CN106647257A - Feedforward control method based on orthogonal least squares - Google Patents
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Abstract
The invention relates to a feedforward control method based on orthogonal least squares. Aiming at the problems that the traditional prediction filtering algorithm is insufficient in precision and large in delay characteristic and cannot meet the requirements of the current actual tracking system, the method utilizes the characteristics of orthogonal least squares, namely the characteristics of independent variable noise and dependent variable noise, low delay, small calculated amount and the like, and simultaneously considers the independent variable noise and the dependent variable noise to carry out track fitting, and can effectively improve the prediction precision under the condition of meeting the requirement of the real-time performance of the system, thereby improving the adaptability of the system to high-frequency target maneuvering, improving the prediction tracking error of the traditional prediction filtering algorithm under the condition that the target is suddenly maneuvered or suddenly stopped, and improving the performance of the system. In addition, the present invention only depends on the angular position information provided by the CCD detector and the encoder, simultaneously realizes the estimation of the current real target position and speed, and replaces the traditional directly measured miss distance information with the miss distance information with reduced lag, thereby further improving the tracking capability of the photoelectric tracking system.
Description
Technical field
The invention belongs to photoelectric tracking control field, is specifically related to one kind based on optoelectronic device itself Angle Position and detection
The object missing distance for arriving obtains target real trace position and speed, so as to be predicted tracking and the feedforward using the information
Method.
Background technology
In Light Electronic Control System object tracking process, main purpose is to realize the high precision tracking of target.With target
The mobility of object is increasing, and common closed loop control method cannot meet the tracer request, and it is right on the basis of this to need
The movable information (position, speed, acceleration) of target accurately estimated, then realizes the feedforward using the estimated information.
" fiery pond " control system of U.S.'s Lincoln laboratory research and development utilizes angle position information and the coaxial tracking of precision distance measurement information realization,
Achieve good control effect.Angular velocity and angle of the studies in China personnel using the Kalman filter based on model to target
Acceleration is estimated, then carries out the feedforward to improve tracking accuracy by estimator.Document《Predictive filtering technology is in light
Application simulation in electro-theodolite set》(photoelectric project, Vol (8) 2002) employ velocity feed forward method and are emulated, and improve
Tracking accuracy.On this basis, Publication No. CN 102736636A《Feedforward side in tracking system based on angle information
Method》Chinese patent predictive filtering model is improved, with the addition of feed forward of acceleration, further improve system tracking essence
Degree.Document《Combined line-of-sight error and angular position to generate
feedforward control for a charge-coupled device-based tracking loop》(Optical
Engineering, Vol (54), 2015) are compensated using Kalman's velocity feed forward to image time delay, and analyze Kalman
Predictive filtering is in the feedforward to the impact of system stability.
The feedforward utilizes its early response to target maneuver, can effectively improve electro-optical system tracking accuracy.High-performance
Feedforward effect rely primarily on to target current kinetic parameters estimate accuracy and real-time.But, due in electro-optical system
The object missing distance that imageing sensor (CCD) is collected has that renewal frequency is low, time delay big and containing certain noise, together
When the predictive filtering algorithm itself that adopted can bring time lag characteristic, so after predictive filtering the target information of gained standard
Really property real-time is lowered, so as to the effect that causes to feedover in systems in practice is limited.Meanwhile, with the motility of target and motor-driven
Property greatly improve, the such as such target of unmanned plane, model plane, it has, and little speed, high acceleration, maneuvering frequency be high, track rule
The rule property characteristic such as weak, traditional predictive filtering algorithm, such as Kalman Prediction filtering, gradually cannot adapt to, and particularly exist
Target is suddenly motor-driven or unexpected stopping in the case of, due to the inaccuracy of estimated information, so as to cause tracking error to be amplified, sternly
Heavy directly results in BREAK TRACK.In this case, to prediction algorithm improvement or a kind of precision of prediction of searching is high,
Little predictive filtering algorithm of time-delay characteristics itself becomes the problem that the needs of the feedforward are solved.
The content of the invention
It is not enough big with time-delay characteristics itself for Classical forecast filtering algorithm precision, it is impossible to meet currently practical tracking system
The problems such as demand, the present invention proposes a kind of feed forward control method based on Orthogonal Least Squares.The method utilizes orthogonal minimum
Two consider that independent variable noise and dependent variable noise carry out track fitting and its delayed little, amount of calculation is little while taking advantage of itself to have
Etc. characteristic, precision of prediction can be effectively improved under the requirement for meeting system real time, so as to improve system to high frequency target maneuver
Adaptability, improve that target is suddenly motor-driven or unexpected stopping in the case of predicting tracing error, improve systematic function.
To realize the purpose of the present invention, the present invention provides a kind of feed forward control method based on Orthogonal Least Squares, specifically
Implementation steps are as follows：
Step (1)：In tracking equipment encoder installed above and imageing sensor (CCD), encoder obtains tracking equipment
The attitude angle information of itself, the trace information of angle information and target that imageing sensor receives encoder does and subtracts each other process,
Output measurement object missing distance e (k)；
Step (2)：Because object missing distance e (k) for measuring carries time delay, generally a few tens of milliseconds, it is therefore desirable to by height
Encoder angular information y (k) of frame frequency is added after time unifying with object missing distance e (k) of low frame rate and obtains containing delayed
Measurement target trajectory information r (k)；
Step (3)：Orthogonal Least Squares prediction algorithm is by a series of measurement target trajectory data r to the nearest time period
K () is fitted, obtain the locus model matched with current track, so as to using the model to current locations of real targets and
Speed carries out extrapolation estimate, and with this reduction of measurement delay is realized.
Step (4)：After real goal track r ' is got (k), obtain current by deducting current encoder values
The real miss distance e ' of target (k), sends it as input into position modifier controller；
Step (5)：Feedforward controller is received predicts that the target velocity for obtaining generates and export feed-forward control signals；
Step (6)：Feed-forward control signals are added with position correction control signal, generate and output driving control signal, use
In drive control object, realize to the closed-loop corrected of object missing distance e (k).
Wherein, the data characteristic of encoder has high precision, frame frequency high in step (1), and its lag time is little negligible；Figure
As sensor general precision is relatively low, frame frequency is low, and frame frequency is generally 50Hz, delayed general in 20ms～60ms.
Wherein, time unifying can only accomplish thick alignment in real work in step (2), because imageing sensor is to difference
In the processing procedure of image, spent time can difference, so as to cause, its data is delayed to have certain drift,
Can still there is certain not calibrated error after thick time calibration.
Wherein, in step (3) nearest historical trajectory data is fitted using Orthogonal Least Square, obtains calibrated
True model of fit, often receives a new data point and then deletes an oldest data point, and the fitting is then updated again
Model parameter, then calculates the position of current goal and speed for extrapolation imageing sensor lag time, to feed back with it is front
Feedback tracing control.
Wherein, the position correction controller in step (4) is entered for controlled device before feed forward control loop is not added
Row design is completed.
The present invention has the advantage that compared with prior art：
(1) the relative Kalman Prediction based on motion model estimates feed forward control method, and the invention is not based on the fortune of target
Movable model, avoids by priori and to initial parameter sensitivity, and the measurement data in its selection nearest time period is used as consideration,
Impact of all historical datas to being fitted is eliminated, target maneuver can be preferably followed, and its amount of calculation is less, meets system
Requirement of real-time；On the other hand, because its time hysteresis characteristic is compared, Kalman is little, in the target maneuver of higher frequency, can be more
The good speed for estimating target, so as to its tracking error is greatly decreased；
(2) independent variable noise and dependent variable noise are considered while having due to Orthogonal Least Squares prediction algorithm itself
Track fitting is carried out, the invention is more nearly truly by the trajectory parameters that fitting is obtained such that it is able to lifted current to target
Position and the accuracy of velocity estimation, are finally embodied in being greatly decreased for tracking error；
(3) compared to current feed-forward control algorithm, the present invention relies only on the Angle Position letter that ccd detector, encoder are provided
Breath, while realize estimating current locations of real targets and speed, it is traditional with the miss distance information substitution for reducing delayed
Miss distance information measured directly, so as to further improve the ability of tracking of photoelectric follow-up.
Description of the drawings
Fig. 1 is the structural representation of the feed forward control method based on Orthogonal Least Squares of the present invention.
Fig. 2 is the bearing data of the flight path that the unmanned plane of the present invention does continuous motor-driven suddenly or unexpected stopping.
Fig. 3 is the prediction of speed Comparative result for UAV Maneuver of the present invention.
Fig. 4 is that the tracking error for unmanned plane during flying of the present invention is contrasted.
Specific embodiment
The specific embodiment of the present invention is elaborated below in conjunction with accompanying drawing.
The structural representation of the feed forward control method of Orthogonal Least Squares is based on as shown in figure 1, including orthogonal
Least-squares prediction algoritic module, feedforward controller, position correction controller, control object；The control device group contain by
Closed feedback loop and be made up of Orthogonal Least Squares prediction algorithm module, feedforward controller that position correction controller is formed
Feed-forward loop.Realize that the specific implementation step of feed forward control method is as follows using described device：
Step (1)：In tracking equipment encoder installed above and imageing sensor (CCD), encoder obtains tracking equipment
The attitude angle information of itself, the trace information of angle information and target that imageing sensor receives encoder does and subtracts each other process,
Output measurement object missing distance e (k)；Wherein, the data characteristic of encoder has high precision, frame frequency high, its lag time I
Ignore；Imageing sensor general precision is relatively low, and frame frequency is low, and frame frequency is generally 50Hz, delayed general in 20ms～60ms；
Step (2)：Because object missing distance e (k) for measuring carries time delay, it is therefore desirable to by the encoder angular of high frame frequency
Information y (k) is added after time unifying with object missing distance e (k) of low frame rate and obtains containing delayed measurement target motion rail
Information r (k) of mark；But, time unifying can only accomplish thick alignment in real work, because imageing sensor is to different images
Processing procedure in, spent time can difference, so as to cause, its data is delayed to have certain drift, when thick
Between calibrate after can still there is certain not calibrated error；
Step (3)：Orthogonal Least Squares prediction algorithm is by a series of measurement target trajectory data r to the nearest time period
K () is fitted, obtain the locus model matched with current track, so as to using the model to current locations of real targets and
Speed carries out extrapolation estimate, and with this reduction of measurement delay and the elimination of time not calibrated error are realized；
Step (4)：After real goal track r ' is got (k), obtain current by deducting current encoder values
The real miss distance e ' of target (k), sends it as input into position modifier controller；
Step (5)：Feedforward controller is received predicts that the target velocity for obtaining generates and export feed-forward control signals；
Step (6)：Feed-forward control signals are added with position correction control signal, generate and output driving control signal, use
In drive control object, realize to the closed-loop corrected of object missing distance e (k).
Below the precision of prediction and tracking accuracy of the present invention are carried out so that an Experimental Control System is to unmanned plane tracking as an example
Describe in detail：
Encoder sample frequency in the experimental system is 2KHz, and imageing sensor sample frequency is 50Hz, by experiment
Measure the delayed about 40ms of its view data (2 frame data).Unmanned plane target is apart from tracking equipment 300m or so, height about 100m
Do level maneuvering flight back and forth.Orthogonal Least Squares select linear quadratic multinomial as model of fit, specific as follows：
The experiment obtains accurate model of fit by being fitted to the history flight path data in nearest 1 second,
Often receive a new data point and then delete an oldest data point, the fitted model parameters are then updated again, then
The position of current goal and speed are calculated for imageing sensor extrapolation lag time, to the tracing control that feeds back and feedover.
Do the bearing data of the flight path of continuous motor-driven suddenly or unexpected stopping, its machine for unmanned plane as shown in Figure 2
Dynamic frequency is higher.
It is directed to the prediction of speed result of UAV Maneuver for each algorithm as shown in Figure 3.Make here with encoder differential
For the basic reference of prediction of speed., it is apparent that in this case, the estimated target speed of Kalman Prediction algorithm
There is larger overshoot in degree, mainly due to Kalman Prediction algorithm, the hysteresis characteristic of itself is caused for this.Conversely, orthogonal minimum
Two take advantage of, and preferably estimate the speed amount of target, and compared to encoder differential speed, it also carries certain filter effect.
It is directed to the tracking error of unmanned plane during flying for each algorithm as shown in Figure 4., it is apparent that due to orthogonal minimum
Two take advantage of the accurate estimation to position and speed, and compared to Kalman Algorithm, its predicting tracing error is greatly decreased, and overall performance is better than
Kalman Prediction filtering algorithm.
Claims (5)
1. a kind of feed forward control method based on Orthogonal Least Squares, it is characterised in that：Its specific implementation step is as follows：
Step (1)：In tracking equipment encoder installed above and imageing sensor (CCD), encoder obtains tracking equipment itself
Attitude angle information, the trace information of angle information and target that imageing sensor receives encoder does and subtracts each other processs, output
Measurement object missing distance e (k)；
Step (2)：Because object missing distance e (k) for measuring carries time delay, it is therefore desirable to by the encoder angular information of high frame frequency
Y (k) is added after time unifying with object missing distance e (k) of low frame rate and obtains containing delayed measurement target trajectory
Information r (k)；
Step (3)：Orthogonal Least Squares prediction algorithm is by a series of measurements target trajectory data r (k) to the nearest time period
Be fitted, obtain the locus model matched with current track, so as to using the model to current locations of real targets and speed
Degree carries out extrapolation estimate, to get real goal track and speed；
Step (4)：After real goal track r ' is got (k), by deducting current encoder values current goal is obtained
Real miss distance e ' (k), sends it as input into position modifier controller；
Step (5)：Feedforward controller is received predicts that the target velocity for obtaining generates and export feed-forward control signals；
Step (6)：Feed-forward control signals are added with position correction control signal, generate and output driving control signal, for driving
Dynamic control object, realizes to the closed-loop corrected of object missing distance e (k).
2. a kind of feed forward control method based on Orthogonal Least Squares according to claim 1, it is characterised in that：Step
(1) data characteristic of encoder has high precision, frame frequency high in, and its lag time is little negligible；Imageing sensor general precision
Relatively low, frame frequency is low, and frame frequency is generally 50Hz, delayed general in 20ms～60ms.
3. a kind of feed forward control method based on Orthogonal Least Squares according to claim 1, it is characterised in that：Step
(2) time unifying can only accomplish thick alignment in real work in, because during imageing sensor is to the processing procedure of different images,
The spent time can difference, so as to cause, its data is delayed to have certain drift, after thick time calibration still
Can there is certain not calibrated error.
4. a kind of feed forward control method based on Orthogonal Least Squares according to claim 1, it is characterised in that：Step
(3) in nearest historical trajectory data is fitted using Orthogonal Least Square, obtains accurate model of fit, often connect
Receive a new data point and then delete an oldest data point, the fitted model parameters are then updated again, be then directed to
Imageing sensor extrapolation lag time calculates the position of current goal and speed, to the tracing control that feeds back and feedover.
5. a kind of feed forward control method based on Orthogonal Least Squares according to claim 1, it is characterised in that：Step
(4) the position correction controller in is to be designed to complete for controlled device before feed forward control loop is not added.
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Cited By (10)
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| CN107367934A (en) * | 2017-07-11 | 2017-11-21 | 中国科学院光电技术研究所 | Fast reflecting mirror stability control method based on double disturbance observers |
| CN107390522A (en) * | 2017-07-11 | 2017-11-24 | 中国科学院光电技术研究所 | Error observation feedforward control method based on visual tracking |
| CN109683482A (en) * | 2019-01-10 | 2019-04-26 | 中国科学院光电技术研究所 | A kind of low-frequency range Disturbance Rejection method based on acceleration analysis |
| CN109831600A (en) * | 2019-02-27 | 2019-05-31 | 中国科学院光电技术研究所 | A kind of method that photoelectric follow-up avoids picture from moving during target approaches |
| CN110876275A (en) * | 2019-04-30 | 2020-03-10 | 深圳市大疆创新科技有限公司 | Aiming control method, mobile robot and computer readable storage medium |
| CN112462798A (en) * | 2020-12-04 | 2021-03-09 | 三生万物(北京)人工智能技术有限公司 | Unmanned aerial vehicle and method for improving flight performance of unmanned aerial vehicle |
| CN112684817A (en) * | 2020-12-17 | 2021-04-20 | 中国工程物理研究院应用电子学研究所 | Method for improving tracking precision of photoelectric tracking system |
| CN113687598A (en) * | 2021-10-25 | 2021-11-23 | 南京信息工程大学 | Prediction feedforward tracking control method and device based on internal model and storage medium thereof |
| CN113805596A (en) * | 2021-09-27 | 2021-12-17 | 深圳市英威腾电气股份有限公司 | Position regulation and control method, device, equipment and medium of controller |
| CN116147689A (en) * | 2023-04-14 | 2023-05-23 | 四川中科友成科技有限公司 | Off-target delay test method and device for outfield tracker |
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| CN107367934A (en) * | 2017-07-11 | 2017-11-21 | 中国科学院光电技术研究所 | Fast reflecting mirror stability control method based on double disturbance observers |
| CN107390522A (en) * | 2017-07-11 | 2017-11-24 | 中国科学院光电技术研究所 | Error observation feedforward control method based on visual tracking |
| CN109683482A (en) * | 2019-01-10 | 2019-04-26 | 中国科学院光电技术研究所 | A kind of low-frequency range Disturbance Rejection method based on acceleration analysis |
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| CN109831600B (en) * | 2019-02-27 | 2021-06-01 | 中国科学院光电技术研究所 | Method for avoiding image motion in target approaching process of photoelectric tracking system |
| CN110876275A (en) * | 2019-04-30 | 2020-03-10 | 深圳市大疆创新科技有限公司 | Aiming control method, mobile robot and computer readable storage medium |
| CN112462798A (en) * | 2020-12-04 | 2021-03-09 | 三生万物(北京)人工智能技术有限公司 | Unmanned aerial vehicle and method for improving flight performance of unmanned aerial vehicle |
| CN112684817A (en) * | 2020-12-17 | 2021-04-20 | 中国工程物理研究院应用电子学研究所 | Method for improving tracking precision of photoelectric tracking system |
| CN112684817B (en) * | 2020-12-17 | 2023-01-03 | 中国工程物理研究院应用电子学研究所 | Method for improving tracking precision of photoelectric tracking system |
| CN113805596A (en) * | 2021-09-27 | 2021-12-17 | 深圳市英威腾电气股份有限公司 | Position regulation and control method, device, equipment and medium of controller |
| CN113687598A (en) * | 2021-10-25 | 2021-11-23 | 南京信息工程大学 | Prediction feedforward tracking control method and device based on internal model and storage medium thereof |
| CN116147689A (en) * | 2023-04-14 | 2023-05-23 | 四川中科友成科技有限公司 | Off-target delay test method and device for outfield tracker |
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