A method for automatic row control of a corn harvester

Abstract

The application discloses a kind of corn harvester automatic control method of line, belong to harvester technical field, the method includes integral term enabling judgment;With pre-look deviation ep (t) As the input of PID controller;Real-time comparison current deviation absolute value |e (t) |With the size of preset ε;If |e (t) |> ε, then disable integral term in PID controller, if |e (t) |≤ε, then enable integral term in PID controller.The application has the advantages that the integral saturation phenomenon under large deviation is eliminated, the system response time is shortened.Over-oscillation when approaching target line is effectively inhibited, and the risk of seedling loss is fundamentally reduced.The adaptability to dynamic interference such as terrain undulation is enhanced, and the control stability is improved.

Description

Technical Field

[0001] This invention belongs to the field of harvester technology, and relates to the field of precision navigation technology with improved control algorithms, specifically an automatic row alignment control method for corn harvesters. Background Technology

[0002] Currently, most corn harvesters in China still rely on manual steering to align the rows, which is not only labor-intensive but also limits the harvester's operating speed and efficiency. A few corn harvesters use automatic row alignment devices, but the alignment effect is poor, often resulting in deviation and ear drop. The mainstream automatic row alignment technology uses sensors such as machine vision and LiDAR to detect crop rows and employs a classic proportional-integral-derivative (PID) controller to calculate steering commands and drive the hydraulic steering system.

[0003] Although PID control is widely used, its inherent defects seriously affect row accuracy and work efficiency in the specific application scenario of corn harvesters. The main defects are as follows.

[0004] Integral saturation leads to sluggish response. In the early stages of operation or when there is a large lateral deviation due to terrain, the integral term will quickly accumulate to the saturation limit of the actuator (such as a hydraulic valve). After that, it takes a long time for the system to exit the saturation state, resulting in delayed correction actions and the harvester deviating from the correct path for an extended period.

[0005] Overshoot can lead to the risk of crop damage. When rapidly approaching the target row, the combined effect of proportional and integral control can easily result in excessive control output, causing the harvester to oscillate in the opposite direction after crossing the centerline of the target row. This overshoot and oscillation significantly increase the risk of the header or divider crushing adjacent rows of corn.

[0006] Poor adaptability to dynamic environments: Farmland terrain is not flat, often featuring undulations and furrow changes. Traditional PID parameters are fixed and cannot adapt to such continuously changing external disturbances, easily generating continuous steady-state errors or periodic fluctuations during operation, affecting the continuity and uniformity of harvesting.

[0007] To address these issues, the industry sometimes employs methods such as empirical parameter tuning, adding dead zones, or limiting amplitude, but these methods have limited effectiveness and cannot fundamentally solve the problems. Summary of the Invention

[0008] To overcome the shortcomings of the prior art, this invention provides an automatic row alignment control method for a corn harvester. The aim is to eliminate integral saturation under large deviations and shorten the system response time. It effectively suppresses overshoot oscillations when approaching the target row, fundamentally reducing the risk of seedling damage. It also enhances adaptability to dynamic disturbances such as terrain undulations and improves control stability.

[0009] To achieve the above objectives, the present invention provides the following technical solution: an automatic row alignment control method for a corn harvester, the core idea of ​​which is to introduce "foresight" and "conditionality" into the traditional control framework.

[0010] The control method includes the following steps.

[0011] S1: Real-time deviation acquisition; When the corn harvester is walking, its image acquisition sensor component acquires images of the corn crop row in front at a fixed sampling period, and calculates the lateral position deviation e(t) between the harvester and the center line of the target row at the current moment in real time through image processing algorithm.

[0012] S2: Predicting the aiming deviation; Based on the harvester's current speed v, a aiming time Tp is set, and the aiming distance Lp = v * Tp is calculated; Using current and historical deviation data, the possible deviation ep(t) at the aiming point Lp is calculated through a prediction model; ep(t) is calculated according to the formula e(t) + [de(t) / dt] * Tp, where t is a time variable, e(t) represents the lateral deviation at the current moment, and de(t) / dt is the first derivative of the deviation e(t) with respect to time t, representing the rate of change of the deviation with time; that is, the speed and direction of the deviation change. If de(t) / dt > 0, it means the deviation is increasing; if de(t) / dt < 0, it means the deviation is decreasing; if de(t) / dt = 0, it means the deviation remains unchanged.

[0013] S3: Integral term enable judgment; The aiming deviation ep(t) obtained in step S2 is used as the input of the PID controller; The PID controller is connected to the walking control mechanism of the harvester, and the PID controller outputs a control signal. Its control output value u(t) is calculated by the following formula: u(t)=Kp*ep(t)+Ki_sep*∫ep(t)dt+Kd*[dep(t) / dt]; where Kp is the proportional coefficient, Ki_sep is the dynamic integral coefficient, and Kd is the derivative coefficient: When When the integral term is enabled, Ki_sep=Ki, which is used to eliminate the steady-state error of the system by utilizing integral action to achieve high-precision tracking; when the integral term is disabled, Ki_sep=0, which is used for fast response and to avoid integral saturation; the method for determining whether the integral term is enabled or disabled is to pre-set a deviation threshold ε; compare the magnitude of the current absolute value of deviation |e(t)| with ε in real time; if |e(t)|>ε, then the integral term in the PID controller is disabled; if |e(t)|≤ε, then the integral term in the PID controller is enabled.

[0014] S4: Steering command output, converts the control output value u(t) calculated in step S3 into an electrical signal that the hydraulic steering system can receive, drives the harvester to steer, corrects the driving direction, and achieves precise tracking of the target crop row.

[0015] As a further optimization, the aiming time Tp is 0.3 s - 0.6 s; the deviation threshold is 10%-20% of the crop row spacing; and the sensor assembly samples at a frequency of not less than 30 Hz.

[0016] As a further optimization, the deviation threshold ε = 100 mm; the aiming time Tp = 0.5 s.

[0017] As a further optimization, Kp, Ki, and Kd are determined using the Ziegler-Nichols method; the sampling period of the image acquisition sensor is synchronized with the visual frame rate and is set to 33ms.

[0018] As a further optimization, Kp=1.2, Ki=0.08, Kd=0.8.

[0019] As a further optimization, the control output value u(t) is an analog voltage signal, which is limited to ±10V and then output to the electro-hydraulic proportional valve through the D / A module of the PID controller to control the direction of the harvester.

[0020] As a further optimization, the PID controller runs an OpenCV-based vision algorithm to perform grayscale conversion, filtering, edge detection, and Hough line transformation on each frame of the image, identify the center lines of the six corn rows, and take the two middle ones as the target navigation paths.

[0021] As a further optimization, the image acquisition sensor assembly includes an industrial monocular camera, mounted on the top of the cab, facing forward, with a field of view covering 6-8 rows of corn plants.

[0022] The advantages of this invention are that, compared with traditional PID control algorithms, it eliminates integral saturation under large deviations and shortens the system response time. It effectively suppresses overshoot oscillations when approaching the target line, fundamentally reducing the risk of seedling damage. It also enhances adaptability to dynamic disturbances such as terrain undulations and improves control stability. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the system structure framework of an embodiment of the present invention.

[0025] Figure 3 This is a comparison curve of the simulation performance of the method of the present invention and the traditional PID control algorithm.

[0026] Figure 4 This is a performance comparison chart between the method of this invention and the traditional PID control algorithm. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some preferred embodiments of the present invention, and not all embodiments. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0028] For examples, please refer to Figure 1-4 This invention provides an automatic row alignment control method for a corn harvester. The hardware system for implementing this method includes the following components.

[0029] Sensor module: Used to sense the positional relationship between the harvester and the target crop row in real time. Machine vision cameras, LiDAR, or multi-line array CCD sensors can be used.

[0030] Controller: An embedded industrial computer or a high-performance microcontroller that runs the control algorithm of this invention.

[0031] Actuator: The hydraulic steering system of a corn harvester typically consists of an electro-hydraulic proportional valve, a hydraulic cylinder, and steering wheels.

[0032] The implementation steps of this control method can be found in [reference needed]. Figure 1 Specifically, it includes the following.

[0033] Real-time deviation acquisition: The sensor acquires images of the corn crop row in front at a fixed sampling period (e.g., 30Hz), and the image processing algorithm calculates the lateral position deviation e(t) between the harvester and the center line of the target row at the current moment.

[0034] Predicting aiming deviation (aiming mechanism): Based on the harvester's current travel speed v (m / s), a "aiming time" Tp (e.g., 0.3-0.6 seconds) is set.

[0035] Calculate the aiming distance Lp = v * Tp.

[0036] Using current and historical deviation data, a predictive model (such as first-order extrapolation) is used to calculate the possible future deviation ep(t) at the target point Lp. The formula is: ep(t) ≈ e(t) + [de(t) / dt] * Tp, where t is the time variable, e(t) represents the lateral deviation at the current moment, and de(t) / dt is the first derivative of the deviation e(t) with respect to time t, representing the rate of change of the deviation over time, i.e., the speed and direction of the deviation change. If de(t) / dt > 0, it indicates that the deviation is increasing; if de(t) / dt < 0, it indicates that the deviation is decreasing; and if de(t) / dt = 0, it indicates that the deviation remains unchanged.

[0037] Integral term enable judgment (integral separation strategy); a deviation threshold ε is preset. This threshold can be set according to crop row width and operational requirements, for example, 10%-20% of the crop row spacing.

[0038] Compare the current absolute value of the deviation |e(t)| with ε in real time.

[0039] If |e(t)| > ε (large deviation): disable the integral term in the PID controller and use only the proportional (P) and derivative (D) actions. The aim is to achieve a fast response and avoid integral saturation.

[0040] If |e(t)| ≤ ε (small deviation): Enable the integral term in the PID controller. The purpose is to eliminate the steady-state error of the system using integral action and achieve high-precision tracking.

[0041] The composite control algorithm is calculated as follows.

[0042] Use the aiming deviation ep(t) obtained in the previous step as the input to the PID controller.

[0043] Based on the enable judgment results from the previous step, the integral phase is dynamically adjusted.

[0044] The control output u(t) is calculated using the following formula: u(t) = Kp * ep(t) + Ki_sep * ∫ ep(t)dt + Kd * [dep(t) / dt]; where Kp is the proportional coefficient, Ki_sep is the dynamic integral coefficient, and Kd is the derivative coefficient: when the integral term is enabled, Ki_sep = Ki; when the integral term is disabled, Ki_sep = 0.

[0045] Steering command output: The calculated control quantity u(t) is converted into an electrical signal (such as analog voltage) that the hydraulic steering system can receive, driving the harvester to steer, thereby correcting the driving direction and achieving precise tracking of the target crop row.

[0046] Based on this, the advantages of this method lie in the integration of the anti-cashing mechanism: introducing dead reckoning prediction into the control, using predicted future deviations rather than current instantaneous deviations for calculation, giving control commands "advance," enabling smooth steering maneuvers and reducing overshoot. The introduction of the integral separation strategy: creatively switching the integral action dynamically based on the magnitude of the deviation. This allows the controller to "perform its specific function" at different stages: as agile as a PD controller with large deviations, and as precise as a full PID controller with small deviations. The structure of the composite control law: not a simple superposition of algorithms, but using the "anti-cashing deviation" as the sole process variable input of the "integral separation PID," thus forming an internally coordinated, conditionally adaptive closed-loop controller, simultaneously improving the system's response speed, steady-state accuracy, and anti-interference capability.

[0047] In a preferred embodiment, the harvester is a self-propelled corn harvester.

[0048] Sensor: Industrial monocular camera, mounted on the top of the cab, facing forward, with a field of view covering 6-8 rows of corn.

[0049] Controller: A quad-core industrial computer based on ARM Cortex-A53, running an embedded Linux system and the ROS (Robot Operating System) framework.

[0050] Actuator: The original load-sensitive full hydraulic steering system of the harvester is modified by adding an electro-hydraulic proportional directional valve.

[0051] The specific implementation parameters and procedures are as follows.

[0052] Image processing: The controller runs a vision algorithm based on OpenCV to perform grayscale conversion, filtering, edge detection and Hough line transformation on each frame of the image, identify the center lines of the six corn rows, and take the two middle ones as the target navigation path.

[0053] Deviation calculation: Obtain the line equation in the image coordinates, calculate the lateral pixel deviation between the theoretical center line of the harvester (image center) and the center line of the target path, and convert it into the actual physical distance by combining the camera calibration parameters to obtain e(t), in mm.

[0054] Pre-aiming parameters: Given a pre-aiming time Tp = 0.5s. Read the vehicle speed signal v (in m / s) from the CAN bus and calculate the pre-aiming distance Lp. Use a first-order prediction model: ep(k) = e(k) + [e(k) - e(k-1)] / T * Tp, where T is the sampling period.

[0055] Integration separation threshold: Based on experiments, ε is set to 100mm (approximately one-sixth of the line width).

[0056] Control parameters: The controller parameters were determined through the Ziegler-Nichols method and field trials: Kp=1.2, Ki=0.08, Kd=0.8. The sampling period was synchronized with the visual frame rate and set to 33ms.

[0057] Execution output: The final control quantity u(k) is limited (±10V) and then outputs an analog voltage signal to the electro-hydraulic proportional valve through the controller's built-in D / A module to control the direction of the harvester.

[0058] The obtained simulation performance is compared with that of traditional PID control algorithms, for example... Figure 3 As shown, this eliminates integral saturation under large deviations, shortening the system response time. It effectively suppresses overshoot oscillations near the target line, fundamentally reducing the risk of seedling loss due to pressure. It enhances adaptability to dynamic disturbances such as terrain undulations, improving control stability.

[0059] The parts of this invention not described in detail are prior art; for those skilled in the art, the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. The scope of this invention is defined by the appended claims and their equivalents.

Claims

1. An automatic row alignment control method for a corn harvester, characterized in that, Includes the following steps: S1: Real-time deviation acquisition; When the corn harvester is walking, its image acquisition sensor component acquires images of the corn crop row in front at a fixed sampling period, and calculates the lateral position deviation e(t) between the harvester and the center line of the target row at the current moment in real time through image processing algorithm. S2: Predicting aiming deviation; Based on the harvester's current speed v, a pre-aiming time Tp is set, and the pre-aiming distance Lp=v*Tp is calculated; Using current and historical deviation data, the possible future deviation ep(t) at the target point Lp is calculated through a prediction model. ep(t) is calculated using the formula e(t) + [de(t) / dt] * Tp, where t is a time variable, e(t) represents the lateral deviation at the current moment, and de(t) / dt is the first derivative of deviation e(t) with respect to time t, representing the rate of change of deviation with time; that is, the speed and direction of deviation change. If de(t) / dt > 0, it indicates that the deviation is increasing; if de(t) / dt < 0, it indicates that the deviation is decreasing; if de(t) / dt = 0, it indicates that the deviation remains unchanged. S3: Integral term enable judgment; The aiming deviation ep(t) obtained in step S2 is used as the input of the PID controller; The PID controller is connected to the walking control mechanism of the harvester, and its control output value u(t) is calculated by the following formula; u(t) = Kp*ep(t) + Ki_sep*∫ep(t)dt + Kd*[dep(t) / dt]; where Kp is the proportional coefficient, Ki_sep is the dynamic integral coefficient, and Kd is the differential coefficient: when the integral term is enabled, Ki_sep = Ki, which is used to eliminate the steady-state error of the system by utilizing the integral action to achieve high-precision tracking; when the integral term is disabled, Ki_sep = 0, which is used for fast response and to avoid integral saturation; The method for determining whether the integral term is enabled or disabled is as follows: a deviation threshold ε is preset; the absolute value of the current deviation |e(t)| is compared with the magnitude of ε in real time; if |e(t)|>ε, the integral term in the PID controller is disabled; if |e(t)|≤ε, the integral term in the PID controller is enabled. S4: Steering command output, converts the control output value u(t) calculated in step S3 into an electrical signal that the hydraulic steering system can receive, drives the harvester to steer, corrects the driving direction, and achieves precise tracking of the target crop row.

2. The automatic row alignment control method for a corn harvester according to claim 1, characterized in that: The aiming time Tp is 0.3 s - 0.6 s; the deviation threshold is 10% - 20% of the crop row spacing; the image acquisition sensor component samples at a frequency of not less than 30 Hz.

3. The automatic row alignment control method for a corn harvester according to claim 2, characterized in that: The deviation threshold ε = 100 mm; the aiming time Tp = 0.5 s.

4. The automatic row alignment control method for a corn harvester according to claim 3, characterized in that: Kp, Ki, and Kd are determined using the Ziegler-Nichols method; the sampling period of the image acquisition sensor component is synchronized with the visual frame rate and is set to 33ms.

5. The automatic row alignment control method for a corn harvester according to claim 4, characterized in that: Kp=1.2, Ki=0.08, Kd=0.

8.

6. The automatic row alignment control method for a corn harvester according to claim 1, characterized in that: The control output value u(t) is an analog voltage signal, which is limited to ±10V and then output to the electro-hydraulic proportional valve through the D / A module of the PID controller to control the direction of the harvester.

7. The automatic row alignment control method for a corn harvester according to claim 1, characterized in that: The PID controller runs an OpenCV-based vision algorithm to perform grayscale conversion, filtering, edge detection, and Hough line transformation on each frame of the image, identify the center lines of the six corn rows, and take the two middle ones as the target navigation paths.

8. The automatic row alignment control method for a corn harvester according to claim 1, characterized in that: The image acquisition sensor assembly includes an industrial monocular camera, mounted on the top of the cab, facing forward, with a field of view covering 6-8 rows of corn plants.

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