Electric harvester component control method based on optimal grain loss rate and electric harvester

Abstract

The present invention discloses a method for controlling electric harvester components based on an optimal grain loss rate and an electric harvester, comprising the following steps: 1. Pre-collecting component speed, grain loss rate, and feed rate information to establish a database; 2. Establishing a fitting relationship using the component speed and feed rate as independent variables and the loss rate as a dependent variable; 3. Collecting harvester component speed and torque information; 4. Determining the component speed with the optimal loss rate under different feed rates based on the fitting relationship; 5. Estimating the feed rate using the feed torque load value of the harvester based on a header dynamic equation; 6. Using the component speed with the optimal loss rate under the estimated feed rate as a target value, controlling the speed of the component motor using fuzzy PID control. The present invention establishes a relationship between the loss rate, component speed, and feed rate under different feed rates based on multiple linear regression, and derives an optimal speed target value based on different feed rates, thereby meeting the loss rate requirements under different feed rates.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control of agricultural machinery, and in particular relates to a method for controlling electric harvester components based on an optimal grain loss rate and an electric harvester. Background Art

[0002] In recent years, agricultural mechanization has developed rapidly, moving toward intelligent and automated production. However, traditional harvesters, driven by diesel engines, consume large amounts of energy and pollute the environment. Therefore, converting traditional harvesters to renewable energy has become a research hotspot in the field of rice harvesters. Electric harvesters are a current research focus. Grain loss, as one of the evaluation indicators for rice harvesters, is of great reference value for the control of electric harvesters. Existing electric harvester control technology does not consider the issue of grain loss under different feed rates. As a result, when the feed rate changes, the grain loss rate exceeds the national mechanized production technology requirements, which is not conducive to the development of intelligent rice harvesters. Summary of the Invention

[0003] This paper proposes a control method for electric harvester components based on optimal grain loss rate. Based on a database of grain loss rate, feed rate, and component speed, this method establishes a fitting relationship between the three factors, thereby calculating the component speed for the optimal loss rate under different feed rate conditions. Based on the harvester dynamics equation, the feed rate is estimated in real time using the feed torque load. The speed at the optimal loss rate under this feed rate is used as the target value, and a fuzzy PID controller is used to track the speed of the component motor.

[0004] The specific plan is as follows:

[0005] A method for controlling components of an electric harvester based on an optimal grain loss rate comprises the following steps:

[0006] Step 1: Create a database based on pre-collected information on component speed, grain loss rate, and feed amount;

[0007] Step 2: Establish a fitting relationship based on different feed rates, taking component speed and feed rate as independent variables and loss rate as dependent variable;

[0008] Step 3: Use sensors to collect speed and torque information of harvester components;

[0009] Step 4: Obtain the component speed with the best loss rate under different feed rates based on the fitting relationship;

[0010] Step 5: Based on the header dynamic equation, use the header feed torque load value to estimate the feed amount;

[0011] Step 6: Taking the component speed with the best loss rate under the estimated feed rate as the target value, the fuzzy PID controller is used to track the speed of the component motor;

[0012] Furthermore, the method for pre-collecting the information of component rotation speed, grain loss rate, and feeding amount in step 1 to establish a database is as follows:

[0013] Through experiments, the feeding amount, grain loss rate, threshing drum speed, cleaning and plowing speed were collected and preprocessed. Outliers, duplicate values ​​and missing values ​​were processed by interpolation and deletion methods, and the data were sorted from large to small and then stored in the database.

[0014] Furthermore, in step 2, a fitting relationship is established with the threshing drum, cleaning, plowing speed, and feeding amount as independent variables and the loss rate as the dependent variable according to different feeding amounts:

[0015] According to the maximum feed amount value of the specific harvester, the value is divided into three parts, named small, medium and large feed amounts, whose ranges are [0, q sm ],[q sm ,q mm ],[q mm ,q bm ].

[0016] At large feed volume:

[0017]

[0018]

[0019]

[0020] Depend on

[0021]

[0022] The solution is

[0023] A b =(X b T X b ) -1 X b T Y b

[0024] Therefore, the fitting relationship is

[0025]

[0026] where a b0 、a b1 、a b2 、ab3 、a b4 、a b5 、a b6 、a b7 、a b8 is the fitting coefficient; w b1 、w b2 、w b3 The threshing drum, cleaning and plowing speeds are optimized for large feed rates; is the grain loss rate under large feeding amount; q b For large feeding amount; A b is the fitting coefficient matrix under large feeding amount; X b Fit the data matrix for the independent variables under large feeding rates; Fitting the data matrix for the dependent variable under large feeding amount; Loss b is the loss function of multiple linear regression under large feeding amount; Y b is the sample data matrix of loss rate in the database under large feed amount.

[0027] At medium feeding amount:

[0028]

[0029]

[0030]

[0031] Depend on

[0032]

[0033] The solution is

[0034] A m =(X m T X m ) -1 X m T Y m

[0035] Therefore, the fitting relationship is

[0036]

[0037] where a m0 、a m1 、a m2 、a m3 、a m4 、a m5 、a m6 、a m7 、a m8 is the fitting coefficient;

[0038] w m1 、w m2 、w m3 The speed of threshing drum, cleaning and plowing under medium feed rate; is the grain loss rate at medium feeding rate. m A is a medium feed amount; m is the fitting coefficient matrix under medium feeding amount; X m Fit the data matrix for the independent variables at the medium feeding rate; Fit the data matrix for the dependent variable under medium feeding amount; Loss m is the loss function of the multiple linear regression under medium feeding amount; Y m is the sample data matrix of loss rate in the database under medium feeding amount.

[0039] At small feeding amount:

[0040]

[0041]

[0042]

[0043] Depend on

[0044]

[0045] The solution is

[0046] A s =(X s T X s ) -1 X s T Y s

[0047] Therefore, the fitting relationship is

[0048]

[0049] where a s0 、a s1 、a s2 、a s3 、a s4 、a s5 、a s6 、a s7 、a s8 is the fitting coefficient; ω s1 、ω s2 、ω s3 The rotation speed of threshing drum, cleaning and plowing under small feeding amount; is the grain loss rate under small feeding amount.s For small feeding amount; A s is the fitting coefficient matrix under small feeding amount; X s Fit the data matrix for the independent variables at small feeding rates; Fitting the data matrix for the dependent variable under small feeding amount; Loss s is the loss function of multiple linear regression under small feeding amount; Y s is the sample data matrix of loss rate in the database under small feeding amount.

[0050] Furthermore, in step 3, the method for collecting the speed and torque information of the harvester components using sensors is as follows:

[0051] The speed and torque sensor uses a proximity sensor, which is installed on the drive shaft of the cleaning, plowing, threshing drum and cutting table. It is used to detect the cleaning speed, plowing speed, threshing drum speed and cutting table speed. The disc torque sensor is installed on the drive shaft, one end of which is connected to the power input end and the other end is connected to the load, which is used to measure the cutting table torque.

[0052] Furthermore, in step 4, the specific method for obtaining the component rotation speed with the best loss rate under different feed rates according to the fitting relationship is:

[0053] Under large feeding amount,

[0054]

[0055]

[0056]

[0057] The solution is

[0058]

[0059]

[0060]

[0061] Under medium feeding amount,

[0062]

[0063]

[0064]

[0065] The solution is

[0066]

[0067]

[0068]

[0069] Under small feeding amount,

[0070]

[0071]

[0072]

[0073] The solution is

[0074]

[0075]

[0076]

[0077] It is concluded that in [0,q sm ],[q sm ,q mm ],[q mm ,q bm ]Under different feeding amounts, the optimal speed sequence is {[ω ops1 ,ω pos2 ,ω pos3 ]、[ω opm1 ,ω opm2 ,ω opm3 ]、[ω opb1 ,ω opb2 ,ω opb3 ]}. Among them ω ops1 ,ω ops2 ,ω ops3 The optimal speed for threshing drum, cleaning and plowing under small feeding amount; opm1 ,ω opm2 ,ω opm3 The optimal speed for threshing drum, cleaning and plowing under medium feed amount; opb1 ,ω opb2 ,ω opb3 It is the optimal speed for threshing drum, cleaning and plowing under large feed rate.

[0078] Furthermore, in step 5, based on the header dynamic equation, the specific method for estimating the feed amount using the header feed torque load value is as follows:

[0079] According to the header dynamic equation:

[0080]

[0081] M qr =(k g +kb +k j )*q r

[0082] Among them, M H is the header torque at the sampling moment; M bf M is the friction torque of the reel wheel during idling; gf M is the friction torque of the cutter during idling operation; jf is the idling friction torque of the cutting platform auger; i b 、i j are the transmission ratios from the driving shaft to the reel and the auger respectively; J b is the moment of inertia of the reel; w in J is the cutting head speed at the sampling time; j M is the moment of inertia of the auger; qr Feed load torque; q r is the feeding amount; k g 、k b 、k j are the feed load factors of the cutter, reel and auger respectively.

[0083] The estimated feed amount is:

[0084]

[0085] Furthermore, in step 6, the component speed with the best loss rate under the estimated feed rate is used as the target value, and the specific method of using the fuzzy PID controller to track the speed of the component motor is as follows:

[0086] According to the estimated value of the feeding amount obtained in step 5, it is judged that it belongs to [0, q sm ],[q sm ,q mm ],[q mm ,q bm ] Which type, then use the corresponding optimal speed as the target value of the fuzzy PID controller. The specific control steps are as follows:

[0087] The threshing drum speed error value and error change value are fuzzified, the membership degree is calculated according to the fuzzy rule table, and the centroid method is used to defuzzify the corresponding Δk pt , Δk it , Δk dt , combined with the PID controller to track the speed of the threshing drum motor. The fuzzy rule table is as follows:

[0088]

[0089]

[0090] The speed error value and error change value are fuzzified, the membership degree is calculated according to the fuzzy rule table, and the center of gravity method is used to defuzzify the corresponding Δk pc , Δk ic , Δk dc , combined with the PID controller to track the speed of the cleaning motor. The fuzzy rule table is as follows:

[0091]

[0092] The error value of the plowing speed and the error change value are fuzzified, the membership degree is calculated according to the fuzzy rule table, and the center of gravity method is used to defuzzify the corresponding Δk pdr , Δk idr , Δk ddr , combined with the PID controller to track the speed of the reel motor. The fuzzy rule table is as follows:

[0093]

[0094] The present invention also provides an electric harvester, which is capable of executing the contents of steps 2-6 above.

[0095] Beneficial effects of the present invention:

[0096] (1) Based on the multivariate linear regression fitting method, a mathematical expression between the loss rate, component speed, and feed rate under different feed rates was established;

[0097] (2) The optimal speed target value is obtained according to different feed rates, which can meet the loss rate requirements under different feed rates;

[0098] (3) Use fuzzy PID controller to improve the dynamic performance of speed tracking control. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 It is a flow chart of the method of the present invention.

[0100] Figure 2 This is the structural block diagram of the fuzzy PID speed tracking control of the present invention. DETAILED DESCRIPTION

[0101] The electric harvester component control technology based on the optimal grain loss rate disclosed in the present invention aims to solve the problem of component drive control with the optimal grain loss rate of the electric harvester under different feeding conditions. The technology starts from the perspective of constructing a database of feed amount, component speed, and grain loss rate information, and establishes a fitting relationship under different feeding conditions, with component speed and feed amount as independent variables and loss rate as dependent variable. According to the fitting relationship of feed amount, loss rate, and speed of each harvester component, the optimal speed target value of the loss rate corresponding to the feeding amount is calculated, the feeding amount is estimated in real time based on the harvester driving speed information, and combined with the optimal speed target value under the corresponding feeding amount, the fuzzy PID controller is used to track the speed of the motor of each component, providing a method for currently realizing intelligent control of electric harvesters with loss rate as an indicator.

[0102] The present invention will be further described below with reference to the accompanying drawings.

[0103] The present invention provides an electric harvester component control method based on the optimal grain loss rate

[0104] A control method for electric harvester components based on the optimal grain loss rate, such as Figure 1 As shown, it includes the following steps:

[0105] Step 1: The method for pre-collecting information on component rotation speed, grain loss rate, and feed amount to establish a database is as follows:

[0106] The data that needs to be collected and stored in the database in advance includes: feed rate, grain loss rate, threshing drum speed, cleaning speed, and straw plowing speed, as well as the squares of these speeds. Data should be preprocessed, using interpolation and deletion to address outliers, duplicates, and missing values. The data should be sorted from largest to smallest before being stored in the database.

[0107] Step 2: The method for establishing a fitting relationship based on different feed rates with component speed and feed rate as independent variables and loss rate as dependent variable is as follows:

[0108] According to the maximum feed amount value of the specific harvester, the value is divided into three parts, named small, medium and large feed amounts, whose ranges are [0, q sm ],[q sm ,q mm ],[q mm ,q bm ].

[0109] Substitute the data from step 1, with a large feed volume:

[0110]

[0111]

[0112]

[0113] in, n is the number of pre-collected data.

[0114] Depend on

[0115]

[0116] The solution is

[0117] A b =(X b T X b ) -1 X b T Y b

[0118] Therefore, the fitting relationship is

[0119]

[0120] where a b0 、a b1 、a b2 、a b3 、a b4 、a b5 、a b6 、a b7 、a b8 is the fitting coefficient; w b1 、w b2 、w b3 The threshing drum, cleaning and plowing speeds are optimized for large feed rates; is the grain loss rate under large feeding amount; q b For large feeding amount; A b is the fitting coefficient matrix under large feeding amount; X b Fit the data matrix for the independent variables under large feeding rates; Fitting the data matrix for the dependent variable under large feeding amount; Loss b is the loss function of multiple linear regression under large feeding amount; Y b is the sample data matrix of loss rate in the database under large feed amount.

[0121] Substitute the data from step 1, with a medium feed rate:

[0122]

[0123]

[0124]

[0125] in, n is the number of pre-collected data.

[0126] Depend on

[0127]

[0128] The solution is

[0129] A m =(X m T X m ) -1 X m T Y m

[0130] Therefore, the fitting relationship is

[0131]

[0132] where a m0 、a m1 、a m2 、a m3 、a m4 、a m5 、a m6 、a m7 、a m8 is the fitting coefficient;

[0133] w m1 、w m2 、w m3 The speed of threshing drum, cleaning and plowing under medium feed rate; is the grain loss rate at medium feeding rate. m A is a medium feed amount; m is the fitting coefficient matrix under medium feeding amount; X m Fit the data matrix for the independent variables at the medium feeding rate; Fit the data matrix for the dependent variable under medium feeding amount; Loss m is the loss function of the multiple linear regression under medium feeding amount; Y m is the sample data matrix of loss rate in the database under medium feeding amount.

[0134] Substitute the data from step 1, with a small feed volume:

[0135]

[0136]

[0137]

[0138] in, n is the number of pre-collected data.

[0139] Depend on

[0140]

[0141] The solution is

[0142] A s =(X s T X s ) -1 X s T Y s

[0143] The fitting relationship is

[0144]

[0145] where a s0 、a s1 、a s2 、a s3 、a s4 、a s5 、a s6 、a s7 、a s8 is the fitting coefficient; ω s1 、ω s2 、ω s3 The rotation speed of threshing drum, cleaning and plowing under small feeding amount; is the grain loss rate under small feeding amount. s For small feeding amount; A s is the fitting coefficient matrix under small feeding amount; X s Fit the data matrix for the independent variables at small feeding rates; Fitting the data matrix for the dependent variable under small feeding amount; Loss s is the loss function of multiple linear regression under small feeding amount; Y s is the sample data matrix of loss rate in the database under small feeding amount.

[0146] Step 3: The method for collecting the speed and torque information of harvester components using sensors is as follows:

[0147] The specific steps for collecting the speed information of the threshing drum, cleaning, plowing and harvesting platform are as follows:

[0148] The proximity switch detection is arranged at the position of the component drive shaft, and the pulse signal emitted by the proximity switch detection is collected by the engine ECU. The value of the speed information is calculated by the following formula:

[0149]

[0150] Among them, n det is the number of proximity signals detected by the ECU within the sampling period; T det is the sampling period; n is the number of approach points on the flange of the component.

[0151] The torque information of the cutting platform is collected using a disc torque sensor installed on the drive shaft. One end of the sensor is connected to the power input end and the other end is connected to the load to measure the cutting platform torque.

[0152] Step 4: The specific method for obtaining the component speed with the best loss rate under different feed rates based on the fitting relationship is:

[0153] Large feed order

[0154]

[0155]

[0156]

[0157] The solution is

[0158]

[0159]

[0160]

[0161] Medium feed order

[0162]

[0163]

[0164]

[0165] The solution is

[0166]

[0167]

[0168]

[0169] Small feed order

[0170]

[0171]

[0172]

[0173] The solution is

[0174]

[0175]

[0176]

[0177] It is concluded that in [0,q sm ],[q sm ,q mm ],[q mm ,q bm ]Under different feeding amounts, the optimal speed sequence is {[ω ops1 ,ω ops2 ,ω ops3 ]、[ω opm1 ,ω opm2 ,ω opm3 ]、[ω opb1 ,ω opb2 ,ω opb3 ]}. Among them ω ops1 ,ω ops2 ,ω ops3 The optimal speed for threshing drum, cleaning and plowing under small feeding amount; opm1 ,ω opm2 ,ω opm3 The optimal speed for threshing drum, cleaning and plowing under medium feed amount; opb1 ,ω opb2 ,ω opb3 It is the optimal speed for threshing drum, cleaning and plowing under large feed rate.

[0178] Step 5: Based on the header dynamic equation, the specific method for estimating the feed rate using the header feed torque load value is as follows:

[0179] According to the header dynamic equation:

[0180]

[0181] M qr =(k g +k b +k j )*q r

[0182] Among them, M H is the header torque at the sampling moment; M bj M is the friction torque of the reel wheel during idling; gf M is the friction torque of the cutter during idling operation; jf is the idling friction torque of the cutting platform auger; i b 、i jare the transmission ratios from the driving shaft to the reel and the auger respectively; J b is the moment of inertia of the reel; ω in J is the cutting head speed at the sampling time; j M is the moment of inertia of the auger; qr Feed load torque; q r is the feeding amount; k g 、k b 、k j are the feed load factors of the cutter, reel and auger respectively.

[0183] The estimated feed amount is:

[0184]

[0185] Step 6: Please refer to Figure 2 As shown in the figure, the specific method of using the fuzzy PID controller to track the speed of the component motor with the optimal loss rate as the target value is as follows:

[0186] According to the estimated value of the feeding amount obtained in step 5, it is judged that it belongs to [0, q sm ],[q sm ,q mm ],[q mm ,q bm ] Which category, then use the corresponding optimal speed as the target value of the fuzzy PID controller. After determining the optimal speed target value based on the real-time feed amount estimation value, the fuzzy PID controller is used for speed tracking control. The specific control steps are as follows:

[0187] The speed error value is obtained by subtracting the target value of the threshing drum speed from the filtered value of the threshing drum speed sensor. The speed error value and the error change value of the threshing drum are fuzzified. The normal membership function is used to calculate Δk according to the fuzzy rule table. pt , Δk it , Δk dt The membership degree is then defuzzified using the centroid method to obtain the corresponding Δk pt , Δk it , Δk dt , combined with the PID controller to obtain the torque value to track the speed of the threshing drum motor. The fuzzy rule table is as follows:

[0188]

[0189] The input of the threshing drum speed feedback control is

[0190]

[0191] k pt =k pt0 +Δkpt

[0192] k it =k it0 +Δk it

[0193] k dt =k dt0 +Δk dt

[0194] Among them, k pt 、k it 、k dt is the input proportional, differential and integral coefficients; e(t i ) is the error between the expected speed of the threshing drum and the current speed; k pt0 、k it0 、k dt0 The proportional, differential, and integral coefficient values ​​at the previous sampling time point; Δk pt , Δk it , Δk dt The change values ​​of the proportional, differential and integral coefficients respectively.

[0195] The speed error value is obtained by subtracting the target speed value from the filtered value of the speed sensor. The speed error value and error change value of the component are fuzzified. The normal membership function is used to calculate Δk according to the fuzzy rule table. pc , Δk ic , Δk dc The membership degree is then defuzzified using the centroid method to obtain the corresponding Δk pc , Δk ic , Δk dc , combined with the PID controller to obtain the torque value to track the speed of the cleaning motor. The fuzzy rule table is as follows:

[0196]

[0197] The input of the cleaning speed feedback control is

[0198]

[0199] k pc =k pco +△k pc

[0200] k ic =k ico +△k ic

[0201] k dc =k dc0 +△k dc

[0202] Among them, k pc 、k ic 、k dc is the input proportional, differential and integral coefficients; e(t i ) is the error between the desired speed and the current speed; k pc0 、k ic0 、k dc0 The proportional, differential, and integral coefficient values ​​at the previous sampling time point; Δk pc , Δk ic , Δk dc The change values ​​of the proportional, differential and integral coefficients respectively.

[0203] The target value of the plowing speed is subtracted from the filtered value of the plowing speed sensor to obtain the speed error value. The plowing speed error value and the error change value are fuzzified, and the normal membership function is used to calculate Δk according to the fuzzy rule table. pdr , Δk idr , Δk ddt The membership degree is then defuzzified using the centroid method to obtain the corresponding Δk pdr , Δk idr , Δk ddr , combined with the PID controller to obtain the torque value to track the speed of the reel motor. The fuzzy rule table is as follows:

[0204]

[0205]

[0206] The input of the plowing speed feedback control is

[0207]

[0208] k pdr =k pdr0 +Δk pdr

[0209] k idr =k idr0 +Δk idr

[0210] k ddr =k ddr0 +Δk ddr

[0211] Among them, k pdr 、k idr 、k ddr is the input proportional, differential and integral coefficients; e(t i ) is the error between the desired plowing speed and the current speed; k ddr0 、kidr0 、k pdr0 The proportional, differential, and integral coefficient values ​​at the previous sampling time point; Δk pdr , Δk idr , Δk ddr The change values ​​of the proportional, differential and integral coefficients respectively.

[0212] The above harvester parameters are determined according to the specific characteristics of the harvester and obtained through experimental calibration.

[0213] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent methods or changes that do not deviate from the technology of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling components of an electric harvester based on an optimal grain loss rate, characterized in that: The steps include: Step 1: Building a database based on pre-collected component rotation speed, grain loss rate, and feed amount information; the component rotation speed includes threshing drum rotation speed, cleaning rotation speed, and plowing rotation speed; Step 2: Establish a fitting relationship based on different feed rates, taking component speed and feed rate as independent variables and loss rate as dependent variable; Step 3: Collect the speed and torque information of harvester components; Step 4: Obtain the component speed with the best loss rate under different feed rates based on the fitting relationship; Step 5: Establish the header dynamic equation and use the header feed torque load value to estimate the feed amount; Step 6: Taking the component speed with the best loss rate under the estimated feed rate as the target value, the fuzzy PID controller is used to track and control the speed of the threshing drum motor, cleaning motor, and straw motor.

2. The electric harvester component control method based on the optimal grain loss rate according to claim 1, characterized in that: Specific implementation of step 1: Through experiments, the feeding amount, grain loss rate, threshing drum speed, cleaning and plowing speed were collected and preprocessed. Outliers, duplicate values ​​and missing values ​​were processed by interpolation and deletion methods, and the data were sorted from large to small and then stored in the database.

3. The electric harvester component control method based on the optimal grain loss rate according to claim 1, characterized in that: Specific implementation of step 2: According to the maximum feed amount value of the specific harvester, the value is divided into three parts, named small, medium and large feed amounts, whose ranges are [0, q sm ],[q sm ,q mm ],[q mm ,q bm ]; At large feed volume: Depend on The solution is A b =(X b T X b ) -1 X b T Y b Therefore, the fitting relationship is where a b0 、a b1 、a b2 、a b3 、a b4 、a b5 、a b6 、a b7 、a b8 is the fitting coefficient; ω b1 、ω b2 、ω b3 The threshing drum, cleaning and plowing speeds are optimized for large feed rates; is the grain loss rate under large feeding amount; q b For large feeding amount; A b is the fitting coefficient matrix under large feeding amount; X b Fit the data matrix for the independent variables under large feeding rates; Fitting the data matrix for the dependent variable under large feeding amount; Loss b is the loss function of multiple linear regression under large feeding amount; Y b is the sample data matrix of loss rate in the database under large feed amount; At medium feeding amount: Depend on The solution is A m =(X m T X m ) -1 X m T Y m Therefore, the fitting relationship is Part a m0 , a m1 , a m2 , a m3 , a m4 , a m5 , a m6 , a m7 , a m8 Combined series; ω m1 、ω m2 、ω m3 The speed of threshing drum, cleaning and plowing under medium feed rate; is the grain loss rate under medium feeding amount, q m A is a medium feed amount; m is the fitting coefficient matrix under medium feeding amount; X m Fit the data matrix for the independent variables at the medium feeding rate; Fit the data matrix for the dependent variable under medium feeding amount; Loss m is the loss function of the multiple linear regression under medium feeding amount; Y m is the sample data matrix of loss rate in the database under medium feeding amount; At small feeding amount: Depend on The solution is A s =(X s T X s ) -1 X s T Y s Therefore, the fitting relationship is where a s0 、a s1 、a s2 、a s3 、a s4 、a s5 、a s6 、a s7 、a s8 is the fitting coefficient; ω s1 、ω s2 、ω s3 The rotation speed of threshing drum, cleaning and plowing under small feeding amount; is the grain loss rate under small feeding amount, q s For small feeding amount; A s is the fitting coefficient matrix under small feeding amount; X s Fit the data matrix for the independent variables at small feeding rates; Fitting the data matrix for the dependent variable under small feeding amount; Loss s is the loss function of multiple linear regression under small feeding amount; Y s is the sample data matrix of loss rate in the database under small feeding amount.

4. The electric harvester component control method based on the optimal grain loss rate according to claim 1, characterized in that: Specific implementation of step 3: Use proximity sensors, installed on the drive shafts of the cleaning, plowing, threshing drum, and cutting table to detect the cleaning speed, plowing speed, threshing drum speed, and cutting table speed. Use a disc torque sensor, installed on the drive shaft, connect one end of it to the power input end, and the other end to the load to measure the cutting table torque.

5. The electric harvester component control method based on the optimal grain loss rate according to claim 3, characterized in that: Specific implementation of step 4: Under large feeding amount, get Under medium feeding amount, get Under small feeding amount, get It is concluded that in [0,q sm ],[q sm ,q mm ],[q mm ,q bm ]Under different feeding amounts, the optimal speed sequence is {[ω ops1 ,ω ops2 ,ω ops3 ]、[ω opm1 ,ω opm2 ,ω opm3 ]、[ω opb1 ,ω opb2 ,ω opb3 ]}, where ω ops1 ,ω ops2 ,ω ops3 The optimal speed for threshing drum, cleaning and plowing under small feeding amount; opm1 ,ω opm2 ,ω opm3 The optimal speed for threshing drum, cleaning and plowing under medium feed amount; opb1 ,ω opb2 ,ω opb3 It is the optimal speed for threshing drum, cleaning and plowing under large feed rate.

6. The electric harvester component control method based on the optimal grain loss rate according to claim 1, characterized in that: Specific implementation of step 5: Establish the header dynamic equation: M qr =(k g +k b +k j )*q r Among them, M H is the header torque at the sampling moment; M bf M is the friction torque of the reel wheel during idling; gf M is the friction torque of the cutter during idling operation; jf is the idling friction torque of the cutting platform auger; i b 、i j are the transmission ratios from the driving shaft to the reel and the auger respectively; J b is the moment of inertia of the reel; ω in J is the cutting head speed at the sampling time; j M is the moment of inertia of the auger; qr Feed load torque; q r is the feeding amount; k g 、k b 、k j are the feed load factors of the cutter, reel and auger respectively; The estimated feed amount is:

7. The electric harvester component control method based on optimal grain loss rate according to claim 5, characterized in that: Specific implementation of step 6: According to the estimated value of the feeding amount obtained in step 5, it is judged that it belongs to [0, q sm ],[q sm ,q mm ],[q mm ,q bm ] Which type, then take the corresponding optimal speed as the target value of the fuzzy PID controller. The specific control steps are as follows: The threshing drum speed error value and error change value are fuzzified, the membership degree is calculated according to the fuzzy rule table, and the centroid method is used to defuzzify the corresponding Δk pt , Δk it , Δk dt , combined with the PID controller to track the speed of the threshing drum motor; the fuzzy rule table is as follows:

8. The electric harvester component control method based on the optimal grain loss rate according to claim 7, characterized in that: Also includes: The speed error value and error change value are fuzzified, the membership degree is calculated according to the fuzzy rule table, and the center of gravity method is used to defuzzify the corresponding Δk pc , Δk ic , Δk dc , combined with the PID controller to track the speed of the cleaning motor; the fuzzy rule table is as follows:

9. The electric harvester component control method based on optimal grain loss rate according to claim 7, characterized in that: Also includes: The error value of the plowing speed and the error change value are fuzzified, the membership degree is calculated according to the fuzzy rule table, and the center of gravity method is used to defuzzify the corresponding Δk pdr , Δk idr , Δk ddr , combined with the PID controller to track the speed of the reel motor; the fuzzy rule table is as follows:

10. An electric harvester, characterized in that: The electric harvester can perform the contents of steps 2-6 of any one of claims 1-9 above.

Images