Manned-UAV swarm system based on cluster emergence and chatter-free fault-tolerant control

Abstract

The present invention relates to a manned-unmanned aerial vehicle (UAV) swarm system based on cluster emergence and chatter-free fault-tolerant control, belonging to the technical field of UAV swarm fault tolerance. The system comprises a leader UAV, a follower UAV, an observer, and a controller. The manned-unmanned aerial vehicle (UAV) swarm is divided into multiple clusters containing a unique leader UAV and a follower UAV. The observer is used to estimate the leader UAV information for follower UAVs in each cluster that cannot directly obtain the leader UAV information based on the leader UAV information obtained by the traction node in the cluster and the network interaction information between UAVs, and use it as the target information for multi-target cluster emergence control. The controller is used to track the target information estimated by the observer and perform fault-tolerant control on the follower UAVs. The present invention implements distributed chatter-free fault tolerance and multi-target cluster emergence for the manned-unmanned aerial vehicle (UAV) swarm, ensuring the high performance and safety of the manned-unmanned aerial vehicle (UAV) swarm operation.

Description

Technical Field

[0001] The present invention relates to the technical field of fault tolerance of unmanned aerial vehicle (UAV) clusters, and in particular to a manned-unmanned aerial vehicle (UAV) cluster system based on cluster emergence and chatter-free fault-tolerant control. Background Art

[0002] In recent years, drones have been widely used in emergency rescue, aerial photography, plant protection, power inspection, geological exploration, and film and television production. Compared to single drones, drone swarms achieve various tasks through inter-drone communication and coordination, offering greater application and research value. Manned-drone swarm control is an effective approach for controlling drone swarms. This approach uses an operator-controlled leader drone to guide follower drones to achieve control objectives. This manned-drone swarm control algorithm is highly robust and can effectively prevent accidents. Emergent control, on the other hand, is an effective approach for managing complex systems by adjusting local behaviors to achieve desired overall behavior. Multi-target emergence in manned-drone swarms involves dividing a drone swarm into clusters. A single operator-controlled drone guides other drones to achieve synchronized attitudes within the same cluster, while maintaining different attitudes within different clusters.

[0003] Furthermore, drone rotors can experience physical failures, manifesting as excessive or insufficient torque input, or reverse torque failures. These failures can cause minor performance degradation of individual drones, while more serious ones can lead to crashes and even endanger the safe operation of the entire drone swarm. Current fault-tolerant control for physical failures also suffers from chattering, a phenomenon that is significant in practical drone applications. Therefore, it is necessary to design a chatter-free controller to suppress chattering during fault-tolerant control. Summary of the Invention

[0004] In view of the above analysis, the present invention aims to disclose a manned-UAV cluster system based on cluster emergence and chatter-free fault-tolerant control; while realizing cluster emergence of manned-UAV clusters, it realizes fault-tolerant control of UAV rotors and avoids the UAV chattering problem, thereby ensuring the high performance and safety of the manned-UAV cluster.

[0005] The present invention discloses a manned-unmanned aerial vehicle (UAV) cluster system based on cluster emergence and chatter-free fault-tolerant control, comprising a leading UAV, a following UAV, an observer and a controller; wherein,

[0006] The manned-unmanned aerial vehicle (UAV) swarm is divided into multiple clusters, each of which includes a leader UAV and multiple follower UAVs. The leader UAV is controlled by a leader, and the follower UAVs track the leader UAV in the cluster.

[0007] The observer is a distributed preset performance observer; it is used to estimate the leader drone information for follower drones in each cluster that cannot directly obtain the leader drone information based on the leader drone information in the cluster obtained by the leading node in each cluster and the converted error information of the cluster synchronization error interacted through the inter-UAV communication network. This information is used as the leader drone information for the follower drones in each cluster that cannot directly obtain the leader drone information, and the leader drone information is used as the target information for multi-target cluster emergence control.

[0008] The controller is a distributed preset performance fault-tolerant controller, which is used to track the target information estimated by the observer and perform fault-tolerant control on the following UAV.

[0009] Furthermore, the traction node is determined according to the communication topology structure between the following UAVs; wherein,

[0010] The conditions for determining the i-th following drone as the towing node are: 1) there is no neighbor node or 2) or 3)

[0011] Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that follow the drones and receive information from other clusters; i -1,…,s; s is the number of leading drones;

[0012] The node without neighbor nodes refers to a follower drone that has no communication with other follower drones in the cluster.

[0013] Furthermore, the leading UAV and the following UAV in the manned-unmanned aerial vehicle cluster are both rotary-wing UAVs; dynamic models of the attitude systems are established for the leading UAV and the following UAV respectively;

[0014] The dynamic model of the attitude system of the leader UAV is:

[0015]

[0016] in, and Respectively represent the angular position and angular velocity of the leader UAV in the roll, pitch and yaw channels; l i The code that represents the cluster where the drone is located; represents the output of the leader drone; It represents the leader's operational input to the leader UAV; represents a nonlinear term;

[0017] The output of the leader drone does not change suddenly; it is constant. make Established;

[0018] The dynamic model of the attitude system of the i-th following drone is:

[0019]

[0020] Among them, x i,11 、x i,21 、x i,31 Respectively represent the roll angle, pitch angle and yaw angle of the i-th following drone; x i,12 、x i,22 、x i,32 Respectively represent the angular velocity of the roll, pitch and yaw channels of the i-th following UAV; d i,p2 Indicates interference; y i,p Represents the output signal; f i,p represents a nonlinear term; Indicates input for following the drone including fault conditions.

[0021] Furthermore, the control link between the following UAV and the controller includes an actuator; the actuator executes the control instruction output by the controller to make the following UAV track the tracking target estimated by the observer; when executing the control instruction, the actuator may fail;

[0022] Then, the actuator is modeled to obtain the output of the actuator including the fault condition for:

[0023]

[0024] Among them, u i,p Represents the normal input signal to the actuator; ρ i,p represents the multiplicative factor of the fault, which satisfies ρ in the roll channel and the pitch channel i,p > 0, satisfying ρ in the yaw channel i,p ≠0; in the yaw channel, there is a reverse fault. At this time, ρ i,p <0;r i,p Indicates a bias fault.

[0025] Furthermore, the distributed preset performance observer is a first-order integral structure, which can be expressed as:

[0026]

[0027] Among them, u i ∈R 3 With ξ i ∈R 3 are the input and output signals of the observer respectively; N is the number of following drones in the cluster;

[0028] Input signal u at time ti The calculation expression is:

[0029]

[0030] Wherein, c2 and c3 are positive constant gains; ij The matrix Ξ=(L+B) -1 The elements of L and B are the Laplace matrix and traction matrix of the manned-unmanned aerial vehicle cluster communication topology respectively; μ 1j and μ 2j are the first and second auxiliary variable matrices; is the kth jth following drone j kth j +1 trigger moment, is the kth jth following drone j The conversion error of the cluster synchronization error at the triggering moment, δ j satisfy is δ j estimated value.

[0031] Furthermore, in the observer input of the i-th following drone, the conversion error i,p , the calculation formula for p=1,2,3 is:

[0032]

[0033] Among them, a ij 、a im is the element of the adjacency matrix of the communication topology of the following UAV cluster; is the output vector of the leader UAV; Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that receive information from other clusters following the drone;

[0034] b i ∈{0,1} is the traction gain. When the i-th following drone is the traction node, b i =1, otherwise, b i =0; is the set of neighbor nodes following drone i; ∩ represents the intersection of two sets;

[0035]

[0036] Among them, γ i (t) = [γ i,1 ,γ i,2 ,γ i,3 ] TUsed to characterize the boundary distance, h(γ i,p (t)) is the error conversion function, c1 is the constant gain, 13 is the three-dimensional column vector of all 1s, and the symbol Indicates the multiplication of the elements of two vectors, a1 is the set safety constant, and β is the set safety margin;

[0037] Auxiliary variables μ in the first and second auxiliary variable matrices 1j,p 、μ 2j,p , the calculation formulas for p=1,2,3 are:

[0038]

[0039] in, The pth element of .

[0040] Furthermore, the triggering condition for the interaction of the communication network between the following UAVs is that the difference between the conversion error at the current moment and the conversion error at the most recent trigger moment is not less than the trigger threshold;

[0041] The trigger threshold is the minimum value of the first trigger limit and the second trigger limit; wherein the first trigger limit is a preset positive constant threshold; the second trigger limit is determined by the ratio of the first trigger limit to the maximum eigenvalue of the first auxiliary variable matrix at the current moment.

[0042] Furthermore, the distributed preset performance fault-tolerant controller adopts Nussbaum function for fault tolerance and suppresses the chattering phenomenon of Nussbaum function when solving reverse faults through the preset performance method;

[0043] Then, the distributed preset performance fault-tolerant controller without chattering is expressed as:

[0044]

[0045] Among them, i,p is an adaptive variable; is an auxiliary variable; is the Nussbaum function used; p = 1, 2, 3.

[0046] Furthermore, the Nussbaum function Expressed as:

[0047]

[0048] Where σ1 and σ2 are parameters for adjusting the amplitude and frequency of the Nussbaum function respectively;

[0049] Adaptive variable υ i,p and auxiliary variables The calculation formula is as follows:

[0050]

[0051] Where q i,p is a positive constant gain, Output signal of drone angular velocity and first-order filter The error between is the intermediate variable, z i,p1 is the conversion error, h(κ i,p (t)) is the conversion function, κ i,p is the boundary distance for adjusting the error, l i is a constant gain, a 2i,p is the positive safety constant of the design;

[0052] is the adaptive gain of the neural network, Z i,p =[x i,11 ,x i,12 ,x i,21 ,x i,22 ,x i,31 ,x i,32 ] T is the input of the neural network, is the radial basis function vector.

[0053] Furthermore, the calculation formula of the radial basis function vector is:

[0054]

[0055] Among them, M1 is the number of neurons, o m and π m represent the center and width of the neuron, respectively;

[0056] The conversion error z i,p1 , the boundary distance of the adjustment error κ i,p , conversion function h(κ i,p The calculation formulas for (t)) are:

[0057]

[0058] Among them, e i,p is the adjustment error of the UAV output signal;

[0059] A first-order filter is represented as:

[0060]

[0061] Among them, ν i,p is a positive time constant, α i,p (t) is the virtual control law as the filter input, and its calculation formula is:

[0062]

[0063] Where, Ω i,p is an auxiliary variable, and its calculation formula is:

[0064]

[0065] in, is an intermediate variable;

[0066] Adaptive gain The update law is:

[0067]

[0068] Among them, r i,p1 and r i,p2 is a positive constant gain.

[0069] The distributed chatter-free fault-tolerant control system for manned-unmanned aerial vehicles disclosed in the present invention can achieve the following beneficial effects:

[0070] 1) The control strategy designed in this invention does not require prior knowledge of the drone's initial conditions, and the designed observer is a first-order integral structure, which simplifies the observer's design difficulty and computational complexity. Furthermore, the control strategy can autonomously adjust the system's convergence time and accuracy as needed, improving the performance of manned-unmanned aerial vehicle (UAV) swarms in the event of a fault.

[0071] 2) The present invention can realize distributed, non-jittering, fault-tolerant, multi-target clustering emergence control of manned-UAV clusters; the leading UAV influences the traction node to achieve multi-target clustering emergence of the entire UAV cluster.

[0072] 3) The present invention utilizes a preset performance method to suppress the state chattering phenomenon that occurs when the Nussbaum function method solves the reverse fault, ensuring that the output signal of the following drone is a smooth signal, thereby improving the performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. Throughout the drawings, the same reference symbols denote the same components.

[0074] Figure 1 The figure is a schematic diagram of the connection components of a manned-unmanned aerial vehicle cluster system for distributed chatter-free fault-tolerant control in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, which constitute a part of this application and are used to illustrate the principles of the present invention together with the embodiments of the present invention.

[0076] One embodiment of the present invention discloses a distributed manned-unmanned aerial vehicle (UAV) cluster system for performing chatter-free fault-tolerant control. Figure 1 As shown, it includes a leading UAV, a following UAV, an observer and a controller; wherein,

[0077] The manned-unmanned aerial vehicle (UAV) swarm is divided into multiple clusters, each of which includes a leader UAV and multiple follower UAVs. The leader UAV is controlled by a leader, and the follower UAVs track the leader UAV in the cluster.

[0078] The observer is a distributed preset performance observer; it is used to estimate the leader drone information for follower drones in each cluster that cannot directly obtain the leader drone information based on the leader drone information in the cluster obtained by the leading node in each cluster and the converted error information of the cluster synchronization error interacted through the inter-UAV communication network. This information is used as the leader drone information for the follower drones in each cluster that cannot directly obtain the leader drone information, and the leader drone information is used as the target information for multi-target cluster emergence control.

[0079] The controller is a distributed preset performance fault-tolerant controller, which is used to track the target information estimated by the observer and perform fault-tolerant control on the following UAV; including chatter-free fault-tolerant control for reverse faults.

[0080] Specifically, the traction node is determined according to the communication topology structure between the following drones;

[0081] The conditions for determining the i-th following drone as the towing node are: 1) there is no neighbor node or 2) or 3)

[0082] Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that follow the drones and receive information from other clusters; i -1,…,s; s is the number of leading drones;

[0083] The node without neighbor nodes refers to a follower drone that has no communication with other follower drones in the cluster.

[0084] Specifically, the leading UAV and the following UAV in the manned-unmanned aerial vehicle cluster are both rotor UAVs; dynamic models of the attitude systems of the leading UAV and the following UAV are established respectively;

[0085] The dynamic model of the attitude system of the leader UAV is:

[0086]

[0087] in, and Respectively represent the angular position and angular velocity of the leader UAV in the roll, pitch and yaw channels; l i The code that represents the cluster where the drone is located; represents the output of the leader drone; Indicates the leader's operational input; represents a nonlinear term;

[0088] The output of the leader drone does not change suddenly; it is constant. make Establishment; constant The symbol “—” indicates upper limit.

[0089] The dynamic model of the attitude system of the i-th following drone is:

[0090]

[0091] Among them, x i,11 、x i,21 、x i,31 Respectively represent the roll angle, pitch angle and yaw angle of the i-th following drone; x i,12 、x i,22 、x i,32 Respectively represent the angular velocity of the roll, pitch and yaw channels of the i-th following UAV; d i,p2 Indicates interference; y i,p Represents the output signal; f i,p represents a nonlinear term; Indicates input including fault conditions.

[0092] The control link between the following drone and the controller includes an actuator; the actuator executes the control instruction output by the controller to make the following drone track the tracking target estimated by the observer; when executing the control instruction, the actuator may fail;

[0093] Then, the actuator is modeled to obtain the output of the actuator including the fault condition for:

[0094]

[0095] Among them, u i,p Represents the normal input signal of the attitude system design of the following drone; ρ i,p represents the multiplicative factor of the fault, which satisfies ρ in the roll channel and the pitch channel i,p> 0, satisfying ρ in the yaw channel i,p ≠0; in the yaw channel, there is a reverse fault. At this time, ρ i,p <0;r i,p Indicates a bias fault.

[0096] The nonlinear term expression in the dynamic model of the attitude system of the leader UAV and the follower UAV is the same; taking the nonlinear term of the follower UAV as an example; the nonlinear term f i,p Expressed as:

[0097]

[0098] Among them, J x 、J y 、J z Represents the moment of inertia in different directions, J r is the rotor inertia, I1, I2, I3 are the aerodynamic damping coefficients, Ω r is the residual angular velocity of all motors of the rotorcraft. In addition, the topological graph of the N following drones contains a directed spanning tree.

[0099] The distributed preset performance observer in this embodiment is a first-order integral structure, which simplifies the design difficulty and calculation complexity of the observer.

[0100] The expression of the distributed preset performance observer is:

[0101]

[0102] Among them, u i ∈R 3 With ξ i ∈R 3 are the input and output signals of the observer respectively; N is the number of following drones in the cluster;

[0103] The signal u input to the distributed preset performance observer at time t i The calculation expression is:

[0104]

[0105] Wherein, c2 and c3 are positive constant gains; ij The matrix Ξ=(L+B) -1 The elements of L and B are the Laplace matrix and traction matrix of the manned-unmanned aerial vehicle cluster communication topology respectively; μ 1j =diag{μ 1j,1 ,μ 1j,2 ,μ 1j,3}、μ 2j =[μ 2j,1 ,μ 2j,2 ,μ2j,3 ] T are the first and second auxiliary variable matrices; is the kth jth following drone j kth j +1 trigger moment, is the kth jth following drone j The conversion error of the cluster synchronization error at the triggering moment, ∈ j =[∈ j,1 ,∈ j,2 ,∈ j,3 ] T , δ j =[δ j,1 ,δ j,2 ,δ j,3 ] T satisfy is δ j The subscripts 1, 2, and 3 in the formula represent the roll, pitch, and yaw channels, respectively.

[0106] More specifically, in the observer input of the i-th following drone, the conversion error of the cluster synchronization error ∈ i,p , the calculation formula for p=1,2,3 is:

[0107]

[0108] Among them, a ij 、a im is the element of the adjacency matrix of the communication topology of the following UAV cluster; is the output vector of the leader UAV; Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that follow the drones and receive information from other clusters;

[0109] b i ∈{0,1} is the traction gain, which forms the traction matrix B=diag{b1,…,b N}, when the i-th following drone is a towing node, b i =1, otherwise, b i =0; is the set of neighbor nodes of drone i; ∩ represents the intersection of the two sets. In addition:

[0110]

[0111]

[0112] Among them, γi (t) = [γ i,1 ,γ i,2 ,γ i,3 ] T Used to characterize the boundary distance, h(γ i,p (t)) is the error conversion function, c1 is the constant gain, 13 is the three-dimensional column vector of all 1s, and the symbol Indicates the multiplication of the elements of two vectors, a1 is the set safety constant, and β is the set safety margin;

[0113] The calculation formula for the safety margin is as follows:

[0114]

[0115] Where T is the design convergence time and ε is the design convergence accuracy.

[0116] Auxiliary variables μ in the first and second auxiliary variable matrices 1j,p 、μ 2j,p , the calculation formulas for p=1,2,3 are:

[0117]

[0118] in, for The pth element of .

[0119] Specifically, the following UAVs trigger network interaction between UAVs to obtain the conversion error information of the cluster synchronization error; the trigger condition for the communication network interaction between the following UAVs is that the difference between the conversion error at the current moment and the conversion error at the most recent trigger moment is not less than the trigger threshold;

[0120] The trigger threshold is the minimum value of the first trigger limit and the second trigger limit; wherein the first trigger limit is a preset positive constant threshold; the second trigger limit is determined by the ratio of the first trigger limit to the maximum eigenvalue of the first auxiliary variable matrix at the current moment.

[0121] More specifically, the event triggering conditions for inter-UAV communication are:

[0122]

[0123] in, is the difference between the conversion error at the current moment and the conversion error at the most recent trigger moment, η j is a positive constant trigger threshold, sup{} represents the supremum of the variable, min{} represents the minimum value of the set, and λ max () represents the largest eigenvalue of the matrix.

[0124] When verifying the performance of the observer,

[0125] Define observation error:

[0126]

[0127] Taking the derivative of the observation error, we can get:

[0128]

[0129] Furthermore, the compact form of N following drones can be expressed as:

[0130]

[0131] in, ∈=col{∈1,...,∈ N},∈ i =[∈ i,1 ,∈ i,2 ,∈ i,3 ] T ,μ1=diag{μ 11 ,...,μ 1N},μ 1i =diag{μ 1i,1 ,μ 1i,2 ,μ 1i,3},μ2=diag{μ 21 ,...,μ 2N},μ 2i =[μ 2i,1 ,μ 2i,2 ,μ 2i,3 ] T ; Ψ=col{Ψ1,…,Ψ N},Ψ i =[Ψ i,1 ,Ψ i,2 ,Ψ i,3 ] T .

[0132] Based on (9) and (16), we can get:

[0133]

[0134] in,

[0135] Taking the derivative of (8), its compact form can be expressed as:

[0136]

[0137] Similarly, the compact form of (7) is:

[0138]

[0139] in,

[0140] Consider the following Lyapunov function:

[0141]

[0142] Taking the derivative of (22) we can get:

[0143]

[0144] Where δ=col{δ1,...,δ N} and c3δ T δ / 2 is a constant.

[0145] Considering the event triggering conditions (15), (23) can be expressed as:

[0146]

[0147] in,

[0148] Therefore, V o (t),∈ i,p as well as If is bounded, the designed observer (5) can estimate the output of the leading UAV and the observation error converges to the preset accuracy within the preset time.

[0149] Specifically, the distributed preset performance fault-tolerant controller adopts Nussbaum function for fault tolerance and suppresses the chattering phenomenon of Nussbaum function when solving reverse faults through the preset performance method;

[0150] Then, the distributed preset performance fault-tolerant controller without chattering is expressed as:

[0151]

[0152] Among them, i,p is an adaptive variable; is an auxiliary variable; is the Nussbaum function used; p = 1, 2, 3.

[0153] More specifically, the Nussbaum function Expressed as:

[0154]

[0155] Where σ1 and σ2 are parameters for adjusting the amplitude and frequency of the Nussbaum function, respectively.

[0156] Adaptive variable υ i,p and auxiliary variables The calculation formula is as follows:

[0157]

[0158] Where q i,p is a positive constant gain, Output signal of drone angular velocity and first-order filter The error between is the intermediate variable, z i,p1 is the conversion error, h(κ i,p (t)) is the conversion function, κ i,p is the boundary distance for adjusting the error, l i is a constant gain, a 2i,p is the positive safety constant of the design, is the adaptive gain of the neural network, Z i,p =[x i,11 ,x i,12 ,x i,21 ,x i,22 ,x i,31 ,x i,32 ] T represents the input of the neural network, is the radial basis function vector, and its calculation formula is:

[0159]

[0160] Among them, M1 is the number of neurons, o m and π m Represent the center and width of the neuron respectively.

[0161] Conversion error z i,p1 , the boundary distance of the adjustment error κ i,p , conversion function h(κ i,p The calculation formula of (t)) is:

[0162]

[0163] e i,p =y i,p -ξ i,p (31)

[0164]

[0165]

[0166] Among them, e i,p is the adjustment error of the UAV output signal.

[0167] A first-order filter is represented as:

[0168]

[0169] Among them, ν i,p is a positive time constant, α i,p (t) is the virtual control law as the filter input, and its calculation formula is:

[0170]

[0171] Where, Ω i,p is an auxiliary variable, and its calculation formula is:

[0172]

[0173] in, is an intermediate variable.

[0174] Adaptive gain The update law is

[0175]

[0176] Among them, r i,p1 and r i,p2 is a positive constant gain.

[0177] The above controller is designed based on the backstepping method. First, consider the Lyapunov function of the following form:

[0178]

[0179] Combining (1), (30)-(33), and (35), taking the derivative of (39) and using Young's inequality, we can obtain:

[0180]

[0181] in,

[0182] Further consider the following Lyapunov function:

[0183]

[0184] in, yes Estimates.

[0185] Combining (25)-(28), the derivative of (41) is expressed as:

[0186]

[0187] Substituting (38) into (42) yields the following inequality:

[0188]

[0189] in, and is a constant.

[0190] Integrating both sides of (43), we obtain:

[0191]

[0192] in, is a constant. Then V i,p (t),υ i,p (t) and Therefore, the adjustment error of the UAV converges to the preset accuracy within the preset time, thus achieving fault-tolerant control under reverse faults.

[0193] Stability analysis shows that the chattering-free preset performance fault-tolerant control method of the designed manned-UAV cluster is effective. It can effectively solve the chattering problem caused by the Nussbaum function method under reverse faults and accurately identify the time of the reverse fault and the channel where the fault is located, thereby improving the robustness of the manned-UAV cluster and ensuring the safe and stable operation of the system.

[0194] In summary, this embodiment realizes the clustering emergence of manned-UAV clusters while achieving fault-tolerant control of UAV rotors, avoiding the UAV vibration problem, and ensuring the high performance and safety of manned-UAV clusters.

[0195] In a more preferred solution, a fault direction identification indicator is set to determine reverse faults in the following drone. The fault direction identification indicator is determined by the signs of the last few sampling values ​​of the Nussbaum function in the controller. When the signs of the last few sampling values ​​are the same, it indicates that there is no reverse fault. When the signs of the last few sampling values ​​are opposite, it indicates that there is a reverse fault. When a reverse fault exists, an alarm is issued. By alarming for reverse faults, the fault direction is identified, and the reverse fault is located.

[0196] It is preferred to use the last two sampling values ​​to determine the fault direction identification index. If there is a sampling value whose sign cannot be determined in the last two sampling values, the sampling value before the last two sampling values ​​is added. The reverse fault is determined by using whether the two sampling values ​​whose signs cannot be determined in the three consecutive sampling values ​​to improve the robustness of the reverse fault.

[0197] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control, characterized in that: It includes a leader drone, a follower drone, an observer and a controller; among them, The manned-unmanned aerial vehicle (UAV) swarm is divided into multiple clusters, each of which includes a leader UAV and multiple follower UAVs. The leader UAV is controlled by a leader, and the follower UAVs track the leader UAV in the cluster. The observer is a distributed preset performance observer; it is used to estimate the leader drone information for follower drones in each cluster that cannot directly obtain the leader drone information based on the leader drone information in the cluster obtained by the leading node in each cluster and the converted error information of the cluster synchronization error interacted through the inter-UAV communication network. This information is used as the leader drone information for the follower drones in each cluster that cannot directly obtain the leader drone information, and the leader drone information is used as the target information for multi-target cluster emergence control. The controller is a distributed preset performance fault-tolerant controller, which is used to track the target information estimated by the observer and perform fault-tolerant control on the following UAV; The distributed preset performance fault-tolerant controller adopts Nussbaum function for fault tolerance and suppresses the chattering phenomenon of Nussbaum function when solving reverse fault by using preset performance method. Then, the distributed preset performance fault-tolerant controller without chattering is expressed as: Among them, i,p is an adaptive variable; is an auxiliary variable; is the Nussbaum function used; p = 1, 2, 3, i is the number of the following drone; Nussbaum function Expressed as: Where σ1 and σ2 are parameters for adjusting the amplitude and frequency of the Nussbaum function respectively; Adaptive variable υ i,p and auxiliary variables The calculation formula is as follows: Where q i,p is a positive constant gain, Output signal of drone angular velocity and first-order filter The error between is the intermediate variable, z i,p1 is the conversion error, h(κ i,p (t)) is the conversion function, κ i,p is the boundary distance for adjusting the error, l i is a constant gain, a 2i,p is the positive safety constant of the design; e i,p is the adjustment error of the UAV output signal; is the adaptive gain of the neural network, Z i,p =[x i,11 ,x i,12 ,x i,21 ,x i,22 ,x i,31 ,x i,32 ] T is the input of the neural network, is the radial basis function vector.

2. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 1 is characterized in that: The traction node is determined based on the communication topology between the following drones; The conditions for determining the i-th following drone as the towing node are: 1) there is no neighbor node or 2) or 3) Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that follow the drones and receive information from other clusters; i =1,…,s; s is the number of leading drones; The node without neighbor nodes refers to a follower drone that has no communication with other follower drones in the cluster.

3. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 2 is characterized in that: The leader UAV and the follower UAV in the manned-unmanned aerial vehicle (UAV) swarm are both rotor UAVs. The dynamic models of the attitude systems of the leader UAV and the follower UAV are established respectively. The dynamic model of the attitude system of the leader UAV is: in, and Respectively represent the angular position and angular velocity of the leader UAV in the roll, pitch and yaw channels; l i The code that represents the cluster where the drone is located; represents the output of the leader drone; It represents the leader's operational input to the leader UAV; represents a nonlinear term; The output of the leader drone does not change suddenly; it is constant. make Established; The dynamic model of the attitude system of the i-th following drone is: Among them, x i,11 、x i,21 、x i,31 Respectively represent the roll angle, pitch angle and yaw angle of the i-th following drone; x i,12 、x i,22 、x i,32 Respectively represent the angular velocity of the roll, pitch and yaw channels of the i-th following UAV; d i,p2 Indicates interference; y i,p Represents the output signal; f i,p represents a nonlinear term; Indicates input for following the drone including fault conditions.

4. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 3 is characterized in that: The control link between the following drone and the controller includes an actuator; the actuator executes the control instruction output by the controller to make the following drone track the tracking target estimated by the observer; when executing the control instruction, the actuator may fail; Then, the actuator is modeled to obtain the output of the actuator including the fault condition for: Among them, u i,p represents the normal input signal to the actuator; ρ i,p represents the multiplicative factor of the fault, which satisfies ρ in the roll channel and the pitch channel i,p > 0, satisfying ρ in the yaw channel i,p ≠0; in the yaw channel, there is a reverse fault. At this time, ρ i,p <0;r i,p Indicates a bias fault.

5. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 4 is characterized in that: The distributed preset performance observer is a first-order integral structure, expressed as: Among them, u i ∈R 3 With ξ i ∈R 3 are the input and output signals of the observer respectively; N is the number of following drones in the cluster; Input signal u at time t i The calculation expression is: Wherein, c2 and c3 are positive constant gains; ij The matrix Ξ=(L+B) -1 The elements of L and B are the Laplace matrix and traction matrix of the manned-unmanned aerial vehicle cluster communication topology respectively; μ 1j and μ 2j are the first and second auxiliary variable matrices; is the kth jth following drone j kth j +1 trigger moment, is the kth jth following drone j The conversion error of the cluster synchronization error at the triggering moment, δ j satisfy is δ j estimated value.

6. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 5 is characterized in that: In the input of the observer of the i-th following drone, the conversion error ∈ i,p , the calculation formula for p=1,2,3 is: Among them, a ij 、a im is the element of the adjacency matrix of the communication topology of the following UAV cluster; is the output vector of the leader UAV; Represents cluster l i The set of nodes that send information to the following drones in other clusters, Represents cluster l i The set of nodes that receive information from other clusters following the drone; b i ∈{0,1} is the traction gain. When the i-th following drone is the traction node, b i =1, otherwise, b i =0;v i * is the set of neighbor nodes following drone i; ∩ represents the intersection of two sets; Among them, γ i (t) = [γ i,1 ,γ i,2 ,γ i,3 ] T Used to characterize the boundary distance, h(γ i,p (t)) is the error conversion function, c1 is the constant gain, 13 is the three-dimensional column vector of all 1s, and the symbol Indicates the multiplication of the elements of two vectors, a1 is the set safety constant, and β is the set safety margin; Auxiliary variables μ in the first and second auxiliary variable matrices 1j,p 、μ 2j,p , the calculation formulas for p=1,2,3 are: in, for The pth element of .

7. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 6 is characterized in that: The trigger condition for the interaction of the communication network between the following UAVs is that the difference between the conversion error at the current moment and the conversion error at the most recent trigger moment is not less than the trigger threshold; The trigger threshold is the minimum value of the first trigger limit and the second trigger limit; wherein the first trigger limit is a preset positive constant threshold; the second trigger limit is determined by the ratio of the first trigger limit to the maximum eigenvalue of the first auxiliary variable matrix at the current moment.

8. The manned-unmanned aerial vehicle swarm system based on cluster emergence and chatter-free fault-tolerant control according to claim 7 is characterized in that: The calculation formula of the radial basis function vector is: Among them, M1 is the number of neurons, o m and π m represent the center and width of the neuron, respectively; The conversion error z i,p1 , the boundary distance of the adjustment error κ i,p , conversion function h(κ i,p The calculation formulas for (t)) are: Among them, e i,p is the adjustment error of the UAV output signal; A first-order filter is represented as: Among them, ν i,p is a positive time constant, α i,p (t) is the virtual control law as the filter input, and its calculation formula is: Where, Ω i,p is an auxiliary variable, and its calculation formula is: in, is an intermediate variable; Adaptive gain The update law is: Among them, r i,p1 and r i,p2 is a positive constant gain.

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