A model-free adaptive multi-tractor platoon path leader-following control system and control method

Abstract

The application discloses a kind of based on model-free adaptive multi-farm machine formation path navigation-tracking control system and control method, belong to agricultural machinery navigation technical field.Main steps are:1, using sensor, real-time acquisition agricultural machinery position information, pre-look lateral deviation and area deviation.2, based on deviation information and control input constructs dynamic linear equation, constructs criterion function, minimizes tracking error in control increment, deduces control law, combines pseudo partial derivative online estimation algorithm, realizes model-free adaptive trajectory tracking.3, based on the position information of leader agricultural machinery of design, real-time control follower motion control operation, realizes agricultural machinery vehicle path tracking precision.4, based on the model-free adaptive control scheme of design pre-look deviation.The application can adapt to complex farmland environment, not dependent on accurate model and have strong adaptive ability, realizes high-precision, strong robustness collaborative operation, provides technical support for modern agricultural intelligent development.

Description

Technical Field

[0001] This invention relates to modern unmanned autonomous operation technology for agricultural machinery, specifically to a model-free adaptive multi-agricultural machinery formation path navigation-tracking control system and control method, belonging to the field of agricultural machinery navigation technology. Background Technology

[0002] With the acceleration of agricultural modernization, large-scale and intensive agricultural production models have placed higher demands on operational efficiency and precision. Multi-machine collaborative operation technology has become a key approach to improving farmland operation efficiency, among which the leader-follower control architecture has attracted much attention due to its simple structure and strong scalability. However, the actual farmland operating environment is complex and variable. Factors such as soil characteristics, crop height, and terrain undulations make it difficult to accurately establish agricultural machinery dynamics models. Traditional model-based control methods face problems such as insufficient adaptability and poor robustness in practical applications.

[0003] Existing agricultural machinery cooperative control methods mainly rely on accurate dynamic models or preset control parameters. For example, model predictive control (MPC) requires the establishment of accurate kinematic and dynamic models of the agricultural machinery. However, in actual operation, factors such as changes in agricultural machinery load and ground adhesion conditions can lead to model mismatch, thus affecting control accuracy. Traditional methods such as PID control, although simple in structure, have fixed parameters, making it difficult to adapt to the control requirements under different operating conditions. They often require frequent manual parameter adjustments, increasing operational complexity.

[0004] In recent years, data-driven control methods have demonstrated advantages in the field of complex system control due to their independence from precise mathematical models. Model-free adaptive control, as a typical data-driven control method, designs the controller using only the system's input and output data, avoiding complex modeling processes. This method estimates the system's pseudo-gradient parameters online and adjusts the control strategy in real time, exhibiting adaptability to dynamic changes in the system.

[0005] However, existing research still has limitations in applying model-free adaptive control to multi-machine cooperative operations. On the one hand, most studies focus on the path tracking problem of a single machine, without fully considering the coupling relationships and communication constraints in multi-machine cooperation. On the other hand, existing methods are relatively conservative in parameter update mechanisms and control law design, making it difficult to achieve fast and accurate cooperative control while ensuring stability. Especially in unstructured farmland environments, the requirements for cooperative control, such as maintaining relative distance and trajectory synchronization among machines, are higher, necessitating more efficient adaptive mechanisms.

[0006] Therefore, there is an urgent need to develop a multi-machine collaborative control method that can adapt to complex farmland environments, does not rely on precise models, and possesses strong adaptive capabilities. This invention addresses this problem by proposing a multi-machine collaborative control scheme based on model-free adaptive control. It aims to achieve high-precision and robust collaborative operations through a data-driven approach, providing technical support for the intelligent development of modern agriculture. Summary of the Invention

[0007] To address the above technical problems, this invention provides a model-free adaptive multi-agricultural machinery platoon path navigation-tracking control system and control method.

[0008] The technical solution adopted by this invention to solve its technical problem is:

[0009] A model-free adaptive multi-farm machinery platoon path navigation-tracking control method, the method comprising the following steps:

[0010] S1: Using sensors, the position information, aiming lateral deviation and area deviation of the agricultural machinery are obtained in real time;

[0011] S2: Based on the deviation information and control input, construct a dynamic linearization equation, construct a criterion function, minimize the tracking error to the control increment, derive the control law, and combine it with the pseudo-partial derivative online estimation algorithm to realize the model-free adaptive trajectory tracking control algorithm, and obtain the system control input, i.e., the steering wheel angle of the agricultural machinery;

[0012] S3: Based on the leader's agricultural machinery location information, the system controls the movement of followers in real time to achieve accurate agricultural machinery path tracking.

[0013] Furthermore, step S2 includes the following steps:

[0014] S2.1 Establish a data-based model, utilizing control and state data from multiple agricultural machines to transform the unknown agricultural machine model into a dynamic linearized model, specifically:

[0015] ;

[0016] in, express time, express Output at any moment and The difference in output at any given moment for Real-time output volume for Real-time output volume for Always anticipate lateral error. for Area of ​​time error , For real numbers, For the pseudo-partial derivatives of the system, , Indicates time Control quantity and time The difference in the control quantity, For the system at time The control quantity, For the system at time The control quantity is the steering wheel angle;

[0017] S2.2 Based on the above dynamic linearized data model, the prediction equation for the next step model is derived, as follows:

[0018] ;

[0019] in, For the system in Output at any given time For the system in Output at any given moment;

[0020] S2.3 The control law is derived by minimizing the tracking error and control increment through a constructor, and combined with an online pseudo-partial derivative estimation algorithm to achieve model-free adaptive control. The criterion function is:

[0021] ;

[0022] in, The desired output is the weighted sum of the lateral error and the area error. Utilizing optimization conditions The following control law is obtained:

[0023] ;

[0024] in, It is the step size factor that makes the system converge; These are weighting coefficients, the purpose of which is to limit... The changes in ensure that the control input criterion function has a reasonable range, while avoiding the occurrence of a denominator of 0;

[0025] S2.4 Design parameter update algorithm, the criterion function is:

[0026] ;

[0027] in, For the system in The pseudo-partial derivative estimate at time t, using optimization conditions The following control law is obtained:

[0028] ;

[0029] in, It is the step size factor that makes the system converge; These are weighting coefficients, the purpose of which is to limit... The changes in denominator ensure that the control input criterion function has a reasonable range, while avoiding the occurrence of a denominator of 0.

[0030] Furthermore, step S3 includes the following steps:

[0031] S3.1 Following the agricultural machinery vehicle, calculate the difference based on the position of the lead agricultural machinery, and add a fixed distance. The error was obtained. ,right Proportional control is performed to obtain the speed at which the agricultural machinery vehicle follows.

[0032] S3.2 Following the lead agricultural vehicle, the following agricultural vehicle follows the path of the lead vehicle and uses the same steering wheel direction control as in S2 to maintain a fixed distance between the lead and follower vehicles. .

[0033] To implement the above control method, the sampling control system includes a position information acquisition module, an error information acquisition module, a model-free adaptive predictive control module, a communication topology update module, and a control module.

[0034] The location information acquisition module uses agricultural machinery sensors to acquire the location information of mobile agricultural machinery vehicles;

[0035] The error information acquisition module uses reference points on the target path to set aiming points and acquires the lateral aiming error and aiming area error of agricultural machinery vehicles.

[0036] The model-free adaptive predictive control module uses the aiming lateral error and aiming area error to design the control output. Based on the control output and control input data, it constructs a dynamic linearized equation, minimizes the tracking error and control increment through the criterion function to derive the control law, and combines the pseudo-partial derivative matrix online estimation and parameter prediction algorithm to realize model-free adaptive predictive control.

[0037] The communication module is used to send the real-time information collected from the navigator's agricultural machinery location and speed to the follower agricultural machinery vehicles;

[0038] It includes a control module for a model-free adaptive predictive control scheme based on the design. By controlling the speed and steering wheel angle of the following agricultural machinery vehicle, a distributed controller is designed for the following agricultural machinery vehicle to adjust its motion state and form a stable distance with the leading agricultural machinery vehicle.

[0039] The beneficial effects of this invention include: This invention designs a model-free adaptive multi-agricultural machinery platoon path navigation-tracking control method. Based on the collection of position information, lateral deviation, and area deviation data of the agricultural machinery, the steering wheel angle is calculated to achieve automatic tracking of agricultural machinery operations. The controller designed in this invention eliminates the dependence on mathematical models of agricultural machinery vehicles, using only the input and output data of the agricultural machinery vehicle system; it designs output data based on a weighted sum of lateral distance error and area error, reducing computational complexity; the model-free adaptive controller described in this invention can effectively track the reference trajectory path, obtain high path tracking accuracy, achieve better control performance, and better meet the needs of actual agricultural machinery operations. Attached Figure Description

[0040] Figure 1 This is a block diagram of the multi-farm machinery platoon path navigation-tracking control method based on model-free adaptive control of the present invention.

[0041] Figure 2 A schematic diagram for path tracking of leading agricultural machinery vehicles;

[0042] Figure 3 A schematic diagram illustrating the actual movement trajectory of agricultural machinery vehicles for navigation and tracking;

[0043] Figure 4 A schematic diagram showing the positions of the lead agricultural machinery vehicles and the tracking agricultural machinery vehicles. Detailed Implementation

[0044] The following refers to the accompanying drawings in the instruction manual. Figures 1-4 The technical solutions in the embodiments of the present invention will be further described in detail below.

[0045] like Figure 1 As shown, a model-free adaptive multi-agricultural machinery platoon path navigation-tracking control system and control method are disclosed. The specific implementation process of the method is as follows:

[0046] The method includes the following steps:

[0047] S1: Using sensors, the position information, aiming lateral deviation and area deviation of the agricultural machinery are obtained in real time;

[0048] S2: Based on the deviation information and control input, construct a dynamic linearization equation, construct a criterion function, minimize the tracking error to the control increment, derive the control law, and combine it with the pseudo-partial derivative online estimation algorithm to realize the model-free adaptive trajectory tracking control algorithm, and obtain the system control input, i.e., the steering wheel angle of the agricultural machinery;

[0049] S3: Based on the leader's agricultural machinery location information, the system controls the movement of followers in real time to achieve accurate agricultural machinery path tracking.

[0050] 2. The method according to claim 1, wherein step S2 comprises the following steps:

[0051] S2.1 Establish a data-based model, utilizing control and state data from multiple agricultural machines to transform the unknown agricultural machine model into a dynamic linearized model, specifically:

[0052] ;

[0053] in, express time, express Output at any moment and The difference in output at any given moment for Real-time output volume for Real-time output volume for Always anticipate lateral error. for Area of ​​time error , For real numbers, For the pseudo-partial derivatives of the system, , Indicates time Control quantity and time The difference in the control quantity, For the system at time The control quantity, For the system at time The control quantity is the steering wheel angle;

[0054] S2.2 Based on the above dynamic linearized data model, the prediction equation for the next step model is derived, as follows:

[0055] ;

[0056] in, For the system in Output at any given time For the system in Output at any given moment;

[0057] S2.3 The control law is derived by minimizing the tracking error and control increment through a constructor, and combined with an online pseudo-partial derivative estimation algorithm to achieve model-free adaptive control. The criterion function is:

[0058] ;

[0059] in, The desired output is the weighted sum of the lateral error and the area error. Utilizing optimization conditions The following control law is obtained:

[0060] ;

[0061] in, It is the step size factor that makes the system converge; These are weighting coefficients, the purpose of which is to limit... The changes in ensure that the control input criterion function has a reasonable range, while avoiding the occurrence of a denominator of 0;

[0062] S2.4 Design parameter update algorithm, the criterion function is:

[0063] ;

[0064] in, For the system in The pseudo-partial derivative estimate at time t, using optimization conditions The following control law is obtained:

[0065] ;

[0066] in, It is the step size factor that makes the system converge; These are weighting coefficients, the purpose of which is to limit... The changes in denominator ensure that the control input criterion function has a reasonable range, while avoiding the occurrence of a denominator of 0.

[0067] 3. The method according to claim 1, wherein step S3 comprises the following steps:

[0068] S3.1 Following the agricultural machinery vehicle, calculate the difference based on the position of the lead agricultural machinery, and add a fixed distance. The error was obtained. ,right Proportional control is performed to obtain the speed at which the agricultural machinery vehicle follows.

[0069] S3.2 Following the lead agricultural vehicle, the following agricultural vehicle follows the path of the lead vehicle and uses the same steering wheel direction control as in S2 to maintain a fixed distance between the lead and follower vehicles. .

[0070] To implement the above control method, the control system includes a position information acquisition module, an error information acquisition module, a model-free adaptive predictive control module, a communication topology update module, and a control module.

[0071] The location information acquisition module uses agricultural machinery sensors to acquire the location information of mobile agricultural machinery vehicles;

[0072] The error information acquisition module uses reference points on the target path to set aiming points and acquires the lateral aiming error and aiming area error of agricultural machinery vehicles.

[0073] The model-free adaptive predictive control module uses the aiming lateral error and aiming area error to design the control output. Based on the control output and control input data, it constructs a dynamic linearized equation, minimizes the tracking error and control increment through the criterion function to derive the control law, and combines the pseudo-partial derivative matrix online estimation and parameter prediction algorithm to realize model-free adaptive predictive control.

[0074] The communication module is used to send the real-time information collected from the navigator's agricultural machinery location and speed to the follower agricultural machinery vehicles;

[0075] It includes a control module for a model-free adaptive predictive control scheme based on the design. By controlling the speed and steering wheel angle of the following agricultural machinery vehicle, a distributed controller is designed for the following agricultural machinery vehicle to adjust its motion state and form a stable distance with the leading agricultural machinery vehicle.

[0076] The aforementioned multi-mobile robot includes various modules in its control system, which can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0077] The foregoing has provided a detailed description of the model-free adaptive multi-agricultural machinery platoon path navigation-tracking control and control method provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention; these examples are merely illustrative to help understand the core ideas of the invention. Furthermore, this invention provides a reference method for research in the same field and can be further extended to other related agricultural machinery path tracking control fields, possessing high practicality and promotional value.

[0078] It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from its principles, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A model-free adaptive multi-farm machinery platooning path navigation-tracking control method, characterized in that, The method includes the following steps: S1: Using sensors, the position information, aiming lateral deviation and area deviation of the agricultural machinery are obtained in real time; S2: Based on the deviation information and control input, construct a dynamic linearization equation, construct a criterion function, minimize the tracking error to the control increment, derive the control law, and combine it with the pseudo-partial derivative online estimation algorithm to realize the model-free adaptive trajectory tracking control algorithm, and obtain the system control input, i.e., the steering wheel angle of the agricultural machinery; S3: Based on the leader's position information, the system controls the movement of followers in real time to achieve high accuracy in tracking agricultural machinery paths.

2. The method according to claim 1, characterized in that, S2 includes the following steps: S2.1 Establish a data-based model, utilizing control and state data from multiple agricultural machines to transform the unknown agricultural machine model into a dynamic linearized model, specifically: ； in, express time, express Output at any moment and The difference in output at any given moment for Real-time output volume for Real-time output volume for The lateral error is predicted at all times, and k1 and k2 are constants; for Area of ​​time error , For real numbers, For the pseudo-partial derivatives of the system, , Indicates time Control quantity and time The difference in the control quantity, For the system at time The control quantity, For the system at time The control quantity is the steering wheel angle; S2.2 Based on the above dynamic linearized data model, the prediction equation for the next step model is derived, as follows: ； in, For the system in Output at any given time For the system in Output at any given moment; S2.3 The control law is derived by minimizing the tracking error and control increment through a constructor, and combined with an online pseudo-partial derivative estimation algorithm to achieve model-free adaptive control. The criterion function is: ； in, The desired output is the weighted sum of the lateral error and the area error. Utilizing optimization conditions The following control law is obtained: ； in, It is the step size factor that makes the system converge; These are weighting coefficients, the purpose of which is to limit... The changes in ensure that the control input criterion function has a reasonable range, while avoiding the occurrence of a denominator of 0; S2.4 Design parameter update algorithm, the criterion function is: ； in, For the system in The pseudo-partial derivative estimate at time t, using optimization conditions The following control law is obtained: ； in, It is the step size factor that makes the system converge; These are weighting coefficients, the purpose of which is to limit... The changes in denominator ensure that the control input criterion function has a reasonable range, while avoiding the occurrence of a denominator of 0.

3. The method according to claim 1, characterized in that, S3 includes the following steps: S3.1 Following the agricultural machinery vehicle, calculate the difference based on the position of the lead agricultural machinery, and add a fixed distance. The error was obtained. ,right Proportional control is performed to obtain the speed at which the agricultural machinery vehicle follows. S3.2 Following the lead agricultural vehicle, the following agricultural vehicle follows the path of the lead vehicle and uses the same steering wheel direction control as in S2 to maintain a fixed distance between the lead and follower vehicles. .

4. A model-free adaptive multi-farm machinery platooning path navigation-tracking control system, used to implement the method as described in any one of claims 1-3, characterized in that, It includes a location information acquisition module, an error information acquisition module, a model-free adaptive predictive control module, a communication topology update module, and a control module; The location information acquisition module uses agricultural machinery sensors to acquire the location information of mobile agricultural machinery vehicles; The error information acquisition module uses reference points on the target path to set aiming points and acquires the lateral aiming error and aiming area error of agricultural machinery vehicles. The model-free adaptive predictive control module uses the aiming lateral error and aiming area error to design the control output. Based on the control output and control input data, it constructs a dynamic linearized equation, minimizes the tracking error and control increment through the criterion function to derive the control law, and combines the pseudo-partial derivative matrix online estimation and parameter prediction algorithm to realize model-free adaptive predictive control. The communication module is used to send the real-time information collected from the navigator's agricultural machinery location and speed to the follower agricultural machinery vehicles; It includes a control module for a model-free adaptive predictive control scheme based on the design. By controlling the speed and steering wheel angle of the following agricultural machinery vehicle, a distributed controller is designed for the following agricultural machinery vehicle to adjust its motion state and form a stable distance with the leading agricultural machinery vehicle.

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