A multi-unmanned aerial vehicle adaptive distributed control method based on relative measurement information
By employing a distributed control method based on relative measurement information and adaptive parameter identification, the problems of network dependence and inertial parameter variation in multi-UAV control are solved, achieving efficient and interference-resistant UAV formation control.
Patent Information
- Application Number
- CN202411299833.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-18
- Publication Date
- 2026-02-06
- Estimated Expiration
- 2044-09-18
AI Technical Summary
Existing multi-UAV control strategies rely on network communication, which makes them vulnerable to network attacks and interference, and they are difficult to process changes in inertial parameters in real time, resulting in poor control performance.
A distributed control method based on relative measurement information is adopted, which utilizes airborne sensors for local information exchange and combines an adaptive controller to identify inertial parameters, designing a closed-loop position and attitude system to achieve adaptive control of UAV formation.
It improves the anti-interference and anti-attack capabilities of the UAV collaborative control system, reduces communication burden, and can process parameter changes in real time to ensure the stability and accuracy of formation flight.
Smart Images
Figure CN119440091B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application relates to a multi-unmanned aerial vehicle adaptive distributed control method based on relative measurement information and belongs to the field of multi-unmanned aerial vehicle formation. BACKGROUND
[0002] In recent years, the rapid development of artificial intelligence enables multi-agent to be widely applied in military and civilian fields. The quadrotor unmanned aerial vehicle is widely concerned due to its exquisite structure, flexible use and economical cost and is applied in disaster relief, environmental detection, military reconnaissance and the like.
[0003] In order to improve the performance of the multi-unmanned aerial vehicle, a leader-following mode or a leaderless mode is usually adopted, and the two modes can realize the coordinated flight of the multi-unmanned aerial vehicle. However, since most control strategies need network communication to realize information exchange, a great communication burden exists, and from the actual application, the network communication may be subjected to potential network attacks or interference, thereby causing the task to fail. In addition, when the unmanned aerial vehicle flies in the air, the phenomenon of inertia parameter change may occur. Therefore, a distributed control scheme using the local and relative measurement of the airborne sensor is needed, and the inertia parameter identification function is included.
[0004] In order to solve the problem, the existing method can use an adaptive controller to solve the parameter change problem. Although the method can realize the unmanned aerial vehicle formation in the case of parameter uncertainty, the main purpose is to ensure the stability of the closed loop, and the accurate identification of the unknown parameter cannot be completed. Meanwhile, the fuzzy control can be used to adapt to the change of the system parameter through the fuzzy rule and the membership function. However, the design of the fuzzy rule and the membership function in the method is relatively subjective, and a large number of experiments are needed to determine. Meanwhile, the intelligent control including the neural network control and the like can be used to solve the parameter change problem. However, the method needs a large amount of training data, and the training process may be time-consuming, and the control performance is affected by the training sample and the data quality. The problem can also be solved through the model predictive control. The model predictive control optimizes the control action by predicting the future system behavior. However, the method needs high precision of the mathematical model and has a large amount of calculation, and the system with high real-time requirement may not be suitable. SUMMARY
[0005] In order to solve the inertia parameter change problem on the basis of good control performance, small amount of calculation and real-time control, the application aims to provide a multi-unmanned aerial vehicle adaptive distributed control method based on relative measurement information. On the one hand, the method no longer depends on the network communication for information exchange but adopts the distributed control strategy and only uses the relative position information for local information exchange. On the other hand, the application uses the data-based system identification to enable the inertia parameter to converge to the true value, thereby improving the accuracy and efficiency of the multi-unmanned aerial vehicle adaptive distributed control.
[0006] The object of the present application is achieved by the following technical solutions:
[0007] The application discloses a multi-unmanned aerial vehicle adaptive distributed control method based on relative measurement information, and comprises the following steps:
[0008] Step one, kinematics and dynamics equations of the quad-rotor unmanned aerial vehicle are established, a connection between a position p i and a thrust T i is constructed, and a connection between a unit quaternion q i and a torque τ i is constructed; an instruction quaternion is obtained from the position, so that an instruction attitude of the quad-rotor is expressed.
[0009] Step two, since a mass m i is unknown, parameter m i identification is realized through design of two dynamic systems, an auxiliary system and a variable z i are further proposed, a kinematics equation established in step one is combined, an adaptive distributed control force is designed, a closed-loop position system is obtained, and thus position formation tracking is realized.
[0010] Step three, since a moment of inertia J i is unknown, J i identification is realized through design of two dynamic systems, an instruction quaternion is obtained according to a specific position obtained in step two and a method of calculating the instruction quaternion from the position proposed in step one, a variable s i is further proposed, a dynamics equation established in step one is combined, an adaptive control torque is designed, a closed-loop attitude system is obtained, and finally, attitude tracking is realized on the basis of position tracking in step two.
[0011] In step one, the process of establishing the kinematics equation and the dynamics equation is as follows:
[0012] According to the Euler-Newton formula, kinematics and dynamics equations of the quad-rotor unmanned aerial vehicle are represented by the following formula:
[0013]
[0014] In the formula, p i is a position of the unmanned aerial vehicle, v i and ω i are respectively a speed and an angular speed, m i is a mass, g is a gravity acceleration, is a unit vector in the z-axis direction, T i is a thrust applied along the direction, is the unit quaternion expressing the attitude of quadrotor, q i0 is the real part of quaternion, q ir is the imaginary part of quaternion, which contains three terms, and can be expressed as q ir = [q ir1 ; q ir2 ; q ir3 ], the function I3is the third order unit matrix; J i is the moment of inertia, τ i is the torque, R i ∈ SO(3) is the rotation matrix, which can be expressed as:
[0015]
[0016] Further based on the kinematic equation proposed by formula (2), the position estimation command quaternion q
[0017] According to the cascade system control design, divide both sides of formula (2) by m i , so that the right side of the formula is defined as an intermediate variable where u ix , u iy and u iz are the three specific terms of the intermediate variable u i , and the command rotation matrix R is the command unit quaternion, then:
[0018]
[0019] In the formula, is the unit vector, and to ensure the consistency of formula (6), formula (6) is further derived as:
[0020]
[0021] The command thrust T i is:
[0022]
[0023] To solve the redundancy of quaternion, let , so we get:
[0024]
[0025] The command quaternion is extracted as:
[0026]
[0027] wherein, in step two, the design of the distributed formation control scheme, the closed-loop position system establishment process is as follows:
[0028] m i are unknown constants, to realize parameter identification, two dynamic systems are designed as:
[0029]
[0030] wherein, Q i and P i represent the dynamic system state in formula (12), and the initial state is zero; define filter and filter θ i and represent the filter state, and the initial state is zero; constant η p > 0; f i is a control force; define variable define ι1, ι2 and are all normal numbers, ι1 < ι2;
[0031] Auxiliary system:
[0032]
[0033] wherein, represent the state of auxiliary system formula (13), and the initial state is zero; represent the set of unmanned aerial vehicles adjacent to unmanned aerial vehicle i; constant constant κ r > 0; relative position error p ij represents the relative position of unmanned aerial vehicle i and unmanned aerial vehicle j, δ ij is the desired position difference between unmanned aerial vehicles; define a new variable wherein κ1 is a normal number, to z i both sides are multiplied by m i and the derivative is obtained:
[0034]
[0035] Combined with formula (2), the intermediate variable is the estimated value of m i , then the control force f i is represented as:
[0036]
[0037] The adaptive law of is:
[0038]
[0039] where κ2, β i and c i are positive constants, the state of the dynamic system in equation (12) at time t pi is and where μ1is the corresponding time when the dynamic system in equation (12) reaches the maximum value; define the estimation error The closed-loop position system is obtained as follows:
[0040]
[0041] According to the closed-loop system, a complete unmanned aerial vehicle position control system is established, and the position formation tracking of multiple unmanned aerial vehicles is realized according to the complete unmanned aerial vehicle position control system.
[0042] In step three, the design of the distributed formation control scheme, the closed-loop attitude system is established as follows:
[0043] The moment of inertia J i is an unknown symmetric positive definite constant matrix, in order to obtain an accurate estimate of the moment of inertia, the dynamic system in equation (18) is introduced:
[0044]
[0045] where and represent the state of the dynamic system in equation (18), and the initial state is zero; define the filter and the filter ζ i and ρ i are filter states, and the initial state is zero; the constant η a > 0; define the variable is a linear operator, for x = [x1, x2, x3] T , define l1, l2 and are constants, l1 < l2;
[0046] The attitude error is is expressed as follows:
[0047]
[0048] where ⊙ is quaternion multiplication, which can be obtained according to the unmanned aerial vehicle position obtained in step two and the method described in step one. denotes the inverse of , the dynamics satisfy:
[0049]
[0050] wherein is the angular velocity error, is the command angular velocity, the attitude error dynamics equation is expressed as:
[0051]
[0052] define a variable φ i = [j x , j y , j z , j yz , j xz , j xy ] T , define a new variable s i is multiplied by J i and the derivative is obtained:
[0053]
[0054] In combination with formula (4), the intermediate variable κ3 is a normal number, is the estimated value of φ i , and the control torque is:
[0055]
[0056] The variation law of
[0057]
[0058] wherein κ4, α i and γ i are normal numbers, the state of the dynamic system in formula (18) at time t ai is respectively and wherein μ2 is the corresponding time when the dynamic system in formula (18) reaches the maximum value; the estimation error Finally, the closed-loop attitude system is obtained as:
[0059]
[0060] According to this closed-loop system, a complete unmanned aerial vehicle attitude control system is established, and the attitude tracking of multiple unmanned aerial vehicles is realized according to the complete unmanned aerial vehicle attitude control system.
[0061] Advantages:
[0062] 1. The application discloses a distributed control method based on relative measurement information, directly utilizes local and relative measurement values obtained by an unmanned aerial vehicle on-board sensor, utilizes relative position information to perform distributed control, thereby completing a complete control system, instead of utilizing absolute information exchanged through a communication device, can improve anti-interference and anti-attack performance of the unmanned aerial vehicle cooperative control system, further resists network interference and network attacks, and reduces communication burden.
[0063] 2. The application discloses a data-based adaptive parameter identification method, utilizes data-based system identification to respectively solve quality and rotational inertia unknown problems, can solve unmanned aerial vehicle parameter change problems in a flight process, can make parameters converge to true values, thereby improving control performance, enabling the unmanned aerial vehicle formation system to complete cooperative control flight in a parameter change process, and completing multi-unmanned aerial vehicle formation control. BRIEF DESCRIPTION OF DRAWINGS
[0064] Figure 1 A schematic diagram is set for a multi-four-rotor unmanned aerial vehicle coordinate system;
[0065] Figure 2 A flowchart of the application discloses a multi-unmanned aerial vehicle adaptive distributed control method based on relative measurement information;
[0066] Figure 3 A mass estimation error schematic diagram;
[0067] Figure 4 A rotational inertia estimation error schematic diagram;
[0068] Figure 5 A flight process schematic diagram. DETAILED DESCRIPTION
[0069] In order to better illustrate the purpose and advantages of the application, the following further illustrates the content of the application in combination with the drawings and examples.
[0070] Example 1
[0071] As shown in the figure, the application discloses a multi-unmanned aerial vehicle adaptive distributed control method based on relative measurement information, and the specific implementation steps are as follows: Figure 2 (1) Establishing a four-rotor unmanned aerial vehicle dynamics model
[0072] According to the Euler-Newton formula, the kinematics and dynamics equations of the four-rotor unmanned aerial vehicle are represented by the following formula:
[0073]
[0074]
[0075] In the formula, p i For the drone's location, v i and ω i These are velocity and angular velocity, respectively, m i It is mass, and g is the acceleration due to gravity. It is the unit vector in the z-axis direction, T i It is along The thrust applied in the direction, q is a unit quaternion that expresses the attitude of a quadcopter drone. i0 q is the real part of the quaternion. ir This is the imaginary part of the quaternion, which specifically contains three terms and can be represented as q. ir =[q ir1 ;q ir2 ;q ir3 ],function I3 is a third-order identity matrix; J i It is the moment of inertia, τ i For torque, R i ∈SO(3) is a rotation matrix, and its expression is as follows:
[0076]
[0077] Furthermore, based on the kinematic equations, the command quaternion is calculated according to the position.
[0078] Based on the control design of cascaded systems, divide both sides of the kinematic formula by m. i Thus, the right-hand side is defined as an intermediate variable. Where u ix u iy and u iz Intermediate variables u i Specifically, there are three items: instruction rotation matrix. Let be the instruction unit quaternion, then:
[0079]
[0080] In the formula, For unit vectors, to ensure consistency in the above equation, This can be further deduced as follows:
[0081]
[0082] Then the command thrust T i for:
[0083]
[0084] To solve the redundancy of quaternion, let Thus, we get:
[0085]
[0086] Then the command quaternion can be extracted as:
[0087]
[0088] The mass and moment of inertia of the UAV are unknown, and the number of multi-quadrotor UAVs is designed to be 4, as shown in Figure 1 The coordinates of the multi-quadrotor UAVs are distributed.
[0089] The mass of the UAV is given as m i = 0.85 kg, and the moment of inertia is J i = diag[0.050, 0.042, 0.086] kgm 2 However, these two data are unknown in the experiment and need to be identified. Let the expected value of the displacement difference between each follower and the leader at the end of flight be δ i Then set δ1 = [4, 4, 0] T m, δ2 = [4, -4, 0] T m, δ3 = [-4, -4, 0] T m, δ4 = [-4, 4, 0] T m, that is, the four followers should be distributed in a square with the leader as the center and a side length of 8 m at the end.
[0090] Set the initial position of the leader p0 = [0, 0, 0] T m, and the initial speed of the leader v0 = [0.4, 0.4, 0.25] T ms -1 The initial positions of the followers are p1(0) = [5, 3.5, 0] T m, p2(0) = [4, -4.5, 0] T m, p3(0) = [-4.5, -3.5, 0] T m, p4(0) = [-4.5, 5, 0] T m; The initial attitudes of the followers are q1(0) = [0.9848, 0, 0, 0.1736] T , q2(0) = [0.9962, 0, 0, -0.0872] T , q3(0) = [0.9914, 0, 0, 0.1305] T , q4(0) = [0.9659, 0, 0, -0.2588] T .
[0091] (2) Design the force to achieve closed-loop position system
[0092] Based on the design of the distributed formation control scheme described in step two, the detailed steps of force control are as follows:
[0093] m i is an unknown constant, in order to realize parameter identification, two dynamic systems are designed as:
[0094]
[0095] In the formula, Q i and P i represent the state of the dynamic system, and the initial state is zero; define the filter And filter θ i And represent the filter state, and the initial state is zero; η p = 0.06; f i is the control force; define the variable Define
[0096] Auxiliary system:
[0097]
[0098] Wherein, represents the state of the auxiliary system, and the initial state is zero; represents the set of unmanned aerial vehicles adjacent to unmanned aerial vehicle i; κ r = 0.4; the relative position error p ij represents the relative position of unmanned aerial vehicle i and unmanned aerial vehicle j, δ ij is the desired position difference between unmanned aerial vehicles; define a new variable κ1=0.2, to z i Both sides are multiplied by m i and the derivative is obtained:
[0099]
[0100] In the formula,
[0101] f i is the control force. is the estimated value of m i , the control force f i is designed to represent:
[0102]
[0103] The adaptation pattern is as follows:
[0104]
[0105] In the formula, κ2 and β i and c i All are positive constants, κ2 = 2.4, β1 = β2 = β3 = β4 = 0.01, c1 = 0.5, c2 = 10, c3 = 2, c4 = 10; the dynamic system at t pi The states at time 1 are respectively and in μ1 represents the time when the dynamic system reaches its maximum value; the estimation error is defined as follows: The closed-loop position system is obtained as follows:
[0106]
[0107] Based on this closed-loop system, a complete UAV position control system can be established, thereby enabling the position formation and tracking of multiple UAVs.
[0108] (3) Design the applied torque to achieve the closed-loop attitude system
[0109] Based on the distributed formation control scheme designed in step three, the detailed steps for torque control are as follows:
[0110] Moment of inertia J i Given an unknown symmetric positive definite constant matrix, a dynamic system is introduced to obtain an accurate estimate of the inertia matrix:
[0111]
[0112] in, and Represents the state of a dynamic system, with all initial states being zero; defines a filter. and filter ζ i and ρ i These are the filter states, and their initial states are all zero; η a =0.06; Define variable It is a linear operator for x = [x1, x2, x3] T , definition 1, 2 and It is a constant, l1 < l2, l1 = 0.1, l2 = 10,
[0113] Attitude error is It can be represented in the following form:
[0114]
[0115] Where ⊙ represents quaternion multiplication, The location of the UAV obtained in step two and the method described in step one can be used to determine its position. express The reverse, The dynamics satisfy:
[0116]
[0117] In the formula, For angular velocity error, Given the commanded angular velocity, the attitude error dynamic equation is expressed as:
[0118]
[0119] Define variable φ i =[j x ,j y ,j z ,j yz ,j xz ,j xy ] T Define a new variable For s i Multiply by J i And by taking the derivative, we can obtain:
[0120]
[0121] Where κ3 is a positive constant, κ3 = 0.5, For φ i If the estimated value is obtained, then the control torque is:
[0122]
[0123] The law of change is:
[0124]
[0125] Among them κ4, α i and γ i All are positive constants, κ4 = 0.5, α1 = α2 = α3 = α4 = 0.5, γ1 = γ2 = γ3 = 1000, γ4 = 4000. The dynamic system at t ai The states at time 1 are respectively and in μ2 represents the time when the dynamic system reaches its maximum value; estimation error The closed-loop attitude system is finally obtained as:
[0126]
[0127] According to the closed-loop system, a complete unmanned aerial vehicle attitude control system can be established, so as to realize the attitude tracking of multiple unmanned aerial vehicles.
[0128] According to the above parameters, the corresponding formula is solved, and flight verification is performed; the error between the parameter estimation value and the actual value in the flight process is recorded, as shown in Figure 3 and Figure 4 The error can gradually decrease to zero in the flight process, that is, the parameter estimation value can converge to the true value; at the same time, the flight process is recorded, and the experimental results are shown in Figure 5 The multiple unmanned aerial vehicles can complete the formation following the leader and complete the expected position and attitude while maintaining the formation.
[0129] The above specific description further details the purpose, technical scheme and beneficial effects of the application. It should be understood that the above description is only a specific embodiment of the application and is not used to limit the protection scope of the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.
Claims
1. A multi-UAV adaptive distributed control method based on relative measurement information, characterized in that: Comprising the following steps, Step one, establish quadrotor unmanned aerial vehicle kinematics and dynamics equations, build the relationship between the position p i and thrust T i , and build the relationship between the unit quaternion q i and torque τ i ; according to the position to get the command quaternion , so as to express the command attitude of quadrotor Step two, due to the mass m i Unknown, by designing two dynamic systems to achieve the parameter m i Identification, further put forward auxiliary system and variable z i , combined with the kinematics equation established in step one, design adaptive distributed control force, get closed-loop position system, so as to realize position formation tracking; m i is an unknown constant, for the realization of parameter identification, design two dynamic systems for: where Q i and P i represent the dynamic system states in equation (12), and the initial states are all zeros; define the filter and the filter θ i and represent the filter states, and the initial states are all zeros; the constant η p > 0; f i is the control force; define the variable define ι1, ι2 and are all normal numbers, and ι1 < ι2; Step three, due to the moment of inertia J i Unknown, by designing two dynamic systems to achieve J i Identification, according to the specific position obtained in step two and the instruction quaternion obtained by the position instruction quaternion method in step one Further put forward variable s i , combined with the dynamic equation established in step one, design adaptive control torque, get closed loop attitude system, finally realize attitude tracking on the basis of position tracking in step two; moment of inertia J i is an unknown symmetric positive definite constant matrix, to obtain an accurate estimate of the moment of inertia, introduce the dynamic system of equation (18): where and a i denote the dynamic system states in equation (18), and their initial states are all zeros; define the filter and filter ζ i and p i are filter states, and their initial states are all zeros; constant a > 0; define variable is a linear operator, for x = [x1, x2, x3] T , define and is a constant, 2. The method of claim 1, wherein: The specific implementation method of step one is, According to Euler-Newton formula, the kinematics and dynamics equations of quadrotor UAV are expressed by the following formula: where p i is the position of the UAV, v i and ω i are the velocity and angular velocity, m i is the mass, g is the gravity acceleration, is the unit vector in the z-axis direction, T i is the thrust exerted along the direction, is the unit quaternion expressing the attitude of the quadrotor UAV, q i0 is the real part of the quaternion, q ir is the imaginary part of the quaternion, which specifically contains three terms, expressed as q ir = [q ir1 ; q ir2 ; q ir3 ], the function I3 is the third-order unit matrix; J i is the moment of inertia, τ i is the torque, ω i × = [0, -ω i3 , ω i2 ; ω i3 , 0, -ω i1 ; -ω i2 , ω i1 , 0], R i ∈ SO(3) is the rotation matrix, whose expression is as follows: Further based on the kinematic equations presented in equation (2), the position-aided command quaternion is calculated as According to the cascade system control design, divide both sides of formula (2) by m i Define the right side of the formula as an intermediate variable Where u ix , u iy , and u iz are intermediate variables u i Specifically, the command rotation matrix is the command unit quaternion, then: wherein is a unit vector, to ensure consistency of equation (6), Equation (6) is further derived as: T = Tc + Tp i is: To solve the redundancy of quaternions, let We get: Then the command quaternion is extracted as:
3. The method of claim 2, wherein: The specific implementation method of step two is, Auxiliary system: wherein, represents the state of the assistance system (13) and the initial state is zero; represents the set of drones adjacent to drone i; constant constant K r > 0; relative position error p ij represents the relative position of drone i to drone j, δ ij is the desired position difference between drones; define a new variable wherein K1 is a normal number, for z i multiply both sides by m i and derive: Combining equation (2), the intermediate variable is the estimate of m i , then the control force f i is expressed as: The adaptation pattern is as follows: where κ2, β i and c i are normal numbers, the state of the dynamic system in formula (12) at time t pi is respectively and where μ1is the corresponding time when the dynamic system in formula (12) reaches the maximum value; define the estimation error The closed-loop position system is obtained as follows: According to this closed loop system, that is, the complete UAV position control system is established, and the position formation tracking of multiple UAVs is realized according to the UAV position control system.
4. The method of claim 3, wherein: The specific implementation method of step three is, The attitude error is is expressed in the form where is quaternion multiplication, According to the position of the UAV obtained in step two and the method obtained in step one, denotes the inverse of The dynamics satisfy: wherein is the angular velocity error, is the commanded angular velocity, the attitude error dynamics equation is expressed as: Define the variable φ i = [j x , j y , j z , j yz , j xz , j xy ] T , define a new variable s i times J i and derive: Combining equation (4), the intermediate variable κ3 is a constant, is an estimate of φ i the control torque is: The law of change is: where κ4, a i and γ i are normal numbers, the state of the dynamic system in formula (18) at time t ai is respectively and where μ2 is the corresponding time when the dynamic system in formula (18) reaches the maximum value; the estimation error Finally, the closed-loop attitude system is obtained as: According to this closed loop system, that is, the complete UAV attitude control system is established, and the attitude tracking of multiple UAVs is realized according to the complete UAV attitude control system.