CN109048082B - Distance control method based on Kalman filtering - Google Patents
Distance control method based on Kalman filtering Download PDFInfo
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- CN109048082B CN109048082B CN201811089844.2A CN201811089844A CN109048082B CN 109048082 B CN109048082 B CN 109048082B CN 201811089844 A CN201811089844 A CN 201811089844A CN 109048082 B CN109048082 B CN 109048082B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K26/00—Working by laser beam, e.g. welding, cutting or boring
- B23K26/36—Removing material
- B23K26/38—Removing material by boring or cutting
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K26/00—Working by laser beam, e.g. welding, cutting or boring
- B23K26/70—Auxiliary operations or equipment
- B23K26/702—Auxiliary equipment
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Abstract
The invention relates to the field of laser cutting, in particular to a distance control method based on Kalman filtering, which comprises the following steps: periodically sending an instruction, acquiring a feedback signal, and performing Kalman filtering processing on the feedback signal to acquire an estimated distance of Kalman filtering; judging the stage of the cutting head; and finishing parameter setting according to the estimated distance of Kalman filtering and the stage of the cutting head, and outputting the servo. After the estimated distance of Kalman filtering is obtained, the stage of the cutting head is judged, corresponding servo output is performed, the Kalman filtering processing can remove noise interference on a feedback signal, different stages correspond to different parameter outputs, stable servo output can be ensured, and the cutting head and the surface of the plate are always at the same specific height.
Description
Technical Field
The invention relates to the field of laser cutting, in particular to a distance control method based on Kalman filtering.
Background
The planar laser cutting is a non-contact type machining, in the whole machining process, the workpiece does not need to be in contact with a cutter, and the surface of the workpiece is subjected to thermal machining by focusing emitted light through a laser head. In this process, in addition to the adjustment of the focal point of the laser head, the distance of the cutting head from the surface of the workpiece is also an important control.
The sheet to be worked is generally not perfectly flat and, during the cutting process, is deformed, resulting in undulations in the surface of the sheet. In order to ensure that the focus of the emitted light is kept focused on the surface of the plate, the cutting head needs to keep a certain distance from the surface of the plate unchanged in the whole processing process, so that the Z axis where the cutting head is located needs to be adjusted according to the feedback of the distance between the cutting head and the surface of the plate, namely, the distance follow-up control in the laser cutting.
The principle of distance control is to adjust the distance in real time by the feedback of a voltage equidistant sensor. The traditional method is to dynamically compensate the distance error through a PID algorithm, and the method has good dynamic performance and can be fast and stable when the feedback signal is stable. However, in an actual machining process, the feedback signal of the sensor is greatly interfered, and the feedback signal is distorted. In addition, the processing speed of the sampling device is lower than the communication period of the motion controller, which causes a delay of the sampled signal. If the traditional PID algorithm is adopted for adjustment, the oscillation on the movement is severe, and the requirement of practical application cannot be met.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a distance control method based on kalman filtering, which solves the problem that a specific distance between a cutting head and a plate surface cannot be maintained.
In order to solve the technical problem, the invention provides a distance control method based on Kalman filtering, which comprises the following steps:
periodically sending an instruction, acquiring a feedback signal, and performing Kalman filtering processing on the feedback signal to acquire an estimated distance of Kalman filtering;
judging the stage of the cutting head;
and finishing parameter setting according to the estimated distance of Kalman filtering and the stage of the cutting head, and outputting the servo.
Preferably, the distance control method further includes the following steps: and the state machine acquires the distance between the cutting head and the cutting surface of the workpiece and judges the stage of the cutting head.
Preferably, the cutting head is located in four stages, namely an approach stage, an adjustment stage, a flexible adjustment stage and a rapid reaction stage; when the distance reaches the adjusting range, the approach stage is carried out; when the distance is within the adjusting range, the adjusting stage is carried out; when the distance is close to the target distance, a flexible adjustment stage is carried out; and when the distance is smaller than the target distance, a quick reaction stage is carried out.
Preferably, the distance control method further includes the following steps:
acquiring acceleration, calculating speed and displacement according to the estimated distance of Kalman filtering, wherein if the acceleration is in an approaching stage, the acceleration is unchanged; if the acceleration is in the adjusting stage, the acceleration is unchanged; if the flexible adjustment stage is in, the acceleration is set to be one third times the acceleration; if in the fast reaction phase, the acceleration is set to be three times the acceleration.
Preferably, the distance control method further includes the following steps:
and periodically acquiring the prediction distance of Kalman filtering, the Kalman coefficient and the measurement distance of Kalman filtering so as to acquire the estimation distance of Kalman filtering.
Preferably, the distance control method further includes the following steps:
constructing an estimation distance formula of Kalman filtering:
EstimatedDisti＝ForeDisti+Kg*(MeasureDisti-ForeDisti)，
wherein EstimateDist is an estimated distance of Kalman filtering, ForeDist is a predicted distance of Kalman filtering, Kg is a Kalman coefficient, and MeasureDist is a measured distance of Kalman filtering, so as to obtain the estimated distance of Kalman filtering in the current period.
Preferably, the distance control method further includes the following steps:
carrying out average value filtering processing on the feedback signal;
and substituting the average value for filtering by referring to a distance calibration table to obtain the measurement distance of Kalman filtering.
Preferably, the distance control method further includes the following steps:
constructing a prediction distance formula of Kalman filtering:
ForeDisti＝ForeDisti-1+Si，
and S is the displacement of the ith period, so as to obtain the prediction distance of the Kalman filtering of the current period.
Preferably, the distance control method further includes the following steps:
constructing a prediction variance formula of Kalman filtering:
ForecastVari＝MeasureVari-1+Q，
the ForecastVar is a prediction variance of Kalman filtering, the MeasureVar is a measurement variance of the Kalman filtering, and the Q is a prediction noise variance, so that the prediction variance of the Kalman filtering in the current period is obtained.
Constructing a Kalman coefficient formula:
and R is the measurement noise variance, so that the Kalman coefficient of the current period is obtained.
Preferably, the distance control method further includes the following steps:
constructing a measurement variance formula of Kalman filtering:
MeasureVa ri＝(1-Kg)2*ForecastVa ri+Kg2*R，
thereby obtaining the measurement variance of the Kalman filtering of the current period.
Compared with the prior art, the method has the advantages that after the estimated distance of Kalman filtering is obtained, the stage of the cutting head is judged, corresponding servo output is performed, the Kalman filtering process can remove noise interference on a feedback signal, different stages correspond to different parameter outputs, stable servo output can be ensured, and the cutting head and the surface of the plate are always at the same specific height.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a block flow diagram of a distance control method of the present invention;
FIG. 2 is a block flow diagram of the stage of the present invention at which a determination is made;
FIG. 3 is a block flow diagram of parameter setting of the present invention;
FIG. 4 is a block flow diagram of the present invention for obtaining an estimated distance for Kalman filtering.
Detailed Description
The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
As shown in fig. 1 to 4, the present invention provides a preferred embodiment of a distance control method based on kalman filtering.
Specifically, referring to fig. 1, a distance control method based on kalman filtering includes the following steps:
step 1, periodically sending an instruction, then acquiring a feedback signal, and performing Kalman filtering processing on the feedback signal to acquire an estimated distance of Kalman filtering;
step 2, judging the stage of the cutting head;
and 3, completing parameter setting according to the estimated distance of Kalman filtering and the stage of the cutting head, and outputting the servo.
The cutting head cuts a workpiece, a sensor arranged on the cutting head periodically transmits a detection signal to a cutting surface of the workpiece, the workpiece reflects a feedback signal to the sensor, the sensor performs Kalman filtering processing on the feedback signal after receiving the feedback signal, noise interference is removed, distortion of the feedback signal is prevented, and an estimated distance of Kalman filtering is obtained, wherein the estimated distance is the estimated distance between the cutting head and the cutting surface of the workpiece; and then, judging the stage of the cutting head in the period, finishing the setting of relevant parameters according to the estimated distance of Kalman filtering and the stage, sending the parameters to the cutting head, adjusting the distance between the cutting head and the workpiece according to the parameters, performing servo output, and performing cutting operation on the cutting surface of the workpiece. Therefore, through the distance control method, Kalman filtering can be quickly converged to be near the true value, vibration in motion caused by continuous adjustment of errors is avoided, then the position of the cutting head is adjusted, the distance between the cutting head and the cutting surface of the workpiece can be adjusted in real time, the distance is kept unchanged, the focus of laser emitted by the cutting head is kept focused on the cutting surface of the workpiece, and stable cutting is realized.
Further, referring to fig. 2, the distance control method further includes the steps of:
and 21, acquiring the distance between the cutting head and the cutting surface of the workpiece by the state machine, and judging the stage of the cutting head.
The sensor acquires the distance between the cutting head and the cutting surface of the workpiece and sends the distance to the state machine, and the state machine judges the stage of the cutting head according to the distance.
In this embodiment, the cutting head is located in four stages, namely, an approach stage, an adjustment stage, a flexible adjustment stage, and a rapid reaction stage; when the distance reaches the adjustment range, the distance is in an approaching stage, for example, the distance is larger than 10mm, and then normal adjustment is carried out; when the distance is within the adjusting range, the distance is in an adjusting stage, for example, less than or equal to 10mm, and then the distance is adjusted normally; when the distance is close to the target distance, in a flexible adjustment stage, the adjustment needs to be modified; when the distance is less than the target distance, a fast reaction phase, followed by a modification of the adjustment, is required. The target distance is a self-set value and can be adjusted according to specific conditions.
Still further, referring to fig. 3, the distance control method further includes the steps of:
step 31, acquiring acceleration, calculating speed and displacement according to the estimated distance of Kalman filtering, wherein if the acceleration is in an approaching stage, the acceleration is unchanged; if the acceleration is in the adjusting stage, the acceleration is unchanged; if the flexible adjustment stage is in, the acceleration is set to be one third times the acceleration; if in the fast reaction phase, the acceleration is set to be three times the acceleration.
Setting relevant output parameters such as acceleration, calculation speed and displacement of the cutting head according to the estimated distance of Kalman filtering, and judging the moving direction of the cutting head, wherein when the cutting head moves periodically, each period may be in different stages, the cutting head is respectively set corresponding to different parameters, and if the cutting head moves in a close stage, the acceleration of the cutting head is unchanged; if the cutting head is in the adjusting stage, the acceleration of the cutting head is unchanged; if the cutting head is in the flexible adjustment stage, the acceleration of the cutting head is set to be one third times the acceleration, namely the set acceleration is multiplied by one third; if in the rapid reaction phase, the acceleration of the cutting head is set to a triple acceleration, i.e. the acceleration set is multiplied by three times. And then, the cutting head moves according to the set parameters, so that the distance between the cutting head and the cutting surface of the workpiece is controlled. Set for a plurality of stages, can carry out different control processes to the position that the cutting head is located, when satisfying quick following control, can also reduce the error that the feedback signal lags and brings to can further improve the stability of cutting head, guarantee that the cutting head moves fast, accurately.
Specifically, referring to fig. 4, the distance control method further includes the steps of:
and step 11, periodically acquiring the prediction distance, the Kalman coefficient and the measurement distance of Kalman filtering so as to acquire the estimation distance of Kalman filtering.
The method comprises the steps of acquiring a prediction distance of Kalman filtering, a Kalman coefficient and a measurement distance of the Kalman filtering in each period, and calculating an estimation distance of the Kalman filtering according to the acquired data.
Here, an acquisition process of an estimated distance of kalman filtering is provided.
Constructing an estimation distance formula of Kalman filtering:
EstimatedDisti＝ForeDisti+Kg*(MeasureDisti-ForeDisti)，
wherein EstimateDist is an estimated distance of Kalman filtering, ForeDist is a predicted distance of Kalman filtering, Kg is a Kalman coefficient, and MeasureDist is a measured distance of Kalman filtering, so as to obtain the estimated distance of Kalman filtering in the current period. Currently, i period, and EstimatedDistiThe estimated distance of Kalman filtering in the current period is the value to be obtained at this time, ForeDistiFor the prediction distance of the current period Kalman Filter, MeasureDis tiThe measurement distance of Kalman filtering in the current period is obtained.
Firstly, obtaining a feedback signal which is mixed with a plurality of signals, carrying out average value filtering processing, then substituting a distance calibration table into the average value filtering, and obtaining the measurement distance MeasureDits of Kalman filteringi。
Then, a prediction distance formula of Kalman filtering is constructed:
ForeDisti＝ForeDisti-1+Si，
wherein, ForeDistiFor the predicted distance of the current cycle, ForeDisti-1The predicted distance of the last period is a known value, S is the displacement of the ith period and is a known value, and the predicted distance ForeDist of the Kalman filtering of the current period can be obtained through the formulai。
Then, constructing a prediction variance formula of Kalman filtering:
ForecastVari＝MeasureVari-1+Q，
wherein ForecastVar is the prediction variance of Kalman filtering, ForecastVariIs the predicted variance of the Kalman filtering of the current cycle, MeasureVar is the measured variance of the Kalman filtering, MeasureVari-1The measurement variance of the Kalman filtering in the last period is a known value, Q is a prediction noise variance and is a known value, and the prediction variance ForecastVar of the Kalman filtering in the current period can be obtained through the formulai。
Subsequently, a kalman coefficient formula is constructed:
wherein, ForecastVariAnd obtaining the Kalman coefficient Kg of the current period by the formula, wherein R is the measurement noise variance which is a known value and is obtained from the predicted variance of the Kalman filtering of the current period.
It should be noted that, the measurement variance of the kalman filter in the current period may be obtained first, and the measurement variance is provided to the next period for use, so as to construct a measurement variance formula of the kalman filter:
MeasureVa ri＝(1-Kg)2*ForecastVa ri+Kg2*R，
wherein Kg is the Kalman coefficient of the current period, which has been obtained in the above, ForecastVariFor the prediction variance of the Kalman filtering in the current period, R is the measurement noise variance which is already obtained and is a known value, and the measurement variance MeasureVar of the Kalman filtering in the current period can be obtained by the formulai。
It is worth mentioning that the prediction noise variance Q and the measurement noise variance R of kalman filtering are given, the two are independent of time, and the error conforms to the standard normal distribution, and the obtained value is accurate after the formula is substituted.
Therefore, the prediction distance ForeDist of the Kalman filtering in the current period is obtainediCurrent cycle kalman coefficient Kg, measurement distance measuredi t of current cycle kalman filteriThen, the estimated distance EstimatedDist of the Kalman filtering in the current period can be obtainedi。
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A distance control method based on Kalman filtering is characterized by comprising the following steps:
periodically sending an instruction, acquiring a feedback signal, and performing Kalman filtering processing on the feedback signal to acquire an estimated distance of Kalman filtering;
judging the stage of the cutting head;
according to the estimated distance of Kalman filtering and the stage of the cutting head, parameter setting is completed, servo output is performed, and the distance between the laser head and workpiece cutting is adjusted;
the distance control method further includes the steps of:
periodically acquiring a prediction distance of Kalman filtering, a Kalman coefficient and a measurement distance of Kalman filtering so as to acquire an estimation distance of Kalman filtering;
the distance control method further includes the steps of:
constructing an estimation distance formula of Kalman filtering:
EstimatedDisti＝ForeDisti+Kg*(MeasureDisti-ForeDisti)，
wherein EstimateDist is an estimated distance of Kalman filtering, ForeDist is a predicted distance of Kalman filtering, Kg is a Kalman coefficient, MeasureDist is a measured distance of Kalman filtering, and the estimated distance of Kalman filtering in the current period is obtained;
the distance control method further includes the steps of:
carrying out average value filtering processing on the feedback signal;
substituting the average value filtering by referring to a distance calibration table to obtain the measurement distance of Kalman filtering;
the distance control method further includes the steps of:
constructing a prediction distance formula of Kalman filtering:
ForeDisti＝ForeDisti-1+Si，
wherein S isiThe displacement of the ith period is used for obtaining the prediction distance of the Kalman filtering of the current periodSeparating;
the distance control method further includes the steps of:
constructing a prediction variance formula of Kalman filtering:
ForecastVari＝MeasureVari-1+Q，
wherein, ForecastVar is the prediction variance of Kalman filtering, MeasureVar is the measurement variance of Kalman filtering, and Q is the prediction noise variance, so as to obtain the prediction variance of Kalman filtering in the current period;
constructing a Kalman coefficient formula:
wherein, R is the variance of the measurement noise so as to obtain the Kalman coefficient of the current period;
the distance control method further includes the steps of:
constructing a measurement variance formula of Kalman filtering:
MeasureVari＝(1-Kg)2*ForecastVari+Kg2*R，
thereby obtaining the measurement variance of the Kalman filtering of the current period.
2. The distance control method according to claim 1, characterized by further comprising the steps of:
and the state machine acquires the distance between the cutting head and the cutting surface of the workpiece and judges the stage of the cutting head.
3. The distance control method according to claim 2, wherein the cutting head is located in four stages, namely an approach stage, an adjustment stage, a flexibility adjustment stage and a quick response stage; when the distance reaches the adjusting range, the approach stage is carried out; when the distance is within the adjusting range, the adjusting stage is carried out; when the distance is close to the target distance, a flexible adjustment stage is carried out; and when the distance is smaller than the target distance, a quick reaction stage is carried out.
4. The distance control method according to claim 3, characterized by further comprising the steps of:
acquiring acceleration, calculating speed and displacement according to the estimated distance of Kalman filtering, wherein if the acceleration is in an approaching stage, the acceleration is unchanged; if the acceleration is in the adjusting stage, the acceleration is unchanged; if the flexible adjustment stage is in, the acceleration is set to be one third times the acceleration; if in the fast reaction phase, the acceleration is set to be three times the acceleration.
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| CN112097622A (en) * | 2020-09-14 | 2020-12-18 | 上海维宏电子科技股份有限公司 | Method and device for realizing distance measurement of capacitive displacement sensor based on Kalman filter |
| CN113927157B (en) * | 2021-12-17 | 2022-03-08 | 武汉锐科光纤激光技术股份有限公司 | Method and device for controlling output power of laser equipment, computer equipment and medium |
| CN115319311B (en) * | 2022-10-13 | 2023-03-17 | 扬州皓月机械有限公司 | Laser cutting equipment |
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