A multi-component cooperative control method and controller for an electric harvester based on feeding amount estimation

Abstract

The application discloses a kind of based on feeding amount estimation electric harvester multi-component collaborative control method and controller, step 1: the establishment electric harvester component dynamics model;2: acquisition harvester component torque and rotating speed;3: to the linear regression fitting of harvester driving speed and rotating speed;4: based on current feeding torque load value estimation header feeding amount;5: based on header feeding amount estimation feeding roller feeding amount;6: based on roller feeding amount estimation value to roller pre-sight compound PID control;7: based on header feeding amount estimation harvester mass;8: based on harvester mass change to pre-sight compound PID control of walking system.The application solves the problem of electric harvester component drive control under the condition of feeding load fluctuation, from the response characteristic of header dynamics model to carry out real-time estimation on feeding amount, and based on feeding amount to pre-sight compound PID control of walking system and roller, optimize the control stability of harvester component under complex conditions.

Description

TECHNICAL FIELD

[0001] The application discloses a multi-component cooperative control method and a controller for an electric harvester based on feeding amount estimation, and belongs to the field of intelligent control of agricultural machinery. BACKGROUND

[0002] In recent years, agricultural mechanization has developed rapidly. At present, the market share of rice combines is about 2 million units, and the mechanized harvesting level of rice has reached more than 90%. While the mechanized harvesting level of rice is rapidly improving, it also brings a large amount of energy consumption and environmental pollution. In order to reduce pollution, electric rice harvesters have become the focus of research in the field of rice harvesting. However, due to the complex and changeable harvesting environment, factors such as working mode and harvested crop characteristics, the load of the components of the rice combine during operation fluctuates greatly, and problems such as component stall and component instability caused by feeding fluctuation occur from time to time. The existing electric control technology of rice harvesters cannot meet the reliable operation requirements in various environments. SUMMARY

[0003] The application proposes a multi-component cooperative control technology for an electric harvester based on feeding amount estimation. The method estimates the current feeding amount in real time based on the dynamics of the header, establishes the correlation between the feeding amount and the walking system and the cylinder, estimates the cylinder feeding amount and the load change of the harvester based on the header feeding amount estimation, and controls the cylinder speed and the harvester speed using the estimation results to improve the working stability of the electric harvester components under the condition of feeding amount fluctuation. The specific scheme is as follows:

[0004] A multi-component cooperative control method for an electric harvester based on feeding amount estimation, comprising the following steps:

[0005] Step 1: Establishing a dynamic model of the components of the electric harvester;

[0006] Step 2: Collecting the torque and speed information of the components of the harvester using sensors;

[0007] Step 3: Linearly regressing and fitting the historical driving speed and speed information of the harvester;

[0008] Step 4: Estimating the header feeding amount information based on the current feeding torque load value;

[0009] Step 5: Estimating the feeding amount of the feeding cylinder based on the header feeding amount;

[0010] Step 6: Pre-aiming and compound PID controlling the cylinder based on the estimated value of the cylinder feeding amount;

[0011] Step 7: Estimating the mass of the harvester based on the header feeding amount;

[0012] Step 8: Pre-look compound PID control of the walking system based on the changes in the harvester mass.

[0013] Further, the cutterbar dynamics model established in step 1 is shown as follows:

[0014]

[0015] M q = (k c +k p +k m )*q h

[0016] where M G is the motor input torque; M pi is the reel idle friction torque; M ci is the cutterbar idle friction torque; M mi is the unloading auger idle friction torque; i p , i m are the drive ratios from the main shaft to the reel and unloading auger, respectively; J p is the reel rotational inertia; ω ci is the input shaft speed; J m is the unloading auger rotational inertia; M q is the feed rate load torque; q h is the harvester feed rate; k c , k p , k m are the feed rate load coefficients for the cutterbar, reel, and unloading auger, respectively.

[0017] Further, the cylinder dynamics model established in step 1 is shown as follows:

[0018]

[0019] where ω r is the cylinder angular speed; A is the mechanical friction resistance torque; B is the air resistance coefficient; M r is the cylinder idle torque; R t is the equivalent radius; λ is the outlet speed ratio at the cylinder outlet (the ratio of the crop tangential speed to the cylinder rotational linear speed), which is generally 1 / 2-1 / 5; Jr is the rotational inertia of the cylinder; q is the current harvester feed rate; δ is the grain straw ratio; and f is the rubbing coefficient.

[0020] Further, the walking system dynamics model established in step 1 is shown as follows:

[0021]

[0022] where F t is the walking system driving force; f Fdenoted as the rolling resistance coefficient of the harvester; v is the harvester's traveling speed; and m is the harvester's mass.

[0023] Furthermore, the sensors used in step 2 mainly include:

[0024] The speed and torque sensors are installed on the drive shafts of the header, threshing drum, and walking system to detect the speed and torque of the header, the speed and torque of the threshing drum, the walking speed of the harvester, and the walking drive torque of the harvester.

[0025] Furthermore, step 3 involves linearly fitting the harvester's travel speed to the header feed rate as follows:

[0026]

[0027]

[0028]

[0029] M q_v =k·v combine +b

[0030] Where k and b are linear regression fitting coefficients; v combinei M represents the harvester speed at the corresponding sampling time point; qi This refers to the feed load torque at the corresponding sampling time point; This represents the average torque of the cutting table. M represents the average harvester speed at the sampling nodes. q_v The torque value of the cutting table is calculated using the fitting formula.

[0031] Furthermore, the specific method for estimating the feed rate of the header in step 4 is as follows:

[0032]

[0033] q Header This is an estimated value for the feed rate of the header.

[0034] Furthermore, the specific method for estimating the feed rate of the threshing drum in step 5 is as follows:

[0035]

[0036] Where, q Threshing (t) is the estimated value of the feed rate to the threshing drum at time t, q Header (t) is the estimated value of the feed rate at time t, l tran ω is the length of the conveyor belt. tran r is the conveyor belt rotation speed. tarn Where is the radius of the conveyor pulley.

[0037] Further, the specific method for controlling the harvester cylinder speed according to the threshing cylinder feeding amount estimation value in step 6 is as follows:

[0038]

[0039] Wherein, T Threshing is the threshing cylinder control torque; k pre , k p , k i , k d are the speed error preview, proportional, differential, integral link coefficients respectively; e ω_Threshing is the error value between the current speed and the expected speed; is the differential of the error value between the current speed and the expected speed.

[0040] Further, the specific method for estimating the harvester load according to the header feeding amount in step 7 is as follows:

[0041] m i = m i-1 + k q-mass · q Header_i

[0042] Wherein, k q-mass is the feeding amount-load change coefficient; m i , m i-1 are the harvester masses corresponding to the i and i-1 sampling points respectively.

[0043] Further, the specific method for controlling the harvester driving speed according to the load change in step 8 is as follows:

[0044]

[0045] Wherein, k transport_pre , k transport_p , k transport_i , k transport_d are the driving speed error preview, proportional, differential, integral link coefficients respectively; r tire is the driving wheel tire radius; e v is the error between the expected speed and the current speed.

[0046] The present application also proposes an electric harvester controller, which can execute the contents of steps 1, 3, 4, 5, 6, 7 and 8 described above.

[0047] The beneficial effects of the present application are as follows:

[0048] (1) Combined with the header dynamics characteristics, the header feeding amount in the harvesting operation process is more accurately estimated;

[0049] (2) The feed load of the drum is estimated by using the feed load of the cutting table, and the desired speed of the drum is tracked and controlled by the pre-aiming PID.

[0050] (3) The quality of the harvester is updated in real time by combining the feeding characteristics, and the harvester’s operating speed is tracked and controlled by combining the pre-aiming PID. Attached Figure Description

[0051] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0052] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0053] This invention provides a multi-component collaborative control method for electric harvesters based on feed rate estimation, such as... Figure 1 As shown, it includes the following steps:

[0054] Step 1: Establish a dynamic model of the header, rollers, and traveling parts of the electric harvester.

[0055]

[0056] M q =(k c +k p +k m )*q h

[0057] Where M G M is the input torque for the motor. pi M is the frictional torque of the reel during idle rotation. ci M is the frictional torque of the cutting tool during idle rotation. mi For the frictional torque of the auger during idling; i p i m These are the transmission ratios from the drive shaft to the reel and the auger, respectively; J p ω is the moment of inertia of the reel; ci J is the input shaft speed; m M is the moment of inertia of the auger. q Feed rate and load torque; q h k is the feed rate of the header. c k p k m These are the feed load coefficients for the cutter, reel, and auger, respectively.

[0058]

[0059] In the formula, ω r ω is the drum angular velocity; A is the mechanical frictional resistance torque; B is the blower resistance coefficient; ω is the drum idling torque; R is the drum angular velocity.r is the equivalent radius; λ is the exit speed ratio (the ratio of the crop tangential velocity to the cylinder rotational linear velocity) at the cylinder exit, which is usually 1 / 2-1 / 5.

[0060]

[0061] F t is the driving force of the walking system; f F is the rolling resistance coefficient of the walking system; v is the walking speed of the harvester; and m is the mass of the harvester.

[0062] The parameters in the harvester dynamics model above are determined by the characteristics of the harvester components and are obtained through a calibration test procedure.

[0063] Step 2: Collect the torque and rotational speed information of the harvester components using sensors;

[0064] The rotational speed information collected during use includes the header rotational speed information, the cylinder rotational speed information, and the driving shaft rotational speed information. The rotational speed information is detected by proximity switches arranged at the positions of the corresponding driving shafts. The proximity switches send a high-level signal after detecting the proximity point of the flange plate. The system collects the signal in real time through the ECU. At this time, the rotational speed of the shaft is calculated in the following formula:

[0065]

[0066] n detec is the number of proximity signals detected by the ECU in a sampling period; T detec is the sampling period; and n is the number of proximity points on the flange plate.

[0067] The torque information that needs to be collected during use includes the driving shaft torque information of the header, the cylinder, and the walking system. The torque sensors are installed on the driving shafts of the header, the cylinder, and the walking system.

[0068] Step 3: Perform linear regression fitting on the historical driving speed and rotational speed information of the harvester. The specific method is as follows:

[0069] First, calculate the feeding load torque using the header dynamics model, and then perform linear fitting using the historical feeding load and driving speed information. The specific formulas in the calculation and fitting process are as follows:

[0070]

[0071]

[0072]

[0073] M q_v= k · v combine + b

[0074] wherein k and b are linear regression fitting coefficients; v combinei is the harvester speed corresponding to the sampling time point; M qi is the feeding load torque corresponding to the sampling time node; is the average value of the header torque; is the average value of the harvester speed at the sampling node; M q_v is the header torque value calculated by the fitting formula.

[0075] Step 4: Estimate the header feeding amount information based on the current feeding torque load value. The estimation formula is as follows:

[0076]

[0077] Step 5: Estimate the feeding roller feeding amount based on the header feeding amount. The estimation formula considers the time lag relationship between the header feeding load and the feeding roller feeding load. The time lag time is the time for the conveying trough to transport the header feeding to the feeding roller. The specific estimation formula is as follows:

[0078]

[0079] wherein q Threshing (t) is the estimated value of the estimated value of the feeding roller feeding at time t, q Header (t) is the estimated value of the estimated value of the header feeding at time t, l tran is the conveying belt length, ω tran is the conveying belt speed, and r tarn is the conveying belt wheel radius.

[0080] Step 6: Perform pre-look PID compound control on the roller based on the roller feeding amount estimation value. The compound control mainly considers the error value between the current speed and the expected speed and the influence of the feeding amount change on the roller speed within the pre-look time period. The specific formula is as follows:

[0081]

[0082] wherein T Threshing is the control torque of the feeding roller; k pre , k p , k i , and k d are the speed error pre-look, proportional, differential, and integral link coefficients, respectively, e ω_ Th res hi ng is the error value between the current speed and the expected speed; is the differential of the error value between the current speed and the expected speed.

[0083] Step 7: Estimate the mass of the harvester based on the feeding amount of the header, the estimation method is based on the mass estimation of the last time node and the feeding amount of the current time point to update the current mass estimation value, the specific method is as follows:

[0084] m i = m i-1 +k q-mass ·q Header_i

[0085] Wherein, k q-mass is the feeding amount-load change coefficient.

[0086] Step 8: Based on the mass change of the harvester, the pre-look PID control of the walking system is carried out, which mainly considers the difference between the current driving speed and the expected driving speed and the influence of the mass change on the driving speed of the harvester within the pre-look time period.

[0087]

[0088] Wherein, k transport_pre , k transport_p , k transport_i , k transport_d are the driving speed error pre-look, proportional, differential and integral link coefficients respectively; r tire is the driving wheel tire radius; e v is the error between the expected speed and the current speed.

[0089] The embodiment of the application also includes an electric harvester controller, which can execute the contents of steps 1, 3, 4, 5, 6, 7 and 8 described above, and the physical device of the controller can be installed in the cab.

[0090] The series of detailed descriptions listed above are only specific descriptions of the feasible embodiments of the application, and are not used to limit the protection scope of the application, and any equivalent means or changes without departing from the technology of the application should be included in the protection scope of the application.

Claims

1. A method for multi-component coordinated control of an electric harvester based on feed rate estimation, characterized by, The method comprises the following steps: Step 1: establishing a dynamic model of the electric harvester components; The dynamic model of the components in step 1 comprises: The header dynamic model is shown as follows: M q = (k c +k q +k m )*q h where M G is the motor input torque; M pi is the reel idle friction torque; M ci is the cutter idle friction torque; M mi is the unloading auger idle friction torque; i p , i m are the drive ratios from the drive shaft to the reel and unloading auger respectively; J p is the reel moment of inertia; ω ci is the input shaft rotational speed; J m is the unloading auger moment of inertia; M q is the feed rate load torque; q h is the header feed rate; k c , k q , k m are the feed rate load coefficients for the cutter, reel, and unloading auger respectively; The cylinder dynamic model is shown as follows: In the formula, ω r A is the drum angular velocity; B is the mechanical frictional resistance torque; C is the blower resistance coefficient; D is the drum angular velocity; E is the mechanical frictional resistance torque; M is the blower resistance coefficient ... r R is the idle torque of the drum; r λ is the equivalent radius; λ is the exit speed ratio at the drum exit (the ratio of crop tangential velocity to drum rotation linear velocity), which is generally 1 / 2 to 1 / 5; Jr is the moment of inertia of the threshing drum; q is the current feed rate of the harvester; δ is the grain-to-straw ratio; and f is the friction coefficient. The walking system dynamic model is shown as follows: where F t is the driving force of the walking system; f F is the rolling resistance coefficient of the walking system; v is the walking speed of the harvester; m is the mass of the harvester; Step 2: collecting the torque and rotating speed information of the harvester components; Step 3: performing linear regression fitting on the harvester traveling speed and the header feeding; The method for linear fitting of the harvester traveling speed and the header feeding in step 3 is as follows: M q_v = k · v combine + b where k and b are linear regression fitting coefficients; v combinei is the harvester speed at the corresponding sampling time point; M qi is the feeding load torque at the corresponding sampling time point; is the average value of the header torque; is the average value of the harvester speed at the sampling points; M q_v is the header torque value calculated by the fitting formula; Step 4: estimating the header feeding information; The method for estimating the header feeding information in step 4 is as follows: Step 5: estimating the cylinder feeding based on the header feeding; Step 6: performing preview compound PID control on the cylinder rotating speed based on the cylinder feeding estimation value; Step 7: estimating the harvester load based on the header feeding; Step 8: performing preview compound PID control on the walking system based on the harvester load change.

2. The multi-component cooperative control method of an electric harvester based on the estimation of the feeding amount according to claim 1, characterized in that, The method for realizing step 2 is as follows: Rotating speed sensors and torque sensors are installed on the driving shafts of the header, the cylinder and the walking system to detect the rotating speed and torque of the header, the rotating speed and torque of the cylinder and the traveling speed and the driving torque of the harvester.

3. The multi-component cooperative control method for electric harvester based on feeding amount estimation according to claim 1, characterized in that, The method for estimating the cylinder feeding in step 5 is as follows: where q Threshing (t) is the estimate of the threshing cylinder feed estimate at time t, is the estimate of the header feed estimate at time t, l tran is the length of the conveyor belt, ω tran is the speed of the conveyor belt, r tarn is the radius of the conveyor belt wheel.

4. The multi-component cooperative control method of an electric harvester based on the estimation of the feeding amount according to claim 3, characterized in that, The specific implementation method of step 6 comprises the following: Wherein, T Threshing is the threshing cylinder control torque; k pre , k p , k i , k d are the speed error preview, proportional, differential, integral link coefficients, e ω_Threshing is the error value between the current speed and the desired speed; is the differential of the error value between the current speed and the desired speed, t pre is the preview time period.

5. The multi-component cooperative control method of an electric harvester based on the estimation of the feeding amount according to claim 1, characterized in that, The specific implementation method of step 7 comprises the following: m i = m i-1 + k q-mass · q Header_i k q-mass is the feed rate-load change coefficient.

6. The multi-component cooperative control method of an electric harvester based on the estimation of the feeding amount according to claim 5, characterized in that, The implementation of step 8 comprises: where k transport_pre , k transport_p , k transport_i , k transport_d are the driving speed error pre-look, proportional, derivative, and integral link coefficients, respectively; r tire is the driving wheel tire radius; and e v is the error between the desired speed and the current rotational speed.

7. An electric harvester controller, characterized in that The controller can perform the control method of any one of claims 1-6.

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