A four-wheel drive cooperative control method of a distributed electric drive self-propelled plant protection machine

Abstract

The application provides a four-wheel drive cooperative control method for a distributed electric drive self-propelled plant protection machine, a control strategy model of four-wheel rotating speeds and a cooperative control strategy model of four-wheel rotating speeds and torques are established, a four-wheel real-time slip ratio model is established by collecting current driving state information and current driving state information of the plant protection machine, tire slip ratios are calculated, slip wheel identification is performed according to a preset slip ratio threshold, then driving mode selection is performed, and an optimization algorithm based on model predictive control is introduced to perform predictive control on control parameters of each wheel in a future period of time. The application can detect and identify wheels that slip in real time, and can quickly suppress the slip phenomenon by dynamically adjusting the rotating speed and torque of the slip wheel and the compensation control of other wheels. Through real-time dynamic coordination of the four-wheel rotating speed and torque, the power output of other wheels can be automatically adjusted when a single wheel slips, and the accuracy of the vehicle motion trajectory is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy agricultural vehicle control, and in particular relates to a four-wheel drive coordinated control method for a distributed electric-driven self-propelled plant protection machine. Background Art

[0002] With the mechanization and intelligentization of agricultural production, plant protection machines are playing an increasingly important role in modern agriculture. Plant protection machines are primarily used for pest control and pesticide spraying in agricultural production. Their precision and efficiency directly impact the quality and efficiency of agricultural production. Traditional self-propelled plant protection machines typically use internal combustion or diesel engines as their power source. These traditional power systems have numerous issues with efficiency, noise, and emissions, gradually limiting their application in modern agriculture. Electric drive technology, however, is becoming the primary power source of choice for plant protection machines due to its high efficiency, environmental friendliness, and low noise levels.

[0003] The distributed electric drive self-propelled plant protection vehicle (DDEPPV) directly drives the four wheels via four wheel-side electric motors, and its drive system features high responsiveness and precise power control. However, in actual use, four-wheel slip is inevitable due to factors such as road conditions, workload, and terrain changes. This slip not only affects the driving stability and control accuracy of the plant protection vehicle, but also leads to waste of electrical energy and deviations in the driving trajectory. Existing electric drive plant protection vehicles mostly use traditional control strategies, such as constant speed, constant torque, or fuzzy control, to adjust the speed and torque of the four wheels. However, these methods often fail to fully consider the dynamic changes of four-wheel slip and cannot be flexibly adjusted according to real-time conditions, making it difficult to meet the accuracy requirements in complex terrain and operating environments. In addition, the drive structure for distributed electric drives is relatively new, and there are relatively few drive control methods.

[0004] Therefore, how to accurately obtain four-wheel slip information and combine it with a real-time adjustment mechanism to optimize the speed and torque distribution of each wheel to improve the straight-line driving accuracy of DDEPPV remains a technical challenge that needs to be solved urgently in this field. Summary of the Invention

[0005] In response to the shortcomings in the existing technology, the present invention provides a four-wheel drive coordinated control method for a distributed electric-driven self-propelled plant protection machine, which solves the problems of four-wheel slippage of distributed electric-driven self-propelled plant protection machines affecting the driving stability and control accuracy of the plant protection machines, and the inability to flexibly adjust according to real-time conditions based on traditional control strategies.

[0006] The present invention achieves the above technical objectives through the following technical means.

[0007] A distributed electric drive self-propelled plant protection machine four-wheel drive coordinated control method includes the following process:

[0008] Step 1: Use the vehicle's onboard sensors to collect the current driving status information of the agricultural drone, including steering wheel angle and vehicle speed, and transmit the collected data to the whole machine collaborative controller;

[0009] Step 2: The whole machine collaborative controller obtains the current driving state information of the agricultural drone through feedback from the motor driver, including the four-wheel speed and four-wheel torque;

[0010] Step 3: The whole machine collaborative controller establishes a control strategy model for the four-wheel speed to independently adjust the speed of the four wheels. This driving mode is an independent driving mode.

[0011] Step 4: The whole machine collaborative controller establishes a coordinated control strategy model for the four-wheel speed and torque, and adjusts the torque and speed of the slipping and non-slipping wheels. This driving mode is a coordinated driving mode.

[0012] Step 5: The whole machine collaborative controller establishes a four-wheel real-time slip rate model based on the data collected in steps 1 and 2;

[0013] Step 6: The whole machine collaborative controller calculates the tire slip rate based on the four-wheel real-time slip rate model, identifies the slipping wheel based on the preset slip rate threshold, and then selects the driving mode;

[0014] Step 7: The whole machine collaborative controller introduces an optimization algorithm based on model predictive control to predict the control parameters of each wheel in the future.

[0015] Furthermore, in step 3, the control strategy model of the four-wheel speed is as follows:

[0016]

[0017] Among them, R is the turning radius of the plant protection machine, v is the speed of the plant protection machine, θ is the steering wheel angle of the plant protection machine, L is the wheelbase, f(θ) is the wheel angle, v in is the linear velocity of the inner wheel during steering, W is the wheelbase, v out is the linear velocity of the outer wheel during steering, ω in is the speed of the inner wheel during steering, r is the wheel radius, ω out is the speed of the outside wheel when turning.

[0018] Furthermore, in step 4, the coordinated control strategy model of the four-wheel speed and torque is as follows:

[0019]

[0020] Among them, j is the sliding wheel, k is the non-slip wheel, are the updated wheel speeds of the slip wheel and non-slip wheel, The current wheel speeds of the sliding wheel and non-slip wheel, are the updated wheel torques of the slip wheel and non-slip wheel, are the current wheel torques of the slipping wheel and the non-slipping wheel respectively; α, γ, β, and λ are dynamic adjustment factors; ρ is the adjustment coefficient, which is 0.3; φ is the adjustment coefficient, which is 0.2; S i is the slip rate of the i-th wheel; S lim is the slip rate threshold.

[0021] Furthermore, in step 5, the four-wheel real-time slip rate model is as follows:

[0022]

[0023] Among them, f(μ i ,λ i ) is the correction factor for ground type and load conditions, μ i is the friction coefficient between the wheel and the ground, λ i is the pressure between the wheel and the ground, ω i is the angular velocity of the i-th wheel, v is the speed of the agricultural machine, and r is the wheel radius.

[0024] Furthermore, in step 6, when S i ≥S lim When , the wheel is identified as a slip wheel, otherwise it is a non-slip wheel;

[0025] When selecting the drive mode, if one wheel is a slip wheel, the vehicle enters the cooperative drive mode; if no wheel is a slip wheel, the vehicle enters the independent drive mode.

[0026] Furthermore, the specific process of step 7 is as follows:

[0027] Step 7.1: Build a plant protection machine system model to predict the future state, that is, the evolution of the future slip rate:

[0028] S i (t+n)=S i (t)+ΔS i (t+n)

[0029] Among them, S i (t+n) is the slip rate of the i-th wheel at the predicted time t+n, S i (t) is the slip rate of the i-th wheel at time t, ΔS i (t+n) is the slip rate change from the current time t to the predicted time t+n;

[0030] Step 7.2: Construct the cost function as shown below:

[0031]

[0032] Among them, J is the cost function; N is the predicted time domain length; is the weight coefficient of the torque to cost function. The priority between the two is balanced by adjusting the weight coefficient, and the value is 0.5;

[0033] And set the following constraints:

[0034]

[0035] Among them, ω min 、ω max are the minimum and maximum speeds allowed for the wheels respectively; T min 、T max are the minimum torque and maximum torque allowed by the wheel respectively; ω i (t+n) is the wheel speed at the predicted time t+n; T i (t+n) is the wheel torque at the predicted time t+n; S i (t+n) is the wheel slip ratio at the predicted time t+n.

[0036] The present invention has the following beneficial effects:

[0037] By setting a reasonable slip rate threshold (e.g., 15%), the present invention can detect and identify slipping wheels in real time, and quickly suppress the slip phenomenon by dynamically adjusting the speed and torque of the slipping wheel and compensating for other wheels.

[0038] Through real-time dynamic coordination of four-wheel speed and torque, the power output of other wheels can be automatically adjusted when a single wheel slips, ensuring the accuracy of the vehicle's motion trajectory;

[0039] The introduction of a dynamic adjustment factor calculation method enables the system to flexibly adjust the control strategy according to different road conditions and real-time operating status, and has excellent environmental adaptability;

[0040] For the first time, model predictive control was applied to the coordinated control of the four wheels of an electric-driven crop protection machine. By predicting future slip and power output trends, control instructions were adjusted in advance, achieving forward-looking optimization of the control strategy.

[0041] The present invention can improve the driving stability, control accuracy and driving performance of the entire machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is the structural diagram of the power chassis of a distributed electric drive self-propelled plant protection machine;

[0043] Figure 2 This is a schematic diagram of Ackermann four-wheel steering;

[0044] Figure 3 This is a flow chart of the four-wheel drive control method for a distributed electric-driven self-propelled plant protection machine. DETAILED DESCRIPTION

[0045] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0046] like Figure 1 、 2 As shown, the distributed electric drive self-propelled plant protection machine targeted by the solution of the present invention is equipped with a left front wheel motor, a right front wheel motor, a left rear wheel motor, and a right rear wheel motor. Each motor can independently control the corresponding wheel, and can realize four-wheel independent drive control and four-wheel Ackerman steering. When steering, the front and rear wheel angles are the same in size and opposite in direction; energy such as batteries are used to supply power to the components of the plant protection machine through a transformer, and the whole machine collaborative controller and the motor driver interact in real time with the drive control strategy.

[0047] The invention provides a method for coordinated control of four-wheel drive of a distributed electric drive self-propelled plant protection machine. Figure 3 As shown, the specific process includes the following:

[0048] Step 1: Collect the current driving status information of the agricultural machinery through the on-board sensors, including the steering wheel angle θ and vehicle speed v, and transmit the collected data to the whole machine collaborative controller;

[0049] Step 2: The whole machine collaborative controller obtains the current driving state information of the plant protection machine through the feedback of the motor driver, including the four-wheel speed ω i , four-wheel torque T i ;

[0050] Step 3: The whole machine collaborative controller establishes the following four-wheel speed control strategy model to independently adjust the speed of the four wheels. This driving mode is an independent driving mode:

[0051]

[0052]

[0053] Among them, R is the turning radius of the plant protection machine, v is the speed of the plant protection machine, θ is the steering wheel angle of the plant protection machine, L is the wheelbase, f(θ) is the wheel angle, v in is the linear velocity of the inner wheel during steering, W is the wheelbase, v out is the linear velocity of the outer wheel during steering, ω inis the speed of the inner wheel during steering, r is the wheel radius, ω out is the speed of the outside wheel when turning.

[0054] Step 4: The whole machine collaborative controller establishes the coordinated control strategy model of the four-wheel speed and torque as shown below, and adjusts the torque and speed of the slipping and non-slipping wheels. This driving mode is a coordinated driving mode:

[0055]

[0056] Among them, j is the sliding wheel, k is the non-slip wheel, are the updated wheel speeds of the slip wheel and non-slip wheel, The current wheel speeds of the sliding wheel and non-slip wheel, are the updated wheel torques of the slip wheel and non-slip wheel, are the current wheel torques of the slipping wheel and the non-slipping wheel respectively; α, γ, β, and λ are dynamic adjustment factors; ρ is the adjustment coefficient, which is 0.3; φ is the adjustment coefficient, which is 0.2; S i is the slip rate of the i-th wheel, i = 1, 2, 3, 4, representing the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively; S lim is the slip rate threshold.

[0057] Step 5: The whole machine collaborative controller establishes the following four-wheel real-time slip rate model based on the data collected in steps 1 and 2:

[0058]

[0059] Among them, f(μ i ,λ i ) is the correction factor for ground type and load conditions, μ i is the friction coefficient between the wheel and the ground, λ i is the pressure between the wheel and the ground, ω i is the angular velocity of the i-th wheel.

[0060] Step 6: The whole machine collaborative controller calculates the tire slip rate according to the four-wheel real-time slip rate model in step 5, and performs slip wheel identification according to the preset slip rate threshold. In this embodiment, the slip rate threshold S lim Set to 15%, if S i ≥S lim , then the wheel is identified as a slip wheel, otherwise it is a non-slip wheel;

[0061] Then select the drive mode, such as Figure 3 As shown, if any wheel is a slip wheel, the vehicle enters the cooperative drive mode; if no wheel is a slip wheel, the vehicle enters the independent drive mode.

[0062] Step 7: The machine collaborative controller introduces an optimization algorithm based on Model Predictive Control (MPC) to predict and control the control parameters of each wheel within a certain period of time. MPC predicts the future state of the plant protection system in real time and makes optimization decisions based on current state data and future predictions, thereby improving the control accuracy and stability of the electric plant protection machine. MPC optimization problems are usually solved by minimizing the cost function, where the goal of the cost function is to make the system output as close to the expected value as possible while considering the system constraints. The specific process of Step 7 is as follows:

[0063] Step 7.1: Establish a plant protection machine system model. The plant protection machine system model is used to predict the future state, that is, the evolution of the future slip rate:

[0064] S i (t+n)=S i (t)+ΔS i (t+n)

[0065] Among them, S i (t+n) is the slip rate of the i-th wheel at the predicted time t+n, S i (t) is the slip rate of the i-th wheel at time t, ΔS i (t+n) is the slip rate change from the current time t to the predicted time t+n;

[0066] Step 7.2: Construct a cost function (objective function). In the present invention, the goal of MPC is to minimize the cost function shown below, penalizing the slip rate (to prevent tire slippage) while limiting the driving torque (to prevent energy waste or system instability). The specific formula is as follows:

[0067]

[0068] Among them, J is the cost function; N is the predicted time domain length; is the weight coefficient of the torque to cost function. The priority between the two is balanced by adjusting the weight coefficient, and the value is 0.5;

[0069] And set the following constraints:

[0070]

[0071] Among them, ω min 、ω max are the minimum and maximum speeds allowed for the wheels respectively; T min 、T max are the minimum torque and maximum torque allowed by the wheel respectively; ω i(t+n) is the wheel speed at the predicted time t+n; T i (t+n) is the wheel torque at the predicted time t+n; S i (t+n) is the wheel slip ratio at the predicted time t+n.

[0072] The embodiments described are preferred implementations of the present invention, but the present invention is not limited to the above implementations. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essence of the present invention are within the scope of protection of the present invention.

Claims

1. A distributed electric drive self-propelled plant protection machine four-wheel drive coordinated control method, characterized in that: The process includes the following: Step 1: Use the vehicle's onboard sensors to collect the current driving status information of the agricultural drone, including steering wheel angle and vehicle speed, and transmit the collected data to the whole machine collaborative controller; Step 2: The whole machine collaborative controller obtains the current driving state information of the agricultural drone through feedback from the motor driver, including the four-wheel speed and four-wheel torque; Step 3: The whole machine collaborative controller establishes a four-wheel speed control strategy model to independently adjust the speed of the four wheels. The driving mode is independent driving mode. Step 4: The whole machine collaborative controller establishes a coordinated control strategy model for the four-wheel speed and torque, adjusts the torque and speed of the slipping and non-slipping wheels, and adopts a coordinated driving mode. Step 5: The whole machine collaborative controller establishes a four-wheel real-time slip rate model based on the data collected in steps 1 and 2; Step 6: The whole machine collaborative controller calculates the tire slip rate based on the four-wheel real-time slip rate model, identifies the slipping wheel based on the preset slip rate threshold, and then selects the driving mode; Step 7: The whole machine collaborative controller introduces an optimization algorithm based on model predictive control to predict the control parameters of each wheel in the future; In step 3, the control strategy model of the four-wheel speed is as follows: Among them, R is the turning radius of the plant protection machine, v is the speed of the plant protection machine, θ is the steering wheel angle of the plant protection machine, L is the wheelbase, f(θ) is the wheel angle, v in is the linear velocity of the inner wheel during steering, W is the wheelbase, v out is the linear velocity of the outer wheel during steering, ω in is the speed of the inner wheel during steering, r is the wheel radius, ω out is the speed of the outer wheel during steering; In step 4, the coordinated control strategy model of the four-wheel speed and torque is as follows: Among them, j is the sliding wheel, k is the non-slip wheel, are the updated wheel speeds of the slip wheel and non-slip wheel, The current wheel speeds of the sliding wheel and non-slip wheel, are the updated wheel torques of the slip wheel and non-slip wheel, are the current wheel torques of the slipping wheel and the non-slipping wheel respectively; α, γ, β, and λ are dynamic adjustment factors; ρ is the adjustment coefficient, which is 0.3; φ is the adjustment coefficient, which is 0.2; S i is the slip rate of the i-th wheel; S lim is the slip rate threshold.

2. The distributed electric drive self-propelled plant protection machine four-wheel drive coordinated control method according to claim 1, characterized in that: In step 5, the four-wheel real-time slip rate model is as follows: Among them, f(μ i ,λ i ) is the correction factor for ground type and load conditions, μ i is the friction coefficient between the wheel and the ground, λ i is the pressure between the wheel and the ground, ω i is the angular velocity of the i-th wheel, v is the speed of the plant protection machine, r is the wheel radius, S i is the slip rate of the i-th wheel.

3. The distributed electric drive self-propelled plant protection machine four-wheel drive coordinated control method according to claim 1, characterized in that: In step 6, when S i ≥S lim When , the wheel is identified as a slip wheel, otherwise it is a non-slip wheel, S i is the slip rate of the i-th wheel, S lim is the slip rate threshold; When selecting the drive mode, if one wheel is a slip wheel, the vehicle enters the cooperative drive mode; if no wheel is a slip wheel, the vehicle enters the independent drive mode.

4. The distributed electric drive self-propelled plant protection machine four-wheel drive coordinated control method according to claim 1, characterized in that: The specific process of step 7 is as follows: Step 7.1: Build a plant protection machine system model to predict the future state, that is, the evolution of the future slip rate: S i (t+n)=S i (t)+ΔS i (t+n) Among them, S i (t+n) is the slip rate of the i-th wheel at the predicted time t+n, S i (t) is the slip rate of the i-th wheel at time t, ΔS i (t+n) is the slip rate change from the current time t to the predicted time t+n; Step 7.2: Construct the cost function as shown below: Among them, J is the cost function; N is the predicted time domain length; is the weight coefficient of the torque to cost function, and the priority between the two is balanced by adjusting the weight coefficient, with a value of 0.5; S i is the slip rate of the i-th wheel; T i is the torque of the i-th wheel; And set the following constraints: Among them, ω min 、ω max are the minimum and maximum speeds allowed for the wheels respectively; T min 、T max are the minimum torque and maximum torque allowed by the wheel respectively; ω i (t+n) is the wheel speed at the predicted time t+n; T i (t+n) is the wheel torque at the predicted time t+n; S i (t+n) is the wheel slip rate at the predicted time t+n; S lim is the slip rate threshold.

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