Unmanned agricultural operation vehicle path tracking control method, system and device based on immersion and invariance

Abstract

The invention discloses an unmanned agricultural operation vehicle path tracking control method, system and device based on immersion and invariance, and the method comprises the steps: constructing a path tracking deviation model based on transverse deviation and course deviation, defining a sliding variable, determining a system model with adaptive parameter estimation, and carrying out the tracking control of an unmanned agricultural operation vehicle path. Designing a non-smooth second-order sliding mode controller; aiming at the problem of system uncertainty in the path tracking deviation dynamical model, constructing an adaptive parameter estimation law based on immersion and invariance, and carrying out effective estimation on the system uncertainty; and combining the adaptive parameter estimation law with a non-smooth second-order sliding mode controller to obtain a non-smooth compound controller for path tracking of the unmanned agricultural operation vehicle, so that the agricultural operation vehicle can finally track an expected path. According to the method, the path tracking deviation of the agricultural operation vehicle is finally rapidly converged, and high-precision tracking, namely anti-interference performance, is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of autonomous navigation control of unmanned agricultural working vehicles, and specifically relates to a path tracking control method, system and device for unmanned agricultural working vehicles based on immersion and invariance technology. Background Art

[0002] In the field of modern agricultural production, the automation and intelligence of agricultural vehicles have become an effective way to improve efficiency and reduce costs. Path tracking control technology is a key link in realizing the automation and intelligence of agricultural vehicles. It is of great significance to improve operation accuracy, reduce human resource input, save costs and improve operation quality. Therefore, the development of efficient path tracking control methods is crucial to enhance the tracking accuracy and robustness of agricultural vehicles. Sliding mode control is one of the commonly used nonlinear control methods, which is known for its strong robustness to parameter changes and external disturbances. In the path tracking of unmanned agricultural vehicles, sliding mode control is widely used because of its excellent anti-interference ability and rapid response characteristics, which can effectively cope with the complex environment of farmland and improve the accuracy and stability of path tracking.

[0003] However, the traditional sliding mode control method only guarantees the asymptotic convergence of the system, which means that although the system state can eventually converge, its convergence time and convergence speed cannot be guaranteed. Therefore, under asymptotic convergence, agricultural operation vehicles cannot quickly adapt to the complex farmland operation environment. At the same time, when agricultural operation vehicles perform path tracking operations in complex environments, the impact of system uncertainty on path tracking accuracy and stability is inevitable, which puts higher requirements on the robustness of the path tracking method. Summary of the invention

[0004] In order to solve the problems existing in path tracking of unmanned agricultural working vehicles and the shortcomings of the prior art, the present invention proposes a path tracking control method, system and device for unmanned agricultural working vehicles based on immersion and invariance technology, which increases the path tracking accuracy of agricultural working vehicles and improves their response speed so that they can be applied to actual agricultural operations.

[0005] The present invention achieves the above technical objectives through the following technical means.

[0006] Path tracking control method for unmanned agricultural operation vehicles based on immersion and invariance technology:

[0007] Construct a path tracking deviation model based on lateral deviation and heading deviation, define sliding variables, and determine the system model with adaptive parameter estimation;

[0008] Design a nonsmooth second-order sliding mode controller based on the system model with adaptive parameter estimation:

[0009]

[0010] Among them, β0>0 is the design parameter of the non-smooth second-order sliding mode controller, u0 is the virtual controller, g(x, t) is the intermediate quantity, θ is the vector containing unknown parameters, is a vector containing state variables;

[0011] An adaptive parameter estimation law based on immersion and invariance is constructed to effectively estimate the uncertainty of the system. The adaptive parameter estimation law is:

[0012]

[0013] Among them, the constant Λ>0; is an estimate of θ, and α is an additional term;

[0014] The adaptive parameter estimation law is combined with a non-smooth second-order sliding mode controller to obtain a non-smooth composite controller for path tracking of an unmanned agricultural operation vehicle based on immersion and invariance technology, so that the agricultural operation vehicle can eventually track the desired path; the non-smooth composite controller is:

[0015]

[0016] Non-smooth composite controller Perform inverse transformation to obtain the actual front wheel steering angle of the agricultural vehicle.

[0017] In a further technical solution, the virtual controller is:

[0018]

[0019] Among them, β1, β2, γ1, and γ2 are all design parameters of the virtual controller, and β1>0, β2>0, γ1>2, and γ2>1; Both s1 and s2 are sliding variables.

[0020] Further technical solutions:

[0021] Define the sliding variable as s1=x1, Then the system model with adaptive parameter estimation is:

[0022]

[0023] Among them, the intermediate The vector containing unknown parameters θ=(c t V 2 , c t ) T , a vector containing state variables V is the longitudinal speed of the agricultural vehicle, L t is the wheelbase of the agricultural vehicle, c t is the curvature of the desired working path, x1 and x2 are state variables, and d(x, t) is the external disturbance of the system.

[0024] Further technical solutions:

[0025] Select state variable x1 = L os 、x2=Vsinθ os , establish the state space equation:

[0026]

[0027] Among them, L os It represents the lateral deviation between the actual working path and the expected working path of the agricultural working vehicle during its movement, θ os It indicates the heading deviation between the actual working path and the expected working path of an agricultural vehicle during its movement.

[0028] A further technical solution is that the path tracking deviation model based on the lateral deviation and heading deviation is:

[0029]

[0030] Among them, σ is the front wheel steering angle of the agricultural vehicle, d(t) = sin(t) is the uncertainty including the external interference of the system, and t is the time constant.

[0031] Further technical solutions for non-smooth composite controllers Perform the inverse transformation to obtain the actual front wheel steering angle σ of the agricultural vehicle:

[0032] A path tracking control system for an unmanned agricultural operation vehicle based on immersion and invariance technology, comprising:

[0033] The error processing module compares the actual operation path of the unmanned agricultural operation vehicle with the current nearest expected operation path, and calculates and processes it into lateral deviation and heading deviation;

[0034] A model conversion module converts the path tracking deviation model of lateral deviation and heading deviation into a system model that is convenient for the design of adaptive parameter estimation law and non-smooth second-order sliding mode controller;

[0035] A non-smooth control module is used to make the lateral deviation and heading deviation of the unmanned agricultural operation vehicle converge to zero quickly;

[0036] The adaptive module is used to adaptively estimate the unknown parameters in the unmanned agricultural vehicle model to solve the system uncertainty problem;

[0037] The inverse transformation module is used to perform an inverse transformation on the control input to solve the front wheel steering angle actually controlled.

[0038] A path tracking control device for an unmanned agricultural operation vehicle based on immersion and invariance technology, comprising:

[0039] The host computer module is used to design the control program and record the experimental data;

[0040] The path planning module designs the desired path for agricultural operation vehicles and calculates the lateral deviation and heading deviation based on the real-time position information of the vehicle;

[0041] Satellite navigation and positioning module to obtain real-time location information of agricultural operation vehicles;

[0042] Serial communication module, to realize the communication between various parts of agricultural operation vehicles;

[0043] The hardware control module receives the lateral deviation and heading deviation information, calculates the control program, and outputs the steering angle control command;

[0044] The vehicle control module executes the steering angle commands of the agricultural operation vehicle and realizes the vehicle's forward and turning functions.

[0045] The beneficial effects of the present invention are:

[0046] 1. The present invention considers the influence of uncertainty in the path tracking process of unmanned agricultural operation vehicles and designs a non-smooth second-order sliding mode controller to ensure the rapid convergence performance of the tracking deviation in the path tracking process;

[0047] 2. The present invention uses an adaptive parameter estimation law based on immersion and invariance technology to improve the estimation accuracy of system uncertainty, thereby enhancing the dynamic performance and anti-interference performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a path tracking control block diagram of the unmanned agricultural operation vehicle of the present invention;

[0049] Figure 2 It is a schematic diagram of the composition of the path tracking control device for the unmanned agricultural operation vehicle of the present invention;

[0050] Figure 3 This is a schematic diagram of the structure of the path tracking control system for the unmanned agricultural operation vehicle of the present invention;

[0051] Figure 4 A schematic diagram of the path tracking of an agricultural operation vehicle according to the present invention;

[0052] Figure 5 A trajectory result diagram of the agricultural operation vehicle path tracking of the present invention;

[0053] Figure 6 A lateral deviation map of the agricultural operation vehicle path tracking of the present invention;

[0054] Figure 7 It is a path tracking heading deviation map of the agricultural operation vehicle of the present invention. DETAILED DESCRIPTION

[0055] The present invention provides a path tracking control method, system and device for unmanned agricultural operation vehicles based on immersion and invariance technology. The overall control scheme schematic diagram is shown in FIG. Figure 1 As shown. In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the technical solution in the example of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. It should be noted that the embodiments described here are only some embodiments of the present invention, not all embodiments. They are only used to explain the present invention and are not used to limit the present invention.

[0056] An embodiment of a path tracking control device for an unmanned agricultural operation vehicle based on immersion and invariance technology comprises the following components: Figure 2 As shown, it will be used to specifically illustrate the present invention, including the following parts:

[0057] The host computer module is used to design the control program and record the experimental data;

[0058] The path planning module designs the desired path for agricultural operation vehicles and calculates the lateral deviation and heading deviation based on the real-time position information of the vehicle;

[0059] Satellite navigation and positioning module to obtain real-time location information of agricultural operation vehicles;

[0060] Serial communication module, to realize the communication between various parts of agricultural operation vehicles;

[0061] The hardware control module receives the lateral deviation and heading deviation information, calculates the control program, and outputs the steering angle control command;

[0062] The vehicle control module executes the steering angle command of the agricultural operation vehicle and realizes the vehicle's forward and turning functions.

[0063] An embodiment of a path tracking control system for an unmanned agricultural operation vehicle based on immersion and invariance technology, such as Figure 3As shown, it includes the following modules:

[0064] The error processing module is used to compare the actual operation path of the unmanned agricultural operation vehicle with the current nearest expected path information, and calculate and process it into deviation information in two dimensions: lateral and heading;

[0065] A model conversion module, used to convert the path tracking deviation model about the lateral deviation and the heading deviation into a system model that is convenient for the design of the adaptive parameter estimation law and the non-smooth second-order sliding mode controller;

[0066] A non-smooth control module is used to make the lateral deviation and heading deviation of the unmanned agricultural operation vehicle converge to zero quickly;

[0067] An adaptive module is used to adaptively estimate the unknown parameters in the unmanned agricultural vehicle model to solve the system uncertainty problem;

[0068] The inverse transformation module is used to perform inverse transformation on the control input, solve the front wheel steering angle information of the actual control, and control the agricultural operation vehicle in a complex operating environment.

[0069] Figure 4 This is a schematic diagram of the path tracking of an agricultural operation vehicle according to the present invention. t is the actual heading angle of the agricultural vehicle, θ d is the desired heading angle of the agricultural vehicle. The reference speed V of the agricultural vehicle is 3 m / s, and the wheelbase L of the agricultural machine is t For the above system embodiment, an embodiment of an unmanned agricultural machinery path tracking control method based on immersion and invariance technology, see Figure 1 , including the following steps:

[0070] Step 1: According to the kinematic characteristics of the unmanned agricultural operation vehicle, taking into account the internal and external uncertainties of the system contained in the model, a path tracking deviation model based on lateral deviation and heading deviation is constructed, and the required state variables are defined based on the model;

[0071] Specifically, in this example, the path tracking deviation model of the unmanned agricultural operation vehicle with system uncertainty is:

[0072]

[0073] Among them, L os It represents the lateral deviation between the actual working path and the expected working path of the agricultural working vehicle during its movement, θ os It represents the heading deviation between the actual operation path and the expected operation path of the agricultural operation vehicle during its movement. V is the longitudinal speed of the agricultural operation vehicle. Lt is the wheelbase of the agricultural vehicle, σ is the front wheel steering angle of the agricultural vehicle, c t is the curvature of the expected operation path, d(t)=sin(t) is the uncertainty including the external interference of the system, and t is the time constant.

[0074] At the same time, in order to facilitate the design of non-smooth second-order sliding mode controller, x1 = L os , x2=Vsinθ os As the state variable, establish the state space equation for the system:

[0075]

[0076] Where u = tanσ is the designed non-smooth second-order sliding mode controller, is the external disturbance of the system.

[0077] Step 2: Based on the path tracking deviation model in step 1, considering the path tracking target of agricultural operation vehicles, define the sliding variable as s1 = x1, Then the system model with adaptive parameter estimation is:

[0078]

[0079] Among them, the intermediate θ=(c t V 2 , c t ) T is a vector containing unknown parameters, is a vector containing the state variables.

[0080] This model facilitates the design of the subsequent adaptive parameter estimation law and reduces the design complexity of the adaptive parameter estimation law.

[0081] Step 3: Based on the system model with adaptive parameter estimation constructed in step 2, a non-smooth second-order sliding mode controller is designed, which is:

[0082]

[0083] Among them, β0>0 is the design parameter of the non-smooth second-order sliding mode controller, and u0 is the virtual controller.

[0084] The innovation of the design of the non-smooth second-order sliding mode controller lies in that it can achieve the control target while ensuring that the lateral deviation and heading deviation of the unmanned agricultural vehicle converge quickly, while ensuring robustness and anti-interference performance.

[0085] Step 4: Design the virtual controller u0, which is:

[0086]

[0087] Among them, β1, β2, γ1, and γ2 are all design parameters of the virtual controller, and β1>0, β2>0, γ1>2, and γ2>1;

[0088]

[0089] Step 5: To solve the problem of system uncertainty in the path tracking deviation model, an immersion and invariant adaptive parameter estimation law is constructed to effectively estimate the system uncertainty. The adaptive parameter estimation law is:

[0090]

[0091] Among them, the constant Λ>0; is an estimate of θ and α is an additional term.

[0092] The innovation of the design of the adaptive parameter estimation law lies in that it can perform high-precision adaptive estimation for system uncertainty, improve the dynamic performance and anti-interference performance of the system, and shorten the convergence time.

[0093] Step 6: Combine the adaptive parameter estimation law with the non-smooth second-order sliding mode controller to obtain a non-smooth composite controller for path tracking of unmanned agricultural operation vehicles based on immersion and invariance technology, so that the agricultural operation vehicle can eventually track the desired path and ensure its high-precision tracking performance and rapid responsiveness. The non-smooth composite controller is:

[0094]

[0095] Step 7: Through the non-smooth composite controller Perform inverse transformation to obtain the actual front wheel steering angle σ of the agricultural vehicle:

[0096]

[0097] An embodiment of the path tracking control method, system and device for unmanned agricultural operation vehicles based on immersion and invariance technology has been specifically explained and illustrated, so that the technical solution is clearer and easier for engineers to implement. Based on this example, the effect of the present invention is further demonstrated through simulation experiments.

[0098] In order to verify that the present invention has excellent tracking accuracy, the tracking accuracy of this embodiment is tested based on the path tracking performance when the initial lateral deviation of the unmanned agricultural operation vehicle is set to 10 cm. Figure 5 This is the trajectory result of agricultural vehicle path tracking under S-shaped path. Figure 6is the lateral deviation map of agricultural vehicle path tracking under S-shaped path, Figure 7 This is a heading deviation diagram of the agricultural vehicle path tracking under the S-shaped path. It is not difficult to see that the control accuracy of this embodiment in the experiment is higher and the reference path is tracked in a faster time, so it can be demonstrated that the present invention has excellent tracking accuracy. In summary, the present invention achieves a significant improvement in dynamic performance and tracking accuracy while ensuring the hardware safety of the agricultural vehicle.

[0099] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0100] Although the present invention has been described according to various specific embodiments, those skilled in the art will appreciate that the present invention can be implemented with modifications within the spirit of the claims. Therefore, 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 belong to the protection scope of the present invention.

Claims

1. A path tracking control method for an unmanned agricultural vehicle based on immersion and invariance technology, characterized in that: Construct a path tracking deviation model based on lateral deviation and heading deviation, define sliding variables, and determine the system model with adaptive parameter estimation; Design a nonsmooth second-order sliding mode controller based on the system model with adaptive parameter estimation: Among them, β0>0 is the design parameter of the non-smooth second-order sliding mode controller, u0 is the virtual controller, g(x, t) is the intermediate quantity, θ is the vector containing unknown parameters, is a vector containing state variables; An adaptive parameter estimation law based on immersion and invariance is constructed to effectively estimate the uncertainty of the system. The adaptive parameter estimation law is: Among them, the constant Λ>0; is an estimate of θ, and α is an additional term; The adaptive parameter estimation law is combined with a non-smooth second-order sliding mode controller to obtain a non-smooth composite controller for path tracking of an unmanned agricultural operation vehicle based on immersion and invariance technology, so that the agricultural operation vehicle can eventually track the desired path; the non-smooth composite controller is: Non-smooth composite controller Perform inverse transformation to obtain the actual front wheel steering angle of the agricultural vehicle.

2. The path tracking control method for an unmanned agricultural operation vehicle based on immersion and invariance technology according to claim 1 is characterized in that: The virtual controller is: Among them, β1, β2, γ1, and γ2 are all design parameters of the virtual controller, and β1>0, β2>0, γ1>2, and γ2>1; Both s1 and s2 are sliding variables.

3. The path tracking control method for an unmanned agricultural operation vehicle based on immersion and invariance technology according to claim 2 is characterized in that: Define the sliding variable as s1=x1, Then the system model with adaptive parameter estimation is: Among them, the intermediate The vector containing unknown parameters Δ=(c t V 2 ,c t ) T , a vector containing state variables V is the longitudinal speed of the agricultural vehicle, L t is the wheelbase of the agricultural vehicle, c t is the curvature of the desired working path, x1 and x2 are state variables, and d(x, t) is the external disturbance of the system.

4. The path tracking control method for unmanned agricultural operation vehicles based on immersion and invariance technology according to claim 3 is characterized in that: Select state variable x1 = L os 、x2=Vsinθ os , establish the state space equation: Among them, L os It represents the lateral deviation between the actual working path and the expected working path of the agricultural working vehicle during its movement, θ os It indicates the heading deviation between the actual working path and the expected working path of an agricultural vehicle during its movement.

5. The path tracking control method for unmanned agricultural operation vehicles based on immersion and invariance technology according to claim 4 is characterized in that: The path tracking deviation model based on lateral deviation and heading deviation is: Among them, σ is the front wheel steering angle of the agricultural vehicle, d(t) = sin(t) is the uncertainty including the external interference of the system, and t is the time constant.

6. The path tracking control method for an unmanned agricultural vehicle based on immersion and invariance technology according to claim 1 is characterized in that: Non-smooth composite controller Perform the inverse transformation to obtain the actual front wheel steering angle σ of the agricultural vehicle:

7. A system for implementing the path tracking control method for an unmanned agricultural vehicle based on immersion and invariance technology as described in any one of claims 1 to 6, characterized in that: include: The error processing module compares the actual operation path of the unmanned agricultural operation vehicle with the current nearest expected operation path, and calculates and processes it into lateral deviation and heading deviation; A model conversion module converts the path tracking deviation model of lateral deviation and heading deviation into a system model that is convenient for the design of adaptive parameter estimation law and non-smooth second-order sliding mode controller; A non-smooth control module is used to make the lateral deviation and heading deviation of the unmanned agricultural operation vehicle converge to zero quickly; An adaptive module is used to adaptively estimate the unknown parameters in the unmanned agricultural vehicle model to solve the system uncertainty problem; The inverse transformation module is used to perform an inverse transformation on the control input to solve the front wheel steering angle actually controlled.

8. A device for implementing the path tracking control method for an unmanned agricultural vehicle based on immersion and invariance technology as described in any one of claims 1 to 6, characterized in that: include: The host computer module is used to design the control program and record the experimental data; The path planning module designs the desired path for agricultural operation vehicles and calculates the lateral deviation and heading deviation based on the real-time position information of the vehicle; Satellite navigation and positioning module to obtain real-time location information of agricultural operation vehicles; Serial communication module, to realize the communication between various parts of agricultural operation vehicles; The hardware control module receives the lateral deviation and heading deviation information, calculates the control program, and outputs the steering angle control command; The vehicle control module executes the steering angle commands of the agricultural operation vehicle and realizes the vehicle's forward and turning functions.

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