Flying operation robot contact force tracking control method based on variable stiffness admittance

Abstract

The invention provides a flight operation robot contact force tracking control method based on variable stiffness admittance. The method comprises the following steps: S1, establishing a dynamic model of a flight operation robot system; s2, designing a self-adaptive variable stiffness admittance model, generating an expected reference trajectory by dynamically adjusting a stiffness coefficient, and compensating the influence of unknown environmental parameters; s3, designing a nonlinear disturbance observer to estimate system disturbance, and providing disturbance compensation for the pose controller; s4, a pose controller is designed based on the obstacle Lyapunov function, reference trajectory tracking is achieved, and the position error is strictly restrained to be within a safe range; s5, the rotating speed of each rotor wing of the flying operation robot is calculated through control input; the flying operation robot is controlled to realize stable contact force tracking; according to the method, uncertainty and contact disturbance in an unknown dynamic environment can be effectively dealt with, and the contact force tracking precision and the operation safety of the flying operation robot are improved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a contact force tracking control method for flight operation robots based on variable stiffness admittance. Background Technology

[0002] Unmanned aerial vehicles (UAVs) are characterized by their high mobility and flexibility, and have received widespread attention and research in recent years for the inspection and maintenance of high-altitude infrastructure. Flying robots, as a revolutionary type of aerial robot consisting of a flight platform and a robotic arm, can gain the ability to physically interact with their environment by being equipped with active operating mechanisms, playing a vital role in complex scenarios such as industrial inspection, agricultural monitoring, and disaster relief.

[0003] In the process of aerial work robots performing contact inspection tasks, a stable control system and high-precision control of the end effector are generally considered key components of the entire operation. However, the following technical challenges exist when performing physical interactions in dynamic environments:

[0004] First, the unpredictability of target motion in unknown dynamic environments introduces significant uncertainties. Traditional fixed-parameter admittance models struggle to maintain stable contact force tracking performance when faced with dynamic changes in environmental position. Existing variable admittance methods are primarily designed for static environments and lack effective compensation mechanisms for uncertainties in dynamic environments.

[0005] Secondly, the instantaneous contact force between the airborne active working mechanism and the object will have a strong disturbance effect on the flying robot system, posing a great challenge to the safety and stability of dynamic grasping. Although existing studies have used methods such as sliding mode control and adaptive control to deal with disturbances, these methods usually assume that the disturbance changes slowly or require prior knowledge of the upper bound of the disturbance, which has limitations in practical applications.

[0006] Third, during dynamic contact, the need for rapid transient response and large control torque may lead to controller saturation. Simultaneously, to ensure operational safety, position tracking errors must be strictly constrained within a reasonable range to prevent contact loss or collisions. Existing control methods often require excessively high control gain to overcome disturbances, which can cause system oscillations and affect control performance.

[0007] Therefore, for complex nonlinear strongly coupled flying robot systems, there is an urgent need to explore efficient control methods that can cope with the uncertainties of unknown dynamic environments without disturbing prior knowledge, achieve stable contact force tracking, and strictly constrain position errors to ensure the safety and success rate of dynamic operations. Summary of the Invention

[0008] This invention proposes a contact force tracking control method for flight operation robots based on variable stiffness admittance, which can effectively cope with uncertainties and contact disturbances in unknown dynamic environments, and improve the contact force tracking accuracy and operational safety of flight operation robots.

[0009] The present invention adopts the following technical solution.

[0010] A contact force tracking control method for a flying robot based on variable stiffness admittance is proposed. When dealing with physical interaction tasks in unknown dynamic environments, this method generates a reference trajectory through an adaptive variable stiffness admittance model and combines it with a pose controller based on an obstacle Lyapunov function to achieve stable contact force tracking control. This effectively addresses environmental uncertainties and contact disturbances, improving the operational safety and control accuracy of the flying robot in dynamic environments. The method includes the following steps:

[0011] Step S1: Establish a dynamic model of the flying operation robot system, considering the effects of the quadcopter flight platform, rigid tools, and contact forces;

[0012] Step S2: Design an adaptive variable stiffness admittance model, and generate the desired reference trajectory by dynamically adjusting the stiffness coefficient to compensate for the influence of unknown environmental parameters;

[0013] Step S3: Design a nonlinear disturbance observer to estimate system disturbances and provide disturbance compensation for the pose controller;

[0014] Step S4: Design a pose controller based on the obstacle Lyapunov function to achieve reference trajectory tracking and strictly constrain the position error within a safe range;

[0015] Step S5: Calculate the rotational speed of each rotor of the flying operation robot by controlling the input; control the flying operation robot to achieve stable contact force tracking.

[0016] The flying robot includes a quadcopter flight platform, with a rigidly fixed control stick for contact operations at the flight platform. The end of the control stick is equipped with a device for real-time measurement of the contact force with the target. Force sensor.

[0017] The contact target in the dynamic environment is a rigid vertical plane capable of linear motion along the x-axis. During the experiment, the flying robot is controlled to approach this plane along the x-axis. Once the force sensor detects the contact force, the robot's pose controller intervenes and tracks and controls the contact force, maintaining it at the desired value. .

[0018] The control lever is equipped with unmanned robot tools.

[0019] Considering the influence of internal and external disturbances on the flying operation robot during contact operations, step S1 uses the Newton-Euler equations to establish a system dynamics model to accurately describe the position dynamics and attitude dynamics characteristics; the position error of the flying operation robot and the measured external force are used as inputs to the admittance model, and the parameters are divided to design a variable admittance controller; specifically, the following steps are included:

[0020] Step S11: Define the inertial coordinate system and body coordinate system ,in Located at the center of gravity of the flying operation robot;

[0021] Step S12: Let The center of gravity of the flying operation robot is in The position in the middle, For the body coordinate system of the flying operation robot Euler angles in;

[0022] Step S13: Establish position dynamics equations:

[0023] in, The nominal total mass of the flying operation robot. It is the acceleration due to gravity. , For generalized force input, For the lumped disturbance of the location loop;

[0024] Step S14: Establish attitude dynamics equations:

[0025] in, The nominal inertia matrix, The Coriolis force matrix, For generalized torque input, For the lumped disturbance of the attitude loop;

[0026] Step S15: Represent the position and attitude dynamics in a unified matrix form:

[0027]

[0028] in, , It is a positive definite inertial matrix. For Coriolis force and centrifugal force terms, The gravity vector For generalized input, External disturbance;

[0029] Step S16: Establish an environmental contact force model, assuming the environmental dynamics are a linear spring system:

[0030]

[0031] in, For the combined equivalent stiffness of the environment and the end effector, This refers to the location where the tool is unable to apply force when it first comes into contact with the environment. It is the contact force.

[0032] To address the uncertainty caused by the unpredictable motion of a target in an unknown dynamic environment, step S2 designs an adaptive variable stiffness admittance model. This model dynamically adjusts the stiffness coefficient based on force and position tracking errors, generating an accurate reference trajectory to maintain stable contact force. It also designs the adaptive rate of the inertia and stiffness parameters of the variable admittance controller and calculates the damping parameter control rate based on the specified performance controller. Specifically, this includes the following steps:

[0033] Step S21: Design an adaptive variable stiffness admittance model:

[0034]

[0035] in, , , These are the mass, damping, and stiffness parameters of the admittance model, respectively. For variable stiffness coefficient, The instruction location generated for the admittance model. For reference position, For contact force tracking error, For reference contact force, For force gain;

[0036] Step S22: Definition Calculate the instruction location:

[0037]

[0038] Step S23: Define position tracking error The result is obtained from the contact force model:

[0039]

[0040] Step S24: Substitute the result and its derivative from step S23 into step S21 to obtain the force error differential equation:

[0041]

[0042] in, Includes unknown parameters related to environmental dynamics and state variables that are difficult to obtain precisely:

[0043]

[0044] in , Feedback items that can be directly measured and used for compensation;

[0045] Step S25: Design the adaptive variable stiffness coefficient:

[0046]

[0047] Step S26: Design an adaptive law to estimate the upper bound of the unknown perturbation:

[0048]

[0049] in, , For positive integers, This is the upper bound for the disturbance estimation;

[0050] Step S27: Design the nonlinear function :

[0051]

[0052] in It is a positive constant;

[0053] Step S28: To mitigate the influence of force sensor noise, a tracking differentiator (STD) based on the sigmoid function is used to measure the contact force. It is then filtered before being used in the control loop.

[0054] To overcome disturbances caused by physical contact and strictly constrain position errors within a reasonable range to prevent mission failure, step S3 designs a pose controller based on a nonlinear disturbance observer and an obstacle Lyapunov function, achieving stable tracking performance without requiring prior knowledge of the disturbance. Combining the given energy tank rules with the variations in inertia, stiffness, and damping parameters in the admittance controller, the passive parameters of the variable admittance controller are designed. Specifically, this includes the following steps:

[0055] Step S31: Design a nonlinear perturbation observer:

[0056]

[0057] in, For disturbance The observed values, For the observer state, Gain is a positive constant.

[0058] Step S32: Define the observer estimation error:

[0059]

[0060] Step S33: Obtain the derivative of the estimation error from steps S31 and S32:

[0061]

[0062] Step S34: Let for The minimum eigenvalue is obtained from step S33, which yields the upper bound of the estimation error:

[0063]

[0064] in, This is the upper bound of the rate of change of the disturbance;

[0065] Step S35: Define the upper bound of the pose controller observer estimation error: .

[0066] Step S4 utilizes the obstacle Lyapunov function to strictly constrain the position tracking error, ensuring that the flying operation robot maintains stable contact with the target in the environment during the contact operation process, preventing contact loss or collision due to excessive error; a variable boundary energy tank is designed, and boundary change adaptive rate and energy flow exchange rules are proposed to realize the safety supervision of the sampling process of the flying operation robot, specifically including the following steps;

[0067] Step S41: Define the state error ,in For command pose;

[0068] Step S42: Rewrite the system dynamics model in state-space form:

[0069]

[0070] Step S43: Design the logarithmic barrier Lyapunov function:

[0071]

[0072] in, for The constraint boundaries define the maximum permissible position tracking error;

[0073] Step S44: Calculation Time derivative:

[0074]

[0075] in, ,according to It can be known Hengchengli;

[0076] Step S45: Introduce virtual control variables:

[0077]

[0078] in ;

[0079] Step S46: Construct the Lyapunov function:

[0080]

[0081] in, , for The estimated value;

[0082] Step S47: Calculation The derivative:

[0083]

[0084] in, ;

[0085] Step S48: Design controller inputs:

[0086]

[0087] in, , It is a positive number;

[0088] Step S49: Design the adaptive law:

[0089] in It is a positive constant;

[0090] Step S50: Control input from step S48 Calculate the reference roll and pitch angles:

[0091]

[0092] in This is the desired yaw angle.

[0093] Step S5 converts the control input into rotational speed commands for the four rotors of the flying operation robot using a control allocation algorithm, thereby achieving precise control of the flying operation robot; specifically:

[0094] Step S51: Obtain the system control force from step S48. and control torque ;

[0095] Step S52: Based on the relationship between the thrust and torque of the four rotors, calculate the rotational speeds of the four rotors:

[0096]

[0097] in, For the first The square of the rotor speed, and These are the thrust coefficient and the moment coefficient, respectively. The distance between the centers of the symmetrical rotors;

[0098] Step S53: Transfer the calculated rotor speed command The data is sent to the flight controller of the flying operation robot to control the flying operation robot to achieve stable contact force tracking.

[0099] The force sensor at the end of the control lever is a one-dimensional force sensor.

[0100] The system parameters of the flying operation robot are shown in Table 1:

[0101] Table 1. Parameters of the Flying Operation Robot System

[0102]

[0103] Compared with the prior art, the present invention has the following advantages:

[0104] (1) A novel adaptive variable stiffness admittance model is proposed, which can generate a reference trajectory in an unknown dynamic environment to maintain a stable contact force. By dynamically adjusting the stiffness coefficient through force and position tracking errors, the influence of unpredictable target motion is effectively mitigated, and the force tracking accuracy is significantly improved compared with existing methods.

[0105] (2) A pose controller based on the obstacle Lyapunov function was designed, which can achieve stable tracking under strong disturbances, while strictly constraining the position error within a reasonable range. Without the need for prior knowledge of the disturbance, the state variables converge to the constraint range in a finite time, avoiding the introduction of excessive control gain.

[0106] (3) The proposed admittance model and pose control scheme do not require prior knowledge of the uncertainty upper bound and can converge in a finite time, which improves the robustness and applicability of the system.

[0107] (4) By effectively estimating and compensating for disturbances caused by physical contact through a nonlinear disturbance observer, and by combining the adaptive law to estimate the upper bound of the residual disturbance, the overestimation of gain and system oscillation are avoided, thereby improving the operational safety and control accuracy of the flying robot in dynamic environments. Attached Figure Description

[0108] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0109] Appendix Figure 1 This is a schematic diagram of the control architecture flow according to an embodiment of the present invention;

[0110] Appendix Figure 2 This is a schematic diagram of the trajectory tracking error of the flight operation robot in an embodiment of the present invention;

[0111] Appendix Figure 3 This is a schematic diagram illustrating the contact force tracking effect of the flying operation robot in an embodiment of the present invention. Detailed Implementation

[0112] As shown in the figure, a contact force tracking control method for a flying robot based on variable stiffness admittance is presented. When dealing with physical interaction tasks in unknown dynamic environments, this method generates a reference trajectory through an adaptive variable stiffness admittance model and combines it with a pose controller based on an obstacle Lyapunov function to achieve stable contact force tracking control. This effectively addresses environmental uncertainties and contact disturbances, improving the operational safety and control accuracy of the flying robot in dynamic environments. The method includes the following steps:

[0113] Step S1: Establish a dynamic model of the flying operation robot system, considering the effects of the quadcopter flight platform, rigid tools, and contact forces;

[0114] Step S2: Design an adaptive variable stiffness admittance model, and generate the desired reference trajectory by dynamically adjusting the stiffness coefficient to compensate for the influence of unknown environmental parameters;

[0115] Step S3: Design a nonlinear disturbance observer to estimate system disturbances and provide disturbance compensation for the pose controller;

[0116] Step S4: Design a pose controller based on the obstacle Lyapunov function to achieve reference trajectory tracking and strictly constrain the position error within a safe range;

[0117] Step S5: Calculate the rotational speed of each rotor of the flying operation robot by controlling the input; control the flying operation robot to achieve stable contact force tracking.

[0118] The flying robot includes a quadcopter flight platform, with a rigidly fixed control stick for contact operations at the flight platform. The end of the control stick is equipped with a device for real-time measurement of the contact force with the target. Force sensor.

[0119] The contact target in the dynamic environment is a rigid vertical plane capable of linear motion along the x-axis. During the experiment, the flying robot is controlled to approach this plane along the x-axis. Once the force sensor detects the contact force, the robot's pose controller intervenes and tracks and controls the contact force, maintaining it at the desired value. .

[0120] The control lever is equipped with unmanned robot tools.

[0121] Considering the influence of internal and external disturbances on the flying operation robot during contact operations, step S1 uses the Newton-Euler equations to establish a system dynamics model to accurately describe the position dynamics and attitude dynamics characteristics; the position error of the flying operation robot and the measured external force are used as inputs to the admittance model, and the parameters are divided to design a variable admittance controller; specifically, the following steps are included:

[0122] Step S11: Define the inertial coordinate system and body coordinate system ,in Located at the center of gravity of the flying operation robot;

[0123] Step S12: Let The center of gravity of the flying operation robot is in The position in the middle, For the body coordinate system of the flying operation robot Euler angles in;

[0124] Step S13: Establish position dynamics equations:

[0125] in, The nominal total mass of the flying operation robot. It is the acceleration due to gravity. , For generalized force input, For the lumped disturbance of the location loop;

[0126] Step S14: Establish attitude dynamics equations:

[0127] in, The nominal inertia matrix, The Coriolis force matrix, For generalized torque input, For the lumped disturbance of the attitude loop;

[0128] Step S15: Represent the position and attitude dynamics in a unified matrix form:

[0129]

[0130] in, , It is a positive definite inertial matrix. For Coriolis force and centrifugal force terms, The gravity vector For generalized input, External disturbance;

[0131] Step S16: Establish an environmental contact force model, assuming the environmental dynamics are a linear spring system:

[0132]

[0133] in, For the combined equivalent stiffness of the environment and the end effector, This refers to the location where the tool is unable to apply force when it first comes into contact with the environment. It is the contact force.

[0134] To address the uncertainty caused by the unpredictable motion of a target in an unknown dynamic environment, step S2 designs an adaptive variable stiffness admittance model. This model dynamically adjusts the stiffness coefficient based on force and position tracking errors, generating an accurate reference trajectory to maintain stable contact force. It also designs the adaptive rate of the inertia and stiffness parameters of the variable admittance controller and calculates the damping parameter control rate based on the specified performance controller. Specifically, this includes the following steps:

[0135] Step S21: Design an adaptive variable stiffness admittance model:

[0136]

[0137] in, , , These are the mass, damping, and stiffness parameters of the admittance model, respectively. For variable stiffness coefficient, The instruction location generated for the admittance model. For reference position, For contact force tracking error, For reference contact force, For force gain;

[0138] Step S22: Definition Calculate the instruction location:

[0139]

[0140] Step S23: Define position tracking error The result is obtained from the contact force model:

[0141]

[0142] Step S24: Substitute the result and its derivative from step S23 into step S21 to obtain the force error differential equation:

[0143]

[0144] in, Includes unknown parameters related to environmental dynamics and state variables that are difficult to obtain precisely:

[0145]

[0146] in , Feedback items that can be directly measured and used for compensation;

[0147] Step S25: Design the adaptive variable stiffness coefficient:

[0148]

[0149] Step S26: Design an adaptive law to estimate the upper bound of the unknown perturbation:

[0150]

[0151] in, , For positive integers, This is the upper bound for the disturbance estimation;

[0152] Step S27: Design the nonlinear function :

[0153]

[0154] in It is a positive constant;

[0155] Step S28: To mitigate the influence of force sensor noise, a tracking differentiator (STD) based on the sigmoid function is used to measure the contact force. It is then filtered before being used in the control loop.

[0156] To overcome disturbances caused by physical contact and strictly constrain position errors within a reasonable range to prevent mission failure, step S3 designs a pose controller based on a nonlinear disturbance observer and an obstacle Lyapunov function, achieving stable tracking performance without requiring prior knowledge of the disturbance. Combining the given energy tank rules with the variations in inertia, stiffness, and damping parameters in the admittance controller, the passive parameters of the variable admittance controller are designed. Specifically, this includes the following steps:

[0157] Step S31: Design a nonlinear perturbation observer:

[0158]

[0159] in, For disturbance The observed values, For the observer state, Gain is a positive constant.

[0160] Step S32: Define the observer estimation error:

[0161]

[0162] Step S33: Obtain the derivative of the estimation error from steps S31 and S32:

[0163]

[0164] Step S34: Let for The minimum eigenvalue is obtained from step S33, which yields the upper bound of the estimation error:

[0165]

[0166] in, This is the upper bound of the rate of change of the disturbance;

[0167] Step S35: Define the upper bound of the pose controller observer estimation error: .

[0168] Step S4 utilizes the obstacle Lyapunov function to strictly constrain the position tracking error, ensuring that the flying operation robot maintains stable contact with the target in the environment during the contact operation process, preventing contact loss or collision due to excessive error; a variable boundary energy tank is designed, and boundary change adaptive rate and energy flow exchange rules are proposed to realize the safety supervision of the sampling process of the flying operation robot, specifically including the following steps;

[0169] Step S41: Define the state error ,in For command pose;

[0170] Step S42: Rewrite the system dynamics model in state-space form:

[0171]

[0172] Step S43: Design the logarithmic barrier Lyapunov function:

[0173]

[0174] in, for The constraint boundaries define the maximum permissible position tracking error;

[0175] Step S44: Calculation Time derivative:

[0176]

[0177] in, ,according to It can be known Hengchengli;

[0178] Step S45: Introduce virtual control variables:

[0179]

[0180] in ;

[0181] Step S46: Construct the Lyapunov function:

[0182]

[0183] in, , for The estimated value;

[0184] Step S47: Calculation The derivative:

[0185]

[0186] in, ;

[0187] Step S48: Design controller inputs:

[0188]

[0189] in, , It is a positive number;

[0190] Step S49: Design the adaptive law:

[0191] in It is a positive constant;

[0192] Step S50: Control input from step S48 Calculate the reference roll and pitch angles:

[0193]

[0194] in This is the desired yaw angle.

[0195] Step S5 converts the control input into rotational speed commands for the four rotors of the flying operation robot using a control allocation algorithm, thereby achieving precise control of the flying operation robot; specifically:

[0196] Step S51: Obtain the system control force from step S48. and control torque ;

[0197] Step S52: Based on the relationship between the thrust and torque of the four rotors, calculate the rotational speeds of the four rotors:

[0198]

[0199] in, For the first The square of the rotor speed, and These are the thrust coefficient and the moment coefficient, respectively. The distance between the centers of the symmetrical rotors;

[0200] Step S53: Transfer the calculated rotor speed command The data is sent to the flight controller of the flying operation robot to control the flying operation robot to achieve stable contact force tracking.

[0201] The force sensor at the end of the control lever is a one-dimensional force sensor.

[0202] The system parameters of the flying operation robot are shown in Table 1:

[0203] Table 1. Parameters of the Flying Operation Robot System

[0204] This example proposes a contact force tracking control method for a flying robot based on adaptive variable stiffness admittance control. The main focus is on achieving stable contact force tracking under the influence of contact force in an unknown dynamic environment, and ensuring operational safety by constraining position errors using an obstacle Lyapunov function. The specific setup is as follows: a general-purpose quadcopter UAV is selected as the flight platform. To achieve contact operations, an operating stick (tool) is rigidly fixed to the flight platform, and a one-dimensional force sensor is installed at the end of the operating stick to measure the contact force with the environment in real time.

[0205] In this example, the contact target is defined as a rigid vertical plane that can move linearly along the x-axis. During the operation, the flying robot is controlled to approach the plane along the x-axis to perform the task. When the force sensor detects the contact force, the controller intervenes and maintains the contact force at the desired value.

[0206] like Figures 2-3As shown, the controller further designed according to the contact force tracking control method of this embodiment can enable the flying operation robot to achieve compliant tracking of the reference contact force in an unknown dynamic environment, while minimizing the tracking error of the position trajectory.

[0207] Specifically, the adaptive variable stiffness admittance model dynamically adjusts the stiffness coefficient based on the contact force error, ensuring that the contact force remains stable without significant oscillations during both the instant of contact and the sustained contact phase. Simultaneously, the obstacle Lyapunov function in the pose controller strictly constrains the position error within a preset safety range. The disturbance observer effectively compensates for the impact from contact. The flying robot moves with a small steady-state error, exhibiting small error fluctuations and a short response time to dynamic environmental changes. Figures 2-3 This demonstrates the effectiveness and superiority of the present invention.

[0208] The above description is only a preferred embodiment of the present invention. For those skilled in the art, designing different forms of control algorithms based on adaptive variable stiffness admittance according to the teachings of the present invention does not require creative labor. All equivalent changes, modifications, substitutions and variations made in accordance with the scope of the patent application of the present invention without departing from the principles and spirit of the present invention shall be covered by the present invention.

Claims

1. A contact force tracking control method for a flight operation robot based on variable stiffness admittance, characterized in that: When dealing with physical interaction tasks in a dynamic environment, the control method generates a reference trajectory through an adaptive variable stiffness admittance model and combines it with a pose controller based on obstacle Lyapunov functions to achieve stable contact force tracking control. Includes the following steps: Step S1: Establish the dynamic model of the flying operation robot system; Step S2: Design an adaptive variable stiffness admittance model, and generate the desired reference trajectory by dynamically adjusting the stiffness coefficient to compensate for the influence of unknown environmental parameters; Step S3: Design a nonlinear disturbance observer to estimate system disturbances and provide disturbance compensation for the pose controller; Step S4: Design a pose controller based on the obstacle Lyapunov function to achieve reference trajectory tracking and strictly constrain the position error within a safe range; Step S5: Calculate the rotational speed of each rotor of the flying operation robot by controlling the input; control the flying operation robot to achieve stable contact force tracking.

2. The contact force tracking control method for a flight operation robot based on variable stiffness admittance according to claim 1, characterized in that: The flying robot includes a quadcopter flight platform, with a rigidly fixed control stick for contact operations at the flight platform. The end of the control stick is equipped with a device for real-time measurement of the contact force with the target. Force sensor.

3. The contact force tracking control method for a flight operation robot based on variable stiffness admittance according to claim 2, characterized in that: The contact target in the dynamic environment is a rigid vertical plane that can move linearly along the x-axis. During the operation, the flying robot is controlled to approach the plane along the x-axis. When the force sensor detects the contact force, the pose controller of the flying robot intervenes and tracks and controls the contact force to maintain the contact force at the desired value.

4. The contact force tracking control method for a flight operation robot based on variable stiffness admittance according to claim 2, characterized in that: The control lever is equipped with unmanned robot tools.

5. The contact force tracking control method for a flight operation robot based on variable stiffness admittance according to claim 2, characterized in that: Step S1 uses the Newton-Euler equations to establish a system dynamics model to accurately describe the position dynamics and attitude dynamics characteristics; specifically, it includes the following steps: Step S11: Define the inertial coordinate system and body coordinate system ,in Located at the center of gravity of the flying operation robot; Step S12: Let The center of gravity of the flying operation robot is in The position in the middle, For the body coordinate system of the flying operation robot Euler angles in; Step S13: Establish position dynamics equations: in, The nominal total mass of the flying operation robot. It is the acceleration due to gravity. , For generalized force input, For the lumped disturbance of the location loop; Step S14: Establish attitude dynamics equations: in, The nominal inertia matrix, The Coriolis force matrix, For generalized torque input, For the lumped disturbance of the attitude loop; Step S15: Represent the position and attitude dynamics in a unified matrix form: in, , It is a positive definite inertial matrix. For Coriolis force and centrifugal force terms, The gravity vector For generalized input, External disturbance; Step S16: Establish an environmental contact force model, assuming the environmental dynamics are a linear spring system: in, For the combined equivalent stiffness of the environment and the end effector, This refers to the location where the tool is unable to apply force when it first comes into contact with the environment. It is the contact force.

6. The contact force tracking control method for a flight operation robot based on variable stiffness admittance according to claim 2, characterized in that: Step S2 involves designing an adaptive variable stiffness admittance model, dynamically adjusting the stiffness coefficient based on force and position tracking errors, and generating an accurate reference trajectory to maintain a stable contact force. This specifically includes the following steps: Step S21: Design an adaptive variable stiffness admittance model: in, , , These are the mass, damping, and stiffness parameters of the admittance model, respectively. For variable stiffness coefficient, The instruction location generated for the admittance model. For reference position, For contact force tracking error, For reference contact force, For force gain; Step S22: Definition Calculate the instruction location: Step S23: Define position tracking error The result is obtained from the contact force model: Step S24: Substitute the result and its derivative from step S23 into step S21 to obtain the force error differential equation: in, Includes unknown parameters related to environmental dynamics and state variables that are difficult to obtain precisely: in , Feedback items that can be directly measured and used for compensation; Step S25: Design the adaptive variable stiffness coefficient: Step S26: Design an adaptive law to estimate the upper bound of the unknown perturbation: in, , For positive integers, This is the upper bound for the perturbation estimate; Step S27: Design the nonlinear function : in It is a positive constant; Step S28: To mitigate the influence of force sensor noise, a tracking differentiator (STD) based on the sigmoid function is used to measure the contact force. It is then filtered before being used in the control loop.

7. The contact force tracking control method for a flight operation robot based on variable stiffness admittance according to claim 2, characterized in that: To overcome disturbances caused by physical contact and strictly constrain position errors within a reasonable range to prevent mission failure, step S3 designs a pose controller based on a nonlinear disturbance observer and an obstacle Lyapunov function to achieve stable tracking performance without requiring prior knowledge of the disturbance; specifically, it includes the following steps: Step S31: Design a nonlinear perturbation observer: in, For disturbance The observed values, For the observer state, Gain is a positive constant. Step S32: Define the observer estimation error: Step S33: Obtain the derivative of the estimation error from steps S31 and S32: Step S34: Let for The minimum eigenvalue is obtained from step S33, which yields the upper bound of the estimation error: in, This is the upper bound of the rate of change of the disturbance; Step S35: Define the upper bound of the pose controller observer estimation error: .

8. The contact force tracking control method for a flight operation robot based on variable stiffness admittance according to claim 2, characterized in that: Step S4 uses the obstacle Lyapunov function to strictly constrain the position tracking error, ensuring that the flying operation robot maintains stable contact with the target in the environment during the contact operation, and preventing contact loss or collision due to excessive error. specific Includes the following steps; Step S41: Define the state error ,in For command pose; Step S42: Rewrite the system dynamics model in state-space form: Step S43: Design the logarithmic barrier Lyapunov function: in, for The constraint boundaries define the maximum permissible position tracking error; Step S44: Calculation Time derivative: in, ,according to It can be known Hengchengli; Step S45: Introduce virtual control variables: in ; Step S46: Construct the Lyapunov function: in, , for The estimated value; Step S47: Calculation The derivative: in, ; Step S48: Design controller inputs: in, , It is a positive number; Step S49: Design the adaptive law: in It is a positive constant; Step S50: Control input from step S48 Calculate the reference roll and pitch angles: in This is the desired yaw angle.

9. The contact force tracking control method for a flight operation robot based on variable stiffness admittance according to claim 2, characterized in that: Step S5 converts the control input into rotational speed commands for the four rotors of the flying operation robot using a control allocation algorithm, thereby achieving precise control of the flying operation robot; specifically: Step S51: Obtain the system control force from step S48. and control torque ; Step S52: Based on the relationship between the thrust and torque of the four rotors, calculate the rotational speeds of the four rotors: in, For the first The square of the rotor speed, and These are the thrust coefficient and the moment coefficient, respectively. The distance between the centers of the symmetrical rotors; Step S53: Transfer the calculated rotor speed command The data is sent to the flight controller of the flying operation robot to control the flying operation robot to achieve stable contact force tracking.

10. The contact force tracking control method for a flight operation robot based on variable stiffness admittance according to claim 2, characterized in that: The force sensor at the end of the control lever is a one-dimensional force sensor.

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