CN109159783B - Ground parameter estimation method for distributed electrically-driven tracked vehicle - Google Patents
Ground parameter estimation method for distributed electrically-driven tracked vehicle Download PDFInfo
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- CN109159783B CN109159783B CN201810969265.0A CN201810969265A CN109159783B CN 109159783 B CN109159783 B CN 109159783B CN 201810969265 A CN201810969265 A CN 201810969265A CN 109159783 B CN109159783 B CN 109159783B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
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- 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
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/08—Electric propulsion units
- B60W2510/081—Speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/08—Electric propulsion units
- B60W2510/083—Torque
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/14—Yaw
Abstract
The invention relates to a ground parameter estimation method for a distributed electric drive tracked vehicle, which comprises the following steps of obtaining a ground parameter statistical model of a running road surface by an off-line training method; predicting the motor torque of the vehicle by utilizing the ground parameter statistical model according to the acquired vehicle information; establishing a tracked vehicle dynamic model, calculating to obtain theoretical torques of motors on the left side and the right side of the vehicle, and performing iterative operation on the theoretical torques and the predicted torques of the motors on the left side and the right side to obtain ground parameters including a ground deformation resistance coefficient f and a steering resistance coefficient mu. The test data adopted by the invention are all daily sports car data which can be directly obtained by a whole car data acquisition system, and unknown ground parameters can be obtained by the method without excessive preparation before the test.
Description
Technical Field
The patent relates to the field of unmanned vehicles, in particular to a ground parameter estimation method for a distributed electrically-driven tracked vehicle.
Background
Distributed electrically driven tracked vehicles are favored by more and more researchers due to their flexible steering capabilities, and research on vehicle-ground systems is a focus of research. The previous research on a vehicle-ground system mainly focuses on two aspects, namely, the method comprises the steps of solving the sliding parameters of the vehicle by using a dynamic model of the vehicle and neglecting the solution of the ground parameters; secondly, a large number of tests are combined with a simulation model or a real vehicle model to solve the ground parameters, and the preparation before the tests is complex.
Disclosure of Invention
In view of the foregoing, the present invention is directed to a method of estimating ground parameters for a distributed electrically-driven tracked vehicle that does not require excessive pre-test preparation to obtain unknown ground parameters.
The purpose of the invention is mainly realized by the following technical scheme:
a method of estimating a ground parameter for a distributed electrically driven tracked vehicle, comprising the steps of:
step S1, obtaining a ground parameter statistical model of the driving road surface by an off-line training method; the input quantity of the model is the rotating speed and the course deviation of the motors on the left side and the right side, and the output quantity is the motor torque on the left side and the right side of the vehicle which is restricted by the ground and is related to the ground parameters;
step S2, according to the collected vehicle information, the ground parameter statistical model is utilized to predict the vehicle motor torque, and the motor torques T at the left side and the right side are obtained1、T2；
Step S3, establishing a tracked vehicle dynamic model, and calculating to obtain theoretical torques T 'of motors on the left side and the right side of the vehicle'1、T'2And the predicted results T of the motors on the left side and the right side1、T2And carrying out iterative operation to obtain ground parameters including a ground deformation resistance coefficient f and a steering resistance coefficient mu.
Further, step S1 includes:
step S110, collecting driving data of different driving conditions of the tracked vehicle on a driving road surface; the running working conditions comprise a vehicle straight running working condition, an S-turn working condition, a lane changing working condition and a constant radius steering working condition under different speeds;
step S120, extracting characteristic data including motor rotating speed, vehicle course angle and motor torque information in the collected data;
step S130, removing redundant data in the characteristic data and carrying out median filtering processing to obtain the rotating speeds of the left and right motors, course deviation and torques of the left and right motors;
and step S140, carrying out GMM model training and establishing a ground parameter statistical model.
Further, the data acquisition of different driving conditions in step S110 is performed for multiple times to obtain multiple sets of data acquired under different driving conditions.
Further, the redundant data includes data mainly including extraction of a stationary state of the vehicle.
Further, the step of establishing the ground parameter statistical model is as follows:
1) randomly selecting partial data of the acquired data under different driving conditions obtained in the step S130 to form a data set Z, and carrying out K-means clustering;
2) calculating the contour coefficients of all data points in Z to obtain an average value of the contour coefficients, and selecting a clustering result corresponding to a K value with the most approximate mean contour coefficient to 1 as an initialization parameter of the GMM;
3) training the GMM model by using an EM algorithm to obtain a ground parameter statistical model, wherein the parameters of the ground parameter statistical model obtained after training comprise the weight p of Gaussian distributioniMean value of Gaussian distribution μiStandard deviation sigma of sum Gaussian distributioni。
Further, the step S2 specifically includes:
acquiring the rotating speed and course deviation data of motors on the left side and the right side of the vehicle when the vehicle runs on the running road surface on line;
inputting the collected data into the ground parameter statistical model for prediction to obtain motor torques T at the left side and the right side of the vehicle1、T2。
Further, the prediction adopts a Gaussian mixture regression prediction method.
Further, the step S3 includes:
s310, collecting motor rotating speed and steering radius data of the left side and the right side of the vehicle on line when the vehicle runs on the running road surface;
step S320, inputting the collected data into a tracked vehicle dynamic model to calculate theoretical torque values of the left motor and the right motor to obtain theoretical torques T 'of the left motor and the right motor'1、T'2；
Step S330, obtaining by using least square methodLeft and right motor torque T1、T2And theoretical torques T 'of motors on the left side and the right side output by the dynamic model of the tracked vehicle'1、T'2And carrying out repeated iterative operation to obtain ground parameters including a ground deformation resistance coefficient f and a steering resistance coefficient mu.
Further, theoretical torques of the left and right motors
In the formula, f is a ground deformation resistance coefficient, G is vehicle weight, L is track grounding length, r is a working radius of a driving wheel, B is track center distance, and mu is a steering resistance coefficient.
Further, the stop condition of the least square method iteration calculation is (T'1-T1)2+(T'2-T2)2Taking the minimum value.
The invention has the following beneficial effects:
the method combines a large amount of data with a vehicle dynamics model, extracts the motor rotating speed, course deviation and motor torque as characteristic data based on daily test data, and establishes a ground parameter statistical model through a Gaussian mixture algorithm; inputting the rotating speeds and the course deviation of the motors on two sides in the actual running process of the vehicle into a ground parameter statistical model to obtain the predicted torque of the motors; the method comprises the steps of inputting the rotating speeds and the steering radiuses of motors on two sides of a vehicle in the actual running process into a dynamic model of the tracked vehicle to obtain the theoretical torque of the motors, and enabling the error between the predicted torque and the theoretical torque of the motors to be minimum by utilizing a least square method, so that the method for estimating the vehicle ground parameters is provided. The test data adopted by the invention are all daily sports car data which can be directly obtained by a whole car data acquisition system without excessive preparation before test. And training the obtained data, establishing a ground parameter statistical model, and obtaining unknown ground parameters in the formula by using a least square method and an iterative algorithm in combination with a dynamic model of the tracked vehicle.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a flowchart of a method for estimating ground parameters according to an embodiment of the present invention;
FIG. 2 is a schematic representation of a dynamic model of a tracked vehicle according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and which together with the embodiments of the invention serve to explain the principles of the invention.
The invention discloses a ground parameter estimation method for a distributed electric drive tracked vehicle, which comprises the following steps of (1) calculating a ground parameter of the distributed electric drive tracked vehicle;
as shown in fig. 1, the method comprises the following steps:
step S1, obtaining a ground parameter statistical model of the driving road surface by an off-line training method;
the method specifically comprises the following steps:
step S110, data acquisition
Collecting data of different running working conditions of the tracked vehicle under the road surface, wherein the data comprise a straight running working condition, an S-turn working condition, a lane changing working condition, a constant radius steering working condition and the like of the tracked vehicle at different speeds;
specifically, in order to eliminate the randomness in the test process, the data acquisition of each working condition of different driving working conditions is repeatedly carried out, and a plurality of groups of data acquired under different driving working conditions are obtained.
Step S120, data extraction
And extracting characteristic data including the motor rotating speed, the vehicle course angle and the motor torque information in the collected data, and subtracting the course angle at the moment from the course angle at the next moment to obtain course deviation at the moment.
Step S130, filtering processing
Removing redundant data in the characteristic data and carrying out median filtering processing to obtain the rotating speed, course deviation and torque of motors on the left side and the right side;
specifically, in the present embodiment, the redundant data mainly includes data extracted from the stationary state of the vehicle.
The motor rotating speed and the motor torque data in the steps are feedback quantities of the motor controller, and the course angle is measured by the inertial navigation system.
Step S140, GMM model training is carried out, and a ground parameter statistical model is established
The input quantity of the ground parameter statistical model provided by the embodiment of the invention is the rotating speed and the course deviation of the motors on the left side and the right side, and the output quantity is the torque of the motors on the left side and the right side.
The steps of establishing the ground parameter statistical model are as follows:
1) clustering the filtered data
Randomly selecting partial data under different driving conditions to form a data set Z, carrying out K-means clustering on the Z for multiple times aiming at different clustered data sets Z, and calculating the profile coefficient s of a data point i in the ZiThe closer the contour coefficient is to 1, the more reasonable the clustering result for that data point.
2) Obtaining initialization parameters for GMM
And calculating the contour coefficients of all data points in Z to obtain an average value of the contour coefficients, and selecting a clustering result corresponding to the K value with the average contour coefficient closest to 1 as an initialization parameter of the GMM.
3) Training GMM model using EM algorithm
The GMM model obtained by the training of the EM algorithm is a ground parameter statistical model, and the parameter is the solved pi、μi、σi(ii) a Wherein p isiIs the weight of the ith Gaussian distribution, muiIs ith highMean of the distribution, σiIs the standard deviation of the ith gaussian distribution.
The established ground parameter statistical model can be regarded as a vehicle-ground relation model established on the basis of a large amount of test data, wherein input quantity (rotating speeds of motors on two sides) represents a vehicle system, and output quantity (torque of the motors on two sides) is restricted by the ground and is related to ground parameters.
Through the established ground parameter statistical model, the motor torques on the left side and the right side related to the ground parameters are obtained, the influence of the ground parameters is considered, and the subsequent extraction of the ground parameters is facilitated.
Step S2, according to the collected vehicle information, the ground parameter statistical model is utilized to predict the vehicle motor torque, and the motor torques T at the left side and the right side are obtained1、T2；
Specifically, the method comprises the following steps of,
step S210, collecting vehicle system data on line
The collected specific data comprises the current rotating speed and course angle of the motors on the left side and the right side; calculating course deviation according to the course angle;
specifically, the course angle of the current time is subtracted from the course angle of the next time as the course deviation of the current time.
Step S220, inputting the collected data into the ground parameter statistical model established in the step S140 for prediction to obtain motor torques T at the left side and the right side of the vehicle1、T2
Specifically, in the embodiment of the invention, the motor torques at two sides are predicted by adopting Gaussian Mixture Regression (GMR) to obtain the motor torques T at the left side and the right side1、T2；
The left and right motor torques T1、T2This can be regarded as the actual torque of the vehicle in the vehicle-ground system during driving.
The prediction calculation result of the motor torques on the two sides through Gaussian Mixture Regression (GMR) is not only a motor torque prediction value, but also a probability distribution of all possible future motion tracks of the tracked vehicle, and the motor torque prediction under a certain driving condition can be obtained by utilizing a probability statistical distribution characteristic.
Step S3, establishing a tracked vehicle dynamic model, and calculating theoretical torques T 'of motors on the left side and the right side of the vehicle'1、T'2And the predicted result T of the motor torque1、T2And carrying out iterative operation to obtain ground parameters including a ground deformation resistance coefficient f and a steering resistance coefficient mu.
Specifically, the method comprises the following steps of,
step S310, collecting vehicle system data on line
The collected specific data comprises the rotating speed and the turning radius of the motors on the left side and the right side.
Step S320, inputting the collected data into a tracked vehicle dynamic model to calculate the theoretical torque values of the left motor and the right motor;
in particular, the dynamic model of the tracked vehicle of the embodiment of the invention does not take into account the slip and slip characteristics of the tracked vehicle and makes the following assumptions:
1) the running resistance coefficient of the crawler during steering is the same as that of the crawler during straight running;
2) the caterpillar is a non-extensible uniform flexible belt, the normal coincidence of the caterpillar vehicle is uniformly distributed along the grounding section, namely the load pattern is rectangular, the transverse resistance S is in direct proportion to the normal load G, and the proportionality coefficient is expressed by mu, namely
Mu is also called the steering resistance coefficient;
3) the projection of the gravity center of the tracked vehicle on the horizontal plane is superposed with the center of the vehicle plane;
4) the related steering process is steady-state steering, and the road surface is a horizontal road surface;
5) the sinking of the track and the dozing effect of the sides of the track are not considered.
As shown in FIG. 2, P2For driving force acting on the outer track-engaging surface, P1For braking forces acting on the inner track-engaging surface, R1、R2As resistance to ground deformation, Sh、SqIs the ground transverse resistance. In the embodiment of the invention, straight running is usedThe ground deformation resistance coefficient in the time of turning is replaced by the ground deformation resistance coefficient in the time of turning, and the following are included:
f is the coefficient of ground deformation resistance, and G is the vehicle weight.
From assumption 2) it is easy to derive:
transverse moment of resistance M during steeringμComprises the following steps:
l is the ground contact length of the crawler belt.
When the tracked vehicle is in steady-state steering, the following equilibrium equations may be listed:
P2-P1＝R1+R2(4)
rP2-T'2＝0 (6)
rP1-T'1＝0 (7)
T'1、T'2the theoretical torque of the motors on two sides is shown as r, the working radius of the driving wheel is shown as r, and the center distance of the crawler belt is shown as B.
The above equation can be found in parallel:
and S330, carrying out multiple iterative operations on the line prediction value obtained in the step S2 and the theoretical value output by the tracked vehicle dynamic model by using a least square method to obtain a ground parameter.
The motor torques at two sides, which are predicted according to the ground parameter statistical model, are assumed to be T respectively1、T2Equations (8) and (9) are obtained by iterative calculation using the least square method, and equation (T'1-T1)2+(T'2-T2)2The ground deformation resistance coefficient f and the steering resistance coefficient μ can be obtained as the minimum.
In summary, the ground parameter estimation method for the distributed electrically-driven tracked vehicle disclosed by the embodiment of the invention combines a large amount of data with a vehicle dynamics model, extracts the motor rotation speed, the course deviation and the motor torque as characteristic data based on daily test data, and establishes a ground parameter statistical model through a Gaussian mixture algorithm; inputting the rotating speeds and the course deviation of the motors on two sides in the actual running process of the vehicle into a ground parameter statistical model to obtain the predicted torque of the motors; the method comprises the steps of inputting the rotating speeds and the steering radiuses of motors on two sides of a vehicle in the actual running process into a dynamic model of the tracked vehicle to obtain the theoretical torque of the motors, and enabling the error between the predicted torque and the theoretical torque of the motors to be minimum by utilizing a least square method, so that the method for estimating the vehicle ground parameters is provided. The test data adopted by the embodiment of the invention are all daily sports car data which can be directly obtained by a whole car data acquisition system without excessive preparation before test. And training the obtained data, establishing a ground parameter statistical model, and obtaining unknown ground parameters in the formula by using a least square method and an iterative algorithm in combination with a dynamic model of the tracked vehicle.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (8)
1. A method for estimating ground parameters for a distributed electrically driven tracked vehicle, comprising the steps of:
step S1, obtaining a ground parameter statistical model of the driving road surface by an off-line training method; the input quantity of the model is the rotating speed and the course deviation of the motors on the left side and the right side, and the output quantity is the motor torque on the left side and the right side of the vehicle which is restricted by the ground and is related to the ground parameter;
step S2, according to the collected vehicle information, the ground parameter statistical model is utilized to predict the vehicle motor torque, and the motor torques T at the left side and the right side are obtained1、T2；
Step S3, establishing a tracked vehicle dynamic model, and calculating to obtain theoretical torques T 'of motors on the left side and the right side of the vehicle'1、T′2And the predicted results T of the motors on the left side and the right side1、T2Carrying out iterative operation to obtain ground parameters including a ground deformation resistance coefficient f and a steering resistance coefficient mu;
wherein, step S1 includes:
step S110, collecting driving data of different driving conditions of the tracked vehicle on a driving road surface; the running working conditions comprise a vehicle straight running working condition, an S-turn working condition, a lane changing working condition and a constant radius steering working condition under different speeds;
step S120, extracting characteristic data including motor rotating speed, vehicle course angle and motor torque information in the collected data;
step S130, removing redundant data in the characteristic data and carrying out median filtering processing to obtain the rotating speeds of the left and right motors, course deviation and torques of the left and right motors;
step S140, GMM model training is carried out, and a ground parameter statistical model is established;
the steps of establishing the ground parameter statistical model are as follows:
1) randomly selecting partial data of the acquired data under different driving conditions obtained in the step S130 to form a data set Z, and carrying out K-means clustering;
2) calculating the contour coefficients of all data points in Z to obtain an average value of the contour coefficients, and selecting a clustering result corresponding to a K value with the most approximate mean contour coefficient to 1 as an initialization parameter of the GMM;
3) training the GMM model by using an EM algorithm to obtain a ground parameter statistical model, wherein the parameters of the ground parameter statistical model obtained after training comprise: weight p of Gaussian distributioniMean value of Gaussian distribution μiStandard deviation sigma of sum Gaussian distributioni。
2. The ground parameter estimation method according to claim 1, wherein the data acquisition of the different driving conditions of step S110 is performed a plurality of times to obtain a plurality of sets of the acquired data of the different driving conditions.
3. A method as claimed in claim 1 or 2, wherein the redundant data comprises data extracted primarily from stationary conditions of the vehicle.
4. The ground parameter estimation method according to claim 1,
the step S2 specifically includes:
acquiring the rotating speed and course deviation data of motors on the left side and the right side of the vehicle when the vehicle runs on the running road surface on line;
inputting the collected data into the ground parameter statistical model for prediction to obtain motor torques T at the left side and the right side of the vehicle1、T2。
5. The method of estimating ground parameters of claim 4, wherein said prediction is a Gaussian mixture regression prediction method.
6. The ground parameter estimation method according to claim 1,
the step S3 includes:
s310, collecting motor rotating speed and steering radius data of the left side and the right side of the vehicle on line when the vehicle runs on the running road surface;
step S320, inputting the collected data into a tracked vehicle dynamics model to calculate the theoretical torque values of the left motor and the right motor to obtain a left motor and a right motorTheoretical torque T 'of two-side motor'1、T′2；
Step S330, obtaining the left and right motor torque T by using a least square method1、T2And theoretical torques T 'of motors on the left side and the right side output by the dynamic model of the tracked vehicle'1、T′2And carrying out repeated iterative operation to obtain ground parameters including a ground deformation resistance coefficient f and a steering resistance coefficient mu.
7. The method of estimating a surface parameter of claim 6, wherein the theoretical torques of the left and right motors are set to be equal to each other
In the formula, f is a ground deformation resistance coefficient, G is vehicle weight, L is track grounding length, r is a working radius of a driving wheel, B is track center distance, and mu is a steering resistance coefficient.
8. A method of estimating ground parameters according to claim 6, wherein the stop condition for said least squares iterative calculation is (T'1-T1)2+(T′2-T2)2Taking the minimum value.
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