Unmanned aerial vehicle adaptive anti-wind disturbance angle formation control method under limited input

Abstract

The invention discloses a self-adaptive anti-wind disturbance angle formation control method for an unmanned aerial vehicle under limited input. The method comprises the following steps: 1) dividing external wind disturbance into different types of wind conditions such as mean wind, gust and wind shear, and combining the wind conditions into a composite wind disturbance model; 2) establishing an input limited model by analyzing the nonlinear characteristics of the unmanned aerial vehicle; 3) establishing a quad-rotor unmanned aerial vehicle formation system model according to the communication topology; 4) designing a globally stable angle rigid formation control law and an adaptive law by using local information obtained by communication; the quad-rotor unmanned aerial vehicle formation control method has the advantages of being simple in structure, high in reliability, high in control precision and easy to apply practically, and the quad-rotor unmanned aerial vehicles subjected to formation can be widely applied to the fields of environment monitoring, search and rescue, military affairs and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-UAV formation control, and in particular to an adaptive anti-wind disturbance angle formation control method for UAVs under input constraints. Background Art

[0002] In recent years, with the rapid development of microelectronics technology, communication technology, digital signal processing and sensor technology, the application of quad-rotor drones in civil and military fields has become increasingly widespread (such as Fig. 9 These technological advances enable drones to carry advanced vision systems or distance sensors to achieve more complex and efficient tasks. At the same time, the integration of independent or integrated flight controllers and networked communication architectures provides drones with greater functionality and operational efficiency.

[0003] Initially, research focused on single quadrotor drones, which rely on their own power and navigation systems to perform tasks such as data collection, surveillance and reconnaissance, and material transportation. Early research focused on improving the flight efficiency, performance, and autonomous navigation capabilities of drones. However, with the advancement of technology and the increase in application needs, the limitations of single drones in performing large-scale mapping, continuous monitoring, or complex search and rescue missions have gradually emerged. In contrast, multi-UAV formation systems are gradually becoming an indispensable technology in production and life with their superior stability, fault tolerance, and high efficiency. Especially when performing critical tasks such as high-risk search and rescue, agricultural monitoring, collaborative strategies, and network inspections, multi-UAV formations show great potential and broad development prospects, and the application of quadrotor drone formations is becoming more and more extensive. In this context, the development of adaptive angle formation control technology for multi-quadrotor drones has become particularly important. This technology enables drones to automatically adjust their formation morphology and flight path according to environmental changes and mission requirements, thereby improving the flexibility of the entire formation system and the success rate of mission execution.

[0004] When exploring the control of UAV systems in depth, the importance of efficiently controlling external wind disturbances and multi-UAV systems under input constraints cannot be ignored. Wind disturbances can be subdivided into wind conditions such as mean wind, gusts, and wind shear, all of which may pose a threat to the stability of the UAV system. Mean wind represents a stable or slowly changing wind speed, while gusts are meteorological phenomena in which wind speed suddenly increases in a short period of time, and wind shear describes a dramatic change in wind speed or wind direction in space. This requires that the control strategy must not only be able to handle the continuous impact of mean wind, but also be able to cope with transient and variable disturbances caused by gusts and wind shear, ensuring that the UAV formation can resist wind disturbances and maintain an accurate flight path and formation under input constraints.

[0005] However, most existing formation control methods are based on relative position information, but there are relatively few studies on the anti-wind disturbance control of multi-UAV angle rigid formation with limited input. In UAV formation flying, how to use relative angles and modern control techniques, especially through adaptive mechanisms, to compensate for the impact of limited input and maintain the stability of the formation and system in the face of complex dynamic environments and unknown wind disturbances is an urgent problem to be solved. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the prior art and provide a method for adaptive anti-wind disturbance angle formation control of UAVs under input constraints. By compensating for the nonlinear part caused by external wind disturbance and input constraints, the formation of the UAVs is uniquely determined based only on the relative angle information between each pair of UAVs, ensuring that the final offset angle converges to the preset target expected angle.

[0007] The object of the present invention is achieved by: a method for controlling an adaptive anti-wind disturbance angle formation of a UAV under input constraints, comprising the following steps:

[0008] 1) The external wind disturbance is divided into different types of wind conditions, including mean wind, gust and wind shear, and combined into a composite wind disturbance model to simulate the complex wind environment that the UAV may encounter in actual flight;

[0009] 2) By analyzing the nonlinear characteristics of the UAV and the dynamic response of the system, an input-constrained model is established to accurately quantify the impact of the constrained input on the control system;

[0010] 3) For a quadrotor UAV in three-dimensional space, the model of the quadrotor UAV system is established by considering the restricted input and various types of external wind disturbance factors;

[0011] 4) Using the local angle information obtained through communication, an adaptive dynamic estimator is introduced to estimate the restricted input and wind disturbance, and a globally stable angle rigid formation control law and an adaptive estimation law are designed;

[0012] 5) Using the potential energy function method, a formation control strategy based only on angle information is designed to achieve the desired formation formation maintenance and stability by calculating the error between the actual angle of the UAV and the expected angle;

[0013] 6) Combining the above steps, calculate the control input of each drone.

[0014] As a further limitation of the present invention, the step 1) specifically includes: designing a composite wind disturbance model, dividing the external wind disturbance into different types of wind condition models such as mean wind, gust, and wind shear, and superimposing them; mean wind v a , Gust v b , wind shear v c The models are:

[0015]

[0016] Where h is the flight altitude of the quadcopter, z 0 is the ground roughness, v 6.096 represents the average wind speed from the ground set by the invention, q is the distance between the UAV and the center of the gust, d represents the gust scale range, and v bmax is the peak wind speed of the gust, H c is the roughness height, H is the flight height of the quadrotor, k is the Karman constant, v f Expressed as friction velocity, v a ,v b ,v c is a bounded function;

[0017] The three types of wind fields are superimposed, and the composite wind field model that the quadrotor drone faces during actual flight is: w =v a +v b +v c =[v wx ,v wy ,v wz ] T , where v wx ,v wy ,v wz Represents the components of the model vector on the x, y, and z axes of the Cartesian coordinate system. The interference force of this composite wind field on the quadrotor drone is:

[0018]

[0019] in, represents the wind disturbance on the x, y, and z axes of the quadrotor drone, k wx , k wy , k wz represents the disturbance coefficient.

[0020] As a further limitation of the present invention, the step 2) specifically includes: the input restricted model is:

[0021]

[0022] Among them, v i =[v i1 ,v i2 ,v i3 ] T ∈R 3 , α nik ,α pik ,v nik ,v pik, k = 1, 2, 3 are unknown bounded non-zero constants; then let 0 < α ik =min{α nik ,α pik},0<v ikmax =max{v nik ,v pik}, define β ik =α ik v ikmax , then the above formula (4) is redefined as:

[0023]

[0024] As a further limitation of the present invention, the model of the quadrotor drone system established in step 3) specifically includes:

[0025]

[0026] where x i =[x i ,y i ,z i ] T Indicates the motion position of the quadrotor drone i; Represents the rotation matrix R i ; φ i (v i ) is a function with input v i Input-constrained system model; represents the offset angle of drone i, is the external wind disturbance of the UAV in terms of position and yaw angle, and assumes Where k = x, y, z; represents the unknown normal vector, Represents an unknown positive constant.

[0027] As a further limitation of the present invention, the design of the globally stable angular rigid formation control law and the adaptive estimation law in step 4) specifically includes: the globally stable angular rigid formation control law is:

[0028]

[0029] in, λ i is a known normal vector,

[0030] represents the gradient of the potential energy function, g ij , g ij * represents the actual relative angle and the expected relative angle between drones i and j, is a known positive constant, representing the desired offset angle of the drone relative to the z-axis;

[0031] make S i =diag[ξ 1 ,ξ 2 ,ξ 3 ],ξ 1 ,ξ 2 ,ξ 3 express The components on the x, y, and z axes, express The estimated value at time t, λ i is a normal vector; The parameter adaptive estimation law is:

[0032]

[0033] Among them, η i ,μ i is a known positive constant.

[0034] As a further limitation of the present invention, step 5) specifically includes: constructing a potential energy function between drone i and its neighbor j in the following form:

[0035]

[0036] definition The total potential energy function of UAV i is expressed as:

[0037] The present invention adopts the above technical solution, and compared with the prior art, the beneficial effects are as follows: (1) The present invention designs a composite wind disturbance model, which superimposes wind disturbances in three different wind conditions. The model not only takes into account the size of the wind speed, but also includes the change of wind direction, and more comprehensively evaluates the performance and stability of the UAV under different wind conditions.

[0038] (2) Through in-depth analysis of the nonlinear characteristics and dynamic response of the UAV system, an input-constrained model is established to accurately quantify the impact of constrained input on the flight control system, providing a theoretical basis and quantitative tools for designing efficient control strategies.

[0039] (3) The present invention constructs a new relative angle-based control method to estimate the nonlinear part of the UAV system under input constraints and wind disturbance, providing a more flexible and robust formation control scheme for it;

[0040] (4) The present invention innovatively combines the adaptive law and the potential energy function in the controller structure, realizes the formation only by relying on the angle information and combining with the adaptive control, simplifies the control algorithm, and provides more effective observation information;

[0041] (5) The present invention constructs an adaptive law for an input-constrained quad-rotor UAV formation anti-wind disturbance system to achieve the offset and compensation of possible external wind disturbances and input constraints, thereby reducing the impact of the nonlinear part on the system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of the present invention.

[0043] Figure 2 It is a flow chart of the control system of the present invention.

[0044] Figure 3 This is the input restricted model diagram of the present invention.

[0045] Figure 4 It is a distribution curve diagram of mean wind speed at different heights of the present invention.

[0046] Figure 5 It is a curve diagram of different vertical gust wind conditions of the present invention.

[0047] Figure 6 1 and 1 are wind condition curves for different vertical shear values ​​of the present invention.

[0048] Figure 7 This is a diagram showing the control effect of the UAV formation of the present invention.

[0049] Figure 8 This is the angle error diagram of the drone of the present invention.

[0050] Fig. 9 Schematic diagram of a quadrotor drone. DETAILED DESCRIPTION

[0051] like Figure 1-2 The method for controlling an adaptive anti-wind disturbance angle formation of a UAV under input constraints is characterized by comprising the following steps:

[0052] 1) The external wind disturbance is divided into different types of wind conditions, including mean wind, gust and wind shear, and combined into a composite wind disturbance model to simulate the complex wind environment that the UAV may encounter in actual flight;

[0053] Mean wind refers to the average value of wind speed over a certain period of time, representing the stable or slowly changing part of the wind speed, such as Figure 4 As shown; gust refers to the phenomenon of sudden increase in wind speed in a short period of time, which is sudden and short-lived, such as Figure 5As shown; Wind shear refers to the phenomenon that wind speed or wind direction changes dramatically in space, such as Figure 6 As shown;

[0054] A composite wind disturbance model is designed to divide the external wind disturbance into different types of wind condition models such as mean wind, gust, and wind shear, and superimpose them; mean wind v a , Gust v b , wind shear v c The models are:

[0055]

[0056] Where h is the flight altitude of the quadcopter, z 0 is the ground roughness, v 6.096 represents the average wind speed from the ground set by the invention, q is the distance between the UAV and the center of the gust, d represents the gust scale range, and v bmax is the peak wind speed of the gust, H c is the roughness height, H is the flight height of the quadrotor, k is the Karman constant, k = 0.4, v f Expressed as friction velocity, v a ,v b ,v c is a bounded function;

[0057] The three types of wind fields are superimposed, and the composite wind field model that the quadrotor drone faces during actual flight is: w =v a +v b +v c =[v wx ,v wy ,v wz ] T , where v wx ,v wy ,v wz Represents the components of the model vector on the x, y, and z axes of the Cartesian coordinate system. The interference force of this composite wind field on the quadrotor drone is:

[0058]

[0059] in, represents the wind disturbance on the x, y, and z axes of the quadrotor drone, k wx , k wy , k wz represents the disturbance coefficient.

[0060] 2) By analyzing the nonlinear characteristics of the UAV and the dynamic response of the system, an input-constrained model is established to accurately quantify the impact of the constrained input on the control system;

[0061] like Figure 3 As shown, the input restricted model is:

[0062]

[0063] Among them, v i =[v i1 ,v i2 ,v i3 ] T ∈R 3 , α nik ,α pik ,v nik ,v pik , k = 1, 2, 3 are unknown bounded non-zero constants; then let 0 < α ik =min{α nik ,α pik},0<v ikmax =max{v nik ,v pik}, define β ik =α ik v ikmax , then the above formula (4) is redefined as:

[0064]

[0065] 3) For a quad-rotor UAV in three-dimensional space, the model of the quad-rotor UAV system is established by considering the restricted input and various types of external wind disturbance factors; the model of the quad-rotor UAV system specifically includes:

[0066]

[0067] where x i =[x i ,y i ,z i ] T Indicates the motion position of the quadrotor drone i; Represents the rotation matrix R i ; φ i (v i ) is an input-constrained system model with input vi; represents the offset angle of drone i, is the external wind disturbance of the UAV in terms of position and yaw angle, and assumes Where k = x, y, z; represents the unknown normal vector, Represents an unknown positive constant.

[0068] 4) Using the local angle information obtained through communication, an adaptive dynamic estimator is introduced to estimate the restricted input and wind disturbance, and a globally stable angle rigid formation control law and an adaptive estimation law are designed;

[0069] Designing the globally stable angle rigid formation control law and the adaptive estimation law specifically includes: The globally stable angle rigid formation control law is:

[0070]

[0071] in, v ikmax ≥v ik ,0<a min <a ik ,λ i is a known normal vector, represents the gradient of the potential energy function, g ij , g ij * represents the actual relative angle and the expected relative angle between drones i and j, is a known positive constant, representing the desired offset angle of the drone relative to the z-axis;

[0072] make S i =diag[ξ 1 ,ξ 2 ,ξ 3 ],ξ 1 ,ξ 2 ,ξ 3 express The components on the x, y, and z axes, express The estimated value at time t, λ i is a normal vector; The parameter adaptive estimation law is:

[0073]

[0074] Among them, η i ,μ i is a known positive constant.

[0075] 5) Using the potential energy function method, a formation control strategy based only on angle information is designed to achieve the desired formation formation maintenance and stability by calculating the error between the actual angle of the UAV and the expected angle;

[0076] Construct the potential energy function between drone i and its neighbor j in the following form:

[0077]

[0078] definition The total potential energy function of UAV i is expressed as:

[0079] 6) Based on the above steps, the control input of each quadrotor is calculated to achieve formation control of multiple quadrotor drones.

[0080] The stability analysis of the control law verifies the Lyapunov stability of the entire UAV system. Taking the system Lyapunov function as V, under the action of the control law (7), the adaptive law (8) and the UAV model (6), it can be verified that V'≤0, thereby ensuring that the angle errors of all UAVs in the system converge to zero. The UAV angle rigid formation system is globally asymptotically stable, and the multi-UAV system achieves the desired formation shape.

[0081] prove:

[0082]

[0083] Depend on Equations (7) and (8) can be used to deduce

[0084]

[0085] From the above, we can see that V(r ij (t)) is non-increasing and has a lower bound of zero, then let the limit V(r ij (∞)), let Integrating both sides of the equation, we can get

[0086]

[0087] Then we can know exists and is finite. For any initial state, T(g ij (t)) and v i are all bounded, then is bounded by It can be seen that ij and r ij Bounded, indicating that g ij is uniformly continuous. Because the function T(g ij (t)) If it is continuous, then for any g ij (t) is also uniformly continuous. According to T(g ij (t)) for any g ij The uniform continuity property of (t) can be inferred that T(g ij (t)) When , . According to Barbalat's lemma, Available Similarly, we can deduce Therefore, it is proved that the UAV angle rigid formation system is globally asymptotically stable.

[0088] In order to better reflect the superiority and effectiveness of the control method proposed in this paper, the algorithm simulation of the quadrotor UAV system was carried out; in the simulation experiment, the dynamic equation of the UAV system is:

[0089]

[0090] The initial position parameters of the model are chosen as:

[0091]

[0092] Where t = 5, k i =1, η i =2,μ i =1.

[0093] The effect diagram of multi-UAV formation control is shown in Figure 7. The angle error is shown in Figure 7. Figure 8 As shown, UAV 1-8 displays the simulated flight trajectories of eight drones.

[0094] This invention proposes an adaptive anti-wind disturbance angle formation control method for UAVs under input constraints, and uses an adaptive compensation method to offset the nonlinear part of the UAV system. In the face of external wind disturbance and input constraints, a composite wind disturbance model is designed, which divides the wind disturbance into three different wind conditions for superposition processing, taking into account the changes in wind speed and wind direction. The composite wind disturbance can truly reflect the changeable and complex wind environment that the UAV may encounter in actual flight. Through in-depth analysis of the nonlinear characteristics and dynamic response of the UAV system, the present invention constructs an input-constrained model, which aims to accurately simulate the impact of the flight control system performance under the input-constrained environment.

[0095] The present invention adopts adaptive control technology and compensation controller to improve the stability and anti-interference ability of the system. This method successfully solves the problem of anti-wind disturbance angle formation control under limited input, ensures that the multi-UAV system can achieve a globally stable formation state, and keep all UAVs in the predetermined position and continue to fly.

[0096] The present invention is not limited to the above-mentioned embodiments. On the basis of the technical solution disclosed in the present invention, technicians in this field can make some substitutions and deformations to some technical features therein according to the disclosed technical content without creative labor, and these substitutions and deformations are all within the protection scope of the present invention.

Claims

1. A method for controlling the adaptive anti-wind disturbance angle formation of unmanned aerial vehicles under input constraints, characterized in that: The following steps are involved: 1) The external wind disturbance is divided into different types of wind conditions, including mean wind, gust and wind shear, and combined into a composite wind disturbance model to simulate the complex wind environment that the UAV may encounter in actual flight; 2) By analyzing the nonlinear characteristics of the UAV and the dynamic response of the system, an input-constrained model is established to accurately quantify the impact of the constrained input on the control system; 3) For a quadrotor UAV in three-dimensional space, the model of the quadrotor UAV system is established by considering the restricted input and various types of external wind disturbance factors; 4) Using the local angle information obtained through communication, an adaptive dynamic estimator is introduced to estimate the restricted input and wind disturbance, and a globally stable angle rigid formation control law and an adaptive estimation law are designed; 5) Using the potential energy function method, a formation control strategy based only on angle information is designed to achieve the desired formation formation maintenance and stability by calculating the error between the actual angle of the UAV and the expected angle; 6) Based on the above steps, the control input of each quadrotor is calculated to achieve formation control of multiple quadrotor drones.

2. The method for controlling the UAV adaptive anti-wind disturbance angle formation under input constraints according to claim 1 is characterized in that: The step 1) specifically includes: designing a composite wind disturbance model, dividing the external wind disturbance into different types of wind condition models such as mean wind, gust, and wind shear, and superimposing them; mean wind v a , Gust v b , wind shear v c The models are: In the formula, h is the flight altitude of the quadrotor drone, z0 is the ground roughness, and v 6.096 represents the average wind speed from the ground set by the invention, q is the distance between the UAV and the center of the gust, d represents the gust scale range, and v bmax is the peak wind speed of the gust, H c is the roughness height, H is the flight height of the quadrotor, k is the Karman constant, v f Expressed as friction velocity, v a ,v b ,v c is a bounded function; The three types of wind fields are superimposed, and the composite wind field model that the quadrotor drone faces during actual flight is: w =v a +v b +v c =[v wx ,v wy ,v wz ] T , where v wx ,v wy ,v wz Represents the components of the model vector on the x, y, and z axes of the Cartesian coordinate system. The interference force of this composite wind field on the quadrotor drone is: in, represents the wind disturbance on the x, y, and z axes of the quadrotor drone, k wx , k wy , k wz represents the disturbance coefficient.

3. The method for controlling the UAV adaptive anti-wind disturbance angle formation under input limitation according to claim 1, characterized in that: The step 2) specifically includes: the input restricted model is: Among them, v i =[v i1 ,v i2 ,v i3 ] T ∈R 3 , α nik ,α pik ,v nik ,v pik , k = 1, 2, 3 are unknown bounded non-zero constants; then let 0 < α ik =min{α nik ,α pik },0<v ikmax =max{v nik ,v pik }, define β ik =α ik v ikmax , then the above formula (4) is redefined as:

4. The method for controlling the UAV adaptive anti-wind disturbance angle formation under input limitation according to claim 1, characterized in that: The model of establishing the quadrotor drone system in step 3) specifically includes: where x i =[x i ,y i ,z i ] T Indicates the motion position of the quadrotor drone i; Represents the rotation matrix R i ; φ i (v i ) is a function with input v i Input-constrained system model; represents the offset angle of drone i, is the external wind disturbance of the UAV in terms of position and yaw angle, and assumes Where k = x, y, z; represents the unknown normal vector, Represents an unknown positive constant.

5. The method for controlling the UAV adaptive anti-wind disturbance angle formation under input limitation according to claim 1, characterized in that: The design of the globally stable angle rigid formation control law and the adaptive estimation law in step 4) specifically includes: the globally stable angle rigid formation control law is: in, v ikmax ≥v ik ,0<a min <a ik ,λ i is a known normal vector, represents the gradient of the potential energy function, g ij , g ij * represents the actual relative angle and the expected relative angle between drones i and j, is a known positive constant, representing the desired offset angle of the drone relative to the z-axis; Let sgn(▽r i P i T R i )=[ξ1,ξ2,ξ3] T ,S i =diag[ξ1,ξ2,ξ3], ξ1,ξ2,ξ3 means The components on the x, y, and z axes, express The estimated value at time t, λ i is a normal vector; The parameter adaptive estimation law is: Among them, η i ,μ i is a known positive constant.

6. The method for controlling the UAV adaptive anti-wind disturbance angle formation under input limitation according to claim 1, characterized in that: The step 5) specifically includes: constructing a potential energy function between drone i and its neighbor j, in the following form: definition The total potential energy function of UAV i is expressed as:

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