A Cluster Fault-Tolerant Consistency Control Method for Heterogeneous Unmanned Systems

Abstract

This invention discloses a cluster fault-tolerant consistency control method for heterogeneous unmanned systems, relating to the field of cross-domain cooperative control technology for unmanned systems. First, the method establishes a cooperative motion control model for a heterogeneous unmanned system consisting of a group of unmanned surface vessels (USVs) and unmanned aerial vehicles (UAVs). Then, based on a newly developed error transformation mechanism that avoids singularities, a distributed reference state estimator that meets preset performance requirements is designed to obtain the output trajectory of the cluster leader. Next, a neural network approximator is used to handle actuator failures in the system, and a new weight update law is designed for the neural network approximator using immersion and invariant adaptive theory. Finally, combining dynamic surface control and the newly developed error transformation mechanism, a cluster fault-tolerant consistency control strategy with preset time and predefined accuracy is proposed to achieve cluster consistency of the heterogeneous unmanned system under actuator failures. This invention has significant practical implications and promising application prospects in the field of cross-domain cooperation and fault-tolerant control of heterogeneous unmanned systems.

Description

Technical Field

[0001] This invention relates to the field of cross-domain collaborative control technology for unmanned systems, and in particular to a cluster fault-tolerant consistency control method for heterogeneous unmanned systems. Background Technology

[0002] Heterogeneous unmanned systems have garnered widespread attention and research from industry and academia due to their high efficiency and enormous application potential. Compared to homogeneous unmanned systems, heterogeneous unmanned systems consist of multiple homogeneous or heterogeneous unmanned devices with different parameters and performance. Through cross-domain perception, cluster collaboration, and autonomous decision-making, they can maximize the system's capabilities in all aspects, thereby providing strong technical support for smart living.

[0003] However, achieving cluster consistency control in heterogeneous unmanned systems remains a challenging problem. This requires the unmanned equipment in the system to evolve into several different sub-clusters, with each sub-cluster achieving a consistent state. On the other hand, as the scale and number of components in heterogeneous unmanned system clusters increase, fault tolerance becomes crucial for maintaining overall stability and reliable cooperative movement when a sub-member fails. Therefore, there is an urgent need to develop and research efficient fault-tolerant control technologies for heterogeneous unmanned systems. Furthermore, considering the predefined transient and steady-state behaviors of cluster consistency in heterogeneous unmanned systems is also a significant area of ​​research, reflecting the inevitable requirement for higher levels of system control. Therefore, to enhance the safety and reliability of collaborative operations in heterogeneous unmanned system clusters and optimize the control effects and performance of existing technologies and solutions, this application studies a fault-tolerant consistency control method for heterogeneous unmanned systems. Summary of the Invention

[0004] To address the aforementioned technical problems and achieve cluster consistency control for heterogeneous unmanned systems with actuator failures, the present invention aims to design a cluster fault-tolerant consistency control method for heterogeneous unmanned systems. This method ensures that, even in the event of a failure, unmanned equipment in a heterogeneous unmanned system can simultaneously track the trajectories of different types of cluster leaders, and that the formation error can converge to a preset limit within a specified time.

[0005] Technical solution

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] First, a unified cooperative motion control model for heterogeneous unmanned systems under actuator failure conditions is constructed. Second, an error transformation mechanism to avoid singularities is designed to achieve performance-constrained control of the system. Then, based on this, a distributed reference state estimator capable of meeting preset performance requirements is developed to estimate the output information of the cluster leader. Finally, under a dynamic surface control architecture, combining the aforementioned error transformation mechanism with neural network adaptive approximation theory, a cluster fault-tolerant consistency control strategy with preset time and predefined accuracy is designed to achieve cross-domain cooperation and fault tolerance of heterogeneous unmanned systems under actuator failure conditions.

[0008] The control method includes the following steps:

[0009] Step 1: For a heterogeneous unmanned system consisting of a group of N1 unmanned surface vessels and N2 unmanned aerial vehicles, establish a unified cooperative motion control model for the heterogeneous unmanned system, including actuator failures. The specific process is as follows.

[0010] The first step is to establish the dynamic model of the i-th unmanned surface vessel, as shown below:

[0011]

[0012] in,

[0013]

[0014] Let η be the Jacobian transformation matrix. i =[x i ,y i ,ψ i ] T Let v be the position and heading of the i-th unmanned surface vessel in the inertial coordinate system. i =[u i ,v i ,r i ] T Let be the pitch, yaw, and bow velocities of the i-th unmanned surface vessel in the attached coordinate system. and η i and v i The first derivative, τ i To control the input, M i C i (v i ) and D i (v i Let x be the inertia matrix, Coriolis centripetal force matrix, and damping matrix, respectively. Define x as... i1 =η i x i2 =v i and u i =τi Equation (1) can be rewritten as:

[0015]

[0016] Among them, f i1 =J(ψ) i )v i -v i and For nonlinear terms, g i1 =I3 and For gain terms, I3 represents a 3×3 identity matrix.

[0017] The second step is to establish the dynamic model of the i-th fixed-wing UAV, as shown below:

[0018]

[0019] Where, x i y i z i Let be the position of the i-th fixed-wing UAV in the inertial coordinate system. and x i y i and z i The derivative of V; i Let be the speed of the i-th fixed-wing UAV. For V i The derivative; and These are the heading angle and track angle of the i-th fixed-wing UAV, respectively. and χ i and γ i The derivative of m; i Let g be the mass of the i-th fixed-wing UAV; g be the acceleration due to gravity; μ be the mass of the i-th fixed-wing UAV. i α i and β i These are the tilt angle, angle of attack, and sideslip angle of the i-th fixed-wing UAV, respectively; T i D i Y i and L i Let be the thrust, drag, lateral force, and lift acting on the i-th fixed-wing UAV, respectively, as specifically expressed below:

[0020]

[0021] Among them, C iL C iD and C iY These are the lift coefficient, drag coefficient, and axial force coefficient, respectively, and their specific forms are as follows:

[0022]

[0023] Among them, T imax This indicates the engine's maximum thrust; For thrust throttle setting; ρ0 represents air density; s represents dynamic pressure. i C is the wing area; iL0 C iLα C iD0 C iDα C iDα2 C iY0 C iYβ Let x be the aerodynamic coefficient. i1 =[x i ,y i ,z i ] T x i2 =[V i ,γ i ,γ i ] T u i =[δ Ti ,α i sinμ i ,α i cosμ i ] T Equation (3) can be rewritten as:

[0024]

[0025] Among them, g i1 =I3 and For the gain term, f i1 =[f i11 ,f i12 ,f i13 ] T and f i2 =[f i21 ,f i22 ,f i23 ] T This is a non-linear term, specifically expressed as follows:

[0026] f i11 =V i cosγ i cosχ i -V i

[0027] f i12 =V i cosγ i sinχ i -χi

[0028] f i13 =V i sinγ i -γ i

[0029] f i21 =-D i / m i -gsinγ i

[0030]

[0031] Third, consider the following actuator failure model:

[0032] u i =ρ i u i0 +b i (5)

[0033] Where, ρ i =diag{ρ i1 ,ρ i2 ,ρ i3} is the actuator efficiency loss factor, which is a diagonal matrix satisfying ρ i1 ,ρ i2 ,ρ i3 ∈(0,1];b i The bounded deviation fault vector; u i0 For command control input signals.

[0034] Fourth step, substituting equation (5) into equations (2) and (4), we can obtain the unified cooperative motion control model for heterogeneous unmanned systems under actuator failure:

[0035]

[0036] Among them, h i =g i2 [(ρ i -I3)u i0 +b i [This refers to the lumped fault items.]

[0037] Step 2: To meet the system's performance constraints, the following error transformation mechanism is constructed to avoid singularities:

[0038]

[0039] Where π(t) = 1 - β(t) 2 z(t) 2z(t) represents the system error variable, and β(t) is a time-varying scalar function with the following properties:

[0040] 1) β(t) is at least C on the interval [t0, ∞). n+1 Class function, where n is the system order;

[0041] 2) β(t) is a continuous non-decreasing function, with β(t0) = 0 and Where t p <∞ represents a preset time constant;

[0042] 3)

[0043] 4) For t>t0+t p ,have and Where 0 < ζ < ∞ and 0 < t p <∞ represent the predefined precision and preset time, respectively. Furthermore, differentiating s(t) yields:

[0044]

[0045] in, In addition, to facilitate the design of the subsequent distributed reference state estimator, the following transformations are performed:

[0046]

[0047] in, Furthermore, it can be known from the properties of β(t) It is bounded. For ease of description later, (t) is omitted.

[0048] Step 3: To estimate the output information of the cluster leader, based on the error transformation mechanism proposed in Step 2, a distributed reference state estimator that can meet the preset performance requirements is developed, as shown below:

[0049]

[0050] Among them are:

[0051]

[0052] in, ι2 and ι2 are control parameters, and have Representation matrix The largest eigenvalue, Representation matrix The smallest eigenvalue of μ. i01 =diag{μ i01q}, q = 1, 2, 3 is a 3×3 diagonal matrix that satisfies satisfy This is the output of the distributed reference state estimator described above. express y i The estimate, y i It is a constant and satisfies and y i They represent y respectively i The upper and lower bounds of . Using the error transformation mechanism mentioned above, we can obtain s. i0 The dispersed expression is as follows:

[0053]

[0054] in, β0 is a user-defined time-varying scalar function, z i0 =[z i01 ,z i02 ,z i03 ] T The cluster consistency tracking error is defined as follows:

[0055]

[0056] in, This is the output of the leader of the cluster to which the i-th system member belongs. i =1 means that the i-th system member is a fixed node and can receive output information from the cluster leader. The selection method for fixed nodes in the system is as follows: Based on the requirements of multi-task clustering, the directed graph... The topological nodes are classified into disjoint task sets. For i ≤ N = N1 + N2, if d i =0, then b i =1; if d i ≠0 and have Then b i =1; if d i ≠0 and have Then b i =1.

[0057] Step four: After obtaining the leader's output information using the distributed reference state estimator designed in step three, under the dynamic surface control architecture, based on the error transformation mechanism proposed in step two and the neural network adaptive approximation theory, a cluster fault-tolerant consistency control strategy with preset time and predefined accuracy is designed to achieve cross-domain collaboration and fault tolerance of heterogeneous unmanned systems under actuator failure. The specific process is as follows:

[0058] Step 1: Define the system's position tracking error zi1 And design virtual control laws

[0059] definition Let x be the position tracking error of the i-th system member, where x id This represents the desired cluster formation configuration. Using the error transformation mechanism mentioned above, we can obtain:

[0060]

[0061] in, β1 is a user-defined time-varying scalar function, s i1 =[s i11 ,s i12 ,s i13 ] T Furthermore, differentiating equation (14) above yields:

[0062]

[0063] in, Indicate z i1q The derivative of . Combining equations (6) and (10), the compact expression of equation (15) is:

[0064]

[0065] Where, μ i11 =diag{μi i111 ,μ i112 ,μ i113}, μ i02 =[μ i121 ,μi i122 ,μ i123 ] T , It is x id The derivative of .

[0066] Next, for system (6), a virtual control law is designed. for:

[0067]

[0068] Where, k i1 For a control parameter, It is μ i11 The derivative of .

[0069] The command filter is designed as follows:

[0070]

[0071] Where, τ i For filter error, For filter output, for The derivative of . Next, it is defined as For the filtering error of equation (18), Differentiation yields:

[0072]

[0073] in,

[0074] Step 2: Define the system's velocity tracking error s i2 And design the actual control law u i0 .

[0075] definition For the velocity tracking error of the i-th system member, along system (6) on s i2 Differentiation yields:

[0076]

[0077] For system (6), the actual control law u i0 Designed as follows:

[0078]

[0079] Where, k i2 For control parameters, To centralize fault items h i The estimated value.

[0080] Step 3: Estimating h based on the approximation capability of neural networks i Furthermore, a new weight update law is designed by introducing immersion and invariant adaptive theory.

[0081] Using a neural network to approximate the lumped fault term h in system (6) i We can obtain:

[0082]

[0083] Among them, W i * Represents the ideal weight vector. Let Gausky function vector be the vector. For approximate error, satisfying ε a It is a constant matrix. Because the ideal weight vector W... i * It is unknown but bounded, therefore immersion and invariant adaptive theory is used to obtain W. i *The estimated value. Based on this, the approximate value of equation (22) can be expressed as:

[0084]

[0085] in, W i * The asymptotic estimate is given. For ease of explanation, θ will be used in the following text. i Φ i and ε i express and

[0086] definition This represents the estimation error of the ideal weights. Along system (6), s i3 The derivative can be written as:

[0087]

[0088] in, Represents θ i right The partial derivatives. Next, using immersion and invariant adaptive theory, we design... The update law and function θ i As shown below:

[0089]

[0090] Where, k i3 It is a control parameter. Let θ ipq Representing matrix θ i From the (p,q)th element, we can obtain:

[0091]

[0092] Where, Φ ip Φ i The p-th element in.

[0093] In summary, by combining the distributed reference state estimator composed of equations (11)-(12), the heterogeneous unmanned system control law composed of equations (16)-(18), and the immersion and invariant adaptive neural network estimator composed of equations (20)-(22), a cluster fault-tolerant consistency control scheme for the heterogeneous unmanned system with preset time and predefined accuracy is obtained, thereby realizing the preset performance cross-domain cluster collaborative control and fault tolerance of the heterogeneous unmanned system under actuator failure.

[0094] The beneficial effects of this invention are as follows:

[0095] (1) This invention discloses a cluster fault-tolerant consistency control method for heterogeneous unmanned systems. This method considers the cluster collaboration and fault tolerance problem of heterogeneous unmanned systems affected by actuator failure. The designed method can not only ensure the stability of cluster consistency control of heterogeneous unmanned systems under actuator failure, but also enable the unmanned equipment in the system to simultaneously track the trajectories of different types of cluster leaders.

[0096] (2) A novel error transformation mechanism that avoids singularities was designed to achieve performance constraint control of system errors. Compared with traditional preset performance control, the designed error transformation mechanism relaxes the constraint on the availability of the initial error value.

[0097] (3) Using the designed error conversion mechanism and restraint control idea, a distributed reference state estimator that can realize the user's preset performance requirements was developed to obtain an estimate of the cluster leader's output information.

[0098] (4) A neural network is used to approximate the lumped fault term of the heterogeneous unmanned system, and the immersion and invariant adaptive theory is introduced to design a new weight update law for the neural network.

[0099] (5) Based on dynamic surface control and the designed error conversion mechanism, a cluster fault-tolerant consistency control strategy with preset time and predefined accuracy is proposed, which solves the cluster consistency problem of heterogeneous unmanned systems under actuator failure.

[0100] (6) This invention takes into account the cluster consistency, fault tolerance and performance constraints of heterogeneous unmanned systems, and has great practical significance and application prospects in the field of cross-domain collaborative control of heterogeneous unmanned systems. Attached Figure Description

[0101] Figure 1 Flowchart of a cluster fault-tolerant consistency control scheme for heterogeneous unmanned systems under actuator failure;

[0102] Figure 2 System block diagram of a cluster fault-tolerant consistency control scheme for heterogeneous unmanned systems under actuator failure;

[0103] Figure 3 This is a communication topology diagram of a heterogeneous unmanned system.

[0104] Figure 4 The output of the distributed reference state estimator Line graph;

[0105] Figure 5 For the cluster consistency error z of heterogeneous unmanned systems i0 Time response curve;

[0106] Figure 6The position tracking error z of the heterogeneous unmanned system i1 Time response curve;

[0107] Figure 7 For the lumped fault term h of heterogeneous unmanned systems i and its estimated value Line graph.

[0108] Figure 8 For the control input u of the heterogeneous unmanned system i0 Line graph. Detailed Implementation

[0109] The control method of the present invention will be further explained in conjunction with the accompanying drawings and tables.

[0110] (a) For a heterogeneous unmanned system consisting of a group of N1 unmanned surface vessels and N2 unmanned aerial vehicles, a unified cooperative motion control model for the heterogeneous unmanned system, including actuator failures, is established. The specific process is as follows.

[0111] First, the dynamic model of the i-th unmanned surface vessel is established as follows:

[0112]

[0113] in,

[0114]

[0115] Let η be the Jacobian transformation matrix. i =[x i ,y i ,ψ i ] T Let v be the position and heading of the i-th unmanned surface vessel in the inertial coordinate system. i =[u i ,v i ,r i ] T Let be the pitch, yaw, and bow velocities of the i-th unmanned surface vessel in the attached coordinate system. and η i and v i The first derivative, τ i To control the input, M i C i (v i ) and D i (v i M represents the inertia matrix, Coriolis centripetal force matrix, and damping matrix, respectively. i C i (v i ) and D i (vi The specific expression is as follows:

[0116]

[0117] Define x i1 =η i x i2 =v i and u i =τ i Equation (1) can be rewritten as:

[0118]

[0119] Among them, f i1 =J(ψ) i )v i -v i and For nonlinear terms, g i1 =I3 and For gain terms, I3 represents a 3×3 identity matrix.

[0120] Secondly, the dynamic model of the i-th fixed-wing UAV is established as follows:

[0121]

[0122] Where, x i y i z i Let be the position of the i-th fixed-wing UAV in the inertial coordinate system. and x i y i and z i The derivative of V; i Let be the speed of the i-th fixed-wing UAV. For V i The derivative; and These are the heading angle and track angle of the i-th fixed-wing UAV, respectively. and χ i and γ i The derivative of m; i Let g be the mass of the i-th fixed-wing UAV; g be the acceleration due to gravity; μ be the mass of the i-th fixed-wing UAV. i α i and β i These are the tilt angle, angle of attack, and sideslip angle of the i-th fixed-wing UAV, respectively; T i D i Y i and L iLet be the thrust, drag, lateral force, and lift acting on the i-th fixed-wing UAV, respectively, as specifically expressed below:

[0123]

[0124] Among them, C iL C iD and C iY These are the lift coefficient, drag coefficient, and axial force coefficient, respectively, and their specific forms are as follows:

[0125]

[0126] Among them, T imax This indicates the engine's maximum thrust; For thrust throttle setting; ρ0 represents air density; s represents dynamic pressure. i C is the wing area; iL0 C iLα C iD0 C iDα C iDα2 C iY0 C iYβ Let x be the aerodynamic coefficient. i1 =[x i ,y i ,z i ] T x i2 =[V i ,γ i ,γ i ] T u i =[δ Ti ,α i sinμ i ,α i cosμ i ] T Equation (3) can be rewritten as:

[0127]

[0128] Among them, g i1 =I3 and For the gain term, f i1 =[f i11 ,f i12 ,f i13 ] T and f i2 =[f i21 ,f i22 ,f i23 ] T This is a non-linear term, specifically expressed as follows:

[0129] f i11 =V i cosγ i cosX i -V i

[0130] f i12 =V i cosγ i sinχ i -χ i

[0131] f i13 =V i sinγ i -γ i

[0132] f i21 =-D i / m i -gsinγ i

[0133]

[0134] Then, consider the following actuator failure model:

[0135] u i =ρ i u i0 +b i (5)

[0136] Where, ρ i =diag{ρ i1 ,ρ i2 ,ρ i3} is the actuator efficiency loss factor, which is a diagonal matrix satisfying ρ i1 ,ρ i2 ,ρ i3 ∈(0,1];b i The bounded deviation fault vector; u i0 For command control input signals.

[0137] Finally, substituting equation (5) into equations (2) and (4), we obtain the unified cooperative motion control model for heterogeneous unmanned systems under actuator failure:

[0138]

[0139] Among them, h i =g i2 [(ρ i -I3)u i0 +b i [This refers to the lumped fault items.]

[0140] (b) In order to meet the system's performance constraints, the following error transformation mechanism is constructed to avoid singularities:

[0141]

[0142] Where π(t)=1-βt) 2 z(t) 2 z(t) represents the system error variable, and β(t) is a time-varying scalar function with the following properties:

[0143] 1) β(t) is at least C on the interval [t0, ∞). n+1 Class function, where n is the system order;

[0144] 2) β(t) is a continuous non-decreasing function, with β(t0) = 0 and Where t p <∞ represents a preset time constant;

[0145] 3)

[0146] 4) For t>t0+t p ,have and Where 0 < ζ < ∞ and 0 < t p <∞ represent the predefined precision and preset time, respectively. Furthermore, differentiating s(t) yields:

[0147]

[0148] in, In addition, to facilitate the design of the subsequent distributed reference state estimator, the following transformations are performed:

[0149]

[0150] in, Furthermore, it can be known from the properties of β(t) It is bounded. For ease of description later, (t) is omitted.

[0151] (c) To estimate the output trajectory of the cluster leader, the error transformation mechanism proposed in (b) is adopted to develop a distributed reference state estimator that can meet the user's preset performance requirements, as shown below:

[0152]

[0153] Among them are:

[0154]

[0155] in, ι2 and ι2 are control parameters, and have Representation matrix The largest eigenvalue, Representation matrix The smallest eigenvalue of μ. i01 =diag{μi i01q}, q = 1, 2, 3 is a 3×3 diagonal matrix that satisfies satisfy This is the output of the distributed reference state estimator described above. express y i The estimate, y i It is a constant and satisfies and y i They represent y respectively i The upper and lower bounds of . Using the error transformation mechanism mentioned above, we can obtain s. i0 The dispersed expression is as follows:

[0156]

[0157] in, β0 is a user-defined time-varying scalar function, z i0 =[z i01 ,z i02 ,z i03 ] T Let the cluster consistency tracking error of the i-th system member be defined as follows:

[0158]

[0159] in, This is the output of the leader of the cluster to which the i-th system member belongs. i =1 means that the i-th system member is a fixed node and can receive output information from the cluster leader. The selection method for fixed nodes in the system is as follows: Based on the requirements of multi-task clustering, the directed graph... The topological nodes are classified into disjoint task sets. For i ≤ N = N1 + N2, if Then b i =1; if And there are Then b i =1; if And there are Then b i =1. Define the output error of the estimator as... Then the generalized form of equation (13) can be obtained as:

[0160]

[0161] in, Represents the Kroll product. For Laplace matrix, For degree matrix, It is an adjacency matrix. It is an N×N diagonal matrix, where N = N1 + N2.

[0162] Then, a stability analysis is performed on the proposed distributed reference state estimator (10)-(11). First, the derivative of equation (12) is obtained:

[0163]

[0164] Writing equation (15) in a compact form, we get:

[0165]

[0166] Combining equation (14), we have:

[0167]

[0168] in, express The derivative of . Next, along equations (10)-(11), the estimator output error is calculated. The derivative is:

[0169]

[0170] in, y i For y i The lower bound is calculated along equation (11). The derivative is:

[0171]

[0172] Further, the compact forms of equations (18) and (19) can be obtained as follows:

[0173]

[0174] in,

[0175] Consider the following Lyapunov function candidates:

[0176]

[0177] in, Differentiating L0 along equations (17), (20)-(21) yields:

[0178]

[0179] in, Control parameter k0, The selection of ι2 should satisfy This ensures that the error signal is bounded.

[0180] Therefore, the designed distributed reference state estimator can estimate the leader's output trajectory with a predefined error tracking accuracy within a user-preset time frame.

[0181] (d) After obtaining the leader's output information using the distributed reference state estimator designed in step three, under the dynamic surface control architecture, based on the error transformation mechanism and neural network adaptive approximation theory proposed in step two, a cluster fault-tolerant consistency control strategy with preset time and predefined accuracy is designed to achieve cross-domain collaboration and fault tolerance of heterogeneous unmanned systems under actuator failure. The specific process is as follows:

[0182] First, define the system's position tracking error z. i1 And design virtual control laws

[0183] definition Let x be the position tracking error of the i-th system member, where x id This represents the desired cluster formation configuration. Using the error transformation mechanism mentioned above, we can obtain:

[0184]

[0185] in, β1 is a user-defined time-varying scalar function, s i1 =s i11 ,s i12 ,s i13 ] T Furthermore, differentiating equation (14) above yields:

[0186]

[0187] in, Indicate z i1q The derivative of . Combining equations (6) and (10), the compact expression of equation (15) is:

[0188]

[0189] Where, μ i11 =diag{μi111 ,μ i112 ,μ i113}, μ i02 =[μ i121 ,μ i122 ,μ i123 ] T , It is x id The derivative of .

[0190] Next, for system (6), a virtual control law is designed. for:

[0191]

[0192] Where, k i1 For a control parameter, It is μ i11 The derivative of .

[0193] The command filter is designed as follows:

[0194]

[0195] Where, τ i For filter error, For filter output, for The derivative of . Next, it is defined as For the filtering error of equation (18), Differentiation yields:

[0196]

[0197] in,

[0198] Consider the following Lyapunov function candidates

[0199]

[0200] Following equations (26), (27), and (29), we obtain L. i1 From the derivative, we can obtain:

[0201]

[0202] Secondly, define the system's velocity tracking error s i2 And design the actual control law u i0 .

[0203] definition For the velocity tracking error of the i-th system member, along system (6) on s i2 Differentiation yields:

[0204]

[0205] For system (6), the actual control law u i0 Designed as follows:

[0206]

[0207] Where, k i2 For control parameters, To centralize fault items h i The estimated value.

[0208] Consider the following Lyapunov function candidates

[0209]

[0210] Following equations (32) and (33), we obtain L. i2 From the derivative, we can obtain:

[0211]

[0212] in, This represents the estimation error of the lumped fault term.

[0213] Then, h is estimated based on the approximation capability of neural networks. i Furthermore, by introducing immersion and invariant adaptive theory, a new weight update law is designed to obtain...

[0214] The lumped fault item h in system (6) i It can be approximated by neural networks with arbitrary precision, specifically as follows:

[0215]

[0216] Among them, W i * Represents the ideal weight vector. Let Gausky function vector be the vector. For approximate error, satisfying ε a It is a constant matrix. Because the ideal weight vector W... i * It is unknown but bounded, therefore immersion and invariant adaptive theory is used to obtain W. i * The estimated value. Based on this, the approximate value of equation (36) can be expressed as:

[0217]

[0218] in, W i* The asymptotic estimate is given. For ease of explanation, θ will be used in the following text. i Φ i and Φ i express and

[0219] definition This represents the estimation error of the ideal weights. Along system (6), s i3 The derivative can be written as:

[0220]

[0221] in, Represents θ i right The partial derivatives. Next, using immersion and invariant adaptive theory, we design... The update law and function θ i As shown below:

[0222]

[0223] Where, k i3 It is a control parameter. Let θ ipq Representing matrix θ i From the (p,q)th element, we can obtain:

[0224]

[0225] Where, Φ ip Φ i The p-th element in.

[0226] Consider the following Lyapunov function candidates

[0227]

[0228] Where tr{·} denotes the trace of the matrix. Find L i3 From the derivative, we can obtain:

[0229]

[0230] Stability analysis: Consider the following Lyapunov function candidates

[0231] L i =L i1 +L i2 +L i3 (43) where tr{·} denotes the trace of the matrix. L is obtained along equations (31), (35), and (42). i From the derivative, we can obtain:

[0232]

[0233] in, Control parameter selection to meet

[0234] Stability analysis proves that the proposed control method can bring the tracking error of a faulty heterogeneous unmanned system to a specified accuracy range within a finite time.

[0235] (e) The aforementioned distributed reference state estimator, neural network, weight adaptive law, and cluster fault-tolerant consistency control law are embedded into the system's motion control model to achieve cross-domain collaboration and fault tolerance of heterogeneous unmanned systems under actuator failure. The effectiveness of this invention is verified through simulation below:

[0236] To verify the effectiveness of the proposed control method, a heterogeneous unmanned system consisting of four unmanned surface vessels (USVs) and two fixed-wing unmanned aerial vehicles (UAVs) was simulated in MATLAB / SIMLINK. The structural and aerodynamic parameters of the USVs and UAVs are shown in Tables 1 and 2. Figure 3 The communication topology diagram of the aforementioned heterogeneous unmanned system is shown, which illustrates that the unmanned equipment in the system is divided into two clusters—unmanned surface vessel groups. and drone swarms

[0237] Table 1. Values ​​of structural and aerodynamic parameters for the unmanned surface vessel.

[0238]

[0239] Table 2. Values ​​of structural and aerodynamic parameters for fixed-wing UAVs

[0240] parameter Value unit parameter Value unit <![CDATA[s i ]]> 1.463 <![CDATA[m 2 ]]> ρ 1.205 <![CDATA[kg·m -3 ]]> <![CDATA[m i ]]> 25 kg g 9.8 <![CDATA[m·s -2 ]]> <![CDATA[C iD0 ]]> 0.0225 - <![CDATA[C iDα ]]> 0.1002 <![CDATA[rad -1 ]]> <![CDATA[C iDα2 ]]> 1.0778 - <![CDATA[C iL0 ]]> 0.2153 - <![CDATA[C iLα ]]> 4.6333 <![CDATA[rad -1 ]]> <![CDATA[C iY0 ]]> 0 - <![CDATA[C iYβ ]]> -0.0046 <![CDATA[rad -1 ]]>

[0241] In the simulation, the output trajectories of the two cluster leaders are as follows:

[0242]

[0243] The fault model selection for the heterogeneous unmanned system is as follows. Unmanned surface vessel 1 experiences the following fault at t>5 seconds:

[0244]

[0245] The unmanned surface vessel 2 experienced the following malfunction when t>10 seconds:

[0246]

[0247] The unmanned surface vessel 3 experienced the following malfunction when t>13 seconds:

[0248]

[0249] The unmanned surface vessel 4 experienced the following malfunction when t>7 seconds:

[0250]

[0251] The following fault occurred in UAV 1 when t>10 seconds:

[0252]

[0253] The following fault occurred in UAV 2 when t > 8 seconds:

[0254]

[0255] The user-specified time-varying scalar functions β0(t) and β1(t) are selected as follows:

[0256]

[0257] Where ζ0 = 0.15, ζ1 = 0.08,

[0258] The initial state of the heterogeneous unmanned system is shown in Table 3. The control parameters are selected as follows: ι2=5,k 11 =k 21 =k 31 =k 41 =1.5, k 51 =k 61 =0.8, k 12 =k 22 =k 32 =k 42 =30,k 52 =k 62 =16,k i3 =1.12, τ i =0.05, T imax =100, i=1,2,3,4,5,6. The number of basis functions is set to m=11, and the desired cluster formation configuration is x. 1d = [5; 0; 0], x 2d = [0; -5; 0]; x 3d =[-5; 0; 0]; x 4d =[0; 5; 0]; x 5d =[25; 0; 25]; x 6d = [25; 0; -25]. In practice, the control input of heterogeneous unmanned systems is bounded; therefore, in the simulation, the upper and lower limits of the unmanned surface vessel control signal are set to u. i0max =[500,500,500] T and ui0min =-[500,500,500] T Set the upper and lower limits of the drone control signal to u respectively. i0max =[1,1,1] T and u i0min =[0,-1,-1] T Due to the drone's sideslip angle β i It does not affect the control input u i0 Therefore, the sideslip angle is not considered in the simulation.

[0259] Table 3 Initial state values ​​of heterogeneous unmanned systems

[0260]

[0261] Figure 4 Provides the output of the distributed reference state estimator Among them, unmanned surface vessels 1-4 output their trajectories to the leader 1 of their respective clusters. With accurate estimation, UAVs 1-2 achieved the output trajectory of their respective cluster leader 2. The proposed estimator can accurately estimate the output of the cluster leader even when the actuators of the heterogeneous unmanned system fail, thus proving the effectiveness of the proposed estimator.

[0262] The time response of cluster consistency error in heterogeneous unmanned systems is as follows: Figure 5 As shown, it can be observed that even in the event of actuator failure, the cluster consistency error z of each system member remains the same. i0 It can also be done at the specified time. It converges to a preset accuracy region. The time response of the position tracking error of the heterogeneous unmanned system is as follows: Figure 6 As shown. Similarly, the position tracking error z of each system member. i1 It can also be done at the specified time. The proposed control method converges to within the preset accuracy limit. Furthermore, the initial error value z... i0 (0) and z i1 (0) No excessive constraints were imposed, thus demonstrating the superiority of the proposed control method.

[0263] Figure 7 The lumped fault term h of the heterogeneous unmanned system is displayed. i and its estimated value It is evident that when the actuator fails, the neural network can approximate the lumped fault term very well, which indicates that the new weight update law designed based on immersion and invariant adaptive theory is effective for neural networks. Figure 8The proposed control method provides control inputs for heterogeneous unmanned systems. It can be observed that when the system encounters actuator failure, the control inputs of each system member can be rapidly adjusted using the proposed control method to stabilize the formation in a timely manner.

[0264] In summary, the control method designed in this invention can effectively achieve cluster consistency and fault tolerance of heterogeneous unmanned systems under actuator failure, and can ensure that the system error converges to a specified range within a preset time.

Claims

1. A cluster fault-tolerant consistency control method for heterogeneous unmanned systems, characterized in that, The method includes the following steps: Step 1: For the heterogeneous unmanned system consisting of N1 unmanned surface vessels and N2 fixed-wing unmanned aerial vehicles, establish a cooperative motion control model for the heterogeneous unmanned system under actuator failure. Step two involves designing an error conversion mechanism to achieve performance constraint control of the heterogeneous unmanned system; step two specifically includes the following process: The error conversion mechanism is as follows: Where π(t) = 1 - β(t) 2 z(t) 2 Let z(t) represent the system error variable, and β(t) be a time-varying scalar function. Taking the derivative with respect to s(t) yields: in, The derivative of the error transformation mechanism is transformed as follows: in, (t) omitted; Step 3: Design a distributed reference state estimator, which is used to estimate the output information of the cluster leader; Step 3 specifically includes the following process: To estimate the output state of the cluster leader, a distributed reference state estimator is established that meets the preset performance requirements. First, give the l-th cluster. The state equations of the leader system are as follows: Among them, f l 0 and It is a nonlinear function. It is the system status. It is system output. It is a control input; Secondly, the distributed reference state estimator is: Among them are: in, ι2 and ι2 are control parameters, and have Representation matrix The largest eigenvalue, Representation matrix The smallest eigenvalue; Given a 3×3 diagonal matrix, satisfying satisfy The output of the distributed reference state estimator; Indicates y i The estimate, y i It is a constant and satisfies and y i They represent y respectively i The upper and lower bounds of s; i0 =[s i01 ,s i02 ,s i03 ] T The transformation error variable obtained through the aforementioned error transformation mechanism is expressed as follows: in, β0 is a user-defined time-varying scalar function, z i0 =[z i01 ,z i02 ,z i03 ] T The cluster consistency tracking error is defined as follows: in, The output is for the leader of the cluster to which the i-th system member belongs, with the subscript l. i This indicates that i system members belong to the l-th cluster, i.e. Step 4: Under the dynamic surface control architecture, based on the error conversion mechanism and neural network approximation strategy, as well as the output information of the cluster leader, a cluster fault-tolerant consistency control scheme with preset time and predefined accuracy is designed to realize cross-domain collaboration and fault tolerance of heterogeneous unmanned systems under actuator failure. Step four specifically includes the following process: Step 4.1: Design the virtual control law definition Let x be the position tracking error of the i-th system member, where x id Representing the desired cluster formation configuration, using the aforementioned error transformation mechanism, we obtain: in, β1 is a user-defined time-varying scalar function, s i1 =[s i11 ,s i12 ,s i13 ] T ; For system (6), design a virtual control law. for: Where, k i1 For a control parameter, It is x id The derivative of It is μ i11 The derivative of Let q = 1, 2, 3 be a 3×3 diagonal matrix that satisfies μ i12 =[μ i121 ,μ i122 ,μ i123 ] T satisfy The command filter is designed as follows: in, For filter error, For filter output, for The derivative of the above equation (18) is used to define the filtering error as follows: Step 4.2: Design the actual control law u i0 , definition For the speed tracking error of the i-th system member, then for system (6), the actual control law u i0 Designed as follows: Where, k i2 For control parameters, For the lumped fault item h i The estimated value; Step 4.3: Design the lumped fault term h i The estimated value Lumped fault term h in the cooperative motion control model of heterogeneous unmanned systems under actuator failure i Using a radial basis function neural network approximation, it can be represented as follows: Among them, W i * Represents the ideal weight vector. Let Gausky function vector be the vector. For approximate error, satisfying ε a It is a constant matrix; W is obtained using immersion and invariant adaptive theory. i * The estimated value, the approximate value of equation (18) is expressed as: in, W i * Asymptotic estimation; using Φ i and ε i express and Using immersion and invariant adaptive theory Update law and function The design is as follows: in, express right The partial derivatives of k i3 These are control parameters; let Representation matrix The (p,q)th element is obtained as follows: Where, Φ ip Φ i The p-th element in.

2. The cluster fault-tolerant consistency control method for heterogeneous unmanned systems according to claim 1, characterized in that, Step one specifically includes the following process: Step 1.1: Establish the dynamic model of the i-th unmanned surface vessel, as shown below: in, Let η be the Jacobian transformation matrix. i =[x i ,y i ,ψ i ] T Let be the position and heading of the i-th unmanned surface vessel in the inertial coordinate system. For η i The derivative of v i =[u i ,v i ,r i ] T Let be the pitch, yaw, and bow velocities of the i-th unmanned surface vessel in the attached coordinate system. For v i The derivative of τ i To control the input, M i Let C be the inertia matrix. i (v i D is the Coriolis centripetal force matrix. i (v i ) is the damping matrix; Define x i1 =η i x i2 =v i u i =τ i Equation (1) can be transformed into the following form: Among them, f i1 =J(ψ) i )v i -v i and For nonlinear terms, g i1 =I3 and g i2 =M i -1 For gain terms, I3 represents a 3×3 identity matrix; Step 1.2: Establish the dynamic model of the i-th fixed-wing UAV, as shown below: Where, x i y i z i Let be the position of the i-th fixed-wing UAV in the inertial coordinate system. and x i y i and z i The derivative of V; i Let be the speed of the i-th fixed-wing UAV. For V i The derivative; and These are the heading angle and track angle of the i-th fixed-wing UAV, respectively. and χ i and γ i The derivative of m; i Let g be the mass of the i-th fixed-wing UAV; g be the acceleration due to gravity; μ be the mass of the i-th fixed-wing UAV. i α i and β i These are the tilt angle, angle of attack, and sideslip angle of the i-th fixed-wing UAV, respectively; T i D i Y i and L i Let be the thrust, drag, lateral force, and lift acting on the i-th fixed-wing UAV, respectively; specifically expressed as follows: Among them, C iL C iD and C iY These are the lift coefficient, drag coefficient, and axial force coefficient, respectively, and have the following forms: Among them, T imax This indicates the engine's maximum thrust; For thrust throttle setting; ρ0 represents the dynamic pressure and ρ0 represents the air density; s i C is the wing area; iL0 C iLα C iD0 C iDα C iDα2 C iY0 C iYβ The aerodynamic coefficient; Define x i1 =[x i ,y i ,z i ] T x i2 =[V i ,χ i ,γ i ] T , Equation (3) can be transformed into the following form: Among them, g i1 =I3 and For the gain term, f i1 =[f i11 ,f i12 ,f i13 ] T and f i2 =[f i21 ,f i22 ,f i23 ] T As a non-linear term, it is expressed as follows: f i11 =V i cosγ i cosχ i -V i f i12 =V i cosγ i sinx i -x i f i13 =V i sing i -c i Step 1.3: Establish the actuator fault model: in i =ρ i in i0 +b i (5) Where, ρ i =diag{ρ i1 ,ρ i2 ,ρ i3 } is the actuator efficiency loss factor, which is a diagonal matrix satisfying ρ i1 ,ρ i2 ,ρ i3 ∈(0,1];b i The bounded deviation fault vector; u i0 For command control input signals; Step 1.4: Substituting equation (5) into equations (2) and (4), we obtain the cooperative motion control model of the heterogeneous unmanned system under actuator failure as follows: Among them, h i =g i2 [(ρ i -I3)u i0 +b i [This refers to the lumped fault items.]

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