CN109542099B - Agricultural machinery control method - Google Patents
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Abstract
The agricultural machinery control system enriches fuzzy control rules of the fuzzy controller based on the Internet of things system, and comprises a lower agricultural machinery system, a network layer special for data transmission, a global positioning system for positioning the networked agricultural machinery and an upper cloud processing center for processing the fuzzy control rules; and summarizing the road condition information and the generated fuzzy control rules which are experienced by all the networked lower agricultural machines by using the Internet of things system, and storing the information to a cloud processing center of the Internet of things for expanding the fuzzy control rules of the lower agricultural machine system.
Description
The technical field is as follows:
the invention belongs to the field of agricultural machinery control, and particularly relates to an agricultural machinery control method based on fuzzy control.
Background art:
agriculture is the foundation of national economy and is also an important problem to be solved in the scientific and technological development of all countries. As a global agricultural big country, agricultural land occupies 7 percent of the world in China, but the problem of full temperature of population occupying 22 percent of the world is solved, and the cultivated land area is continuously reduced along with the continuous development of economy in China, so that the improvement of the effective utilization rate of limited cultivated land becomes the development trend of modern agriculture, and more grains are produced by utilizing the continuously reduced land as far as possible. Under the large environment, the concept of 'precision agriculture' is widely proposed, precision agriculture is also called precision agriculture, also called precision farming or computer-aided agriculture, the method reduces the agricultural cost under the condition of not reducing the yield as much as possible at the earliest, avoids pollution caused by over fertilization and pesticides, and develops to the present, the modern information technology, the agricultural technology and the engineering technology are integrated and applied on the technical level to obtain the high-yield, high-quality and efficient production of farmlands. Particularly, the demand of agricultural production for automatic navigation systems of agricultural machines is becoming stronger and stronger nowadays when intelligent agricultural equipment is rapidly developed.
The agricultural vehicle automatic control technology mainly integrates agricultural machine positioning and control technologies, the positioning technology is to accurately acquire the current vehicle position of the agricultural machine or the relative position relative to a target position by using a sensor, and the control technology is to acquire a path from the agricultural machine to the target position by using an algorithm and complete an instruction through an execution mechanism. In order to improve the accuracy and stability of agricultural machinery navigation, many researchers have conducted a great deal of research on navigation control methods. Through years of research, an agricultural machinery control system is not only based on traditional PID control, but also a fuzzy control method as a control method closer to a kinematic model becomes an important research direction for numerous agricultural machinery navigation experts. Fuzzy control, as a control method of practical significance in the field of intelligence, has solved problems that many conventional control methods cannot or are difficult to solve in automation in the field of industrial control. However, the precision of the fuzzy control method depends on fuzzy rules, the poor design of the fuzzy rules or the insufficient number of the rules can not obtain ideal control effect, the working environment of agricultural machinery is poor, particularly the ground unpredictability of farmlands is high, the interaction process of tires and the ground is complex, and great troubles are brought to the formulation of expert rules in the fuzzy control. How to ensure the validity and richness of expert control rules of a fuzzy control algorithm in agricultural machinery control becomes an important problem for restricting the existing agricultural machinery control based on the fuzzy control method.
The internet of things is a network which realizes intelligent identification, positioning, tracking, monitoring and management through information sensing equipment according to an agreed protocol, and is extended and expanded on the basis of the internet. The problem that fuzzy rule number is not enough in the fuzzy control of a single agricultural machine is solved by building an agricultural machine Internet of things system.
Disclosure of Invention
In order to make up for the deficiency of fuzzy control rules in agricultural machine controllers of a single agricultural machine or a single farm, the invention provides an agricultural machine control method and system based on the Internet of things.
The invention provides an agricultural machinery control method, which is used for optimizing a fuzzy controller of a lower agricultural machinery based on an Internet of things system and comprises the following steps:
the system comprises an Internet of things system, a data transmission network layer, a global positioning system and a cloud processing center, wherein the Internet of things system comprises a lower agricultural machine system, the global positioning system is specially used for positioning the Internet of agricultural machines, the cloud processing center is used for processing fuzzy control rules and comprises an engineer station, and the data transmission network layer is used for uploading and downloading data based on a wireless transmission module; the method comprises the steps that road condition information and generated fuzzy control rules which are subjected to all networked lower agricultural machines are collected by using an internet of things system, and are stored in a cloud processing center of the internet of things and used for expanding the fuzzy control rules of the lower agricultural machine system;
the lower agricultural machine system comprises an agricultural machine self-navigation module, a wireless transmission module for uploading and downloading data and an agricultural machine wheel visual monitoring system; a fuzzy controller is arranged in the agricultural machinery self-navigation system; the agricultural machine wheel visual monitoring system comprises a sensor-based visual scanner, a path in front of an agricultural machine wheel is observed in advance through the visual scanner, imaging is carried out through an imaging system, parameters of an imaged road condition are analyzed through an agricultural machine wheel visual prejudging system, an analysis result is uploaded to a fuzzy controller of a lower agricultural machine, the agricultural machine wheel is adjusted in advance through the fuzzy controller, when the fuzzy controller in the agricultural machine self-navigation system does not have a corresponding fuzzy control rule or the corresponding control rule is invalid, road condition parameters are uploaded to a cloud processing center of the Internet of things through a wireless transmission module to be compared, when the road condition parameters have records or similar records in the cloud processing center, the fuzzy control rule corresponding to the records is called, and the agricultural machine wheel is intervened in advance; when the road condition parameters are not recorded or similar records are not recorded in the cloud processing center of the Internet of things, the advancing speed of the agricultural machinery is reduced, the agricultural machinery wheels are adaptively corrected through the self-adjusting module of the lower agricultural machinery, and new fuzzy control rules formed in the correction process are uploaded to the cloud processor of the Internet of things, so that the fuzzy control rules of the agricultural machinery fuzzy control are enriched.
The Internet of things is a four-layer framework and comprises a lower agricultural machine, a wireless network layer, an upper cloud processing center and a global positioning system, wherein a large number of sensors are arranged on the lower agricultural machine for sensing road conditions, the wireless network layer is used for data transmission, and the global positioning system is used for positioning the networked agricultural machines.
The fuzzy control rule of the fuzzy controller is directed at the agricultural machinery which travels in a straight line, and the agricultural machinery can be different types of agricultural machinery, but the travel mode of the agricultural machinery is the straight line.
The fuzzy controller of the lower agricultural machine comprises an agricultural machine attitude determination system, a plurality of sensors are arranged on front wheels of the lower agricultural machine, one of the sensors is an angle sensor, and the angle sensor is arranged in the center of a tire and used for detecting a course angle; the other sensors are infrared distance measuring sensors and ultrasonic distance measuring sensors, and visual monitoring is carried out on the agricultural machinery through the ultrasonic distance measuring sensors and the infrared distance measuring sensors.
Compared with the prior art, the fuzzy controller of the lower agricultural machine is networked based on the Internet of things, and the problem that the control rule in the fuzzy controller of a single lower agricultural machine is not completely set or the fuzzy control rule is invalid is solved. Parameters of different road conditions can be uploaded to the cloud processing platform and shared to the lower agricultural machinery of the network, so that fuzzy control rules of the agricultural machinery are enriched.
Description of the drawings:
FIG. 1 is an agricultural machine Internet of things system provided by the present application;
FIG. 2 is a diagram of an arrangement of agricultural wheel sensors in a subordinate agricultural system;
1. a cloud processing center; 2. a lower agricultural machine system; 3. a global positioning system; 4. a wireless transmission module; 5. an engineer station; 6. the agricultural machinery self-navigation module; 7. an angle sensor; 8. an infrared ranging sensor; 9. an ultrasonic ranging sensor.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. However, it will be apparent to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
The invention optimizes the fuzzy controller of the lower agricultural machinery based on the Internet of things system:
as shown in fig. 1, the internet of things system includes a lower agricultural machine system 2, a global positioning system 3 dedicated to a data transmission network layer, for positioning the networked agricultural machines, and a higher cloud processing center 1 for processing fuzzy control rules, the cloud processing center includes an engineer station 5, and the data transmission network layer uploads and downloads data based on a wireless transmission module 4; and summarizing the road condition information and the generated fuzzy control rules which are experienced by all the networked lower agricultural machines by using the Internet of things system, and storing the collected road condition information and the generated fuzzy control rules into a cloud processing center of the Internet of things for expanding the fuzzy control rules of the lower agricultural machine system.
The agricultural machinery system of next position includes: the agricultural machinery self-navigation module 6, a wireless transmission module for uploading and downloading data and an agricultural machinery wheel visual monitoring system; a fuzzy controller is arranged in the agricultural machinery self-navigation system; the agricultural machine wheel visual monitoring system comprises a visual scanner based on a sensor, a path in front of an agricultural machine wheel is observed in advance through the visual scanner, imaging is carried out through an imaging system, a visual prejudging system analyzes imaged road condition parameters, the agricultural machine wheel is adjusted in advance through a fuzzy controller, when the fuzzy controller in a self-navigation system of a next agricultural machine does not have corresponding fuzzy control rules, the road condition parameters are uploaded to a cloud processing center of the Internet of things through a wireless transmission module for comparison, when the road condition parameters have records or similar records in the cloud processing center, the fuzzy control rules generated by the records are called, and the agricultural machine wheel is intervened in advance; when the road condition is not recorded or similar records are not recorded in the cloud processing center of the Internet of things, the advancing speed of the agricultural machinery is reduced, the deviation target path of the agricultural machinery wheel is adaptively corrected through the self-adjusting module of the lower agricultural machinery, and a new fuzzy control rule formed in the correction process is uploaded to the cloud processing center of the Internet of things, so that the fuzzy control rule of the agricultural machinery fuzzy control is enriched.
The fuzzy controller of the lower computer is set as follows (the fuzzy control of the application is based on the agricultural machinery advancing in a straight line):
the lower agricultural machine is driven by two wheels in a differential mode, the left wheel and the right wheel are driven by two direct current servo motors independently, 10 sensors are mounted on the front wheel, and the structure and the layout of the sensors are shown in fig. 2. The device comprises an angle sensor 7 for detecting the heading angle of the agricultural machine; the other sensors are an infrared distance measuring sensor 8 and an ultrasonic distance measuring sensor 9. And (4) fusing distance information measured by different sensors, and inputting the information serving as the information of the azimuth obstacle into the control system.
And selecting the center of the front end semicircle as a reference point, taking the advancing direction of the agricultural machine as the X axis of the coordinate system of the agricultural machine wheel, and rotating the X axis by 90 degrees anticlockwise to obtain the Y axis of the coordinate system of the agricultural machine wheel. The pose of the agricultural machine can be expressed as:
p＝[x,y,θ]T
in the formula: x, y: the position coordinates of a reference point of the agricultural machine under the global coordinate system XOY; θ: agricultural steering angle (the angle between the agricultural heading and the abscissa of the global coordinate system XOY).
The kinematic model of the agricultural machine is as follows:
in the formula: v: speed as an agricultural machine reference point; ω: the steering angular speed of the agricultural machine.
In the formula: v. ofl: is the speed of the left drive wheel; v. ofr: is the speed of the right drive wheel; l: the distance between the left wheel and the right wheel of the agricultural machine.
The pose of the agricultural machinery at the k moment of the sampling period and the pose at the k +1 moment after one sampling period are expressed by the following formulas:
in the formula: t: a sampling period.
When theta (k +1) is equal to theta (k), the agricultural machine moves linearly. The attitude of the agricultural machine can be calculated by the formula, and preparation is made for determining the distance between the agricultural machine and the target point and the navigation angle in the next step.
In order to realize the fuzzy control of the agricultural machinery, the information of obstacles in the range of 180 degrees in front of the agricultural machinery and the heading angle of the agricultural machinery are obtained through a sensor. Therefore, the input of the fuzzy controller is the distance d of the front left, front right and front 3 azimuth obstaclesl、df、drAnd a heading angle α of the agricultural machine (the heading angle α is positive when the target point is located on the right side of the agricultural machine, and negative otherwise); the output of the fuzzy controller is the speed v of the left and right driving wheels of the agricultural machineryl、vr。
Fuzzy language description of input output quantity, when defining language variable set, if the language variable value is too much, the control rule becomes complicated, so the selection principle of language variable is: on the premise of meeting the control requirements, the number of the language variable values is reduced as much as possible. Fuzzy subsets of barrier distances in the left front direction, the right front direction and the right front direction of the input linguistic variables are taken as far (F), middle (M) and near (N), the actual distance measured by the ultrasonic sensor is quantized into a [0,4] interval through a linear transformation formula, and the discretization domain is (0,1,2,3, 4).
In the formula: x is the number ofi *The actual input amount is varied within a range of
Its domain of discourse is [ xmin,xmax]。
The fuzzy subset of the heading angle alpha is taken as Positive Big (PB), Positive Small (PS), zero (Z), Negative Small (NS) and Negative Big (NB), the heading angle measured by the direction sensor is quantized to the range of [ -3,3], and the discretization domain is (-3, -2, -1,0,1,2, 3).
Fuzzy subsets of the left and right driving wheel speeds of the output linguistic variable are fast (F), medium (M), and slow (S). Membership function and fuzzy division of the distance and membership function and fuzzy division di(i ═ l, f, r) is similar.
The establishment of the fuzzy control rule is a very critical problem, and the design principle is as follows: on the premise of meeting the completeness of the fuzzy controller, the number of control rules is reduced as much as possible, and meanwhile, the consistency of the control rules is also considered. Maximum possible number of rules Nmax＝n1n2…nmWhere m is the number of inputs to the controller, n1n2…nmThe number of fuzzy subsets for each input. The fuzzy controller has a fuzzy rule number of 135. The establishment of the fuzzy rule is described only by taking one case as an example, and the writing rule is shown in table 1.
| d1 | df | dr | α | v1 | vr |
| N | F | F | NB | S | M |
| N | F | F | NS | M | S |
| N | F | F | Z | M | M |
| N | F | F | PS | M | S |
| N | F | F | PB | F | S |
Fuzzy inference, for the multiple input-multiple output (MIMO) system (4 inputs, 2 outputs) designed herein, the rule base can be seen as being composed of 2 sub-rule bases, each of which is a multiple input-single output (MISO) rule base, and each of which is independent of the other. Therefore, the fuzzy inference problem of only one MISO sub-rule base is generally considered, and the single output of each rule is firstly solved and then combined to form the desired result. The fuzzy implications for a rule are:
Rijmn＝(AiandBjandCmandDn)→Eijmn
namely:
the total fuzzy implication relationship of all fuzzy control rules is as follows:
let a certain fuzzy input be: x is A ', y is B ', u is C ', w is D ', the output blur quantity z (represented by the blur set E ') is:
E′＝(A′and B′and C′and D′)□R
in the above formula, the "and" operation adopts an intersection method, the synthesis operation "□" adopts a maximum-minimum method, and the implication operation "→" adopts an intersection method.
Defuzzification, wherein the fuzzy quantity obtained by fuzzy reasoning is changed into a clear quantity through defuzzification processing, and the clear quantity can be changed into a control quantity for actual control through linear transformation. The fuzzy controller converts the fuzzy quantity into the clearness quantity by adopting a weighted average method:
based on the fuzzy rule base, the inference rule and the defuzzification method, if a certain input is dl-1, df-2, dr-3 and alpha-1, the output is vl＝0.767、vrAnd 2, converting the linear scale into the control quantity which is actually input into the direct current servo motor to control the movement of the agricultural machine.
Compared with the prior art, the fuzzy controller of the lower agricultural machine is networked based on the Internet of things, and the problem that the control rule in the fuzzy controller of a single lower agricultural machine is not completely set or the fuzzy control rule is invalid is solved. Parameters of different road conditions can be uploaded to the cloud processing platform and shared to the lower agricultural machinery of the network, so that fuzzy control rules of the agricultural machinery are enriched.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the system can refer to the method in the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and are not limited thereto; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (2)
1. A control method of agricultural machinery is used for optimizing a fuzzy controller of a lower agricultural machinery based on an Internet of things system, and is characterized in that:
the system comprises an Internet of things system and a cloud processing center, wherein the Internet of things system comprises a lower agricultural machine system, a special data transmission network layer, a global positioning system for positioning the Internet of agricultural machines and an upper cloud processing center for processing fuzzy control rules, the cloud processing center comprises an engineer station, and the data transmission network layer uploads and downloads data based on a wireless transmission module; the system of the Internet of things is used for summarizing road condition information and generated fuzzy control rules which are experienced by all the networked lower agricultural machines, storing the collected road condition information and the generated fuzzy control rules into the cloud processing center of the Internet of things, and expanding the fuzzy control rules of the system of the lower agricultural machines;
the lower agricultural machine system comprises an agricultural machine self-navigation module, a wireless transmission module for uploading and downloading data and an agricultural machine wheel visual monitoring system; a fuzzy controller is arranged in the agricultural machinery self-navigation system; the agricultural machine wheel visual monitoring system comprises a sensor-based visual scanner, a path in front of an agricultural machine wheel is observed in advance through the visual scanner, imaging is carried out through an imaging system, an agricultural machine wheel visual prejudging system analyzes parameters of an imaged road condition and uploads an analysis result to a fuzzy controller of a lower agricultural machine, the agricultural machine wheel is adjusted in advance through the fuzzy controller, when the fuzzy controller in the agricultural machine self-navigation system does not have a corresponding fuzzy control rule or the corresponding control rule is invalid, road condition parameters are uploaded to the cloud processing system of the Internet of things through a wireless transmission module for comparison, when the road condition parameters have records or similar records in the cloud processing center, the fuzzy control rule corresponding to the records is called, and the agricultural machine wheel is intervened in advance; when the road condition parameters are not recorded or similar records are not recorded in the cloud processing center of the Internet of things, the advancing speed of the agricultural machinery is reduced, the agricultural machinery wheels are adaptively corrected through the self-adjusting module of the lower agricultural machinery, and new fuzzy control rules formed in the correction process are uploaded to the cloud processor of the Internet of things, so that the fuzzy control rules of agricultural machinery fuzzy control are enriched;
the agricultural machine is a straight-line advancing agricultural machine and is driven by two wheels in a differential mode, the left wheel and the right wheel are independently driven by two direct-current servo motors, and a plurality of sensors are mounted on the two front wheels; the sensors of each front wheel comprise an angle sensor, three infrared ranging sensors and six ultrasonic ranging sensors, wherein the angle sensor is positioned on the wheel axis, one of the three infrared ranging sensors is positioned on the arc surface at the top end of the wheel, the other two infrared ranging sensors are respectively and symmetrically arranged on the arc surfaces at the two sides of the wheel, the eight ultrasonic ranging sensors are respectively and symmetrically arranged on the arc surfaces at the two sides of the wheel, and 4 arc surfaces at each side are arranged; the angle sensor is used for detecting a course angle of the agricultural machinery, and distance information measured by different sensors is input into the control system as information of the azimuth obstacle;
selecting the center of a front-end semicircle as a reference point, taking the advancing direction of the agricultural machine as an X axis of an agricultural machine wheel coordinate system, rotating the X axis anticlockwise by 90 degrees to obtain a Y axis of the agricultural machine wheel coordinate system, and expressing the pose of the agricultural machine as follows:
in the formula: x, y: the position coordinates of a reference point of the agricultural machine under the global coordinate system XOY; θ: the agricultural machinery steering angle, the included angle between the agricultural machinery course and the horizontal coordinate of the global coordinate system XOY;
the kinematic model of the agricultural machine is as follows:
in the formula: v: speed as an agricultural machine reference point; ω: the steering angular speed of the agricultural machine;
in the formula: v. ofl: is the speed of the left drive wheel; v. ofr: is the speed of the right drive wheel; l: the distance between the left wheel and the right wheel of the agricultural machine;
the pose of the agricultural machinery at the k moment of the sampling period and the pose at the k +1 moment after one sampling period are expressed by the following formulas:
in the formula: t: sampling period;
when theta (k +1) = theta (k), the agricultural machine moves linearly;
the sensor obtains the information of the obstacles in the range of 180 degrees in front of the agricultural machinery and the course angle of the agricultural machinery to realize the fuzzy control of the agricultural machinery, and the input of the fuzzy controller is the distance d of the obstacles in 3 directions of left front, right front and right frontl、df、drAnd a heading angle alpha of the agricultural machine, wherein the heading angle alpha is positive when the target point is positioned at the right side of the agricultural machine, and is negative otherwise; the output of the fuzzy controller is the speed v of the left and right driving wheels of the agricultural machineryl、vr；
Fuzzy subsets of barrier distances in the left front direction, the right front direction and the right front direction of the input linguistic variables are taken as far (F), middle (M) and near (N), the actual distance measured by the ultrasonic sensor is quantized into a [0,4] interval through a linear transformation formula, and the discretization domain is (0,1,2,3, 4);
in the formula:
the actual input amount is varied within a range of
The argument of which is
；
The fuzzy subset of the heading angle alpha is taken as Positive Big (PB), Positive Small (PS), zero (Z), Negative Small (NS) and Negative Big (NB), the heading angle measured by the direction sensor is quantized to the range of [ -3,3], and the discretization discourse domain is (-3, -2, -1,0,1,2, 3);
outputting fuzzy subsets of the left driving wheel speed and the right driving wheel speed of the linguistic variables as fast (F), medium (M) and slow (S); its membership function and fuzzy partition and the membership function and fuzzy partition of each distance are
；
Establishment of fuzzy control rules, maximum possible number of rules
Wherein m is the input number of the controller,
the number of fuzzy subsets for each input;
fuzzy reasoning, a multiple-input-multiple-output system is adopted, specifically, 4 inputs and 2 outputs are adopted, a rule base of the fuzzy reasoning is composed of 2 sub rule bases, each sub rule base is a multiple-input-single-output MISO rule base, each sub rule base is independent, the fuzzy reasoning of one MISO sub rule base is considered during specific work, the single output of each rule is firstly solved, then the single output is combined to form a required result, and a single fuzzy implication relation in the rule base is as follows:
namely:
the overall fuzzy implication of all fuzzy control rules is as follows:
let a certain fuzzy input be: x is A ', y is B ', u is C ', w is D ', the output blur amount z represented by the blur set E ' is:
E′= (A′and B′and C′and D′)□R
in the formula, the 'and' operation adopts an intersection solving small method, the synthesis operation '□' adopts a maximum-minimum method, and the implication operation '→' adopts an intersection solving method;
defuzzification, wherein a fuzzy quantity obtained through fuzzy reasoning is changed into a clear quantity through defuzzification processing, and the clear quantity can be changed into a control quantity for actual control through linear transformation; the fuzzy controller converts the fuzzy quantity into the clearness quantity by adopting a weighted average method:
2. the control method according to claim 1, wherein the internet of things is a four-layer architecture and comprises a lower agricultural machine system which is provided with a large number of sensors and used for sensing road conditions, a wireless network layer used for data transmission, an upper cloud processing center and a global positioning system used for positioning the networked agricultural machines.
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| CN201811417071.6A CN109542099B (en) | 2018-11-26 | 2018-11-26 | Agricultural machinery control method |
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