Four-rotor unmanned aerial vehicle cluster anti-interference and fault-tolerant control method

Abstract

The invention discloses an anti-interference and fault-tolerant control method for a quad-rotor unmanned aerial vehicle cluster, and belongs to the technical field of unmanned aerial vehicle control. The method comprises the following steps: establishing a quadrotor unmanned aerial vehicle cluster dynamic model containing unconstrained interference (including unbounded interference) and multiple faults; establishing an augmentation system to enhance the characterization capability of the system to the composite uncertainty; constructing a distributed unknown input observer, and observing the state of the quad-rotor unmanned aerial vehicle, sensor faults and unconstrained lumped interference at the same time; constructing a four-rotor unmanned aerial vehicle cluster anti-interference and fault-tolerant controller; according to the method, the four-rotor unmanned aerial vehicle cluster can maintain good fault estimation precision, disturbance suppression capability and cooperative control performance under the condition of multi-fault concurrency and unbounded disturbance coupling, and the flight safety and task reliability of the four-rotor unmanned aerial vehicle cluster in a complex environment are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and particularly to an anti-interference and fault-tolerant control method for a quadrotor unmanned aerial vehicle cluster under multiple faults and unconstrained disturbances (including unbounded disturbances). Background Art

[0002] In recent years, with the continuous progress of new materials, microelectromechanical systems, and control technologies, quadrotor unmanned aerial vehicle clusters have been widely used in many fields such as disaster prevention and relief, environmental exploration, police security, and cluster operations due to their functional diversity and wide range of uses. However, when quadrotor unmanned aerial vehicle clusters perform tasks in harsh environments, they are extremely vulnerable to the influence of strong disturbances such as measurement noise, electromagnetic interference, weather changes, and wind field disturbances, which may then lead to problems such as navigation deviation, out-of-control flight states, and communication damage, seriously affecting the overall performance and task completion rate of the quadrotor unmanned aerial vehicle cluster system. Therefore, many excellent results have been achieved in anti-interference control for the presence of external disturbances, including sliding mode control methods, adaptive control methods, control methods based on disturbance observers, and control methods based on filters. However, the anti-interference control methods mentioned above often require the disturbance or its derivative to satisfy boundedness conditions, while in engineering practice, there are often unconstrained disturbance forms such as ramp disturbances with unbounded amplitudes and square wave disturbances with unbounded derivatives, making traditional anti-interference methods have obvious limitations in such unbounded disturbance scenarios. Although there have been many research results in recent years on designing anti-interference strategies based on anti-interference observers, estimators, and predictive control for unbounded disturbances, they often need to assume that the disturbance derivative is bounded, satisfies the Lipschitz constraint, and satisfies specific distribution characteristics, so that the existing anti-interference control strategies cannot solve the problem of completely unconstrained disturbances (including unbounded disturbances).

[0003] In extreme and complex operating environments, quadrotor unmanned aerial vehicle clusters often face high-intensity and long-time task executions, which increases the probability of physical component failures. In particular, key components such as actuators, sensors, and control systems are easily affected by external factors and suffer from multiple faults. However, existing fault-tolerant control methods often target single types of faults, such as separate actuator additive / multiplicative faults, sensor faults, or process faults, and most require the process fault amplitude to be bounded, the derivative to be bounded, or the upper and lower bounds of the multiplicative fault factor to be known. In actual systems, there are often multiple fault phenomena such as actuator and sensor process faults, additive and multiplicative faults, and they are easily coupled with dynamic uncertainties such as external disturbances and component aging, resulting in complex fault characteristics. More severely, when unconstrained disturbances (including unbounded disturbances) are coupled with multiple faults, the large amplitude characteristics of unconstrained disturbances (including unbounded disturbances) will significantly amplify the impact of faults, making traditional fault-tolerant control methods ineffective.

[0004] Based on the above analysis, in order to ensure the robustness and fault tolerance of the quadrotor UAV swarm under unconstrained disturbances (including unbounded disturbances) and multiple faults, the present invention proposes a novel anti-disturbance and fault-tolerant control method based on an unknown input observer. Summary of the Invention

[0005] The purpose of the present invention is to provide an anti-disturbance and fault-tolerant control method for a quadrotor UAV swarm to solve the problems mentioned in the background art; the present invention innovatively overcomes the problems of unconstrained disturbances including unbounded disturbances and multiple faults that are difficult to handle by traditional methods, and realizes the safe and reliable flight control of UAVs under the coupled action of multiple faults and unconstrained disturbances (including unbounded disturbances).

[0006] In order to achieve the above invention purpose, the present invention provides the following technical solutions:

[0007] An anti-disturbance and fault-tolerant control method for a quadrotor UAV swarm, comprising the following steps:

[0008] S1. Establish a dynamic model of a quadrotor UAV swarm including unconstrained disturbances (including unbounded disturbances) and multiple faults;

[0009] S2. Based on the dynamic model described in S1, establish an augmented system to enhance the representation ability of the dynamic model for composite uncertainties;

[0010] S3. Design a distributed unknown input observer, and use the observer to simultaneously observe the state of the quadrotor UAV, sensor faults, and unconstrained lumped disturbances;

[0011] S4. Based on the output of the observer described in S3, construct an anti-disturbance and fault-tolerant controller for the quadrotor UAV swarm to achieve stable cooperative control of the UAV swarm under disturbance and fault conditions.

[0012] Preferably, the specific content of S1 is as follows:

[0013] The kinematic model of a single quadrotor UAV under unconstrained disturbances (including unbounded disturbances) is expressed as:

[0014]

[0015] where x, y, and z represent the position of the quadrotor UAV in the inertial coordinate system; φ, θ, and ψ respectively represent the roll angle, pitch angle, and yaw angle of the UAV; I x , I y and I z respectively represent the moments of inertia of the quadrotor UAV along the axes of the body coordinate system; I r represents the moment of inertia of the propeller; u z represents the total thrust generated by the four propellers; u φ , uθ and \(u\) ψ represent the roll moment, pitch moment, and yaw moment of the quadrotor UAV, respectively; \(d\) h (\(h = 1, 2, \ldots, 4\)) represents unconstrained disturbances (including unbounded disturbances); \(m\) represents the mass of the quadrotor UAV; \(g\) represents the acceleration due to gravity; \(\Omega\) r represents the differential angular velocity of the quadrotor UAV's propellers.

[0016] Assume that the quadrotor UAV is moving in an approximate hovering state, which means that in the vertical direction \(u\) z \(\approx mg\), the pitch angle and roll angle are both small enough to satisfy \(\sin\varphi\approx\varphi\) and \(\sin\theta\approx\theta\), and the quadrotor UAV does not produce yaw motion (\(\psi = 0\)) during flight. Therefore, equation (1) can be linearized, and the simplified linear model is:

[0017]

[0018] Let be the system state vector, \(u = [u\) z , \(u\) φ , \(u\) θ , \(u\) ψ T \(\in\mathbb{R}\) p be the control input vector, \(d = [d_1, d_2, d_3, d_4]\) T \(\in\mathbb{R}\) q be the external disturbance vector, \(y = [x, y, z, \varphi, \theta, \psi]\) T \(\in\mathbb{R}\) m be the measurement output vector. Then, the linear dynamic model of the quadrotor UAV can be expressed as:

[0019]

[0020] where:

[0021]

[0022] \(G = [0\ 0\ 0\ 0\ 0\ -1\ 0\ 0\ 0\ 0\ 0\ 0]\) T

[0023] ​The propulsion system of a quadrotor UAV consists of four identical brushless motors and four fixed-pitch propellers. Since the motors or propellers may fail, resulting in the abnormal operation of the actuators, this will significantly reduce the safety and reliability of the quadrotor UAV. At the same time, accurate path tracking depends on the reliable measurement of sensors. However, due to the harsh environment in which the UAV swarm is located and the aging of the sensors themselves, the UAV swarm is vulnerable to interference and sensor failures. Since \(G_g\) is a constant matrix, it can be regarded as the interference part. Thus, based on Equation (3), the mathematical model description of the \(i\)-th UAV in the quadrotor UAV swarm under multiple faults and unconstrained interference (including unbounded interference) is as follows:

[0024]

[0025] where \(N\) represents the number of UAVs in the quadrotor UAV swarm; \(\delta_{a,i}\) represents the actuator additive fault, and \(\delta_{s,i}\) represents the sensor fault. \(u_{m,i}\) is the input containing multiplicative faults of the system, which can be specifically expressed as:

[0026]

[0027] where \(l\) represents the \(l\)-th actuator channel, and \(\rho\) il represents the actuator efficiency loss factor. Considering the following situations, if \(\rho\) il (t) = 0, then there is no actuator multiplicative fault in the \(l\)-th actuator; if 0 < \(\rho\) il (t) < 1, then the \(l\)-th actuator has a partial efficiency loss fault; if \(\rho\) il (t) < 0, it means that the \(l\)-th actuator has an overload fault. Denote \(u\) i (t) = [u i1 (t), …, u i4 (t)] T , and the actuator efficiency loss factor \(\rho\) i (t) = diag(\(\rho\) i1 (t), …, \(\rho\) i4 (t)), then the compact model of the control input containing actuator multiplicative faults can be described as:

[0028]

[0029] Preferably, the S2 specifically includes the following content:

[0030] Establish an augmented system containing the state of the quadrotor UAV and actuator faults:

[0031]

[0032] where C1 = [C E];

[0033] Lumped disturbance w in the augmented system i (t) is expressed as:

[0034] w i (t) = -ρ i (t)u i (t) + f a,i (t) + d i (t) (8)

[0035] It can be seen that this lumped disturbance covers unconstrained disturbances (including unbounded disturbances), multiplicative and additive actuator faults, making it very challenging to accurately estimate such lumped disturbances.

[0036] Preferably, S3 specifically includes the following content:

[0037] Design a novel unknown input observer:

[0038]

[0039] Among them, and are the estimates of the system state x i (t) and the sensor fault f s,i (t) respectively; and are the estimates of the system output and the lumped disturbance respectively; is the first derivative of the system output, and S and R are the gain matrices of the unknown input observer.

[0040] Define as the estimation error of the augmented state of the UAV, and we can get:

[0041]

[0042] Among them, is the estimation error of the lumped disturbance.

[0043] Preferably, S4 specifically includes the following content:

[0044] Construct a fault-tolerant controller:

[0045] Combined with the observer design part of S3, the controller is designed as:

[0046]

[0047] Among them, is the state consistency error of the quadrotor UAV swarm system. K is the controller gain, which can be obtained by solving the following linear matrix inequality:

[0048]

[0049] Among them,

[0050]

[0051] where Q1 and Q2 are positive definite matrices, Q3 = Q2S is a matrix of appropriate dimension, L is the Laplacian matrix of the quadrotor UAV swarm, λ max (·) and λ min (·) represent the maximum and minimum eigenvalues of the matrix respectively, is a symmetric matrix, 1 N is an N-dimensional all-ones vector, I is an identity matrix of appropriate dimension, R and S are the gain matrices of the unknown input observer, and R is also solved by the linear matrix inequality described in Equation (11). The expression of S is:

[0052]

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] 1) The present invention establishes a new lumped disturbance model, which covers unconstrained disturbances (including unbounded disturbances) and multiplicative / additive actuator faults. The method proposed in the present invention eliminates the assumptions such as bounded disturbance derivatives, Lipschitz conditions, and normal distributions required by existing methods, and is applicable to more complex scenarios.

[0055] 2) The present invention designs a new unknown input observer by augmenting the system with state information and sensor fault information, reducing the design difficulty and computational complexity of the observer. Further, it realizes the synchronous online estimation of system states, sensor faults, and unconstrained lumped disturbances (including unbounded disturbances), improving the performance of UAVs in complex scenarios.

[0056] 3) The present invention formulates a fault-tolerant control strategy by using the real-time estimation information obtained by the observer and designs a distributed fault-tolerant controller based on a disturbance compensation mechanism, which can achieve anti-disturbance and fault-tolerant control of the quadrotor UAV swarm. In addition, the proposed fault-tolerant control method based on the unknown input observer decouples the lumped disturbance and the error system, reducing the complexity of solving the observer gain matrix. Brief Description of the Drawings

[0057] Figure 1 is a schematic flow chart of the anti-disturbance and fault-tolerant control method for the quadrotor UAV swarm of the present invention. Detailed Embodiment

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0059] The present invention proposes a method for anti-interference and fault-tolerant control of a quadrotor UAV swarm, enabling the quadrotor UAV swarm to achieve accurate decoupling estimation of the states and faults of the quadrotor UAVs under the combined action of unconstrained interference (including unbounded interference) and multiple faults, and constructing a distributed fault-tolerant control strategy with strong robustness to break through the strict constraints on interference and fault types in existing methods and improve the overall performance of the quadrotor UAV swarm in complex environments. The method for anti-interference and fault-tolerant control of the quadrotor UAV swarm proposed by the present invention will be described below in conjunction with relevant accompanying drawings and specific examples. The specific content is as follows.

[0060] Embodiment 1:

[0061] As Figure 1 shown, the present invention proposes a method for fault estimation and fault-tolerant control of a quadrotor UAV, including:

[0062] S101. Establish a dynamic model of a quadrotor UAV swarm including unconstrained interference (including unbounded interference) and multiple faults, which specifically includes the following content:

[0063] The kinematic model of a single quadrotor UAV under unconstrained interference (including unbounded interference) is expressed as:

[0064]

[0065] where x, y, and z represent the positions of the quadrotor UAV in the inertial coordinate system; φ, θ, and ψ represent the roll angle, pitch angle, and yaw angle of the UAV, respectively; I x , I y and I z represent the moments of inertia of the quadrotor UAV along the respective axes of the body coordinate system; I r represents the moment of inertia of the propeller; u z represents the total thrust generated by the four propellers; u φ , u θ and u ψ represent the roll moment, pitch moment, and yaw moment of the quadrotor UAV, respectively; d h (h = 1, 2,..., 4) represents external unconstrained interference (including unbounded interference); m represents the mass of the quadrotor UAV; g represents the acceleration due to gravity; Ω r represents the differential angular velocity of the quadrotor UAV propeller.

[0066] Assume that the quadrotor UAV is moving in an approximate hovering state, which means that in the vertical direction, uz ≈mg, the pitch angle and roll angle are small enough to satisfy sinφ≈φ and sinθ≈θ, and the quadrotor drone does not produce yaw motion during flight (ψ=0). Therefore, equation (1) can be linearized, and the simplified linear model is:

[0067]

[0068] Will As the system state vector, u=[u z ,u φ ,u θ ,u ψ ] T ∈R p As the control input vector, d = [d1, d2, d3, d4] T ∈R q As the external disturbance vector, y = [x, y, z, φ, θ, ψ] T ∈R m As the measurement output vector, the linear dynamic model of the quadrotor drone can be expressed as:

[0069]

[0070] Where:

[0071]

[0072]

[0073] G=[0 0 0 0 0 -1 0 0 0 0 0 0] T

[0074] The propulsion system of a quadrotor drone is composed of four identical brushless motors and four fixed-pitch propellers. Since the motor or propeller may fail, the actuator cannot operate normally, which will significantly reduce the safety and reliability of the quadrotor drone. At the same time, accurate path tracking depends on reliable measurements of sensors. However, the harsh environment in which the drone cluster is located and the aging of the sensors themselves make the drone cluster susceptible to interference and sensor failure. Since Gg is a constant matrix, it can be regarded as the interference part. Therefore, based on formula (3), the mathematical model of the i-th drone in the quadrotor drone cluster under multiple faults and unconstrained interference (including unbounded interference) is described as:

[0075]

[0076] Where N represents the number of drones in the quadrotor drone cluster; 尻,i represents the actuator additive fault; 尻,i represents the sensor fault, The input for the system with multiplicative faults can be specifically expressed as:

[0077]

[0078] Among them, l represents the l-th actuator channel, and ρ il represents the actuator efficiency loss factor.

[0079] If ρ il (t) = 0, then there is no actuator multiplicative fault in the l-th actuator; if 0 < ρ il (t) < 1, then the l-th actuator has a partial efficiency loss fault; if ρ il (t) < 0, it means that the l-th actuator has an overload fault. Denote u i (t) = [u i1 (t), …, u i4 (t)] T , and the actuator efficiency loss factor ρ i (t) = diag(ρ i1 (t), …, ρ i4 (t)), then the compact model of the control input including actuator multiplicative faults can be described as:

[0080]

[0081] S102. Establish an augmented system by constructing an augmented state vector from the states of the quadrotor UAV and sensor faults, enhancing the system's ability to represent composite uncertainties. The specific content is as follows:

[0082] Establish an augmented system that includes the states of the quadrotor UAV and actuator faults:

[0083]

[0084] Among them, C1 = [C E];

[0085] The lumped disturbance w i (t) in the augmented system is expressed as:

[0086] w i (t) = -ρ i (t)u i (t) + f a,i (t) + d i (t) (8)

[0087] It can be seen that this lumped disturbance covers unconstrained disturbances (including unbounded disturbances), multiplicative and additive actuator faults, making it very challenging to accurately estimate such lumped disturbances.

[0088] S103. Design a distributed unknown input observer, which can not only observe the system state and sensor faults simultaneously, but also observe the lumped disturbances, where the lumped disturbances include actuator additive faults, actuator partial failure faults, and unconstrained disturbances (including unbounded disturbances). Specifically, it includes the following contents:

[0089] Construct a novel unknown input observer:

[0090]

[0091] Among them, and are the estimates of the system state x i (t) and the sensor fault f s,i (t) respectively; and are the estimates of the system output and the lumped disturbance respectively; is the first derivative of the system output, and S, R are the gain matrices of the unknown input observer.

[0092] Define as the estimation error of the augmented state of the UAV, and we can get:

[0093]

[0094] Among them, is the estimation error of the lumped disturbance, which can be derived as follows:

[0095]

[0096] Among them, (C1B1) + (C1B1) = I. If the following constraint conditions are satisfied then the estimation error of the lumped disturbance can be simplified to:

[0097]

[0098] Then the estimation error (10) of the augmented system can be simplified to:

[0099]

[0100] S104. Based on the state / fault estimation information output by the observer, design a robust controller with disturbance compensation characteristics to ensure the robustness and safety of the quadrotor UAV swarm. Specifically, it includes the following contents:

[0101] The fault-tolerant controller can be established as:

[0102]

[0103] Among them, is the state consistency error of the quadrotor UAV swarm system, is the estimated value of the lumped disturbance, both of which can be obtained by the observer. The solution of the controller gain matrix K is based on the Lyapunov stability theory. By ensuring that the derivative of the Lyapunov function of the closed-loop system is negative definite, the asymptotic stability of the system is guaranteed.

[0104] Substituting the designed controller (14) into Equation (4) gives:

[0105]

[0106] By introducing the Kronecker product, the above equation can be written in a compact matrix form:

[0107]

[0108] Define and Further obtain and where L is the Laplacian matrix of the system.

[0109] Therefore, the consistency error of the quadrotor UAV swarm system is:

[0110]

[0111] The present invention provides the following verification steps to verify that the fault-tolerant controller obtained in S104 conforms to the quadrotor UAV system described in the present invention and can still operate stably in the case of multiple faults and disturbances. The specific content is as follows:

[0112] According to the system error state equation (13) and the consistency error equation (17), construct an appropriate Lyapunov function:

[0113]

[0114] where Q1 and Q2 are symmetric positive definite matrices.

[0115] Taking the derivative of the above Lyapunov function gives:

[0116]

[0117] where

[0118]

[0119] where λ max (·) and λ min (·) respectively represent the maximum and minimum eigenvalues of the matrix, is a symmetric matrix, 1N is an N-dimensional all-ones vector, and I is the identity matrix of appropriate dimension.

[0120] For the convenience of solving the linear matrix inequality, let Q3 = Q2S, and the following decoupled matrices are obtained:

[0121]

[0122] where

[0123]

[0124] When the above linear matrix inequality holds, there is V i (t) < 0, which proves the stability of the system and ensures the robustness and safety of the quadrotor UAV swarm.

[0125] The present invention aims at the anti-interference and fault-tolerant control problems of quadrotor UAV swarms, and proposes a novel fault-tolerant control scheme based on an unknown input observer. By innovatively designing the unknown input observer, the present invention realizes the synchronous and accurate estimation of the system state, sensor faults, and unconstrained lumped disturbances (including unbounded disturbances), breaking through the assumption limitation of the existing methods that the upper bound of the disturbance is known. Based on the output information of the observer, a fault-tolerant control strategy is constructed to ensure the flight stability of the closed-loop system under the action of multiple concurrent faults and unconstrained disturbances (including unbounded disturbances).

[0126] It should be noted that in this invention patent, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0127] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for anti-interference and fault-tolerant control of a quadrotor UAV cluster, characterized in that, It includes the following steps: S1. Establish a dynamic model of a quadrotor UAV cluster that includes unconstrained disturbances with unbounded disturbances and multiple faults; S2. Based on the dynamic model described in S1, construct an augmented system to enhance the representation ability of the dynamic model for composite uncertainties; S3. Design a distributed unknown input observer to simultaneously observe the states of quadrotor UAVs, sensor faults, and unconstrained lumped disturbances using the observer; S4. Based on the output of the observer described in S3, construct an anti-disturbance and fault-tolerant controller for the quadrotor UAV cluster to achieve stable cooperative control of the UAV cluster under disturbance and fault conditions.

2. The anti-interference and fault-tolerant control method for a quadrotor UAV cluster according to claim 1, wherein, The specific content of S1 is as follows: The kinematic model of a single quadrotor UAV under unconstrained disturbances with unbounded disturbances is expressed as: where x, y, and z represent the position of the quadrotor UAV in the inertial coordinate system; φ, θ, and ψ represent the roll angle, pitch angle, and yaw angle of the UAV, respectively; I x , I y , and I z represent the moments of inertia of the quadrotor UAV about the axes of the body coordinate system, respectively; I r represents the moment of inertia of the propeller; u z represents the total thrust generated by the four propellers; u φ , u θ , and u ψ represent the roll moment, pitch moment, and yaw moment of the quadrotor UAV, respectively; d h represents the unconstrained disturbance, h = 1, 2, …, 4; m represents the mass of the quadrotor UAV; g represents the acceleration due to gravity; Ω r represents the differential angular velocity of the quadrotor UAV propellers. Assume that a quadrotor UAV is moving in an approximate hovering state, and its vertical velocity \(u\) z ≈ \(mg\), the pitch angle and roll angle satisfy \(\sin\varphi\approx\varphi\) and \(\sin\theta\approx\theta\), and there is no yaw motion generated by the quadrotor UAV during flight; Based on this, linearize Equation (1), and the simplified linear model is: Take as the system state vector; u = [u z , u φ , u θ , u ψ T ∈ R p as the control input vector;​ d = [d1, d2, d3, d4] T ∈R q as an external interference vector y = [x, y, z, φ, θ, ψ] T ∈R m as the measurement output vector Based on the above, the linear dynamic model of the quadrotor UAV is further expressed as: In the formula: G＝[0 0 0 0 0 -1 0 0 0 0 0 0] T Therefore, based on Equation (3), the mathematical model of the i-th UAV in the quadrotor UAV cluster under multiple faults and unconstrained disturbances with unbounded disturbances is described as: Among them, N represents the number of unmanned aerial vehicles in the quadrotor unmanned aerial vehicle cluster; f a，i represents the actuator additive fault; f s，i represents the sensor fault; is the input of the system containing multiplicative faults, specifically expressed as: where, l represents the l-th actuator channel; ρil represents the actuator efficiency loss factor; If ρ il (t) = 0, then there is no actuator multiplicative fault in the l-th actuator; if 0 < ρ il (t) < 1, then a partial efficiency loss fault occurs in the l-th actuator; if ρ il (t) < 0, it means that an overload fault occurs in the l-th actuator; Denote \(u\) i (t)=\([u i1 (t),\ldots,u i4 (t)] T , and the actuator efficiency loss factor \(\rho\) i (t)=\text{diag}(\rho i1 (t),\ldots,\rho i4 (t)), then the compact model description of the control input containing actuator multiplicative faults is:

3. A method for anti-interference and fault-tolerant control of a quadrotor UAV swarm according to claim 1, characterized in that The specific content of S2 is as follows: Establish an augmented system that includes the states of quadrotor UAVs and actuator faults: Among them, C1 = [C E]; The lumped disturbance w i in the augmented system is expressed as: w i (t) = -ρ i (t)u i (t) + f a,i (t) + d i (t) (8).

4. A method for anti-interference and fault-tolerant control of a quadrotor UAV cluster according to claim 1, characterized in that, The specific content of S3 is as follows: Design the distributed unknown input observer as: Among them, and represent the estimates of the system state x i (t) and the sensor fault f s,i (t), respectively; and represent the estimates of the system output and the lumped disturbance, respectively; represents the first derivative of the system output; S and R represent the gain matrices of the unknown input observer.

5. The anti-interference and fault-tolerant control method for a quadrotor UAV swarm according to claim 1, characterized in that, The specific content of S4 is as follows: Design the fault-tolerant controller as: wherein, is the state consistency error of the quadrotor UAV swarm system; K is the controller gain, which is obtained by solving the following linear matrix inequality: where, where \(Q_1\) and \(Q_2\) are positive definite matrices, \(Q_3 = Q_2S\) is a matrix of appropriate dimension, \(L\) is the Laplacian matrix of the quadrotor UAV swarm, \(\lambda\) max (·) and \(\lambda\) min (·) represent the maximum and minimum eigenvalues of the matrix respectively, is a symmetric matrix, \(\mathbf{1}\) N is an \(N\)-dimensional all-one vector; \(I\) is an identity matrix of appropriate dimension; \(S\) and \(R\) are the gain matrices of the unknown input observer; where the solution formula for \(S\) is: R is solved by the linear matrix inequality in Equation (11).

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