed time lag, and the application provides an inverse modeling control algorithm, and an error signal after compensation is estimated through a time sequence and is used for the operation of the output force of the whole control system.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for time lag compensation of errors aiming at the defects in the prior art, and the convergence speed and the control precision can be obviously improved.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method of time-lag compensation of an error signal, comprising the steps of:
1) Identifying a time-lag channel in a control system; identifying a primary channel and a secondary channel in a control system to obtain a primary channel estimation model P (z) and a secondary channel estimation model
2) Collecting controller output signal y (n) and error signal e in control process t (n) combining the channel estimation model to identify the time-lag channel filter on line to obtain a time-lag transfer function estimation model
3) According to
Calculating an inverse system B (z) and using it on the acquired error signal e t (n) inverse filtering to obtain the estimated value of the real error signal
4) In an actual control system, time lag is directly reflected on a time sequence, and in order to further improve the estimation precision of an error signal, the error signal e is acquired t (n) and true error signal estimate
Establishing a Moving Average (MA) model, and correcting the estimation error signal based on the time series model to obtain
And the real error signal after time lag compensation is used as a real error signal, and is put into a control system to control the iterative calculation of filter parameters.
According to the scheme, the primary channel estimation model P (z) and the secondary channel estimation model
And obtaining the result through an identification algorithm.
According to the scheme, the step 2)
Is updated by the formula
Wherein d (n) is the input signal of time-lag channel identification link, h t And (n) is an error signal of the time lag identification filter, and mu is an iteration step.
According to the above scheme, said step 4) further comprises estimating the true error signal
And correcting, wherein the correcting method comprises the following steps:
obtaining a weighting coefficient according to a moving average model and a least square recursion algorithm, and predicting a real error signal at n moments
Using it as a pair
Correction of (2):
according to the collected error signal e t (n) and an estimate of the true error signal
Establishing a moving average model with a mathematical expression of
In the formula, a i 、c i Is MA coefficient, M and N are MA model order,
is the corrected real error signal;
it is expressed in the form of least squares
Wherein
θ＝[a 1 ,a 2 ,...a M ,c 0 ,c 1 ,...,c N ] T
Using a recursive least square method with forgetting factors to carry out parameter identification to obtain theta, and obtaining the estimation error in the step 3)
Is an objective function such that
Approximation
And amends it, the recursion algorithm is
Wherein δ is a forgetting factor.
The invention has the following beneficial effects: the method can approach a real time-lag channel with high precision; the time-lag prediction compensation can truly restore error signals, and further improves the convergence speed and the control precision of the main control link.
Drawings
FIG. 1 is a block diagram of a control system that accounts for time-lag in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of an on-line time lag estimation method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an error signal skew compensation process according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples below so that those skilled in the art can more clearly understand the present invention. The following should not be construed as limiting the scope of the claimed invention.
As shown in fig. 1, the control algorithm is affected by the existence of the time lag, and the effect of the time lag on the control system is regarded as a section of transfer path, the transfer function is T (z), the amplitude is basically unchanged, and the phase changes with the frequency. When the excitation frequency fluctuates, the time lag is a change parameter, which cannot be directly measured, and T (z) needs to be identified. In a more complex control algorithm, a complex filtering operation is performed between a reference signal and an output signal, so that the influence of time delay on a control effect is large, even the situation of control divergence occurs, and online identification and compensation of the time delay are very necessary.
A method of time-lag compensating an error signal, comprising the steps of:
1) Identifying a time-lag channel in a control system; identifying a primary channel and a secondary channel in a control system to obtain a primary channel estimation model P (z) and a secondary channel estimation model
As can be seen from FIG. 2, the error signal entering the controller is actually the error signal e passing through the skew path, according to the skew generation feature in the active control system t (n) the expression is shown in formula (1):
e t (n)＝e(n)T(z)＝d(n)T(z)+y(n)S(z)T(z) (1)
the error signal passing through the time-lag channel consists of two parts, namely a desired signal d (n) passing through the primary time-lag channel and an output signal y (n) S (z) filtered by the secondary channel, wherein d (n) is obtained by the reference signal x (n) passing through the primary channel P (z), and y (n) S (z) is obtained by the output signal y (n) passing through the secondary channel S (z); any part of the signals can be used as the signals passing through the time-lag channel, and the filter weight coefficient of the time-lag link can be obtained through the adaptive filter by only finding the corresponding signal component of the non-time-lag channel.
In an actual control system, a primary channel is only related to the system characteristics of the double-layer vibration isolation platform and generally does not change; the secondary channel is related to the output characteristic and the system characteristic of the actuator, the nonlinear influence factor is strong, even if the secondary channel is identified through a secondary channel identification algorithm, a certain identification error still exists, and the secondary channel characteristic can change along with the change of the working condition. From the aspects of calculation complexity and practical engineering application, d (n) is determined as a reference input signal of a time-lag identification link, d (n) T (z) is determined as a target signal of the time-lag identification link, see formula (2), and a filter weight coefficient of a time-lag channel is obtained through identification iteration of a self-adaptive filter.
d(n)T(z)＝e t (n)-y(n)S(z)T(z) (2)
To obtain d (n) T (z), the secondary channel filtered output signal y (n) S (z) should be constructed first, and in practical control systems, the secondary channel S (z) is not strictly known, but only by using a discriminant model
Instead of that.
2) Controller output signal y (n) and error signal e in acquisition control process d (n) combining the channel estimation model to identify the time-lag channel filter on line to obtain a time-lag transfer function estimation model
Using time lag channel T (z) as time lag ring section identification filter weight coefficient
It is shown that the process of the present invention,
an estimation model regarded as T (z) takes d (n) as an input signal of a time-lag channel identification link,
as target signal, with h t (n) as an error signal for the time-lag discriminating filter
Without considering the identification error of the secondary channel, with the iteration of the lag filter weight coefficients,
continuously approaching to the actual time-lag channel T (z), and identifying error signal h t (n) approaches to zero, and the time-lag identification link is finished. The iteration of the lag filter weight coefficients is performed, still using the convergence criterion of the least mean square algorithm:
in the formula, d (n) = x (n) P (z). Therefore, before performing skew channel on-line identification, the primary channel P (z) needs to be identified, and the identification result is put into the skew identification.
3) According to
Calculating an inverse system B (z) and using it on the acquired error signal e d (n) inverse filtering to obtain the estimated value of the real error signal
The time lag compensation algorithm is as shown in fig. 3, the acquired error signal can be regarded as a real error signal, the time lag channel filtering is carried out, in order to restore the real error signal, the inverse filtering is carried out before the real error signal enters the control algorithm, and the used filter B (z) meets the following requirements:
in the formula, a time lag estimation filter is used
Instead of a true skew path. Namely that
To e t (n) inverse filtering to obtain the estimated value of the actual error signal e (n)
The estimated value of the real error signal can be obtained by the formula (4.2.11). In an actual control system, time lag can be visually represented in a time sequence, and an error signal e (n) at the time n is acquired at the time n + tau.
4) Obtaining a weighting coefficient according to the MA model and the least square recursive algorithm, and predicting a real error signal at n moments
Using it as a pair
Is corrected to obtain
And putting the error signal into a control system as a real error signal to control the iterative calculation of filter parameters.
In order to further improve the estimation accuracy of the error signal, the estimated error signal is corrected based on a time series model.
According to the collected error signal e t (n) and an estimate of the true error signal
Building a Moving Average (MA) model, mathematical expressions can be written as
In the formula, a i 、c i Is MA coefficient, M and N are model orders (taking positive integer),
is the corrected true error signal.
It is expressed in least squares form
Wherein
θ＝[a 1 ,a 2 ,...a M ,c 0 ,c 1 ,...,c N ] T
Using a recursive least square method with forgetting factors to identify parameters to obtain theta, and taking the estimation error obtained by the formula (7) as a target function to ensure that
Approximation
And corrects it. The recursion algorithm is
In the formula, delta is a forgetting factor, and the value range is generally more than 0.96 and less than 0.99.
The above embodiments are only for illustrating the technical idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention, and not to limit the protection scope of the present invention by this means. All equivalent changes and modifications made according to the spirit of the present invention should be covered in the protection scope of the present invention.
Claims (3)
1. A method of skew compensating an error signal, comprising the steps of:
1) Identifying a time-lag channel in a control system; identifying a primary channel and a secondary channel in a control system to obtain a primary channel estimation model P (z) and a secondary channel estimation model
2) Controller output signal y (n) and error signal e in acquisition control process t (n) combining the channel estimation model to identify the time-lag channel filter on line to obtain a time-lag transfer function estimation model
wherein d (n) is input signal of time-lag channel identification link, h t (n) is an error signal of the time lag identification filter, and mu is an iteration step length;
3) According to
Calculating an inverse system B (z) and using it on the acquired error signal e t (n) inverse filtering to obtain the estimated value of the real error signal
4) According to the collected error signal e t (n) and true error signal estimate
Establishing a moving average model, and correcting the estimation error signal to obtain
And the real error signal after time lag compensation is used as a real error signal, and is put into a control system to control the iterative calculation of filter parameters.
2. The method for skew compensation of an error signal of claim 1, wherein said primary channel estimation model P (z) and secondary channel estimation model P (z)
And obtaining the result through an identification algorithm.
3. A method of skew compensation for an error signal according to claim 1, wherein the estimated error signal is modified in step 4) by:
obtaining a weighting coefficient according to a moving average model and a least square recursion algorithm, and predicting a real error signal at n moments
Use it as a pair
Correction of (2):
according to the collected error signal e t (n) and an estimate of the true error signal
Establishing a moving average model with a mathematical expression of
In the formula, a i 、c i Is MA coefficient, M and N are MA model order,
is the corrected real error signal;
it is expressed in the form of least squares
Wherein
θ＝[a 1 ,a 2 ,...a M ,c 0 ,c 1 ,...,c N ] T
Using a recursive least square method with forgetting factors to carry out parameter identification to obtain theta, and obtaining the estimation error in the step 3)
Is an objective function such that
Approximation
And amends it, the recursion algorithm is
Wherein δ is a forgetting factor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110671903.2A CN113467236B (en) | 2021-06-17 | 2021-06-17 | Method for time lag compensation of error signal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110671903.2A CN113467236B (en) | 2021-06-17 | 2021-06-17 | Method for time lag compensation of error signal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113467236A CN113467236A (en) | 2021-10-01 |
| CN113467236B true CN113467236B (en) | 2022-10-21 |
Family
ID=77870380
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110671903.2A Expired - Fee Related CN113467236B (en) | 2021-06-17 | 2021-06-17 | Method for time lag compensation of error signal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113467236B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114459712B (en) * | 2022-01-11 | 2023-12-29 | 东南大学 | Earthquake simulation vibrating table experimental compensation method based on autoregressive model |
| CN114900401B (en) * | 2022-03-24 | 2024-09-20 | 重庆邮电大学 | DFMA-PONs-oriented channel interference elimination method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101924533A (en) * | 2010-07-19 | 2010-12-22 | 浙江工业大学 | Multivariable time-lag parameter estimation method based on FIR (Finite Impulse Response) model identification |
| KR20110062291A (en) * | 2009-12-03 | 2011-06-10 | 한국과학기술원 | Time delay control with gradient estimator for robot manipulator and robot manipulator controller using the same |
| JP2015197941A (en) * | 2014-04-03 | 2015-11-09 | ヤマハ株式会社 | Sampling frequency estimation device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1945470A (en) * | 2006-11-02 | 2007-04-11 | 上海交通大学 | Two freedom decoupling smith pre-evaluating control system of industrial multiple variable time lag process |
| CN101286044B (en) * | 2008-05-12 | 2010-06-16 | 杭州电子科技大学 | Coal-burning boiler system steam-temperature mixing modeling method |
| CN105467844A (en) * | 2016-01-22 | 2016-04-06 | 陈昊哲 | Boiler overheating steam temperature control method based on Neuron identification |
-
2021
- 2021-06-17 CN CN202110671903.2A patent/CN113467236B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110062291A (en) * | 2009-12-03 | 2011-06-10 | 한국과학기술원 | Time delay control with gradient estimator for robot manipulator and robot manipulator controller using the same |
| CN101924533A (en) * | 2010-07-19 | 2010-12-22 | 浙江工业大学 | Multivariable time-lag parameter estimation method based on FIR (Finite Impulse Response) model identification |
| JP2015197941A (en) * | 2014-04-03 | 2015-11-09 | ヤマハ株式会社 | Sampling frequency estimation device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113467236A (en) | 2021-10-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113467236B (en) | Method for time lag compensation of error signal | |
| CN108205259B (en) | Composite control system based on linear extended state observer and design method thereof | |
| CN108363301B (en) | Contour error cross-coupling control method based on interference observation sliding mode variable structure | |
| CN110119588B (en) | On-line optimization design method based on extended Kalman filtering state estimation value | |
| CN109946979B (en) | Self-adaptive adjusting method for sensitivity function of servo system | |
| CN112731814B (en) | Helicopter vibration active control method based on self-adaptive harmonic recognition frequency response correction | |
| CN108181617B (en) | Filtering method of non-linear frequency modulation system based on tensor product model transformation | |
| CN114371618B (en) | Neural network-based extended state observer compensation method and active disturbance rejection controller | |
| CN113093540B (en) | Sliding mode disturbance observer design method based on wavelet threshold denoising | |
| CN110989353B (en) | Design method of periodic disturbance observer | |
| CN113110021B (en) | Method for identifying servo system and designing controller | |
| CN113851104B (en) | Feedback type active noise control system and method containing secondary channel online identification | |
| CN111222214A (en) | Improved strong tracking filtering method | |
| CN109885807A (en) | Weighting latest estimated linear least squares method method of the Hammerstein system under white noise acoustic jamming | |
| CN115685128A (en) | Radar target tracking algorithm and electronic equipment under maneuvering target scene | |
| CN112684710B (en) | Light beam jitter suppression method based on LQG + PI mixed control strategy | |
| CN111077782B (en) | Continuous system U model disturbance rejection controller design method based on standard | |
| CN114519262A (en) | Air target threat prediction method based on improved GM (1,1) model | |
| CN113867155B (en) | Disturbance identification and self-adaptive compensation method suitable for photoelectric tracking system | |
| CN114859722B (en) | Fuzzy self-adaptive fault-tolerant control method for time-lag nonlinear solidification process system | |
| CN115951364B (en) | Method for improving positioning accuracy of piezoelectric type rapid steering mirror platform | |
| CN114878900B (en) | Adaline neural network and FFT (fast Fourier transform) compensated flicker value measurement method | |
| CN113724680B (en) | Active noise control algorithm based on maximum correlation entropy criterion | |
| CN117347993A (en) | Noise self-adaptive estimation method and system for radar track tracking | |
| CN114266103A (en) | Aircraft parameter and noise characteristic online estimation method and storage 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20221021 |