Environmental Interactive Control Method and System for Tilting Multirotor Unmanned Aerial Vehicles

Abstract

This invention relates to an environmental interactive control method and system for tiltable multi-rotor UAVs, specifically for fully driven tiltable hexacopter UAVs used in contact inspection tasks. The algorithm can simultaneously control position and force, exhibiting accurate force tracking capabilities. Furthermore, it is equipped with an active disturbance rejection controller, providing strong anti-interference functionality and performing well even under wind disturbances along the pole direction. This invention fills the gaps in traditional UAVs' underactuation and inaccurate force detection, significantly improving the control precision of the UAV and enhancing its ability to perform contact inspection tasks, thus providing a new solution for contact-operation UAVs.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, specifically to an environmental interactive control method and system for tiltable multi-rotor UAVs. Background Technology

[0002] In recent years, multi-rotor drones have been widely used in aerial photography, plant protection, and inspection industries, but these applications are limited to non-contact observation or simple payloads. In industrial scenarios such as surface cleaning and non-destructive testing, drones need to physically interact with the environment, which places higher demands on their proactive operational capabilities. Currently, contact-based operation control technology for industrial drone systems has become a cutting-edge research topic internationally. Improving the efficiency and accuracy of drone contact-based operations, and enabling them to handle environmental interaction tasks in high-altitude or other adverse environments, can solve the problems of high risk, high cost, and low efficiency associated with traditional manual operations, laying the foundation for the widespread application of industrial drones.

[0003] For contact detection, the most commonly used method is the traditional underactuated rotorcraft UAV. Its lift direction is fixed parallel to the ground, making it impossible to apply omnidirectional torque and force in three-dimensional space. This increases the demands on the control algorithm and limits the range of achievable detection tasks. To address this problem, we aim to propose an environmental interaction control method based on a tiltable multi-rotor UAV. By adding a linkage as a force contact device to a specially structured, fully driven thrust vectoring UAV, we design a force / motion hybrid control method to achieve contact force control of the airframe in the normal direction of the contact surface.

[0004] Currently, the multi-rotor UAVs widely used in engineering are all based on traditional planar configuration airframe designs. The lift generated by each rotor blade is perpendicular to the airframe, possessing 4D trajectory tracking capabilities, namely roll, pitch, yaw, and elevation movements, with strong dynamic coupling between rotation and translation. Corresponding attitude changes are needed to achieve spatial position control. Pitch / roll angles generate lift components in various directions to achieve spatial translation of the UAV. The torque of lift about the airframe's center of gravity and the counter-torque of the motors enable spatial rotation of the UAV.

[0005] However, existing drones exhibit weak attitude stability under external interference, preventing them from achieving contact operations with the physical environment. Furthermore, drone systems often struggle to distinguish between contact forces, the effects of interaction with walls, and forces in the direction of the equipped robotic arm, resulting in highly complex control of these interactions. Therefore, the lack of compensation for interference on the rigid arm will negatively impact the performance of the aerial control platform during operation. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide an environmental interactive control method and system for tiltable multi-rotor unmanned aerial vehicles (UAVs) to solve the problems in the background art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] The environmental interaction control method for a tiltable multi-rotor unmanned aerial vehicle of the present invention includes the following steps:

[0009] Construct rigid kinematics, rigid body dynamics, and control efficiency models for multi-rotor UAVs;

[0010] A flight control model is constructed based on the rigid kinematics model and the rigid body dynamics model.

[0011] The flight control model is simplified, and a target control model is constructed based on the simplified flight control model, the control efficiency model, and the impedance control model. The target control model includes a position control part, an attitude control part, a control allocation part, an impedance control part, a motor control part, and a mathematical solver part.

[0012] Obtain the desired flight information of the multi-rotor UAV, and obtain the actual feedback force and desired feedback force generated when the actuator end of the multi-rotor UAV comes into contact with the environment;

[0013] The desired flight information, the actual feedback force, and the desired feedback force are input into the target control model to obtain a control signal;

[0014] An active interference suppression control algorithm is introduced to filter the control signal, and the filtered control signal is fed back to the controller of the multi-rotor UAV, so that the multi-rotor UAV can fly along the predicted trajectory and generate the desired feedback force to the environment.

[0015] In one embodiment of this application, the kinematic model characterizes the relationship between the UAV's position and linear velocity, and the relationship between the UAV's angular velocity and Euler angles.

[0016] In one embodiment of this application, the rigid body dynamics model includes a position dynamics model and an attitude dynamics model. The position dynamics model characterizes the relationship between linear acceleration in the world coordinate system and propeller thrust in the world coordinate system. The attitude dynamics model characterizes the relationship between the UAV's angular acceleration and angular velocity, the UAV's gyro torque, and the propeller torque, as well as the relationship between the UAV's gyro torque and the body's moment of inertia and the arm's rotational angular velocity.

[0017] In one embodiment of this application, the flight control model is used based on the airframe linear velocity v and Euler angles. Mass m of the body, angular acceleration of the body The angular velocity ω of the aircraft is used to calculate the gyroscopic torque Ga, propeller thrust f, and motor rotational inertia J of the multiple propellers of the multi-rotor UAV in order to control the flight state of the multi-rotor UAV.

[0018] In one embodiment of this application, the efficiency control model includes a thrust and reaction torque model and a thrust and torque model. The thrust and reaction torque model characterizes the relationship between blade thrust and propeller angular velocity, and the relationship between blade torque and propeller angular velocity. The thrust and torque model characterizes the relationship between blade thrust and propeller angular velocity, and the relationship between blade torque and propeller angular velocity.

[0019] In one embodiment of this application, the flight control model is simplified, including:

[0020] The flight control model is simplified to obtain a simplified flight control model, which is as follows:

[0021]

[0022] Based on the simplified flight control model, the position control equations are written as follows:

[0023]

[0024] In the formula, v is the linear velocity of the machine. For Euler angles, ω b Let W be the angular velocity of the machine body, and W be the transformation matrix. Let be the linear acceleration in the world coordinate system, g be the gravitational acceleration, e3 be the unit column vector along the z-axis in the world coordinate system, f be the propeller thrust in the world coordinate system, m be the mass of the aircraft, τ be the torque generated by the propeller, and J be the moment of inertia of the motor. Let be the angular acceleration on the x-axis. Let be the angular acceleration on the y-axis. Let be the angular acceleration on the z-axis. θ is the roll angle, and θ is the pitch angle. This is the yaw angle.

[0025] In one embodiment of this application, a target control model is constructed based on a simplified flight control model, the control efficiency model, and the impedance control model, including:

[0026] A PID-based control loop is constructed based on the simplified flight control model and the control efficiency model.

[0027] An impedance control model is embedded in the control loop to obtain the target control model. The impedance control model uses the multi-rotor UAV as a spring model. The inputs of the impedance control model are the actual feedback force and the desired feedback force, and the output of the impedance control model is the corrected displacement.

[0028] This application also provides an environmental interaction control system for a tiltable multi-rotor unmanned aerial vehicle, including:

[0029] The model building module is used to build rigid kinematic models, rigid body dynamic models, and control efficiency models for multi-rotor UAVs.

[0030] The flight control module construction module is used to construct a flight control model based on the rigid kinematics model and the rigid body dynamics model.

[0031] The target model construction module is used to simplify the flight control model and construct a target control model based on the simplified flight control model, the control efficiency model, and the impedance control model. The target control model includes a position control part, an attitude control part, a control allocation part, an impedance control part, a motor control part, and a mathematical solver part.

[0032] The acquisition module is used to acquire the expected flight information of the multi-rotor UAV, and to acquire the actual feedback force and expected feedback force generated when the actuator end of the multi-rotor UAV comes into contact with the environment.

[0033] The control signal calculation module is used to input the desired flight information, the actual feedback force, and the desired feedback force into the target control model to obtain the control signal;

[0034] The filtering and control module is used to introduce an active interference suppression control algorithm to filter the control signal and feed the filtered control signal back to the controller of the multi-rotor UAV, so that the multi-rotor UAV can fly along the predicted trajectory and generate the desired feedback force to the environment.

[0035] This application also provides an electronic device, the electronic device comprising:

[0036] One or more processors;

[0037] A storage device for storing one or more programs, which, when executed by one or more processors, enable the electronic device to implement the environmental interactive control method for a tilting multi-rotor UAV as described above.

[0038] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the environmental interaction control method for a tilting multi-rotor UAV as described above.

[0039] The beneficial effects of this invention are as follows: The environmental interaction control method and system for tiltable multi-rotor UAVs of this invention are used in contact inspection tasks for fully driven tiltable hexacopter UAVs. This algorithm can simultaneously control position and force, possessing accurate force tracking capabilities. Furthermore, it is equipped with an active disturbance rejection controller, exhibiting strong anti-interference capabilities and performing well even under wind disturbances along the pole direction. It fills the gaps in traditional UAVs' underactuation and inaccurate force detection, significantly improving the control accuracy of the UAV and enhancing its ability to perform contact inspection tasks, providing a new solution for contact-operation UAVs. Attached Figure Description

[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0041] Figure 1 This is a flowchart illustrating an environmental interaction control method for a tiltable multi-rotor unmanned aerial vehicle (UAV) according to an embodiment of this application;

[0042] Figure 2 This is a schematic diagram of the impedance model structure in one embodiment of this application;

[0043] Figure 3 This is a schematic diagram of the impedance with internal position loop control in this application;

[0044] Figure 4 This is a schematic diagram of the controlled force impedance control module subsystem in one embodiment of this application;

[0045] Figure 5 This is a schematic diagram of a contact detection task in one embodiment of this application;

[0046] Figure 6 This is the overall framework of the control algorithm in one embodiment of this application;

[0047] Figure 7 This is a schematic diagram of the basic framework of ADRC in one embodiment of this application;

[0048] Figure 8 This is a schematic diagram of the test results for the noise suppression of the sinusoidal signal by the active interference rejection device in this application;

[0049] Figure 9 This is a schematic diagram of the test results for the square wave signal noise suppression of the active interference rejection device in this application;

[0050] Figure 10 This is a schematic diagram of the test results for the sawtooth wave signal noise suppression of the self-interference rejection device in this application;

[0051] Figure 11 This is a schematic diagram showing the position of the ADRC algorithm in the entire control algorithm system.

[0052] Figure 12 This is a schematic diagram of the force control mechanism capability test results in this application;

[0053] Figure 13 This is a schematic diagram of the anti-interference capability test results in this application;

[0054] Figure 14 This is a schematic diagram of the force tracking results under wind disturbance in this application. Detailed Implementation

[0055] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0056] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the layers related to the present invention and are not drawn according to the actual number, shape and size of the layers in the actual implementation. In the actual implementation, the form, number and proportion of each layer can be arbitrarily changed, and the layer layout may also be more complex.

[0057] Numerous details are explored in the following description to provide a more thorough explanation of embodiments of the invention; however, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details.

[0058] Traditional unmanned aerial vehicles (UAVs) exhibit underactuated characteristics. Traditional underactuated multi-rotor UAVs rely on relatively few inputs to control multi-degree-of-freedom motion, resulting in an overly strong correlation between attitude and position. This leads to significant design challenges and severely limited control effectiveness. UAVs control their altitude by overcoming gravity through vertical forces. However, under interference, these vertical forces are also affected, hindering altitude control and weakening the system's anti-interference capabilities. For example, when a rotorcraft UAV is disturbed by external wind fields, it needs to change its attitude to generate horizontal forces to resist wind disturbances. Therefore, these conventional rotorcraft UAVs only possess passive observation capabilities, have weak anti-interference capabilities, poor attitude stability, and cannot perform contact operations with the physical environment.

[0059] Contact inspection tasks require force control of UAVs during the inspection process. All-drive tilt-rotor UAVs offer a solution to this problem. The total lift of an all-drive multi-rotor UAV always generates components in the three translational directions without depending on non-zero attitude angles. This makes position control completely independent of attitude control, significantly reducing system coupling and nonlinearity, and decoupling translational and rotational dynamics, thus allowing the UAV to apply force in any direction.

[0060] Unmanned aerial vehicle (UAV) systems often struggle to distinguish between contact forces, the effects of interactions with walls, and forces in the direction of the equipped robotic arm, resulting in highly complex control of these interactions. Consequently, the lack of compensation for disturbances on the rigid arm will impact the performance of the aerial control platform during operation.

[0061] This application constructs a low-level control framework for a fully driven six-rotor drone, realizing the control of the drone's position and forces. Considering the shortcomings of traditional solutions, an interference observer is inserted to filter out useless signals during the contact detection process, avoiding undercompensation caused by noise interference in the rigid arm direction of the system. The final control algorithm exhibits excellent control capabilities and accurate force tracking, filling the gaps in traditional solutions and providing a new solution for contact detection. The specific solution is as follows.

[0062] Figure 1 This is a flowchart illustrating an environmental interaction control method for a tiltable multi-rotor unmanned aerial vehicle (UAV) according to an embodiment of this application, as shown below. Figure 1 As shown: The environmental interaction control method for a tiltable multi-rotor UAV in this embodiment may include steps S110 to S140:

[0063] S110, construct the rigid kinematics model, rigid body dynamics model, and control efficiency model of the multi-rotor UAV;

[0064] Specifically, the six-rotor architecture consists of the following four parts:

[0065] (1) Kinematic model of a rigid body (2) Dynamic model of a rigid body

[0066] (3) Controller model (4) Control efficiency model

[0067] I. (Rigid Body) Kinematic Model

[0068] The kinematic model focuses on the position, linear velocity, altitude, and angular velocity of the six-rotor rotor. Its inputs are linear velocity and angular velocity, and its outputs are attitude and position. Assumptions:

[0069] (1) The hexagonal rotor is a rigid body

[0070] (2) The system has constant mass inertia and torque inertia.

[0071] (3) The center of gravity coincides with the geometric center.

[0072] First, the position vector of the six rotors on the machine body is p. e ∈R 3 Then, the relationship between linear velocity and position is shown in equation (2.1), where p e ∈R 3 This represents the linear velocity of the six-rotor. The angular velocity can be represented by equation (2.2).

[0073]

[0074] In the formula, v is the linear velocity of the machine. For Euler angles, ω b Let ω be the angular velocity of the machine body, and W be the transformation matrix.

[0075] II. (Rigid Body) Dynamics Model

[0076] The dynamics model focuses on the motion and related forces. Assuming the propeller disk is leveled, a dynamics model of the six-rotor is performed. The dynamics model of the six-rotor consists of an attitude dynamics model and a position dynamics model.

[0077] For the position dynamics model, by analyzing the forces on the aircraft, we can obtain equation (2.3), where Fz represents the force on the z-axis and g represents the gravitational acceleration. Fz can also be regarded as the propeller thrust.

[0078]

[0079] In the formula, Let be the linear acceleration in the world coordinate system, g be the gravitational acceleration, e3 be the unit column vector along the z-axis in the world coordinate system, f be the propeller thrust in the world coordinate system, m be the mass of the aircraft, and b be the rotation matrix. The rotation matrix in the world coordinate system;

[0080] f is a three-dimensional vector containing the propeller thrust Fz along the z-axis;

[0081] For the attitude dynamics model, the mathematical relationship between torque and angular velocity is shown in equation (2.4). In equation (2.4), τ=[τ x , τ y , τ z The torque is generated by the six-rotor propeller, and J is the moment of inertia. This is the gyroscopic torque. Furthermore, the gyroscopic torque caused by a single rotating propeller is shown in equation (2.5).

[0082]

[0083] In the formula, J is the moment of inertia of the motor. G is the angular acceleration of the aircraft, ω is the angular velocity of the aircraft, Ga is the gyroscopic torque, τ is the torque generated by the propeller, and G a,k J represents the gyroscopic torque on the k-th arm. RP Let w be the moment of inertia of the machine. k This represents the rotational angular velocity of the k-th arm;

[0084] Where, τ=[τ x , τ y , τ z τ represents the torque generated by the propeller along the x-axis. y τ is the torque generated by the propeller along the y-axis. z This represents the torque generated by the propeller along the z-axis.

[0085] Ga,θ is the roll angle gyroscopic torque, and Ga,θ is the pitch angle gyroscopic torque. The yaw angle gyro torque is given by:

[0086]

[0087] In the formula, n is the number of blades, W y W represents the y-component of the motor's angular velocity. x This represents the x-component of the motor's angular velocity.

[0088] S120, a flight control model is constructed based on the rigid kinematics model and the rigid body dynamics model;

[0089] III. Six-rotor flight control model

[0090] By combining the dynamics model and kinematic model of the six-rotor, its flight control rigid model can be obtained, as shown in (2.7).

[0091]

[0092]

[0093] In the formula, It is a rotation matrix;

[0094] The flight control model is used based on the airframe linear velocity v and Euler angles. Mass m of the body, angular acceleration of the body The angular velocity ω of the aircraft is used to calculate the gyroscopic torque Ga, propeller thrust f, and motor rotational inertia J of the multiple propellers of the multi-rotor UAV in order to control the flight state of the multi-rotor UAV.

[0095] IV. Control Efficiency Model

[0096] The input to the control efficiency model is the propeller's angular velocity, and the outputs are torque and thrust. Both the output torque and force are 6-dimensional, meaning the system achieves full actuation.

[0097] The efficiency control model includes a thrust and reaction torque model and a thrust and moment model.

[0098] Thrust and reaction torque model

[0099] The hexacopter hovers in the air. The thrust of the propeller is shown in (2.8), and the reaction torque is shown in (2.9). (2.9) represents the static model of the reaction torque, and its corresponding dynamic model is shown in (2.10).

[0100]

[0101] In the formula, Ti is the blade thrust, and c T M is the blade tension function. i For the blade torque, w i Let c be the angular velocity of the i-th propeller. M Let τ be the propeller torque function, which is the torque on the i-th propeller. i This represents the torque on the i-th propeller. The components of the propeller blade rotation speed in the three directions.

[0102] Wherein, the blade tension function c T The mathematical expression is:

[0103]

[0104] In the formula, ρ is the air density, and D p C is the propeller diameter. T It is the tensile coefficient;

[0105] Blade torque function c M for:

[0106]

[0107] In the formula, C M This is the torque coefficient.

[0108] Thrust and torque model

[0109] The rotor's force and torque are achieved by its motor driving the propellers; let the angular velocity of each propeller be w. i Then the total force and torque in the three directions can be represented by (2.12).

[0110]

[0111] In the formula, A is the distribution matrix, Ω is the square of the angular velocity, γ represents the angle between arm i and the x-axis of the fuselage in the xy plane, β represents the angle between arm i and the xoy plane of the fuselage, and C... f It is the rotor thrust coefficient, C d The value represents the rotor resistance coefficient, α is the tilt angle, i is the arm number, s is the abbreviation for sin, and c is the abbreviation for cos.

[0112] f and τ are the force and torque required for the multirotor to reach the specified position and attitude state.

[0113] S130, the flight control model is simplified, and a target control model is constructed based on the simplified flight control model, the control efficiency model, and the impedance control model. The target control model includes a position control part, an attitude control part, a control allocation part, an impedance control part, a motor control part, and a mathematical solver part.

[0114] First, a low-level flight control framework is established, which creates a reasonable connection between the UAV's position, attitude, control allocation, and motor control.

[0115] The motor control unit receives w dk After (k = 1, 2, ..., nr), it is necessary to obtain the throttle command σ required for each motor. dk The position control section receives the desired position trajectory and needs to calculate the required total thrust and the two required Euler angles: roll and pitch. The attitude control section first receives the required Euler angles and then obtains the desired torque. The control distribution section distributes the required propeller angular velocity to each motor in the six rotors, enabling the model to generate the required torque and force.

[0116] In fact, this control framework is designed for underactuated multirotors. Therefore, the final control framework will be adjusted based on the actual airframe model design.

[0117] To simplify the controller design, the previously established nonlinear model was adjusted. The adjusted simplified model can be represented by (3.1).

[0118]

[0119] Based on the simplified flight control model, the position control equations are written as follows:

[0120]

[0121] In the formula, Let be the angular acceleration on the x-axis. Let be the angular acceleration on the y-axis. Let be the angular acceleration on the z-axis. θ is the roll angle, and θ is the pitch angle. Yaw angle;

[0122] For the horizontal position channel model, ignoring higher-order terms, we get:

[0123]

[0124] In the formula, v represents the height position. h Let the linear velocity be in the height direction. Let x be the linear acceleration in the height direction in the world coordinate system. The yaw angle allocation matrix, θ h For the set calculation matrix, Let P be the transformation matrix. x P is the x-axis position. y Position on the y-axis;

[0125] The attitude channel model is simplified to:

[0126]

[0127] In the formula, and v z The velocity along the z-axis, The acceleration on the z-axis is... Angular acceleration;

[0128] The motor control equation is:

[0129] w k =C R σ+w b (3.7)

[0130]

[0131] σ represents the motor input throttle, C R and w b w is a static parameter representing the relationship between throttle and motor speed. kd For the desired angular velocity, σ d This refers to the motor throttle output of the controller.

[0132] V. Impedance Control Model

[0133] Impedance control models can address the shortcomings of traditional motion control in environments with high control accuracy and high environmental stiffness. They exhibit strong stability and provide an achievable strategy for resolving position uncertainties by maintaining the relationship between external force and displacement.

[0134] Figure 2This is a schematic diagram of the impedance model structure in one embodiment of this application. The impedance model in this application is as follows: Figure 2 As shown; Figure 2 In this context, M represents mass, x represents displacement, S represents the complex frequency variable in the Laplace transform, B represents damping, and K represents stiffness.

[0135] The basic principle of variable impedance control is to conceptualize the robot through a spring-mass physical model, in which the manipulator can dynamically adjust virtual mass, damping, and stiffness parameters to regulate system characteristics between a compliant state and a steady state in resistance to external forces.

[0136] Figure 3 This is a schematic diagram of the impedance control with internal position loop control in this application. The impedance control in this application embeds the position control loop into the force control loop, such as... Figure 3 As shown, M d It is the expected quality, B d It is the expected damping, K e It is the error stiffness, K d It is the desired stiffness, X r It is the actual impedance, X e It is an incorrect impedance, X d q is the desired impedance, S is the complex frequency variable of the Laplace transform, and q d It is the expected intermediate quantity of the Laplace transform, q e It is an incorrect intermediate quantity in the Laplace transform, where F is force, F r The actual force, the contact force between the environment and the end effector of the manipulator, can be converted into a position signal. By superimposing this correction signal onto the desired position signal and then subjecting it to PID control, simultaneous regulation of the contact force and position can be achieved.

[0137] Two simplification methods were used when building the simulation model. The first was to replace the complex UVA model with a six-degree-of-freedom model. In this case, the UAV itself can be regarded as a point mass. The other was to represent the contact wall as a spring model with a stiffness of 5000 N / m.

[0138] In this impedance control model, the force exerted by the wall is calculated by multiplying the difference between the expected and actual displacements in the Cartesian coordinate system by a stiffness coefficient. Subsequently, the expected and actual forces from the wall are input into the impedance control module, which then simulates their difference within the context of a spring-damper mass system, generating a displacement correction. This correction is then combined with the actual displacement of the system to obtain the corrected displacement. The UAV becomes adaptive in this respect by adjusting the error between the expected and actual displacements. Therefore, even when encountering a more elastic wall, the controller can adjust the expected displacement using external forces, thereby avoiding damage to the fuselage.

[0139] This combination of compensating displacement is the core of impedance control. To improve the control process, an impedance control subsystem is also introduced to mitigate the effects of force feedback. Figure 4 This is a schematic diagram of a controlled force impedance control module subsystem in one embodiment of this application, as shown below. Figure 4 As shown, this subsystem operates by handling the difference between the required contact force and the actual contact force. The force error is input into the spring-damper mass system, and the resulting output is a displacement error. Although this displacement error lacks a tangible physical meaning, unlike the previously mentioned compensating displacement, it serves only as an input signal to the PID controller, facilitating the adjustment of force control.

[0140] Now, a low-level flight control framework and an impedance model will be combined. In a contact inspection mission, a hexacopter UAV needs to first move along the z-axis to reach a specified height, then move along a wall and use a rigid rod to detect defects on the wall.

[0141] Figure 5 This is a schematic diagram of a contact detection task in one embodiment of this application. The specific performance of the contact detection task is as follows: Figure 5 As shown.

[0142] On the y and z axes, the logic of our control algorithm is similar to that of an underactuated hexcopter UAV. On the x-axis, there is no apparent movement, meaning position control is not required. Contact forces are along the x-axis, so an impedance model is applied on the x-axis.

[0143] Figure 6 This is the overall framework of the control algorithm in one embodiment of this application. The overall framework of the control algorithm in this application is as follows: Figure 6 As shown.

[0144] The overall control framework includes position control, attitude control, control allocation, motor control, and mathematical solver.

[0145] Position control section: The horizontal controller relates to the hexacopter UAV's position on the x and y axes, while the vertical altitude controller relates to the hexacopter UAV's position on the z-axis. The position control section takes the desired trajectory and yaw angle as input and outputs the remaining two Euler angles and the thrust, i.e., the force on the z-axis.

[0146] The attitude control section describes the relationship between Euler angles and three-dimensional torque.

[0147] Impedance control section: The inputs to the impedance model are the actual contact force and the desired contact force, and the output is the force exerted by the hexagonal rotor on the x-axis. This project assumes that the hexagonal UAV is a mass damper-spring system. However, as mentioned earlier, the mass damper and spring parameters are hypothetical and need to be adjusted.

[0148] Control and distribution section: This section describes the relationship between three-dimensional force, torque, and the speed of the hexacopter propeller. The inputs are the applied force and torque, and the output is the three-dimensional propeller speed. If the hexacopter drone's drive is insufficient, the input will generate torque in three dimensions, and the thrust will be the force along the z-axis.

[0149] Mathematical solver section: This section is responsible for solving the system state based on the output of the motor control section. Its output is the actual position, Euler angles, etc., which will be transmitted to the control section above.

[0150] S140, acquire the expected flight information of the multi-rotor UAV, and acquire the actual feedback force and expected feedback force generated when the actuator end of the multi-rotor UAV comes into contact with the environment;

[0151] S150, the desired flight information, the actual feedback force, and the desired feedback force are input into the target control model to obtain a control signal;

[0152] S160, an active interference suppression control algorithm is introduced to filter the control signal, and the filtered control signal is fed back to the controller of the multi-rotor UAV, so that the multi-rotor UAV flies along the predicted trajectory and generates the desired feedback force to the environment.

[0153] In this application, the disturbance filtering module estimates external disturbances and automatically adjusts the control input variables in the required direction based on the force or displacement input required by the UAV and the contact force feedback from the sensors. Based on this requirement, an active disturbance rejection control algorithm was selected as both the external disturbance observer and the filter.

[0154] The ADRC (Active Disturbance Suppression Control) algorithm employs three distinct algorithmic units: TD (Tracking Differentiator), NLSEF (Nonlinear State Error Feedback), and ESO (Extended State Observer). TD provides real-time estimation of the system state, NLSEF introduces nonlinear feedback to improve control performance, and ESO estimates and compensates for disturbances and unmodeled dynamics within the system. The relevant equations are as follows:

[0155] Controlled object:

[0156]

[0157] TD:

[0158] v1(k+1)=v1(k)+hv2(k) (4.7)

[0159] v2(k+1)=v2(k)+hfst(v1(k)-v0,v2(k),r,h0) (4.8)

[0160] ESO:

[0161] ε1=z1(k)-y(k) (4.9)

[0162] z1(k+1)=z1(k)+h(z2(k)-β 01 ε1) (4.10)

[0163] z2(k+1)=z2(k)+h(z3(k)-β02fal(ε1,α1,δ)=bu(k)) (4.11)

[0164] z2(k+1)=z2(k)-hβ 03 fal(ε1,α2,δ) (4.12)

[0165] NLSEF:

[0166] e1=v1(k)-z1(k) (4.13)

[0167] e2=v2(k)-z2(k) (4.14)

[0168] u0 = β 01 fal(e1,α1,δ)+β 02 fal(e1,α2,δ) (4.15)

[0169] u(k)=u0-z3(k) / b (4.16)

[0170] The following are the meanings of the symbols in the ADRC equation:

[0171] x: The state vector of the controlled object;

[0172] The derivative of the state vector with respect to time;

[0173] w: Process noise;

[0174] t: time;

[0175] b: Control input coefficient;

[0176] y: The output of the controlled object;

[0177] v1: Tracks state variable 1 in the differentiator;

[0178] v2: Tracks state variable 2 in the differentiator;

[0179] h: time step;

[0180] r: Reference value;

[0181] h0: The initial value of the hst function;

[0182] ε1: The estimation error or difference between the measured output and the actual output;

[0183] z1: State variable 1 in the extended state observer;

[0184] z2: State variable 2 in the extended state observer;

[0185] z3: State variable 3 in the extended state observer;

[0186] β 01 β 02 β 03 Observer gain or coefficient;

[0187] fal: nonlinear function or nonlinear approximation function;

[0188] α1, α2: Parameters of the fal function;

[0189] δ: Tolerance parameter of the fal function;

[0190] e1: Error between v1 and z1;

[0191] e2: Error between v1 and z2;

[0192] u0: Control signal generated by nonlinear state error feedback;

[0193] u(k): The final control signal at time step k;

[0194] Figure 7 This is a schematic diagram of the basic framework of ADRC in one embodiment of this application. The ADRC framework in this application is as follows: Figure 7 As shown.

[0195] Based on this framework and principle, an ADRC algorithm module was built in Simulink, specifically designed for multi-rotor tiltrotor UAVs. By considering 28 expected parameters of the UAV, ESO automatically identifies interference factors and calculates real-time compensation. This compensation is then fed back into the UAV control system, effectively eliminating internal and external interference.

[0196] The testing process is as follows:

[0197] This application describes the construction of a separate test platform for active disturbance rejection controller modules. To simulate the filtering effect subsequently loaded into the UAV force control algorithm, a signal generator is used to simulate the expected force input in the direction of interest, and Gaussian distributed noise is added to serve as a simulated force sensor signal.

[0198] Figure 8This is a schematic diagram of the test results for the active interference rejection device (AID) in this application, specifically the sine wave signal noise suppression. First, the expected sine wave signal is input for testing, and the test results are as follows: Figure 8 As shown in the figure, the filtering effect of the test basically meets expectations, with a significant filtering effect. Although there is some signal distortion in the front end, the ADRC's overall noise suppression effect is still very good, almost completely filtering out the interference.

[0199] Figure 9 This is a schematic diagram of the test results for the square wave signal noise suppression of the active interference rejection device in this application. Figure 10 This is a schematic diagram of the test results for the sawtooth wave signal noise suppression of the active interference rejection device in this application, as shown below. Figures 9-10 As shown, this application also has a good filtering effect on square waves and sawtooth waves.

[0200] ADRC performs exceptionally well in processing these three types of signals. However, for square wave and sawtooth wave signals, due to their non-conductivity, ADRC processing can introduce some unwanted fluctuations and distortions. For most continuous signals, ADRC can meet the filtering requirements. Overall, ADRC boasts excellent computational speed, signal continuity processing capabilities, and filtering power.

[0201] This project requires measuring forces from walls and other states originating from the UAV. These measurements may not be entirely accurate due to unforeseen errors and other external interference. To improve the accuracy of the measurement data and eliminate errors, ADRC (Advanced Dynamic Reduction) is introduced into the control algorithm to filter these signals. For force signals from the contact detection task wall, these can be measured by sensors or the feedback signal from the UAV control model can be used as an observation for ADRC. For the UAV's position, velocity, and other state signals, ADRC is used to reduce the impact of measurement errors.

[0202] Considering the coupling of virtual displacement and force after applying the wall model, we selected the position variable in the x-direction, which is of interest in this project, as the input variable for ADRC. This variable is actually calculated by the dynamic model and the impedance correction model. The required displacement is added as another necessary input for ADRC. The corrected displacement control signal will be directly fed back to the UAV position controller as feedback to correct the input signal. The position of the ADRC algorithm in the entire control algorithm system is as follows: Figure 11 As shown.

[0203] The overall algorithm framework includes a dynamic model of a hexarotor tilting UAV, an impedance control algorithm, an interference filtering algorithm, and a wall model. The main control algorithms are encapsulated into subsystems, namely a position controller, attitude controller, control distributor, signal converter, motion information solver, signal distributor, impedance correction model, wall model, x-axis specific impedance module, and active disturbance rejection controller, as well as simple module signal generator, noise generator, and oscilloscope.

[0204] The signal generator produces the required signals and inputs them into the UAV controller. The UAV's control variables are calculated using the UAV dynamics model and information solver. These signals are then distributed to the position and attitude controllers via a 36-signal distributor, completing the control loop. Simultaneously, the output position signal from the signal distributor is input into an impedance correction algorithm and corrected in conjunction with the desired signal to achieve accurate position tracking. This is then converted into a force signal using a PID controller and a wall model. The algorithm incorporates force signals and noise to simulate external disturbances in real-world scenarios. This force signal is fed into the control distributor through an impedance module dedicated to the x-axis force signal. To handle the effects of external disturbances, an ADRC (Advanced Dynamic Range Control) is placed after the impedance correction algorithm to filter out noise from the position signal. This corrected signal is then re-inputted to the position controller.

[0205] Experiment 1: The Algorithm's Force Control Capability

[0206] The first experiment tested the algorithm's force control performance in the rod direction (i.e., the x-axis direction). The complete algorithm, without the filtering algorithm, was used as the test platform.

[0207] Table 1 Parameter values ​​for Experiment 1

[0208]

[0209]

[0210] The variable step size experiment selection and the solver's automatic selection minimum step size were set to 1e-7, with all other parameters set to automatic. The solution time was set to approximately 40 seconds. Figure 12 The simulation results are shown. Even with drastic changes in the generator signal, the force control system still achieved fast and accurate force tracking with good convergence, and the convergence time was approximately 4 seconds.

[0211] Experiment 2: The anti-interference capability of the interference observer

[0212] The second experiment will test the filtering performance of the interference observer, examining whether it can bring the tracking force signal closer to the true value in noisy conditions. The test platform is a complete algorithm with a filtering module.

[0213] The relevant parameters are shown in Table 2. Most of the parameters are the same as those in the first experiment, only the signal from the generator is different, and the impedance control parameters have been fine-tuned to include the parameters of the active disturbance rejection controller.

[0214] Table 2 Overall Algorithm Parameter Values

[0215]

[0216]

[0217] The solver remains set to variable step size, automatically selecting solver mode, and the minimum step size is changed to 1e-6 to shorten the simulation time. Due to some unexplained non-convergence characteristics in the initial value calculation, the ADRC setting is changed to discrete time to simulate dynamics for 15 seconds. The final results are as follows. Figure 13 As shown in the diagram, red represents the input desired force, blue represents the desired force after adding noise, and white represents the filtered force signal. It can be seen that the force is tracked very clearly and converges much faster. ADRC filters out random noise almost perfectly. The corrected force signal is very stable.

[0218] Experiment 3: Force tracking capability under wind disturbance

[0219] The third experiment will test the control algorithm's force tracking capability in the presence of wind, as wind disturbances affect the actual speed of the UAV relative to the ground. Therefore, a wind modeling component is introduced, and the wind speed from (5.1) is used to modify the actual speed of our hexarotor.

[0220]

[0221] In Experiment 3, the parameters were the same as those in Table 2. The force tracking results are as follows: Figure 14 As shown. The orange line represents the combination of white noise and the required contact force, while the white curve represents the actual force exerted by the drone under wind disturbance. From Figure 14 It can be seen that the output contact eventually reaches a steady-state value, which is approximately the same as the required force. Therefore, it can be concluded that the control algorithm can achieve high-precision force tracking even in the presence of wind disturbances.

[0222] Therefore, this application proposes an environmental interaction control method for a tiltable multi-rotor UAV, specifically for a fully driven tiltable hexacopter UAV used in contact inspection tasks. This algorithm can simultaneously control position and force, exhibiting accurate force tracking capabilities. Furthermore, it is equipped with an active disturbance rejection controller, providing strong anti-interference functionality and performing well even under wind disturbances along the pole direction. This method fills the gaps in traditional UAVs' underactuation and inaccurate force detection, significantly improving the control accuracy of the UAV and enhancing its ability to perform contact inspection tasks, thus providing a new solution for contact-operation UAVs.

[0223] This application also provides an environmental interaction control system for a tiltable multi-rotor unmanned aerial vehicle, including:

[0224] The model building module is used to build rigid kinematic models, rigid body dynamic models, and control efficiency models for multi-rotor UAVs.

[0225] The flight control module construction module is used to construct a flight control model based on the rigid kinematics model and the rigid body dynamics model.

[0226] The target model construction module is used to simplify the flight control model and construct a target control model based on the simplified flight control model, the control efficiency model, and the impedance control model. The target control model includes a position control part, an attitude control part, a control allocation part, an impedance control part, a motor control part, and a mathematical solver part.

[0227] The acquisition module is used to acquire the expected flight information of the multi-rotor UAV, and to acquire the actual feedback force and expected feedback force generated when the actuator end of the multi-rotor UAV comes into contact with the environment.

[0228] The control signal calculation module is used to input the desired flight information, the actual feedback force, and the desired feedback force into the target control model to obtain the control signal;

[0229] The filtering and control module is used to introduce an active interference suppression control algorithm to filter the control signal and feed the filtered control signal back to the controller of the multi-rotor UAV, so that the multi-rotor UAV can fly along the predicted trajectory and generate the desired feedback force to the environment.

[0230] This application proposes an environmental interaction control system for a tiltable multi-rotor UAV, specifically a fully driven tiltable hexacopter UAV used in contact inspection tasks. The algorithm can simultaneously control position and force, exhibiting accurate force tracking capabilities. Furthermore, it is equipped with an active disturbance rejection controller, providing strong anti-interference capabilities and performing well even under wind disturbances along the pole direction. This system fills the gaps in traditional UAVs' underactuation and inaccurate force detection, significantly improving the control precision of the UAV and enhancing its ability to perform contact inspection tasks, thus providing a new solution for contact-operation UAVs.

[0231] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any one of the methods in this embodiment, wherein the method is the execution logic of this system.

[0232] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0233] The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory so that the terminal performs any of the methods in this embodiment.

[0234] As will be understood by those skilled in the art, the computer-readable storage medium described in this embodiment allows for the implementation of all or part of the steps in the above method embodiments by computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0235] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication between them. The memory is used to store computer programs, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer programs, so that the electronic terminal performs the steps of the above method.

[0236] In this embodiment, the memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0237] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0238] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. The embodiments of the invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims.

[0239] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. An environmental interactive control method for a tiltable multi-rotor unmanned aerial vehicle (UAV), characterized in that, Including the following steps: Construct rigid kinematics, rigid body dynamics, and control efficiency models for multi-rotor UAVs; A flight control model is constructed based on the rigid kinematics model and the rigid body dynamics model. The flight control model is simplified, and a target control model is constructed based on the simplified flight control model, the control efficiency model, and the impedance control model. The target control model includes a position control part, an attitude control part, a control allocation part, an impedance control part, a motor control part, and a mathematical solver part. Obtain the desired flight information of the multi-rotor UAV, and obtain the actual feedback force and desired feedback force generated when the actuator end of the multi-rotor UAV comes into contact with the environment; The desired flight information, the actual feedback force, and the desired feedback force are input into the target control model to obtain a control signal; An active interference suppression control algorithm is introduced to filter the control signal, and the filtered control signal is fed back to the controller of the multi-rotor UAV so that the multi-rotor UAV can fly along the predicted trajectory and generate the desired feedback force to the environment. The input to the control efficiency model is the angular velocity of the propeller, and the output is the torque and thrust of the rotor. The torque and thrust of the rotor are achieved by its motor driving the propeller. The angular velocity of each propeller is taken as w. i Then the total force and torque in the three directions are represented by (2.12): In the formula, A is the distribution matrix, Ω is the square of the angular velocity, γ represents the angle between arm i and the x-axis of the fuselage in the xy plane, β represents the angle between arm i and the xoy plane of the fuselage, and C... f It is the rotor thrust coefficient, C d α represents the rotor drag coefficient, i is the tilt angle, s is the arm number, c is the abbreviation for sin, f and τ are the force and torque required for the multi-rotor to reach the specified position and attitude.

2. The environmental interaction control method for a tiltable multi-rotor UAV according to claim 1, characterized in that, The kinematic model characterizes the relationship between the UAV's position and linear velocity, as well as the relationship between the UAV's angular velocity and Euler angles.

3. The environmental interaction control method for a tiltable multi-rotor UAV according to claim 2, characterized in that, The rigid body dynamics model includes a position dynamics model and an attitude dynamics model. The position dynamics model represents the relationship between linear acceleration in the world coordinate system and propeller thrust in the world coordinate system. The attitude dynamics model represents the relationship between the UAV's angular acceleration and angular velocity, the UAV's gyro torque, and the propeller torque, as well as the relationship between the UAV's gyro torque and the body's moment of inertia and the arm's rotational angular velocity.

4. The environmental interaction control method for a tiltable multi-rotor UAV according to claim 1, characterized in that, The flight control model is used based on the airframe linear velocity v and Euler angles. Mass m of the body, angular acceleration of the body The angular velocity ω of the aircraft is used to calculate the gyroscopic torque Ga, propeller thrust f, and motor rotational inertia J of the multiple propellers of the multi-rotor UAV in order to control the flight state of the multi-rotor UAV.

5. The environmental interactive control method for a tiltable multi-rotor UAV according to claim 1, characterized in that, The control efficiency model includes a thrust and reaction torque model and a thrust and moment model. The thrust and reaction torque model represents the relationship between blade thrust and propeller angular velocity, and the relationship between blade torque and propeller angular velocity. The thrust and moment model represents the relationship between blade thrust and propeller angular velocity, and the relationship between blade torque and propeller angular velocity.

6. The environmental interactive control method for a tiltable multi-rotor UAV according to claim 1, characterized in that, The flight control model is simplified, including: The flight control model is simplified to obtain a simplified flight control model, which is as follows: Based on the simplified flight control model, the position control equations are written as follows: In the formula, v is the linear velocity of the machine. For Euler angles, ω b Let W be the angular velocity of the machine body, and W be the transformation matrix. Let be the linear acceleration in the world coordinate system, g be the gravitational acceleration, e3 be the unit column vector along the z-axis in the world coordinate system, m be the mass of the machine body, and J be the moment of inertia of the motor. Let be the angular acceleration on the x-axis. Let be the angular acceleration on the y-axis. Let be the angular acceleration on the z-axis. θ is the roll angle, and θ is the pitch angle. Yaw angle It is a rotation matrix.

7. The environmental interactive control method for a tiltable multi-rotor UAV according to claim 6, characterized in that, A target control model is constructed based on the simplified flight control model, the control efficiency model, and the impedance control model, including: A PID-based control loop is constructed based on the simplified flight control model and the control efficiency model. An impedance control model is embedded in the control loop to obtain the target control model. The impedance control model uses the multi-rotor UAV as a spring model. The inputs of the impedance control model are the actual feedback force and the desired feedback force, and the output of the impedance control model is the corrected displacement.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the environmental interactive control method for a tilting multi-rotor unmanned aerial vehicle as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the environmental interactive control method for the tilting multi-rotor UAV as described in any one of claims 1 to 7.

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