Ocean aircraft cluster distributed control method and system based on inverse optimization theory

Abstract

The invention discloses a distributed control method and system for a marine vehicle cluster based on an inverse optimization theory, relates to the technical field of marine vehicle control, and aims to overcome the defects of an existing state feedback control algorithm. The method is technically characterized by comprising the following steps: aiming at a marine vehicle system working in a water surface environment, constructing a multi-marine vehicle system cluster dynamic model with uncertain modeling parameters; constructing a cluster state tracking error system based on navigation-following, and defining a global dynamic cost function including uncertain modeling parameters; designing a distributed collaborative optimization controller, and solving an optimal control quantity enabling the cost function to be optimal by using a global inverse optimization theory; and performing distributed control on the ocean vehicle cluster by using the optimal control quantity. According to the method, the state consistency of a plurality of follower marine vehicles and a leader marine vehicle and the optimality of a system global cost function under the condition of uncertain modeling parameters can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of ocean vehicle control, and in particular to a method and system for distributed control of an ocean vehicle cluster based on inverse optimization theory. Background Art

[0002] Marine robot swarm systems have many advantages such as wide operating range and strong fault tolerance, and have become one of the indispensable equipment in the field of marine engineering. Efficient distributed collaborative control technology is the key prerequisite for marine robot swarm systems to complete various operating tasks.

[0003] In the prior art, the document “Distributed observer-based formation trajectory tracking method of leader-following multi-AUV system, Ocean Engineering, 2022, 260: 11019” discloses a multi-ocean vehicle feedback collaborative control method. This method designs an observer-based state feedback control algorithm to achieve the “leader-follower” state consistency of multiple ocean vehicles. The technical problem with the state feedback control algorithm described in the document is that the controller parameter calculation needs to rely on accurate ocean vehicle cluster dynamics system model parameters, and it is impossible to achieve the desired control performance with optimal control input. Summary of the invention

[0004] In view of the above problems, the present invention proposes a distributed control method and system for an ocean vehicle cluster based on inverse optimization theory to overcome the shortcomings of existing state feedback control algorithms that rely on precise ocean vehicle cluster dynamic system model parameters and cannot achieve the desired control performance with optimal control input.

[0005] According to one aspect of the present invention, a distributed control method for a cluster of ocean vehicles based on inverse optimization theory is proposed, the method comprising:

[0006] Step 1: For the ocean vehicle system working in the water surface environment, a multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters is constructed;

[0007] Step 2: for the multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters, construct a cluster state tracking error system based on "leader-follower", and define a global dynamic cost function including uncertain modeling parameters;

[0008] Step 3: Design a distributed collaborative optimization controller for the cluster state error system and the global dynamic cost function, and use global inverse optimization theory to solve the optimal control quantity that makes the cost function optimal;

[0009] Step 4: Use the optimal control quantity to perform distributed control on the ocean vehicle cluster.

[0010] Furthermore, the multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters in step 1 is:

[0011]

[0012] In the formula, x i Represents state variables; Δ1=cos(ψ0)cos(ψ t )-sin(ψ0)sin(ψ t )-cos(ψ0),Δ2=sin(ψ0)cos(ψ t )+cos(ψ0)sin(ψ t )-sin(ψ0) and denote the inertia matrix, fluid damping matrix and mooring force matrix of the ocean vehicle respectively; is the rotation matrix, ψ0 is a time-invariant constant; u i represents the control variable.

[0013] Furthermore, the cluster state tracking error system in step 2 is:

[0014]

[0015] Where N is the total number of aircraft; a ij represents the communication relationship between the ith ocean vehicle and the jth ocean vehicle. When the ith ocean vehicle can obtain the status information of the jth ocean vehicle, a ij =1, otherwise a ij =0.

[0016] Furthermore, the global dynamic cost function in step 2 is:

[0017]

[0018] In the formula, I N represents the N-order identity matrix, R>0 and Q≥0 are symmetric matrices, To control the gain parameter, The full rank matrix in the Laplacian matrix corresponding to the directed graph describing the communication relationship between multiple ocean vehicles; Represents the global form of the state tracking error; Represents the global form of the vehicle cluster control variables.

[0019] Furthermore, the distributed collaborative optimization controller described in step 3 is designed as follows:

[0020]

[0021] In the formula, is the control gain matrix; the control gain parameters in the optimal control quantity that makes the cost function optimal The following conditions must be met:

[0022]

[0023] In the formula, σ min {Q} represents the smallest singular value of the symmetric matrix Q; Representation Matrix The smallest positive singular value of; α2 is a constant and satisfies

[0024]

[0025] According to another aspect of the present invention, a distributed control system for a cluster of ocean vehicles based on inverse optimization theory is proposed, the system comprising:

[0026] A dynamic model building module, which is configured to build a multi-ocean vehicle system cluster dynamic model with uncertain modeling parameters for an ocean vehicle system operating in a water surface environment;

[0027] An error model building module, which is configured to build a cluster state tracking error system based on "leader-follower" for the multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters, and define a global dynamic cost function including uncertain modeling parameters;

[0028] An optimal controller design module is configured to design a distributed collaborative optimization controller for the cluster state error system and the global dynamic cost function, and use global inverse optimization theory to solve the optimal control quantity that makes the cost function optimal;

[0029] A distributed control module is configured to perform distributed control on a cluster of ocean vehicles using the optimal control quantity.

[0030] Furthermore, the multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters in the dynamics model building module is:

[0031]

[0032] In the formula, x i Represents state variables; Δ1=cos(ψ0)cos(ψ t )-sin(ψ0)sin(ψ t )-cos(ψ0),Δ2=sin(ψ0)cos(ψ t )+cos(ψ0)sin(ψ t )-sin(ψ0) and denote the inertia matrix, fluid damping matrix and mooring force matrix of the ocean vehicle respectively; is the rotation matrix, ψ0 is a time-invariant constant; u i represents the control variable.

[0033] Furthermore, the cluster state tracking error system in the error model building module is:

[0034]

[0035] Where N is the total number of aircraft; a ij represents the communication relationship between the ith ocean vehicle and the jth ocean vehicle. When the ith ocean vehicle can obtain the status information of the jth ocean vehicle, a ij =1, otherwise a ij =0.

[0036] Furthermore, the global dynamic cost function in the error model construction module is:

[0037]

[0038] In the formula, I N represents the N-order identity matrix, R>0 and Q≥0 are symmetric matrices, To control the gain parameter, The full rank matrix in the Laplacian matrix corresponding to the directed graph describing the communication relationship between multiple ocean vehicles; Represents the global form of the state tracking error; Represents the global form of the vehicle cluster control variables.

[0039] Furthermore, the distributed collaborative optimization controller in the optimal controller design module is designed as follows:

[0040]

[0041] In the formula, is the control gain matrix; the control gain parameters in the optimal control quantity that makes the cost function optimal The following conditions must be met:

[0042]

[0043] In the formula, σ min {Q} represents the smallest singular value of the symmetric matrix Q; Representation Matrix The smallest positive singular value of; α2 is a constant and satisfies

[0044]

[0045] The beneficial technical effects of the present invention are:

[0046] The present invention proposes a distributed control method and system for a cluster of ocean vehicles based on inverse optimization theory. First, for an ocean vehicle system working in a water surface environment, a cluster dynamics model of a multi-ocean vehicle system with uncertain modeling parameters is constructed; then, for the cluster dynamics model of a multi-ocean vehicle system with uncertain modeling parameters, a cluster state tracking error system based on "leader-follower" is constructed, and a global dynamic cost function containing uncertain modeling parameters is defined at the same time; then, for the cluster state error system and the global dynamic cost function, a distributed collaborative optimization controller is designed, and the optimal control quantity that makes the cost function optimal is solved by using the global inverse optimization theory; finally, the ocean vehicle cluster is distributedly controlled by using the optimal control quantity. By using the distributed collaborative optimization control method for a cluster of multiple ocean vehicles proposed by the present invention, each follower ocean vehicle achieves the same state as the leader ocean vehicle in about 10 seconds, and the global dynamic cost function can finally converge to the theoretical optimal value. Therefore, the distributed collaborative optimization controller proposed by the present invention can ensure the state consistency of multiple follower ocean vehicles and the leader ocean vehicle under uncertain modeling parameters and the optimality of the global cost function of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention can be better understood by referring to the description given below in conjunction with the accompanying drawings, which together with the following detailed description are included in this specification and form a part of this specification, and are used to further illustrate the preferred embodiments of the present invention and explain the principles and advantages of the present invention.

[0048] Figure 1 is a flow chart of a distributed control method for a cluster of ocean vehicles based on inverse optimization theory according to an embodiment of the present invention;

[0049] Figure 2is a schematic diagram of a communication topology structure between a leader ocean vehicle (labeled as 4) and three follower ocean vehicles (labeled as 1 to 3) in an embodiment of the present invention;

[0050] Figure 3 are the four ocean vehicle state components under the control protocol proposed in the embodiment of the present invention || x i ||A plot of the norm evolving over time;

[0051] Figure 4 are the heading angles ψ of the four ocean craft under the control protocol proposed in the embodiment of the present invention. i Plots of the evolution over time;

[0052] Figure 5 It is a curve diagram of the evolution of the global dynamic cost function over time under the control protocol proposed in the embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the scheme of the present invention, exemplary implementations or embodiments of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described implementations or embodiments are only implementations or embodiments of a part of the present invention, not all of them. Based on the implementations or embodiments of the present invention, all other implementations or embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0054] The present invention provides a distributed collaborative optimization control method and system for a cluster of ocean vehicles. Considering a class of ocean vehicle systems working in a water surface environment, a cluster dynamics model of multiple ocean vehicle systems with uncertain modeling parameters is constructed; for the cluster dynamics model of multiple ocean robots with uncertain modeling parameters, a cluster state tracking error system based on "leader-follower" is constructed, and a global dynamic cost function containing uncertain modeling parameters is defined at the same time; based on the constructed cluster state tracking error system and the global dynamic cost function, a distributed collaborative optimization controller is designed, and the control gain parameters that can ensure the optimal cost function are solved using the global inverse optimal control theory; the Lyapunov stability theory is used to prove that the designed distributed collaborative optimization controller can ensure the asymptotic stability of the state error tracking system. The present invention can optimize the control input in the presence of uncertain modeling parameters, which is more conducive to the application of the controller in actual systems.

[0055] The embodiment of the present invention proposes a distributed control method for a marine vehicle cluster based on inverse optimization theory, such as Figure 1 As shown, the method includes:

[0056] Step 1: For the ocean vehicle system working in the water surface environment, a multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters is constructed;

[0057] Step 2: for the multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters, construct a cluster state tracking error system based on "leader-follower", and define a global dynamic cost function including uncertain modeling parameters;

[0058] Step 3: Design a distributed collaborative optimization controller for the cluster state error system and the global dynamic cost function, and use global inverse optimization theory to solve the optimal control quantity that makes the cost function optimal;

[0059] Step 4: Use the optimal control quantity to perform distributed control on the ocean vehicle cluster.

[0060] The embodiments of the present invention are described in detail below.

[0061] First, in step one, a class of ocean vehicle systems working in a water surface environment is considered, and a multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters is constructed.

[0062] The following six-DOF ocean vehicle cluster dynamics model is given:

[0063]

[0064] in,

[0065]

[0066] and They represent the inertia matrix, fluid damping matrix and mooring force matrix of the ocean vehicle respectively; r ix and v ix Represents longitudinal position and velocity respectively; r iy and v iy denote the lateral position and velocity respectively; ψ i and ω i denote the heading angle and heading angular velocity respectively, is the rotation matrix; N represents the total number of aircraft.

[0067] Select state variables Assume that all ocean vehicles have the same heading angle, ψ i =ψ, then the model in equation (1) can be rewritten as follows:

[0068]

[0069] In the formula,

[0070]

[0071] Let ψ0 be a time-invariant constant and ψ t =ψ-ψ0, then the nonlinear dynamic model in equation (2) can be rewritten as:

[0072]

[0073] in,

[0074]

[0075] Δ1=cos(ψ0)cos(ψ t )-sin(ψ0)sin(ψ t )-cos(ψ0),Δ2=sin(ψ0)cos(ψ t )+cos(ψ0)sin(ψ t )-sin(ψ0). It is worth pointing out that since the expressions of Δ1 and Δ2 only involve sine and cosine functions, they are norm-bounded, which means that ΔA is an uncertain modeling parameter with norm-bounded. Therefore, Equation (3) can be regarded as a multi-ocean robot cluster dynamics model with uncertain modeling parameters. In addition, the dynamics model of the leader ocean vehicle that only provides reference state trajectory information is defined as x N+1 =(A0+ΔA)x N+1 .

[0076] Then, in step 2, for the linearized dynamic model of the multi-ocean vehicle system cluster with uncertain modeling parameters constructed in step 1, a cluster state tracking error system based on "leader-follower" is constructed, and a global dynamic cost function including uncertain modeling parameters is defined.

[0077] Define the leader vehicle's subscript as N+1, and the follower vehicle's subscript as 1,…,N. Construct a multi-ocean vehicle state tracking error system based on "leader-follower" as:

[0078]

[0079] In the formula, a ij represents the communication relationship between the ith ocean vehicle and the jth ocean vehicle. When the ith ocean vehicle can obtain the status information of the jth ocean vehicle, a ij =1, otherwise a ij =0.

[0080] Directed Graph Describe the communication relationships between multiple ocean vehicles, and Contains a directed spanning tree with the leader ocean vehicle as the root node, then the directed graph The corresponding Laplace matrix can be expressed as follows:

[0081]

[0082] In the formula, is a full rank matrix, as well as is a matrix parameter with suitable dimensions.

[0083] The global form of equation (4) is expressed as follows using the Kronecker product:

[0084]

[0085] In the formula, represents the global form of the state tracking error, I p is the identity matrix with matching dimensions, 1 N represents a column vector whose N-dimensional elements are all 1. The derivative of equation (6) is

[0086]

[0087] in, Represents the global form of the control variables of the aircraft cluster. is a full rank matrix, so when the system (7) is stable, we have It is established, that is, multiple follower ocean vehicles achieve the same state as the leader ocean vehicle.

[0088] For the system given in equation (7), the following global dynamic cost function including uncertain modeling parameters is defined:

[0089]

[0090] In the formula, R>0 and Q≥0 are symmetric matrices, To control the gain parameter,

[0091] Then, in step three, a distributed collaborative optimization controller is designed for the state error system and the global dynamic cost function constructed in step two, and the global inverse optimization theory is used to solve the optimal control quantity that makes the cost function optimal, that is, the global inverse optimization theory is used to solve the control gain parameters that can ensure the optimal cost function.

[0092] According to equations (7) and (8), the following distributed collaborative optimization controller is designed:

[0093]

[0094] In the formula, is the control gain matrix.

[0095] According to the optimal control theory, And the matrix is a positive definite solution to the following algebraic Riccati equation:

[0096]

[0097] Then the distributed collaborative optimization controller (9) can ensure the optimality of the global dynamic cost function (8).

[0098] In addition, when the matrix BR -1 B T When the eigenvalue of is non-zero, ensure that the weight matrix parameters in the global dynamic cost function The conditions for being non-negative are:

[0099]

[0100] In the formula, σ min {Q} represents the smallest singular value of the matrix Q, Representation Matrix The minimum singular value of . Considering Therefore, there is Then the condition for formula (11) to be valid can be expressed as

[0101]

[0102] According to formula (12), when the control gain parameter When the following conditions are met, the weight matrix parameters in the global dynamic cost function are is non-negative:

[0103]

[0104] In the formula, α1 is a constant and satisfies

[0105]

[0106] When the matrix BR -1 B T When the eigenvalue of has zero, the condition in equation (11) will no longer apply, ensuring that the weight matrix parameter in the global dynamic cost function The condition for being non-negative can be restated as

[0107]

[0108] In the formula, Representation Matrix The smallest positive singular value of . Considering that Established Similar to the derivation process of equations (11) to (14), when the control gain parameter When the following conditions are met, the weight matrix parameters in the global dynamic cost function are is non-negative:

[0109]

[0110] In the formula, α2 is a constant and satisfies

[0111]

[0112] Then, in step four, the optimal control quantity is used to perform distributed control on the ocean vehicle cluster.

[0113] Furthermore, Lyapunov stability theory is used to prove that the distributed collaborative optimization controller designed in step three can ensure the asymptotic stability of the cluster state tracking error system in step two as shown below.

[0114] For system (7), choose the following Lyapunov function:

[0115]

[0116] When the control input When , the derivative of equation (18) is

[0117]

[0118] Will Substituting the expression of into formula (19) we can get

[0119]

[0120] according to And R>0 can be deduced According to Lyapunov stability theory, system (7) is asymptotically stable, that is, Therefore, the distributed collaborative optimization controller in (9) can ensure that multiple follower ocean vehicles can successfully track the state of the leader ocean vehicle.

[0121] The following examples are used to verify the beneficial technical effects of the present invention.

[0122] Assume that there is a leader ocean vehicle and three follower ocean vehicles forming a cluster system. The communication topology between the ocean vehicles is as follows: Figure 2 As shown, the Laplace matrix corresponding to the communication topology is expressed as follows:

[0123]

[0124] The parameters of the system's inertia matrix, fluid damping matrix, and mooring force matrix are selected as follows:

[0125]

[0126] Let ψ0=0, R=I3 and Q=1000I6, and use equation (10) to calculate the matrix and The parameters are

[0127]

[0128] according to The uncertain modeling parameter ||ΔA||≤0.4405 can be calculated. The control gain parameter can be calculated using inequality (13): Need to meet So choose

[0129] In addition, according to the optimal control theory, the theoretical optimal value of the global dynamic cost function is:

[0130]

[0131] Furthermore, let the leader ocean vehicle (labeled as x4(0)) and the three follower ocean vehicles (labeled as x i The initial states of (0), i=1,...,3) are:

[0132]

[0133] Then, under the control protocol proposed in the present invention, the state norm evolution trajectory curves, heading angle evolution trajectory curves and global dynamic cost function evolution trajectory curves of the leader ocean vehicle and the three follower ocean vehicles are respectively as follows: Figures 3 to 5 As shown. It can be seen from the state norm evolution trajectory simulation curve that under the proposed distributed collaborative optimization controller, the tracking error norm of each state component gradually converges to zero in about 10 seconds; it can be seen from the heading angle evolution trajectory simulation curve that the four ocean vehicles have the same heading angle, which is consistent with the prerequisite in step one; it can be seen from the global dynamic cost function evolution trajectory curve that under the proposed distributed collaborative optimization controller, the global dynamic cost function finally converges to 5951.1, which is consistent with the theoretical optimal value. Therefore, the control method proposed in the present invention can realize the distributed collaborative optimization control of the multi-ocean vehicle cluster system in the presence of uncertain modeling parameters.

[0134] It should be noted that the contents not introduced in detail in the embodiments of the present invention (such as graph theory, optimal control theory, Lyapunov stability theory, matrix theory) are common knowledge in the field.

[0135] Another embodiment of the present invention provides a distributed control system for a cluster of ocean vehicles based on inverse optimization theory, the system comprising:

[0136] A dynamic model building module, which is configured to build a multi-ocean vehicle system cluster dynamic model with uncertain modeling parameters for an ocean vehicle system operating in a water surface environment;

[0137] An error model building module, which is configured to build a cluster state tracking error system based on "leader-follower" for the multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters, and define a global dynamic cost function including uncertain modeling parameters;

[0138] An optimal controller design module is configured to design a distributed collaborative optimization controller for the cluster state error system and the global dynamic cost function, and use global inverse optimization theory to solve the optimal control quantity that makes the cost function optimal;

[0139] A distributed control module is configured to perform distributed control on a cluster of ocean vehicles using the optimal control quantity.

[0140] In this embodiment, preferably, the multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters in the dynamics model building module is:

[0141]

[0142] In the formula,

[0143]

[0144] Δ1=cos(ψ0)cos(ψ t )-sin(ψ0)sin(ψ t )-cos(ψ0),Δ2=sin(ψ0)cos(ψ t )+cos(ψ0)sin(ψ t )-sin(ψ0) and denote the inertia matrix, fluid damping matrix and mooring force matrix of the ocean vehicle respectively; is the rotation matrix, ψ0 is a time-invariant constant; x i represents the state variable, u i represents the control variable.

[0145] In this embodiment, preferably, the cluster state tracking error system in the error model building module is:

[0146]

[0147] Where N is the total number of aircraft; a ij represents the communication relationship between the ith ocean vehicle and the jth ocean vehicle. When the ith ocean vehicle can obtain the status information of the jth ocean vehicle, a ij =1, otherwise a ij =0.

[0148] In this embodiment, preferably, the global dynamic cost function in the error model construction module is:

[0149]

[0150] In the formula, I N represents the N-order identity matrix, R>0 and Q≥0 are symmetric matrices, To control the gain parameter, The full rank matrix in the Laplacian matrix corresponding to the directed graph describing the communication relationship between multiple ocean vehicles; Represents the global form of the state tracking error; Represents the global form of the vehicle cluster control variables.

[0151] In this embodiment, preferably, the distributed collaborative optimization controller in the optimal controller design module is designed as follows:

[0152]

[0153] In the formula, is the control gain matrix; the control gain parameters in the optimal control quantity that makes the cost function optimal The following conditions must be met:

[0154]

[0155] In the formula, σ min {Q} represents the smallest singular value of the symmetric matrix Q; Representation Matrix The smallest positive singular value of; α2 is a constant and satisfies

[0156]

[0157] The functions of the ocean vehicle cluster distributed control system based on inverse optimization theory described in an embodiment of the present invention can be explained by the aforementioned ocean vehicle cluster distributed control method based on inverse optimization theory. Therefore, for the parts not described in detail in the system embodiment, please refer to the above method embodiment and will not be repeated here.

[0158] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments are contemplated within the scope of the invention thus described. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

Claims

1. A distributed control method for ocean vehicle clusters based on inverse optimization theory, characterized in that: include: Step 1: For the ocean vehicle system working in the water surface environment, a multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters is constructed; Step 2: for the multi-ocean vehicle system swarm dynamics model with uncertain modeling parameters, a swarm state tracking error system based on "leader-follower" is constructed, and a global dynamic cost function including uncertain modeling parameters is defined; Step 3: Design a distributed collaborative optimization controller for the cluster state error system and the global dynamic cost function, and use global inverse optimization theory to solve the optimal control quantity that makes the cost function optimal; Step 4: Use the optimal control quantity to perform distributed control on the ocean vehicle cluster.

2. The method for distributed control of ocean vehicle clusters based on inverse optimization theory according to claim 1, characterized in that: The multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters described in step 1 is: In the formula, x i Represents state variables; Δ1=cos(ψ0)cos(ψ t )-sin(ψ0)sin(ψ t )-cos(ψ0),Δ2=sin(ψ0)cos(ψ t )+cos(ψ0)sin(ψ t )-sin(ψ0) and denote the inertia matrix, fluid damping matrix and mooring force matrix of the ocean vehicle respectively; is the rotation matrix, ψ0 is a time-invariant constant; u i represents the control variable.

3. The method for distributed control of ocean vehicle clusters based on inverse optimization theory according to claim 2, characterized in that: The cluster state tracking error system described in step 2 is: Where N is the total number of aircraft; a ij represents the communication relationship between the ith ocean vehicle and the jth ocean vehicle. When the ith ocean vehicle can obtain the status information of the jth ocean vehicle, a ij =1, otherwise a ij =0.

4. The method for distributed control of ocean vehicle clusters based on inverse optimization theory according to claim 3 is characterized in that: The global dynamic cost function in step 2 is: In the formula, I N represents the N-order identity matrix, R>0 and Q≥0 are symmetric matrices, To control the gain parameter, The full rank matrix in the Laplacian matrix corresponding to the directed graph describing the communication relationship between multiple ocean vehicles; Represents the global form of the state tracking error; Represents the global form of the vehicle cluster control variables.

5. The method for distributed control of ocean vehicle clusters based on inverse optimization theory according to claim 4 is characterized in that: The distributed collaborative optimization controller described in step 3 is designed as follows: In the formula, is the control gain matrix; the control gain parameters in the optimal control quantity that makes the cost function optimal The following conditions must be met: In the formula, σ min {Q} represents the smallest singular value of the symmetric matrix Q; Representation Matrix The smallest positive singular value of; α2 is a constant and satisfies 6. A distributed control system for ocean vehicle clusters based on inverse optimization theory, characterized in that: include: A dynamic model building module, which is configured to build a multi-ocean vehicle system cluster dynamic model with uncertain modeling parameters for an ocean vehicle system operating in a water surface environment; An error model building module, which is configured to build a cluster state tracking error system based on "leader-follower" for the multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters, and define a global dynamic cost function including uncertain modeling parameters; An optimal controller design module is configured to design a distributed collaborative optimization controller for the cluster state error system and the global dynamic cost function, and use global inverse optimization theory to solve the optimal control quantity that makes the cost function optimal; A distributed control module is configured to perform distributed control on a cluster of ocean vehicles using the optimal control quantity.

7. The ocean vehicle cluster distributed control system based on inverse optimization theory according to claim 6, characterized in that: The multi-ocean vehicle system cluster dynamics model with uncertain modeling parameters in the dynamics model building module is: In the formula, x i Represents state variables; Δ1=cos(ψ0)cos(ψ t )-sin(ψ0)sin(ψ t )-cos(ψ0),Δ2=sin(ψ0)cos(ψ t )+cos(ψ0)sin(ψ t )-sin(ψ0) and denote the inertia matrix, fluid damping matrix and mooring force matrix of the ocean vehicle respectively; is the rotation matrix, ψ0 is a time-invariant constant; u i represents the control variable.

8. The ocean vehicle cluster distributed control system based on inverse optimization theory according to claim 7, characterized in that: The cluster state tracking error system in the error model building module is: Where N is the total number of aircraft; a ij represents the communication relationship between the ith ocean vehicle and the jth ocean vehicle. When the ith ocean vehicle can obtain the status information of the jth ocean vehicle, a ij =1, otherwise a ij =0.

9. The ocean vehicle cluster distributed control system based on inverse optimization theory according to claim 8, characterized in that: The global dynamic cost function in the error model construction module is: In the formula, I N represents the N-order identity matrix, R>0 and Q≥0 are symmetric matrices, To control the gain parameter, The full rank matrix in the Laplacian matrix corresponding to the directed graph describing the communication relationship between multiple ocean vehicles; Represents the global form of the state tracking error; Represents the global form of the vehicle cluster control variables.

10. The ocean vehicle cluster distributed control system based on inverse optimization theory according to claim 9, characterized in that: The distributed collaborative optimization controller in the optimal controller design module is designed as follows: In the formula, is the control gain matrix; the control gain parameters in the optimal control quantity that makes the cost function optimal The following conditions must be met: In the formula, σ min {Q} represents the smallest singular value of the symmetric matrix Q; Representation Matrix The smallest positive singular value of; α2 is a constant and satisfies

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