CN114228511B - Self-learning-based bilateral independent electric drive tracked vehicle deviation correction control method - Google Patents
Self-learning-based bilateral independent electric drive tracked vehicle deviation correction control method Download PDFInfo
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- CN114228511B CN114228511B CN202111683698.8A CN202111683698A CN114228511B CN 114228511 B CN114228511 B CN 114228511B CN 202111683698 A CN202111683698 A CN 202111683698A CN 114228511 B CN114228511 B CN 114228511B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
- B60L15/32—Control or regulation of multiple-unit electrically-propelled vehicles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/72—Electric energy management in electromobility
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Abstract
The invention discloses a self-learning-based bilateral independent electric drive tracked vehicle deviation rectifying control method, and belongs to the technical field of electric drive tracked vehicle control. Judging whether the correction times reach a set upper limit, judging whether the vehicle is left/right biased, judging whether the correction coefficient reaches a set upper/lower limit, recording the vehicle speed, judging whether the left/right bias time exceeds a threshold value, calculating the average value and the mean square error of the vehicle speed, and judging the mean square error of the vehicle speed and recording the correction coefficient; the control system based on the double-side independent electric drive tracked vehicle self-learns, corrects the speed of the left side and the right side through real-time monitoring, realizes the integral deviation correction control of the vehicle, and avoids the deviation phenomenon when the double-side independent electric drive tracked vehicle runs straight.
Description
Technical Field
The invention relates to the technical field of electric drive tracked vehicle control, in particular to a self-learning-based bilateral independent electric drive tracked vehicle deviation correction control method.
Background
The tracked vehicle has good application in various non-structural pavements and non-structural terrains due to the advantages of high reliability, strong trafficability and the like.
The left and right side driving, transmission and action systems of the bilateral independent electric drive tracked vehicle are not coupled, and the efficiency of the left and right side transmission and action systems is not completely equal due to the problems of machining, assembly precision and the like; the accuracy of the double sided motor control is not necessarily exactly the same.
For the above reasons, the deviation phenomenon occurs when the vehicle runs straight.
In the prior art, most of the solutions are used for calibrating the efficiency and error values of the power, transmission and action systems at two sides according to test results, and the test period is long. And the track tension is different along with the abrasion of the transmission and the action system, so that the calibration effect can be greatly reduced.
Disclosure of Invention
Aiming at the defects and shortcomings of the existing products, the invention provides a self-learning-based bilateral independent electric drive tracked vehicle deviation rectifying control method, which can improve the straight running stability.
The invention discloses a self-learning-based bilateral independent electric drive tracked vehicle deviation correction control method, which is realized based on a control system of the bilateral independent electric drive tracked vehicle and comprises the following steps of:
step one, initializing a system;
step two, reading historical data, wherein the historical data comprises correction times and correction coefficients;
step three, entering a monitoring state;
judging whether the correction times reach a set upper limit, returning to the third step if the correction times reach the upper limit, otherwise executing the next step;
step five, judging whether a left deviation phenomenon occurs, if so, executing step sixteen, otherwise, executing the next step;
step six, judging whether a right deviation phenomenon occurs, if so, executing the next step, otherwise, returning to the step three;
step seven, judging whether the correction coefficient reaches a set lower limit, if so, returning to the step three, otherwise, executing the next step;
continuously recording the speed of the vehicle;
step nine, judging whether the right deviation time exceeds a threshold value, if so, executing the next step, otherwise, returning to the step three;
step ten, calculating the average value and the mean square error of the vehicle speed in the time period with the right deviation phenomenon and the duration being the threshold value;
step eleven, judging whether the mean square error of the vehicle speed is smaller than a threshold value, and if so, executing the next step; otherwise, returning to the step three;
step twelve, correcting the coefficient to be-0.005, and correcting the frequency to be +1;
thirteenth, recording a correction coefficient under the average vehicle speed;
fourteen, judging whether the system is powered down, if so, executing the next step, otherwise, returning to the third step;
fifteen, storing correction coefficients and correction times;
sixthly, judging whether the correction coefficient reaches a set upper limit, if so, returning to the third step, otherwise, executing the next step;
seventeenth, continuously recording the speed of the vehicle;
eighteenth, judging whether the left offset time exceeds a threshold value, if so, executing the next step, otherwise, returning to the third step;
nineteenth, calculating a vehicle speed mean value and a mean square error in a time period in which a left deviation phenomenon occurs and the duration is a threshold value;
twenty, judging whether the mean square error of the vehicle speed is smaller than a threshold value, and executing the next step if the mean square error of the vehicle speed is smaller than the threshold value; otherwise, returning to the step three;
twenty-one, correction coefficient +0.005, correction number +1, and return to thirteenth step.
Further, the history data is stored in a memory of the control system.
Further, the correction number is set to an upper limit number of times of 1000 times.
Further, the judging method of the left deviation phenomenon and the right deviation phenomenon comprises the following steps: the target curvature of the vehicle is 0, and when the actual curvature is greater than 0.001, the vehicle is considered to be right-biased; when the actual curvature is less than-0.001, the vehicle is considered to be left-biased.
Further, the upper limit of the correction coefficient is 1.2, and the lower limit of the correction coefficient is 0.8.
Further, the threshold value of the left bias time is 2 seconds, and the threshold value of the right bias time is 2 seconds.
Further, the threshold value of the vehicle speed mean square error is 2.
Further, the correction is achieved by modifying the correction coefficient, thereby correcting the driving motor torque.
Compared with the prior art, the invention has the beneficial effects that:
the control system based on the double-side independent electric drive tracked vehicle self-learns, corrects the speed of the left side and the right side through real-time monitoring, realizes the integral deviation correction control of the vehicle, and avoids the deviation phenomenon when the double-side independent electric drive tracked vehicle runs straight.
Drawings
FIG. 1 is a flow chart of a control method according to the present invention.
Detailed Description
The technical scheme of the present invention will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the self-learning-based double-side independent electric drive tracked vehicle deviation correcting control method is realized based on a control system of the double-side independent electric drive tracked vehicle, and comprises the following steps:
step one, initializing a system;
step two, reading historical data, wherein the historical data comprises correction times and correction coefficients;
step three, entering a monitoring state;
judging whether the correction times reach a set upper limit, if so, returning to the step III to enter a monitoring state, otherwise, executing the next step;
step five, judging whether a left deviation phenomenon occurs, if so, executing step sixteen, otherwise, executing the next step;
step six, judging whether a right deviation phenomenon occurs, if so, executing the next step, otherwise, returning to the step three;
step seven, judging whether the correction coefficient reaches a set lower limit, if so, returning to the step three to enter a monitoring state, otherwise, executing the next step;
continuously recording the speed of the vehicle;
step nine, judging whether the right deviation time exceeds a threshold value, if so, executing the next step, otherwise, returning to the step three;
step ten, calculating the average value and the mean square error of the vehicle speed in the time period with the right deviation phenomenon and the duration being the threshold value;
step eleven, judging whether the mean square error of the vehicle speed is smaller than a threshold value, and if so, executing the next step; otherwise, returning to the step three;
step twelve, correcting the coefficient to be-0.005, and correcting the frequency to be +1;
thirteenth, recording a correction coefficient under the average vehicle speed;
fourteen, judging whether the system is powered down, if so, executing the next step, otherwise, returning to the third step to enter a monitoring state;
fifteen, storing correction coefficients and correction times;
sixthly, judging whether the correction coefficient reaches a set upper limit, if so, returning to the step III to enter a monitoring state, otherwise, executing the next step;
seventeenth, continuously recording the speed of the vehicle;
eighteenth, judging whether the left offset time exceeds a threshold value, if so, executing the next step, otherwise, returning to the third step to enter a monitoring state;
nineteenth, calculating a vehicle speed mean value and a mean square error in a time period in which a left deviation phenomenon occurs and the duration is a threshold value;
twenty, judging whether the mean square error of the vehicle speed is smaller than a threshold value, and executing the next step if the mean square error of the vehicle speed is smaller than the threshold value; otherwise, returning to the third step to enter a monitoring state;
twenty-one, correction coefficient +0.005, correction number +1, and return to thirteenth step.
The history data is stored in a memory of the control system; setting the correction times to 1000 times; the judging method of the left deviation phenomenon and the right deviation phenomenon comprises the following steps: the target curvature of the vehicle is 0, and when the actual curvature is greater than 0.001, the vehicle is considered to be right-biased; when the actual curvature is smaller than-0.001, the vehicle is considered to be left biased; the upper limit of the correction coefficient is 1.2, and the lower limit of the correction coefficient is 0.8; the threshold value is called as a critical value, and refers to the lowest value or the highest value which can be generated by one effect, wherein the threshold value of the left bias time is 2 seconds, and the threshold value of the right bias time is 2 seconds; the threshold value of the mean square error of the vehicle speed is 2; the correction is achieved by modifying the correction coefficient, thereby correcting the drive motor torque.
While the invention has been described with respect to the preferred embodiments, it will be understood by those skilled in the art that variations, modifications and additions may be made without departing from the spirit of the invention.
Claims (8)
1. The self-learning-based double-side independent electric drive tracked vehicle deviation rectification control method is realized based on a control system of the double-side independent electric drive tracked vehicle and is characterized by comprising the following steps of:
step one, initializing a system;
step two, reading historical data, wherein the historical data comprises correction times and correction coefficients;
step three, entering a monitoring state;
judging whether the correction times reach a set upper limit, returning to the third step if the correction times reach the upper limit, otherwise executing the next step;
step five, judging whether a left deviation phenomenon occurs, if so, executing step sixteen, otherwise, executing the next step;
step six, judging whether a right deviation phenomenon occurs, if so, executing the next step, otherwise, returning to the step three;
step seven, judging whether the correction coefficient reaches a set lower limit, if so, returning to the step three, otherwise, executing the next step;
continuously recording the speed of the vehicle;
step nine, judging whether the right deviation time exceeds a threshold value, if so, executing the next step, otherwise, returning to the step three;
step ten, calculating the average value and the mean square error of the vehicle speed in the time period with the right deviation phenomenon and the duration being the threshold value;
step eleven, judging whether the mean square error of the vehicle speed is smaller than a threshold value, and if so, executing the next step; otherwise, returning to the step three;
step twelve, correcting the coefficient to be-0.005, and correcting the frequency to be +1;
thirteenth, recording a correction coefficient under the average vehicle speed;
fourteen, judging whether the system is powered down, if so, executing the next step, otherwise, returning to the third step;
fifteen, storing correction coefficients and correction times;
sixthly, judging whether the correction coefficient reaches a set upper limit, if so, returning to the third step, otherwise, executing the next step;
seventeenth, continuously recording the speed of the vehicle;
eighteenth, judging whether the left offset time exceeds a threshold value, if so, executing the next step, otherwise, returning to the third step;
nineteenth, calculating a vehicle speed mean value and a mean square error in a time period in which a left deviation phenomenon occurs and the duration is a threshold value;
twenty, judging whether the mean square error of the vehicle speed is smaller than a threshold value, and executing the next step if the mean square error of the vehicle speed is smaller than the threshold value; otherwise, returning to the step three;
twenty-one, correction coefficient +0.005, correction number +1, and return to thirteenth step.
2. The self-learning-based bilateral independent electrically-driven tracked vehicle deviation-correcting control method according to claim 1, wherein the method comprises the following steps of: the history data is stored in a memory of the control system.
3. The self-learning-based bilateral independent electrically-driven tracked vehicle deviation-correcting control method according to claim 1, wherein the method comprises the following steps of: the correction number is set to 1000 times.
4. The self-learning-based bilateral independent electrically-driven tracked vehicle deviation-correcting control method according to claim 1, wherein the method comprises the following steps of: the judging method of the left deviation phenomenon and the right deviation phenomenon comprises the following steps: the target curvature of the vehicle is 0, and when the actual curvature is greater than 0.001, the vehicle is considered to be right-biased; when the actual curvature is less than-0.001, the vehicle is considered to be left-biased.
5. The self-learning-based bilateral independent electrically-driven tracked vehicle deviation-correcting control method according to claim 1, wherein the method comprises the following steps of: the upper limit of the correction coefficient is 1.2, and the lower limit of the correction coefficient is 0.8.
6. The self-learning-based bilateral independent electrically-driven tracked vehicle deviation-correcting control method according to claim 1, wherein the method comprises the following steps of: the threshold value of the left bias time is 2 seconds, and the threshold value of the right bias time is 2 seconds.
7. The self-learning-based bilateral independent electrically-driven tracked vehicle deviation-correcting control method according to claim 1, wherein the method comprises the following steps of: the threshold value of the mean square error of the vehicle speed is 2.
8. The self-learning-based bilateral independent electrically-driven tracked vehicle deviation-correcting control method according to claim 1, wherein the method comprises the following steps of: the correction is achieved by modifying the correction coefficient, thereby correcting the drive motor torque.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5125467A (en) * | 1988-04-26 | 1992-06-30 | Daniel Mancheron | Telecontrolled tracked vehicle |
| JP2010154719A (en) * | 2008-12-26 | 2010-07-08 | Aichi Corp | Travel controller for track-traveling vehicle |
| WO2015046185A1 (en) * | 2013-09-27 | 2015-04-02 | 株式会社クボタ | Series hybrid combine |
| CN109760758A (en) * | 2018-12-07 | 2019-05-17 | 江西悦丰农业科技有限公司 | The crawler unit that a kind of electrodynamic type or so is operated alone |
| CN110775173A (en) * | 2019-11-29 | 2020-02-11 | 徐州徐工基础工程机械有限公司 | High-precision straight-line walking deviation correcting system based on double-side distance detection and engineering vehicle |
-
2021
- 2021-12-31 CN CN202111683698.8A patent/CN114228511B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5125467A (en) * | 1988-04-26 | 1992-06-30 | Daniel Mancheron | Telecontrolled tracked vehicle |
| JP2010154719A (en) * | 2008-12-26 | 2010-07-08 | Aichi Corp | Travel controller for track-traveling vehicle |
| WO2015046185A1 (en) * | 2013-09-27 | 2015-04-02 | 株式会社クボタ | Series hybrid combine |
| CN109760758A (en) * | 2018-12-07 | 2019-05-17 | 江西悦丰农业科技有限公司 | The crawler unit that a kind of electrodynamic type or so is operated alone |
| CN110775173A (en) * | 2019-11-29 | 2020-02-11 | 徐州徐工基础工程机械有限公司 | High-precision straight-line walking deviation correcting system based on double-side distance detection and engineering vehicle |
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