Landing process control method and device for unmanned boat-aircraft platform collaborative system

Abstract

The present invention provides a landing process control method and device for an unmanned boat-aircraft platform collaborative system, relating to the field of control system technology. The method includes: during the landing of a target unmanned boat, controlling the target unmanned boat to maintain its attitude based on an unmanned boat attitude controller; and controlling the descent and soft landing of the target unmanned boat on the target unmanned boat based on a UAV vertical controller and a UAV contact controller. The UAV attitude controller and the UAV vertical controller are trained using an extended state observer; the UAV contact controller is derived based on the admittance control equation. This method ensures a highly robust soft landing of the UAV on the unmanned boat on the sea surface, effectively improving the intelligence level of the unmanned boat-aircraft platform and the autonomous exploration capabilities of offshore equipment.

Description

Technical Field

[0001] This invention relates to the field of control system technology, and in particular to a method and apparatus for controlling the landing process of an unmanned surface vessel-aircraft platform cooperative system. Background Art

[0002] Unmanned surface vehicles (USVs) can replace or collaborate with humans in complex, repetitive maritime tasks, such as environmental and climate monitoring, hydrological surveys, sea surface surveillance, search and rescue, and naval operations. In these tasks, USVs require comprehensive awareness of their surroundings. However, sensors mounted on USVs, such as cameras and radar, have low field of view, resulting in limited detection range. Therefore, they cannot effectively detect nearby floating objects, people in the water, or other mission targets, severely impacting USV safety and efficiency. To address this issue, researchers are using unmanned aerial vehicles (UAVs) to provide a wider aerial view and more accurate environmental awareness. This advantage has led to considerable interest in cooperative unmanned surface vehicle-aircraft platform systems in robotics, automation, and marine engineering.

[0003] In recent years, robotics and automation technologies have made rapid progress, leading to the development of various unmanned surface vessel (USV) collaborative systems suitable for different applications. In these systems, the landing of the UAV on a USV is one of the most critical technologies. The collaborative control system typically employs a cascaded structure consisting of a high-level navigation strategy and a low-level motion controller. The high-level strategy generates a path based on the mission objective and surrounding conditions, and then uses this path as a reference signal input to the motion controller.

[0004] Various control methods exist for USV motion controllers, ranging from classic PID control to advanced control algorithms such as sliding mode control, adaptive and backstepping control, and model predictive control. To handle external disturbances from waves and wind, neural network-based adaptive methods and disturbance observers have also been introduced into relevant controller designs. However, due to the highly complex physical mechanisms of USV fluid dynamics, the dynamics exhibit strong nonlinearity. Determining the USV model structure and parameters is quite difficult. Therefore, due to model errors and uncertainties, many of the aforementioned model-based control methods fail to achieve ideal control performance. Furthermore, due to low positioning accuracy and limitations of traditional controllers, unexpected oscillations occur in the USV's approach trajectory.

[0005] During UAV flight, especially during the landing phase, trajectory planning and motion control are crucial. Modeling the complex interactions between UAVs and USVs using traditional aerodynamic principles has long been a challenge. During hovering and landing, the USV's motion can change rapidly and become unstable, leading to inaccurate measurements and difficulty in achieving precise control. Similarly, existing models and control methods have limited accuracy, require complex calculations, or necessitate manual intervention to achieve stable hovering or landing. Summary of the Invention

[0006] This invention provides a landing process control method and device for an unmanned surface vessel (USV) platform collaborative system, which solves the defects of existing models and control methods that have limited accuracy, require complex calculations, or require manual assistance to achieve stable hovering or landing. It enables UAVs to perform a highly robust soft landing process on USVs on the sea surface, thereby improving the intelligence level of unmanned surface vessel platforms and the autonomous exploration capabilities of marine equipment.

[0007] This invention provides a landing process control method for an unmanned surface vessel-aircraft platform cooperative system, comprising the following steps:

[0008] During the landing of the target drone, the unmanned surface vessel (USV) is controlled to maintain its attitude based on the USV attitude controller.

[0009] The descent and soft landing process of the target UAV on the target unmanned surface vessel is controlled based on the UAV vertical controller and the UAV contact controller.

[0010] The unmanned surface vessel attitude controller and the unmanned aerial vehicle (UAV) vertical controller are obtained by training an extended state observer; the UAV contact controller is obtained based on the admittance control equation.

[0011] According to the present invention, a landing process control method for an unmanned surface vessel (USV)-aircraft platform cooperative system is provided, wherein the method for acquiring the USV attitude controller includes:

[0012] Based on the motion equations of the unmanned surface vessel, a first extended state observer is designed.

[0013] Based on the first extended state observer, determine the linear feedback control law;

[0014] Using a composite sine function as the disturbance observation input, and the integral of the absolute value of the error and the difference in coefficients of the first 5 Fourier series as the optimization objective function, the parameters of the first extended state observer are determined through an optimization algorithm, resulting in a trained unmanned surface vessel attitude controller.

[0015] According to the unmanned surface vessel-aircraft platform cooperative system landing process control method provided by the present invention, the method for acquiring the UAV vertical controller includes:

[0016] Based on the system dynamics equations of the UAV in the vertical direction, a second extended state observer is designed.

[0017] Based on the second extended state observer, determine the linear feedback control law;

[0018] Using the absolute value integral of the error as the optimization objective function, the parameters of the second extended state observer are determined through an optimization algorithm, resulting in a trained UAV vertical controller.

[0019] According to a landing process control method for an unmanned surface vessel-aircraft platform cooperative system provided by the present invention, the method for obtaining the UAV contact controller includes:

[0020] Based on the desired mass matrix, desired damping matrix, and desired stiffness matrix, the admittance control equations of the UAV are determined.

[0021] The UAV contact controller is obtained by adjusting the parameters of the desired mass matrix, the desired damping matrix, and the desired stiffness matrix in the admittance control equation based on the desired contact force only in the vertical direction.

[0022] According to the landing process control method of the unmanned surface vessel-aircraft platform cooperative system provided by the present invention, the initialization parameters of the first extended state observer are determined based on the bandwidth of the first extended state observer; and / or, the initialization parameters of the second extended state observer are determined based on the bandwidth of the second extended state observer.

[0023] According to the present invention, a landing process control method for an unmanned surface vessel-aircraft platform cooperative system is provided, wherein the optimization algorithm is a genetic algorithm.

[0024] The present invention also provides a landing process control device for an unmanned surface vessel-aircraft platform cooperative system, comprising the following modules:

[0025] The unmanned surface vessel (USV) control module is used to control the USV to maintain its attitude during the landing process, based on the USV attitude controller.

[0026] The UAV control module is used to control the descent and soft landing process of the target UAV on the target unmanned surface vessel based on the UAV vertical controller and the UAV contact controller;

[0027] The unmanned surface vessel attitude controller and the unmanned aerial vehicle (UAV) vertical controller are obtained by training an extended state observer; the UAV contact controller is obtained based on the admittance control equation.

[0028] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the landing process control method of any of the above-described unmanned surface vessel-aircraft platform cooperative systems.

[0029] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the landing process control method of the unmanned surface vessel-aircraft platform cooperative system as described above.

[0030] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the landing process control method of any of the above-described unmanned surface vessel-aircraft platform cooperative systems.

[0031] The unmanned surface vessel (USV)-aircraft platform collaborative system landing process control method and device provided by this invention obtains the USV attitude controller and the UAV vertical controller by training an extended state observer, and obtains the UAV contact controller based on the admittance control equation. During the landing process of the target UAV, the USV attitude controller controls the target USV to maintain its attitude, which can reduce the difficulty of the target UAV landing. Then, the UAV vertical controller and the UAV contact controller control the descent and soft landing process of the target UAV on the target USV, which can ensure that the UAV performs a highly robust soft landing process on the unmanned surface vessel, thereby effectively improving the intelligence level of the USV-aircraft platform and the autonomous exploration capability of the marine equipment. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating the landing process control method of the unmanned surface vessel-aircraft platform collaborative system provided by the present invention.

[0034] Figure 2 This is a schematic diagram of the overall process of the unmanned surface vessel-aircraft platform cooperative landing control design provided by the present invention.

[0035] Figure 3 This is a schematic diagram of the complete collaborative control process of the unmanned surface vessel-aircraft platform provided by the present invention.

[0036] Figure 4 This is a schematic diagram of the landing process control device of the unmanned surface vessel-aircraft platform collaborative system provided by the present invention.

[0037] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0039] Figure 1 This is a flowchart illustrating the landing process control method of the unmanned surface vessel-aircraft platform cooperative system provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0040] Step 100: During the landing of the target UAV, control the target UAV to maintain its attitude based on the UAV attitude controller.

[0041] Step 101: Based on the UAV vertical controller and UAV contact controller, control the descent and soft landing process of the target UAV on the target unmanned surface vessel.

[0042] Among them, the unmanned surface vessel attitude controller and the unmanned aerial vehicle vertical controller are obtained by training the extended state observer; the unmanned aerial vehicle contact controller is obtained based on the admittance control equation.

[0043] Specifically, the target drone is a drone that needs to land on the target unmanned surface vessel. In this embodiment of the invention, the target drone and the target unmanned surface vessel are controlled in a coordinated manner to complete the landing process of the target drone on the target unmanned surface vessel.

[0044] First, in this embodiment of the invention, the unmanned surface vessel (USV) needs to maintain a horizontal attitude during navigation, which requires controlling its attitude in three directions (yaw, pitch, and roll). To achieve this, an Extended State Observer (ESO) can be used to compensate for the system's nonlinearity, time-varying characteristics, and disturbances.

[0045] Therefore, an extended state observer can be trained based on the motion equation of the unmanned surface vessel (USV) on the sea surface to obtain the USV attitude controller. The extended state observer can estimate and compensate for disturbances in the system in real time, thereby improving the robustness and performance of the system and ensuring that the USV can maintain its attitude level during navigation even when there are disturbances in the system.

[0046] Generally speaking, during the landing process of a UAV on an unmanned surface vessel, its ideal flight attitude should be horizontal, that is, the roll angle and pitch angle should be close to 0. This attitude control process can be achieved by the UAV's own typical proportional-derivative (PD) controller.

[0047] Therefore, since the main motion of the UAV is in the vertical z-direction, a UAV vertical controller can be designed. The design of the UAV vertical controller is similar to that of the unmanned surface vessel attitude controller. An extended state observer can be trained based on the system dynamic equations of the UAV in the vertical direction to obtain the UAV vertical controller. The extended state observer can estimate the disturbances in the system in real time and compensate for them, thereby improving the robustness and performance of the system. This ensures that the UAV can remain unaffected by disturbances during descent even when there are disturbances in the system.

[0048] However, due to factors such as ocean waves, the attitude of unmanned surface vessels (USVs) will still have certain control errors. Therefore, it is necessary to design a compliant controller in the attitude direction of the USV to reduce contact forces and achieve a soft landing process.

[0049] During the contact process between the UAV and the unmanned surface vessel, a contact controller for the UAV can be obtained based on the admittance control equation. By controlling the difference between the desired contact force and the actual contact force, the state of the UAV can be adjusted, thereby ensuring the stability of the UAV during the landing process.

[0050] The unmanned surface vessel (USV)-aircraft platform collaborative system landing process control method provided by this invention obtains the USV attitude controller and the UAV vertical controller by training an extended state observer, and obtains the UAV contact controller based on the admittance control equation. During the landing process of the target UAV, the USV attitude controller controls the target USV to maintain its attitude, which can reduce the difficulty of the target UAV landing. Then, the UAV vertical controller and the UAV contact controller control the descent and soft landing process of the target UAV on the target USV, which can ensure that the UAV performs a highly robust soft landing process on the unmanned surface vessel, thereby effectively improving the intelligence level of the USV-aircraft platform and the autonomous exploration capability of the marine equipment.

[0051] According to the present invention, a landing process control method for an unmanned surface vessel (USV)-aircraft platform cooperative system is provided, wherein the method for acquiring the USV attitude controller includes:

[0052] Based on the motion equations of the unmanned surface vessel, a first extended state observer is designed.

[0053] Based on the first extended state observer, determine the linear feedback control law;

[0054] Using the composite sine function as the disturbance observation input, and the integral of the absolute value of the error and the difference of the coefficients of the first 5 Fourier series as the optimization objective function, the parameters of the first extended state observer are determined by the optimization algorithm, thus obtaining the trained unmanned surface vessel attitude controller.

[0055] Specifically, the equations of motion for the unmanned surface vessel on the sea are as follows:

[0056]

[0057] Here Indicates the speed of the unmanned surface vessel; Represents the acceleration of the unmanned surface vessel; Represents the control inputs of the unmanned surface vessel; These represent the inertial matrix, damping matrix, Coriolis force matrix, and centrifugal force matrix of the unmanned surface vessel, respectively. This represents the disturbances caused by the marine environment to the unmanned surface vessel.

[0058] To maintain its horizontal attitude during navigation, stabilization controllers need to be designed for its three attitude directions. Without coupled control, the controlled object in each attitude direction takes the following form:

[0059]

[0060] in, , , and These are the equivalent inertial parameters, equivalent damping parameters, equivalent Coriolis force and centrifugal force parameters, and equivalent disturbance parameters for a single-degree-of-freedom independent control scenario.

[0061] To simplify analysis and control design, the system state is defined as follows: , Right now , representing the angular velocity of the unmanned surface vessel. Representing angular acceleration, the nonlinear components in the system's dynamic equations along this attitude direction, its dynamic time-varying characteristics, and the disturbances present in the system during operation are integrated into a total disturbance. and will Expanding into a new system state The system then expands to the following form:

[0062]

[0063] In the formula, Represents the system state variable, in a physical sense as ; Indicates total disturbance The rate of change.

[0064] The extended system can be estimated and observed using a third-order linear extended state observer (first extended state observer) of the following form:

[0065]

[0066] Here System status ESO observations; This is the observation error; For observer gain, For the control input of the unmanned surface vessel. Definition:

[0067]

[0068] The following linear feedback control law can be used: , For the feedback gain coefficient:

[0069]

[0070] Considering the ESO system constructed for the system, under total disturbance Under the assumption of continuous differentiability, when and This ensures that the error system of the observer is asymptotically stable.

[0071] When designing the control parameters for ESO and error feedback law, the principle of separation of active disturbance rejection control (ADRC) is followed. That is, the extended state observer and error feedback law are designed separately based on the system response, and then the parameters are adjusted according to the complete closed-loop system.

[0072] Consider a closed-loop system, a linear ESO system, and an error feedback law. By adjusting appropriate parameters... and This ensures that the closed-loop system is asymptotically stable over a wide range. Understandably, the control parameters... and The adjustment principle can be similar to the corresponding PD controller tuning process, by improving... To accelerate response and reduce residuals, by improving To accelerate response and reduce overshoot, and The larger the system, the higher its relative stability.

[0073] In this controller, increase the observer gain. This can reduce the observation error in the extended state and improve the convergence speed of the observer, but the corresponding observer will be more sensitive to measurement noise.

[0074] The composite sine function can be used as the disturbance observation input, and the integral of the absolute value of the error and the difference of the coefficients of the first 5 Fourier series can be used as the optimization objective function. The parameters of the first extended state observer can be determined by the optimization algorithm, and the trained unmanned surface vessel attitude controller can be obtained.

[0075] Since the marine environment has sinusoidal disturbance characteristics on unmanned surface vessels, a composite sine function can be used as the disturbance observation input during simulation training.

[0076] To ensure that the obtained ESO has the ability to quickly estimate the system state, the integral of the absolute value of the error and the difference in coefficients of the first 5 Fourier series can be used as optimization indicators. :

[0077]

[0078] in, For time, and They are respectively and The observation error value that varies over time; This indicates the order of the Fourier series; To simulate the Fourier coefficients of a composite sine function; These are the Fourier coefficients output by the ESO observations; To optimize the weighting factors.

[0079] Optionally, the initialization parameters of the first extended state observer can be determined based on the bandwidth of the first extended state observer. For example, during the preliminary design, these parameters can be approximated. The problem of adjusting the observer parameters is transformed into the bandwidth of the observer in the first extended state. The choice.

[0080] Optionally, the optimization algorithm is a genetic algorithm. Genetic algorithms can be used for offline optimization in the ESO parameter design, with the optimization variables being... By using the above optimization index as the fitness function, and through multiple generations of iterative optimization, the optimal parameters are found.

[0081] According to the unmanned surface vessel-aircraft platform cooperative system landing process control method provided by the present invention, the method for obtaining the UAV vertical controller includes:

[0082] Based on the system dynamics equations of the UAV in the vertical direction, a second extended state observer is designed.

[0083] Based on the second extended state observer, determine the linear feedback control law;

[0084] By using the absolute value integral of the error as the optimization objective function, the parameters of the second extended state observer are determined through an optimization algorithm, resulting in a trained UAV vertical controller.

[0085] Specifically, as shown above, the main control direction of the UAV during landing is the z-axis (vertical). The dynamic equations of the UAV during landing are simplified as follows:

[0086]

[0087]

[0088]

[0089] In the formula, and These represent the drone's attitude and vertical speed, respectively. This represents the quality of the drone; It is the acceleration due to gravity; The rotor speed represents the total thrust generated by the winglets of the drone. ; Represents the lift coefficient; This represents the resultant force of all factors, excluding gravity and lift, generated when a drone lands on an unmanned surface vessel.

[0090] The nonlinear components, dynamic time-varying characteristics, and disturbances present in the system's dynamic equations in this direction are integrated into a total disturbance. and will Expanding into a new system state The system then expands to the following form:

[0091]

[0092] In the formula, Indicates the vertical speed of the drone , Indicates the vertical acceleration of the drone ; Indicates total disturbance The rate of change.

[0093] Similar to the design of unmanned surface vessels, the extended system is estimated and observed using a third-order linear ESO (second extended state observer) of the following form:

[0094]

[0095] Here System status ESO observations; This is the observation error; Observer gain. Definition:

[0096]

[0097] In the formula, This is the landing altitude of the drone.

[0098] The following linear feedback control law can be used: , For the feedback gain coefficient:

[0099]

[0100] Considering the ESO system constructed for the system, under total disturbance Under the assumption of continuous differentiability, when and This ensures that the error system of the observer is asymptotically stable.

[0101] When designing the control parameters for ESO and error feedback law, the principle of separation of ADRC is followed, that is, the extended state observer and error feedback law are designed separately based on the system response, and then the parameters are adjusted according to the complete closed-loop system.

[0102] Consider a closed-loop system, a linear ESO system, and an error feedback law. By adjusting appropriate parameters... and This ensures that the closed-loop system is asymptotically stable over a wide range. Understandably, the control parameters... and The adjustment principle can be similar to the corresponding PD controller tuning process, by improving... To accelerate response and reduce residuals, by improving To accelerate response and reduce overshoot, and The larger the system, the higher its relative stability.

[0103] In this controller, increase the observer gain. This can reduce the observation error in the extended state and improve the convergence speed of the observer, but the corresponding observer will be more sensitive to measurement noise.

[0104] The absolute value of the error can be integrated as the objective function for optimization. The parameters of the second extended state observer can be determined through an optimization algorithm, resulting in a well-trained UAV vertical controller.

[0105] Since the sinusoidal characteristics of UAV disturbances are not obvious, random disturbances can be used as disturbance observation inputs during simulation training. This allows the optimization index to be based solely on the integral of the absolute value of the error. :

[0106]

[0107] in, For time, and They are respectively and The observation error value that varies over time; To optimize the weighting factors.

[0108] Optionally, the initialization parameters of the second extended state observer can be determined based on the bandwidth of the second extended state observer. For example, during the preliminary design, these parameters can be approximated. The problem of adjusting the observer's parameters is transformed into the bandwidth of the second extended state observer. The choice.

[0109] Optionally, the optimization algorithm is a genetic algorithm. Genetic algorithms can be used for offline optimization in the ESO parameter design, with the optimization variables being... By using the above optimization index as the fitness function, and through multiple generations of iterative optimization, the optimal parameters are found.

[0110] According to the present invention, a landing process control method for an unmanned surface vessel-aircraft platform cooperative system includes the following method for obtaining the UAV contact controller:

[0111] Based on the desired mass matrix, desired damping matrix, and desired stiffness matrix, the admittance control equations of the UAV are determined.

[0112] Based on the desired contact force in the vertical direction only, the parameters of the desired mass matrix, desired damping matrix, and desired stiffness matrix of the admittance control equation are adjusted to obtain the UAV contact controller.

[0113] Specifically, the admittance control equations of the UAV can be determined based on the desired mass matrix, desired damping matrix, and desired stiffness matrix, as shown in the following equation:

[0114]

[0115] in, These are the expected mass matrix, expected damping matrix, and expected stiffness matrix of a positive definite symmetric unmanned aerial vehicle system, respectively. It is the desired contact location; The desired contact force and torque for the drone during landing; The contact force and torque between the actual drone and the unmanned surface vessel.

[0116] The desired mass matrix is ​​used to control the dynamic response of the UAV; the desired damping matrix is ​​used to control the damping characteristics of the UAV and reduce oscillation and overshoot; the desired stiffness matrix is ​​used to determine the compliance of the UAV with contact forces.

[0117] The Cartesian variables in the admittance control equations are independent of each other. Given the control objective of the landing process, the only control degree of freedom is the landing vertical z-axis. Besides the landing vertical z-axis, due to the influence of the actual environment, the control adjustments in other directions need to be sufficiently small to avoid generating excessive contact forces. Therefore, Select as , here The preset desired contact clamping force.

[0118] To achieve compliant control, The selection of these matrices is crucial for the stability of the system. When setting these matrices, the virtual mass, virtual damping, and virtual stiffness parameters in each matrix need to be adjusted according to the characteristics of the contact process.

[0119] The control parameters are set as follows:

[0120]

[0121]

[0122] in, For virtual mass parameters of the contact process; For virtual damping parameters in the contact process; This is the feedback gain coefficient.

[0123] Expected quality matrix The virtual mass represents the system's response inertia in each direction; increasing the virtual mass makes the system's response more stable when subjected to external disturbances; the desired damping matrix... The damping effect of the control system is used to reduce system oscillations; an appropriate damping value helps eliminate unwanted oscillations at contact. (Desired stiffness matrix) The rigidity of the control system is crucial; excessive stiffness can lead to an overly rigid system, which is detrimental to compliant control. To generate compliant motion under contact action, the desired stiffness matrix of the system is required. It is set to near zero.

[0124] By using admittance control equations, the UAV can achieve compliant control during landing. By adjusting the desired mass matrix, desired damping matrix, and desired stiffness matrix, precise control of the vertical contact force can be ensured during contact. This also avoids generating excessive contact forces in other directions. By optimizing these parameters, the UAV contact controller can ensure a smooth and stable landing when the UAV contacts the unmanned surface vessel.

[0125] The following examples, using specific application scenarios, further illustrate the landing process control method of the unmanned surface vessel-aircraft platform cooperative system provided by the present invention.

[0126] Figure 2 This is a schematic diagram of the overall process of the unmanned surface vessel-aircraft platform cooperative landing control design provided by the present invention, as shown below. Figure 2 As shown, it includes three main modules: modeling of the unmanned surface vessel-aircraft platform collaborative system; design of USV attitude control law; and design of UAV landing control law.

[0127] Figure 3 This is a schematic diagram of the complete collaborative control process of the unmanned surface vessel-aircraft platform provided by the present invention. The following is in conjunction with... Figures 2-3 The modules of this embodiment will be described.

[0128] S1: Modeling of Unmanned Surface Vessel-Aircraft Platform Collaborative System

[0129] The design process is explained as follows:

[0130] This study focuses on the landing control problem of a UAV (Unmanned Aerial Vehicle) on a USV (Unmanned Aerial Vehicle), specifically achieving a robust soft landing for the UAV on the USV platform. The equations of motion for the UAV are as follows:

[0131]

[0132]

[0133] here, This represents the Cartesian position and velocity of the UAV in the global coordinate system; The term representing gravity for the UAV; This represents the total thrust generated by the UAV's fins; This represents the resultant force of all factors, excluding gravity and lift, generated when a UAV lands on a USV.

[0134] The attitude equations of the UAV are as follows:

[0135]

[0136] here, These represent the yaw angles of the UAV. Pitch angle and roll angle ; Represents the angular velocity in the UAV's own coordinate system; This represents the output torque generated by the four blades; The moment of inertia matrix representing a quadcopter; Represents the mapping between angular velocity and Euler angle change rate:

[0137]

[0138] Lift and output torque With airfoil rotation speed They are related, and the relationship between them is as follows:

[0139]

[0140] Here These represent the components of the output torque on the x, y, and z axes, respectively. and These represent the lift coefficient and drag coefficient, respectively. This represents the length of the rotor; in this study, all rotors have the same length. and The transformation relationship between them is determined by the rotation matrix. connect:

[0141]

[0142]

[0143] Here Representing cosine calculation ; Represents sine calculation . The rotation sequence of the axes is ZYX.

[0144] The equations of motion for the USV on the sea surface are as follows:

[0145]

[0146] Here Indicates the speed of the USV; Represents the acceleration of the USV; Represents the control input of the USV; These represent the inertia matrix, damping matrix, Coriolis force matrix, and centrifugal force matrix of the USV, respectively. This represents the disturbances caused by the sea surface environment to the USV.

[0147] Generally, during a UAV's descent onto a USV over the sea, its ideal flight attitude should be horizontal, meaning its roll and pitch angles should be close to zero. This attitude control can be achieved by the UAV's typical PD controller. Therefore, its primary motion is in the vertical z-direction, and the UAV's dynamic equations during landing can be simplified as follows:

[0148]

[0149]

[0150]

[0151] Here and These represent the UAV's attitude and vertical velocity, respectively. This represents the quality of the UAV.

[0152] S2: USV Attitude Control Law Design

[0153] As analyzed in Section S1, the control objective of the USV is to maintain its horizontal attitude during navigation. Therefore, stabilization controllers need to be designed for its three attitude directions. Without coupled control, the control object in each attitude direction is as follows:

[0154]

[0155] in, , , and These are the equivalent inertial parameters, equivalent damping parameters, equivalent Coriolis force and centrifugal force parameters, and equivalent disturbance parameters for a single-degree-of-freedom independent control scenario.

[0156] remember ,Right now , representing the angular velocity of the unmanned surface vessel, integrates the nonlinear components, dynamic time-varying characteristics, and disturbances present in the system's dynamic equations along this attitude direction into a total disturbance. and will Expanding into a new system state The system then expands to the following form:

[0157]

[0158] The extended system is estimated and observed using the following form of third-order linear ESO:

[0159]

[0160] Here System status ESO observations; This is the observation error; Observer gain. Definition:

[0161]

[0162] And use the following linear feedback control law form:

[0163]

[0164] The controller constructed in this way has the following properties:

[0165] 1. Considering the ESO system constructed for the system, under the total disturbance... Under the assumption of continuous differentiability, when and This ensures that the error system of the observer is asymptotically stable.

[0166] 2. Consider the closed-loop system, the linear ESO system, and the error feedback law. Adjust appropriate parameters... and This ensures that the closed-loop system is asymptotically stable over a wide range.

[0167] In this controller, increase the observer gain. This can reduce the observation error in the extended state and improve the convergence speed of the observer, but the corresponding observer will be more sensitive to measurement noise. In the preliminary design, an approximate selection can be used... The problem of observer parameter tuning is transformed into observer bandwidth. The choice.

[0168] At the same time, control parameters and The adjustment principle can be similar to the corresponding PD controller tuning process, by improving... To accelerate response and reduce residuals, by improving To accelerate response and reduce overshoot, and The larger the system, the higher its relative stability.

[0169] In designing the control parameters for the ESO and error feedback law, the principle of separation of ADRC (Advanced Dependent Relationship Control) is applied. First, the extended state observer and error feedback law are designed separately based on the system response. Then, the parameters are adjusted based on the complete closed-loop system. A genetic algorithm is used for offline optimization in the ESO parameter design, with the optimization variables being... .

[0170] Since the marine environment's impact on USVs exhibits sinusoidal disturbance characteristics, a composite sine function is used as the disturbance observation input during simulation training. To ensure that the obtained ESO (Effective State Occurrence) can quickly estimate the system state, the integral of the absolute value of the error and the difference in coefficients of the first five Fourier series are used as optimization indices. :

[0171]

[0172] in, To simulate the Fourier coefficients of a composite sine function; These are the Fourier coefficients output by the ESO observations; To optimize the weighting factors.

[0173] S3: UAV Landing Control Law Design

[0174] As analyzed in Section S1, the main control direction of the UAV during landing is the z-vertical direction. The nonlinear part of the system dynamic equations in this direction, the dynamic time-varying characteristics, and the disturbances existing in the system during operation are integrated into the total disturbance. and will Expanding into a new system state The system then expands to the following form:

[0175]

[0176] Similar to the USV design, the extended system is estimated and observed using a third-order linear ESO of the following form:

[0177]

[0178] Here System status ESO observations; This is the observation error; Observer gain. Definition:

[0179]

[0180] in, Let UAV be the landing altitude, and use the following linear feedback control law:

[0181]

[0182] This completes the controller design for the vertical UAV. Its parameter tuning process can follow a similar process to that of the USV attitude controller.

[0183] Since the sinusoidal characteristics of UAV disturbances are not obvious enough, only the integral of the absolute value of the error is used as the optimization index. :

[0184]

[0185] However, due to factors such as ocean waves, the attitude of the USV will still have some control error. Therefore, it is necessary to design a compliant controller in the attitude direction of the UAV to reduce contact forces and achieve a soft landing process for the UAV.

[0186] This invention utilizes admittance control during the UAV contact process to achieve compliant control in the attitude direction. The admittance control equation for the UAV is as follows:

[0187]

[0188] in, The desired contact force and torque during UAV landing; The actual contact force and torque between the UAV and USV; Let be the mass, damping, and stiffness matrices of a positive definite symmetric desired UAV system, respectively.

[0189] The Cartesian variables in admittance control are independent of each other. Given the control objective of the landing process, the only control degree of freedom is the landing vertical z-axis. Apart from the landing vertical z-axis, due to the influence of the actual environment, the control adjustments in other directions need to be sufficiently small to avoid generating excessive contact forces. Therefore, Select as , here The preset desired contact clamping force.

[0190] The control parameters are set as follows:

[0191]

[0192]

[0193] in, For virtual mass parameters of the contact process; For virtual damping parameters in the contact process; It is the gain factor.

[0194] The design of the controller for the coordinated landing process of the unmanned surface vessel and the aircraft platform has now been completed.

[0195] The landing process control device of the unmanned surface vessel-aircraft platform cooperative system provided by the present invention is described below. The landing process control device of the unmanned surface vessel-aircraft platform cooperative system described below can be referred to in correspondence with the landing process control method of the unmanned surface vessel-aircraft platform cooperative system described above.

[0196] Figure 4 This is a schematic diagram of the landing process control device of the unmanned surface vessel-aircraft platform cooperative system provided by the present invention, as shown below. Figure 4 As shown, the device includes the following modules:

[0197] The unmanned surface vessel (USV) control module 400 is used to control the USV to maintain its attitude during the landing process of the USV, based on the USV attitude controller.

[0198] The UAV control module 410 is used to control the descent and soft landing process of the target UAV on the target unmanned surface vessel based on the UAV vertical controller and the UAV contact controller.

[0199] The attitude controller of the unmanned surface vessel and the vertical controller of the unmanned aerial vehicle are obtained by training an extended state observer; the contact controller of the unmanned aerial vehicle is obtained based on the admittance control equation.

[0200] According to the present invention, a landing process control device for an unmanned surface vessel (USV)-aircraft platform cooperative system includes a method for acquiring the USV attitude controller, comprising:

[0201] Based on the motion equations of the unmanned surface vessel, a first extended state observer is designed.

[0202] Based on the first extended state observer, determine the linear feedback control law;

[0203] Using the composite sine function as the disturbance observation input, and the integral of the absolute value of the error and the difference of the coefficients of the first 5 Fourier series as the optimization objective function, the parameters of the first extended state observer are determined by the optimization algorithm, thus obtaining the trained unmanned surface vessel attitude controller.

[0204] According to the unmanned surface vessel-aircraft platform cooperative system landing process control device provided by the present invention, the method for obtaining the UAV vertical controller includes:

[0205] Based on the system dynamics equations of the UAV in the vertical direction, a second extended state observer is designed.

[0206] Based on the second extended state observer, determine the linear feedback control law;

[0207] By using the absolute value integral of the error as the optimization objective function, the parameters of the second extended state observer are determined through an optimization algorithm, resulting in a trained UAV vertical controller.

[0208] According to the present invention, a landing process control device for an unmanned surface vessel-aircraft platform cooperative system includes a method for obtaining the UAV contact controller, comprising:

[0209] Based on the desired mass matrix, desired damping matrix, and desired stiffness matrix, the admittance control equations of the UAV are determined.

[0210] Based on the desired contact force in the vertical direction only, the parameters of the desired mass matrix, desired damping matrix, and desired stiffness matrix of the admittance control equation are adjusted to obtain the UAV contact controller.

[0211] According to the unmanned surface vessel-aircraft platform cooperative system landing process control device provided by the present invention, the initialization parameters of the first extended state observer are determined based on the bandwidth of the first extended state observer; and / or, the initialization parameters of the second extended state observer are determined based on the bandwidth of the second extended state observer.

[0212] According to the present invention, the landing process control device of the unmanned surface vessel-aircraft platform cooperative system uses a genetic algorithm for optimization.

[0213] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 5 As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute a landing process control method for the unmanned surface vessel-aircraft platform cooperative system, which includes:

[0214] During the landing of the target drone, the unmanned surface vessel (USV) is controlled to maintain its attitude based on the USV attitude controller.

[0215] Based on the UAV vertical controller and UAV contact controller, the descent and soft landing process of the target UAV on the target unmanned surface vessel is controlled;

[0216] The attitude controller of the unmanned surface vessel and the vertical controller of the unmanned aerial vehicle are obtained by training an extended state observer; the contact controller of the unmanned aerial vehicle is obtained based on the admittance control equation.

[0217] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0218] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is able to execute the unmanned surface vessel-aircraft platform cooperative system landing process control method provided by the above methods, the method comprising:

[0219] During the landing of the target drone, the unmanned surface vessel (USV) is controlled to maintain its attitude based on the USV attitude controller.

[0220] Based on the UAV vertical controller and UAV contact controller, the descent and soft landing process of the target UAV on the target unmanned surface vessel is controlled;

[0221] The attitude controller of the unmanned surface vessel and the vertical controller of the unmanned aerial vehicle are obtained by training an extended state observer; the contact controller of the unmanned aerial vehicle is obtained based on the admittance control equation.

[0222] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the unmanned surface vessel-aircraft platform cooperative system landing process control method provided by the above methods, the method comprising:

[0223] During the landing of the target drone, the unmanned surface vessel (USV) is controlled to maintain its attitude based on the USV attitude controller.

[0224] Based on the UAV vertical controller and UAV contact controller, the descent and soft landing process of the target UAV on the target unmanned surface vessel is controlled;

[0225] The attitude controller of the unmanned surface vessel and the vertical controller of the unmanned aerial vehicle are obtained by training an extended state observer; the contact controller of the unmanned aerial vehicle is obtained based on the admittance control equation.

[0226] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0227] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0228] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A landing process control method for an unmanned surface vessel-aircraft platform cooperative system, characterized in that, include: During the landing of the target drone, the unmanned surface vessel (USV) is controlled to maintain its attitude based on the USV attitude controller. The descent and soft landing process of the target UAV on the target unmanned surface vessel is controlled based on the UAV vertical controller and the UAV contact controller. The unmanned surface vessel attitude controller and the unmanned aerial vehicle (UAV) vertical controller are obtained by training an extended state observer; the UAV contact controller is obtained based on the admittance control equation. The methods for acquiring the UAV vertical controller include: Based on the system dynamics equations of the UAV in the vertical direction, a second extended state observer is designed. Based on the second extended state observer, determine the linear feedback control law; Using the absolute value of the error integral as the optimization objective function, the parameters of the second extended state observer are determined through an optimization algorithm, resulting in a trained UAV vertical controller. The methods for obtaining the UAV contact controller include: Based on the desired mass matrix, desired damping matrix, and desired stiffness matrix, the admittance control equations of the UAV are determined. The UAV contact controller is obtained by adjusting the parameters of the desired mass matrix, the desired damping matrix, and the desired stiffness matrix in the admittance control equation based on the desired contact force only in the vertical direction.

2. The landing process control method of the unmanned surface vessel-aircraft platform cooperative system according to claim 1, characterized in that, The methods for acquiring the attitude controller of the unmanned surface vessel include: Based on the motion equations of the unmanned surface vessel, a first extended state observer is designed. Based on the first extended state observer, determine the linear feedback control law; Using a composite sine function as the disturbance observation input, and the integral of the absolute value of the error and the difference of the coefficients of the first 5 Fourier series as the optimization objective function, the parameters of the first extended state observer are determined by the optimization algorithm, thus obtaining the trained unmanned surface vessel attitude controller.

3. The landing process control method of the unmanned surface vessel-aircraft platform cooperative system according to claim 1 or 2, characterized in that, The initialization parameters of the first extended state observer are determined based on the bandwidth of the first extended state observer; and / or, the initialization parameters of the second extended state observer are determined based on the bandwidth of the second extended state observer.

4. The landing process control method of the unmanned surface vessel-aircraft platform cooperative system according to claim 1 or 2, characterized in that, The optimization algorithm is a genetic algorithm.

5. A landing process control device for an unmanned surface vessel-aircraft platform collaborative system, characterized in that, include: The unmanned surface vessel (USV) control module is used to control the USV to maintain its attitude during the landing process, based on the USV attitude controller. The UAV control module is used to control the descent and soft landing process of the target UAV on the target unmanned surface vessel based on the UAV vertical controller and the UAV contact controller; The unmanned surface vessel attitude controller and the unmanned aerial vehicle (UAV) vertical controller are obtained by training an extended state observer; the UAV contact controller is obtained based on the admittance control equation. The UAV control module is specifically used for: Based on the system dynamics equations of the UAV in the vertical direction, a second extended state observer is designed. Based on the second extended state observer, determine the linear feedback control law; Using the absolute value of the error integral as the optimization objective function, the parameters of the second extended state observer are determined through an optimization algorithm, resulting in a trained UAV vertical controller. Based on the desired mass matrix, desired damping matrix, and desired stiffness matrix, the admittance control equations of the UAV are determined. The UAV contact controller is obtained by adjusting the parameters of the desired mass matrix, the desired damping matrix, and the desired stiffness matrix in the admittance control equation based on the desired contact force only in the vertical direction.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the landing process control method of the unmanned surface vessel-aircraft platform cooperative system as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the landing process control method of the unmanned surface vessel-aircraft platform cooperative system as described in any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the landing process control method of the unmanned surface vessel-aircraft platform cooperative system as described in any one of claims 1 to 4.

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