Distributed optimization method and system for fixed time and predetermined time of multiple unmanned vehicles

Abstract

The invention discloses a distributed optimization method and system for fixed time and preset time of multiple unmanned vehicles. The method comprises the following steps: establishing a dynamic model and a communication topological graph of a multi-unmanned vehicle system; constructing a global objective function; respectively designing a fixed time centralized optimization algorithm and a predetermined time centralized optimization algorithm based on the communication topological graph and the global objective function; respectively designing a fixed time distributed gradient estimator and a preset time distributed gradient estimator; based on a distributed gradient estimator and a centralized optimization algorithm, respectively designing a fixed time distributed optimization algorithm and a preset time distributed optimization algorithm based on gradient estimation; and adopting a gradient estimation-based fixed time and preset time distributed optimization algorithm to control the multiple unmanned vehicles to obtain an optimal solution of the global target function. The method ensures that the state of the multi-unmanned vehicle system reaches the optimal solution of the global objective function within the fixed time or the preset time.

Description

Technical Field

[0001] The present invention relates to the field of unmanned vehicles, and in particular to a method and system for distributed optimization of fixed time and scheduled time of multiple unmanned vehicles. Background Art

[0002] Multi-agent system is an important research direction in the field of artificial intelligence. The agents that make up the system are autonomous, social, reactive and proactive. They can perceive the environment, make decisions and perform actions. In a multi-agent system, each agent can use local limited information to complete tasks through communication and collaboration. This unique advantage has set off a wave of research on the collaborative control of multi-agent systems along with the development of computer network technology. Distributed optimization problem is one of the key issues in the collaborative control of multi-agent systems. It has a wide range of applications in resource allocation, source location of sensor networks and other fields. By cleverly decomposing complex global optimization problems into multiple sub-problems that are respectively responsible for different agents, distributed optimization algorithms can significantly reduce the complexity of the problem, improve the efficiency of solving, and make the entire system more flexible and scalable. As a typical application example of multi-agent system, multi-unmanned vehicle system has shown great potential and value in many fields such as logistics distribution, intelligent transportation, and agricultural operations in recent years. In the unmanned vehicle system, distributed optimization algorithms can be applied to problems such as collaborative operation and resource allocation between multiple unmanned vehicles. Among them, each unmanned vehicle has a local objective function, and the global objective function to be optimized is the sum of all local objective functions. The goal of distributed optimization is to make the state of each unmanned vehicle converge to the optimal point of the system's global objective function through local information interaction, when only the local objective function of the neighboring unmanned vehicles is known, through a distributed algorithm.

[0003] In the existing centralized optimization algorithms, the central node needs to process a large amount of data information, which increases the operation burden of the network, resulting in poor scalability and weak anti-interference ability of the network. Distributed optimization algorithms have significant advantages in reducing the burden of network communication and improving the robustness of the system to node failures. However, the optimization objective function in the optimization problem is often a global function, and it is challenging to design a distributed optimization algorithm without using global information. On the other hand, most of the existing distributed optimization algorithms can only solve the optimization problem in infinite time, and theoretically can only achieve suboptimal solutions. In practical applications, it is often necessary to make the state of the unmanned vehicle tend to the optimal point of the global objective function within a limited time. Although the limited time distributed optimization algorithm solves the problem of infinite convergence time to a certain extent, it needs to obtain the initial state of the unmanned vehicle when estimating the convergence time, and the convergence time corresponding to different initial states is not the same, which undoubtedly increases the computational burden of the system. More importantly, this method cannot pre-set the time for the system to solve the optimization problem according to actual needs. In addition, the design of existing distributed optimization algorithms mostly contains many adjustable parameters, and requires the local objective function of the unmanned vehicle to be a convex function or a strongly convex function. These characteristics limit the scope of application of the relevant algorithms. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention aims to provide a fixed-time and preset-time distributed optimization method and system for multiple unmanned vehicles, which can ensure that the state of the multi-unmanned vehicle system reaches the optimal solution of the global objective function within a fixed time or a preset time when the initial state is unknown and the local objective function is non-convex.

[0005] The technical solution to achieve the purpose of the present invention is:

[0006] A distributed optimization method for multiple unmanned vehicles with fixed time and scheduled time, comprising:

[0007] Establish the dynamic model and communication topology diagram of multi-unmanned vehicle system;

[0008] Based on the dynamics model, a global objective function is constructed;

[0009] Based on the communication topology graph and the global objective function, fixed-time and scheduled-time centralized optimization algorithms are designed respectively;

[0010] Design fixed-time and scheduled-time distributed gradient estimators respectively;

[0011] Based on the distributed gradient estimator and the centralized optimization algorithm, fixed-time and scheduled-time distributed optimization algorithms based on gradient estimation are designed respectively;

[0012] Fixed-time and scheduled-time distributed optimization algorithms based on gradient estimation are used to control multiple unmanned vehicles and obtain the optimal solution of the global objective function.

[0013] A distributed optimization system for multiple unmanned vehicles with fixed time and scheduled time, comprising:

[0014] Model building unit, used to build the dynamic model and communication topology diagram of the multi-unmanned vehicle system;

[0015] A global objective function construction unit constructs a global objective function based on a dynamics model;

[0016] A centralized optimization algorithm design unit, which designs fixed-time and scheduled-time centralized optimization algorithms based on the communication topology graph and the global objective function;

[0017] A distributed gradient estimator design unit, used to design fixed-time and scheduled-time distributed gradient estimators;

[0018] A distributed optimization algorithm design unit, based on a distributed gradient estimator and a centralized optimization algorithm, designs fixed-time and scheduled-time distributed optimization algorithms based on gradient estimation, respectively;

[0019] The control unit uses a fixed-time and scheduled-time distributed optimization algorithm based on gradient estimation to control multiple unmanned vehicles and obtain the optimal solution of the global objective function.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] (1) This patented technology designs a fixed-time and scheduled-time distributed optimization method based on a distributed gradient estimator. The distributed gradient estimator can be used to estimate the required global objective function gradient information within a fixed time or a scheduled time. The resulting distributed optimization method is suitable for large-scale systems and has the advantages of privacy protection, robustness to node failures, and strong scalability.

[0022] (2) The proposed distributed optimization method solves the optimization problem of a multi-unmanned vehicle system with arbitrary initial states and non-convex local objective functions. This method removes the restriction that the local objective function of the system must be a convex function or a strongly convex function, and does not require the initial state of the unmanned vehicle to be known in advance, so it has a wider range of applications.

[0023] (3) Under the fixed-time optimization algorithm, an estimate of the time the system needs to solve the optimization problem is given, and this estimate is independent of the initial state of the system. Under the preset-time optimization algorithm, the time to solve the optimization problem can be pre-set according to actual needs within the range permitted by physical conditions, and this preset time is independent of the initial state and system parameters.

[0024] (4) Compared with the existing optimization algorithm based on the traditional fixed time stability method, the fixed time and predetermined time distributed optimization method based on exponential function provided by the present patent technology has fewer parameters, simple structure, and easier time estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Flowchart of the distributed optimization method for multiple unmanned vehicles with fixed and scheduled times.

[0026] Figure 2 The communication topology diagram between unmanned vehicles.

[0027] Figure 3 The distributed optimization algorithm u is the position state (x-axis component) of each unmanned vehicle at a fixed time i (t) Change curve diagram under the action of.

[0028] Figure 4 The distributed optimization algorithm u is the position state (y-axis component) of each unmanned vehicle at a fixed time i (t) Change curve diagram under the action of.

[0029] Figure 5 It is a curve diagram of the change of the position state (x-axis component) of each unmanned vehicle under the action of the distributed optimization algorithm at a predetermined time.

[0030] Figure 6 It is a curve diagram of the change of the position state (y-axis component) of each unmanned vehicle under the action of the distributed optimization algorithm at a predetermined time. DETAILED DESCRIPTION

[0031] The technical problem to be solved at present is: Under arbitrary initial states and non-convex local objective functions of a multi-unmanned vehicle system, how to design a distributed fixed-time and scheduled-time optimization algorithm using only the information of neighboring unmanned vehicles and apply it to the unmanned vehicles, so that the state of each unmanned vehicle converges to the optimal point of the global objective function of the system, and estimates the upper bound of the convergence time of the distributed fixed-time optimization algorithm. Based on the technical problem raised, this embodiment fully considers practical applications and proposes a fixed-time and scheduled-time distributed optimization method that is effective for arbitrary initial states and non-convex local objective functions, wherein the time to solve the optimization problem is independent of the initial state of the system and can be pre-set according to actual needs. The proposed method has the advantages of strong scalability, fast convergence speed, and a wide range of applications. It comprises the following steps:

[0032] 1. Establish the dynamic model and communication topology diagram of the multi-unmanned vehicle system

[0033] Consider a system consisting of N Mecanum wheeled unmanned vehicles, where the dynamic model of the i-th unmanned vehicle is as follows:

[0034]

[0035] In the formula, x i1 、x i2 、v i1 、v i2 Respectively represent the position and linear velocity of the i-th unmanned vehicle moving on the two-dimensional plane; θ i represents the orientation angle of the i-th unmanned vehicle, that is, the angle with the x-axis; represents the angular velocity of the i-th unmanned vehicle. Since the Mecanum wheel unmanned vehicle can achieve translational motion at any angle, the dynamic model of the i-th unmanned vehicle can be simplified to the following dynamic model:

[0036]

[0037] Let x i (t) = [x i1 ,x i2 ] T ,u i (t) = [v i1 ,v i2 ] T , the general form of the dynamic equation of the i-th unmanned vehicle is

[0038]

[0039] The communication topology of the multi-unmanned vehicle system is an undirected connection graph. where ν={ν1,…,ν N} represents the set of unmanned vehicles, N is the number of unmanned vehicles, ε∈ν×ν represents the set of edges; (ν i ,ν j )∈ε represents the unmanned vehicle ν i and ν j Information can be transmitted between them; A = a ij ∈R N×N Representation diagram The adjacency matrix of the unmanned vehicle ν i and ν j There is information exchange between them, that is, (ν i ,ν j )∈ε, then the element a of the adjacency matrix ij > 0, otherwise a ij =0, assuming a ii =0; For driverless cars i The neighbor set of The corresponding Laplace matrix Among them l ij =-a ij ,

[0040] 2. Construct a global objective function and describe the optimization problem

[0041] The local objective function of the i-th unmanned vehicle is f i (x i ), the global objective function of the system is the sum of all local objective functions, and its expression is:

[0042]

[0043] The global objective function f(x) satisfies φ strong convexity, where φ is the convex parameter of the function f(x).

[0044] The main goal of the present invention is to minimize the global objective function f(x) of the system by designing a distributed optimization algorithm. The equivalent distributed optimization problem with consistency constraints can be described as:

[0045]

[0046] Where x i (t), x j (t) represent the position states of the i-th and j-th unmanned vehicles, respectively, x=(x1,…,x N ) T ; Represents the function The minimum point x; x i (t) = x j (t), i = 1, 2, ..., N, i ≠ j means that the position states of N unmanned vehicles are the same.

[0047] 3. Design of fixed-time and scheduled-time centralized optimization algorithms based on the global objective function of the unmanned vehicle

[0048] 3.1 Design of a fixed-time centralized optimization algorithm

[0049] An algorithm is designed to solve the above distributed optimization problem within a fixed time or a predetermined time. The fixed-time centralized optimization algorithm based on the agent global objective function is as follows:

[0050]

[0051] where u i (t) is the control input of the i-th unmanned vehicle, and the parameter λ 11 and λ 12 is a positive constant; and q are positive constants and satisfy 0<q<1 / 2;||x i (t)-x j (t)|| represents x i (t)-x jThe Euclidean norm of (t); represents the function f(x i (t)) with respect to time t, f(x i (t)) is x i (t) is the system global objective function of the independent variable; express The Euclidean norm of ij Represents the adjacency matrix A=(a ij ) N×N elements of ij > 0 means that the i-th unmanned vehicle can obtain status information from the j-th unmanned vehicle, otherwise a ij =0; T1 is the fixed time estimate for all unmanned vehicles to achieve consistency. The fixed time estimate for the system to solve the optimization problem under the fixed time centralized optimization algorithm is T=T1+T2, where T2 is the estimated time for the unmanned vehicle to converge from a consistent state to the optimal solution of the global objective function after time t>T1, and its expression is φ is the convex parameter of the global objective function.

[0052] 3.2 Design of a scheduled time centralized optimization algorithm

[0053] The predetermined time centralized optimization algorithm based on the agent global objective function is as follows:

[0054]

[0055] where u i (t) is the control input of the i-th unmanned vehicle, q, and η are positive constants and satisfy 0<q<1 / 2, 0<η<1; Undirected connected communication topology diagram for multiple unmanned vehicle systems The algebraic connectivity of Indicates that the base is e The logarithm of T p is the time it takes for the pre-set multi-autonomous vehicle system to solve the optimization problem, ηT p Achieve consistent time for the pre-set unmanned vehicle, and other parameters are the same as in step 3.1.

[0056] 4. Designing a Fixed-Time and Predetermined-Time Distributed Gradient Estimator

[0057] The fixed-time and scheduled-time centralized optimization algorithms described in step 3 require the use of the system's global objective function gradient information, which is not suitable for large systems or systems that require privacy protection. In order to avoid the use of global information and solve the optimization problem in a distributed manner, fixed-time and scheduled-time distributed gradient estimators are designed respectively, so that each unmanned vehicle estimates the gradient information of the system's global objective function in a distributed manner within a fixed time and scheduled time.

[0058] 4.1 Design of a fixed-time distributed gradient estimator

[0059] The fixed-time distributed gradient estimator designed based on information interaction between unmanned vehicles is as follows:

[0060]

[0061] The parameter θ 11 ,θ 12 is a positive constant, and in ‖ρ i (t)-ρ j (t)‖ ∞ Represents ρ i (t)-ρ j The infinite norm of (t), Indicates that in the time interval [t0,T e ] Upper pair ‖ρ i (t)-ρ j (t)‖ ∞ Take the maximum value, T e ω i (t) converges to the average value of the global objective function gradient The required fixed time; ω i (t) is the estimate of the average value of the gradient of the global objective function by the i-th unmanned vehicle, ν i (t) is the intermediate variable; definition ω i The update rate of (t) is as follows:

[0062]

[0063] Under the action of the fixed-time distributed gradient estimator, each unmanned vehicle only needs to communicate with its neighboring unmanned vehicles to calculate the gradient at a fixed time T. e Estimate the average value of the global objective function gradient Time T e The expression is

[0064]

[0065] 4.2 Design of a scheduled time distributed gradient estimator

[0066] The predetermined time distributed gradient estimator designed based on information interaction between unmanned vehicles is as follows:

[0067]

[0068] Parameters in the formula in ‖ρ i (t)-ρ j (t)‖ ∞ Represents ρ i (t)-ρ j The infinite norm of (t), Indicates that in the time interval [t0,ηT p ] Upper pair ‖ρ i (t)-ρ j (t)‖ ∞ Take the maximum value; 0<η<1, ηT p The preset ω i (t) Converges to the average value of the global objective function gradient time; i (t), ν i (t),ρ i Definition of (t) and ω i The update rate of (t) is the same as in step 4.1.

[0069] Under the influence of the above-mentioned time-determined distributed gradient estimator, each unmanned vehicle only needs to communicate with its neighboring unmanned vehicles to reach the predetermined time ηT p Estimate the average value of the global objective function gradient

[0070] 5. Determine a distributed optimization algorithm based on a gradient estimator so that the multi-unmanned vehicle system can solve the optimization problem in a distributed manner within a fixed time and a predetermined time.

[0071] When the i-th unmanned vehicle estimates the average value of the global objective function gradient After converging to 0, the following distributed optimization algorithm based on gradient estimation can be obtained based on the gradient average estimation of the global objective function and the centralized optimization algorithm.

[0072] 5.1. Determine the distributed optimization algorithm that solves the optimization problem in a fixed time

[0073] The fixed-time distributed optimization algorithm based on gradient estimation is as follows:

[0074]

[0075] Where Nω i(t) is the estimate of the gradient of the global objective function of the system by the ith unmanned vehicle at time t, T1'=max{T1,T e}, T1 is a consistent fixed time estimate for all unmanned vehicles, T e ω i (t) converges to the average value of the global objective function gradient The required fixed time, other parameters are the same as those in the centralized optimization algorithm in step 4.1. The fixed time for the system to solve the optimization problem under the fixed time distributed optimization algorithm is estimated to be T = T' + T2, where T2 is the estimated time for the unmanned vehicle to converge from the consistent state to the optimal solution of the global objective function after time t> T1', and its expression is φ is the convex parameter of the global objective function.

[0076] 5.2. Determine the distributed optimization algorithm that solves the optimization problem within the predetermined time

[0077] The predetermined time distributed optimization algorithm based on gradient estimation is as follows:

[0078]

[0079] in Nω i (t) is the gradient of the global objective function estimated by the i-th unmanned vehicle at time t; ηT p Achieve consistency and consistency for all preset unmanned vehicles i (t) Converges to the average value of the global objective function gradient Time, T p It is the time for the preset unmanned vehicle state to converge to the optimal solution of the global objective function; the other parameters are the same as those in the centralized optimization algorithm in step 4.2.

[0080] The present invention also provides a distributed optimization system for multiple unmanned vehicles with fixed time and scheduled time, comprising:

[0081] Model building unit, used to build the dynamic model and communication topology diagram of the multi-unmanned vehicle system;

[0082] A global objective function construction unit constructs a global objective function based on a dynamics model;

[0083] A centralized optimization algorithm design unit, which designs fixed-time and scheduled-time centralized optimization algorithms based on the communication topology graph and the global objective function;

[0084] A distributed gradient estimator design unit, used to design fixed-time and scheduled-time distributed gradient estimators;

[0085] A distributed optimization algorithm design unit, based on a distributed gradient estimator and a centralized optimization algorithm, designs fixed-time and scheduled-time distributed optimization algorithms based on gradient estimation, respectively;

[0086] The control unit uses a fixed-time and scheduled-time distributed optimization algorithm based on gradient estimation to control multiple unmanned vehicles and obtain the optimal solution of the global objective function.

[0087] Example 1

[0088] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0089] like Figure 1 As shown, a distributed optimization method for multiple unmanned vehicles with fixed time and scheduled time of the present invention has the following specific steps:

[0090] 1. Establish a dynamic model and communication topology diagram of a multi-unmanned vehicle system. The multi-unmanned vehicle system includes 6 unmanned vehicles. The dynamic model of the i-th unmanned vehicle is as follows:

[0091]

[0092] where x i (t),u i (t) represents the position state and control input of the i-th unmanned vehicle, Represents x i (t) The derivative of time t, x i (t) is usually obtained from the sensors and inertial measurement units equipped by the unmanned vehicle, and is used to describe the movement of the unmanned vehicle under control input. Select x1(0) = (0, 0.5) T , x2(0)=(-1,1.6) T , x3(0)=(1.2,-0.5) T , x4(0)=(-1,1) T , x5(0)=(0.2,1.5) T , x6(0)=(3,2) T ; The communication topology between unmanned vehicles is as follows Figure 2 shown.

[0093] 2. Construct the global objective function and describe the optimization problem. The local objective functions of the six unmanned vehicles are f1(x1)=sin(x 11 )+sin(x 12 ),f2(x2)=2(x 21 +0.7) 2 +2(x 22 +2) 2 ,f3(x3)=2(x 31 +1.5)2 +(x 32 +1) 2 ,f4(x4)=2(x 41 +0.2) 2 +2(x 42 +0.1) 2 , f5(x5)=(x 51 +2) 2 +2(x 52 +0.4) 2 , f6(x6)=1.5(x 61 +0.8) 2 +1.5(x 62 +1) 2 , the global objective function is the sum of all local objective functions, and its expression is The distributed optimization problem can be described as:

[0094]

[0095] After calculation, the optimal solution x of the global objective function is * =(-0.9742,-0.9171), the convex parameter of the global objective function φ = 16. The goal of this example is to design fixed-time and scheduled-time distributed optimization algorithms respectively, so that the six unmanned vehicles can solve the above optimization problem within a fixed time or a scheduled time, that is, the six unmanned vehicles can converge to the optimal solution x of the global objective function within a fixed time or a scheduled time. * =(-0.9742,-0.9171).

[0096] 3. Design fixed-time and scheduled-time centralized optimization algorithms based on the global objective function of the unmanned vehicle

[0097] The fixed-time centralized optimization algorithm is designed as follows:

[0098]

[0099] The parameter λ 11 =2,λ 12 =1,q=0.25, a ii =0,a ij =1,i,j=1,…,6.

[0100] The predetermined time centralized optimization algorithm is designed as follows:

[0101]

[0102] The parameter λ 21 =5.6257,λ 22=0.1541,q=0.25, η=0.5,T p =4,.

[0103] 4. Design of fixed-time and scheduled-time distributed gradient estimators

[0104] The fixed-time distributed gradient estimator is designed as follows:

[0105]

[0106] The parameter θ 11 =2,θ 12 =130,q=0.25, Using the designed distributed gradient estimator, ω i (t) can be used at a fixed time T e =5.6247 converges to

[0107] The predetermined time distributed gradient estimator is designed as follows:

[0108]

[0109] The parameter θ 21 =5.6256,θ 22 =130,q=0.25, a ii =0,a ij =1. Select η = 0.5, T p =4 Using the designed distributed gradient estimator, ω i (t) Able to Converges to

[0110] 5. Determine a distributed optimization algorithm based on the gradient estimator so that the multi-unmanned vehicle system can solve the optimization problem in a distributed manner in a fixed time and a predetermined time respectively.

[0111] The fixed-time distributed optimization algorithm based on gradient estimation is as follows:

[0112]

[0113] The parameter λ 11 =2,λ 12 =1,q=0.25, a ii =0,a ij =1,T1'=max{T1,T e}=5.6257,ω iThe value of (t) can be obtained from the calculation process in the previous step. The position state of each unmanned vehicle is in the above u i The change curve under the action of (t) is as follows Figure 3 and Figure 4 As shown, it can be seen that each unmanned vehicle can reach the optimal solution x of the global objective function within a fixed time T = 5.934 * =(-0.9742,-0.9171).

[0114] The predetermined time distributed optimization algorithm based on gradient estimation is as follows:

[0115]

[0116] where λ 21 =5.6257,λ 22 =0.1541; q = 0.25, a ii =0,a ij =1,ω i The value of (t) can be obtained from the calculation process in the previous step. Select η = 0.5, T p =4, the position state of each unmanned vehicle is in the above u i The change curve under the action of (t) is as follows Figure 5 and Figure 6 As shown, it can be seen that each unmanned vehicle can p =4 to reach the optimal solution x of the global objective function * =(-0.9742,-0.9171).

[0117] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A distributed optimization method for multiple unmanned vehicles with fixed time and scheduled time, characterized in that: include: Establish the dynamic model and communication topology diagram of multi-unmanned vehicle system; Based on the dynamics model, a global objective function is constructed; Based on the communication topology graph and the global objective function, fixed-time and scheduled-time centralized optimization algorithms are designed respectively; Design fixed-time and scheduled-time distributed gradient estimators respectively; Based on the distributed gradient estimator and the centralized optimization algorithm, fixed-time and scheduled-time distributed optimization algorithms based on gradient estimation are designed respectively; Fixed-time and scheduled-time distributed optimization algorithms based on gradient estimation are used to control multiple unmanned vehicles and obtain the optimal solution of the global objective function.

2. A method for distributed optimization of multiple unmanned vehicles with fixed time and scheduled time according to claim 1, characterized in that: The fixed-time centralized optimization algorithm is: Among them, u i (t) is the control input of the i-th unmanned vehicle, and the parameter λ 11 and λ 12 is a positive constant; x i (t), x j (t) is the position of the i-th and j-th unmanned vehicles, and q are positive constants and satisfy 0<q<1 / 2;||x i (t)-x j (t)|| represents x i (t)-x j The Euclidean norm of (t); represents the function f(x i (t)) with respect to time t, f(x i (t)) is x i (t) is the system global objective function of the independent variable; express The Euclidean norm of ij Represents the adjacency matrix A=(a ij ) N×N elements of ij >0 means that the i-th unmanned vehicle can obtain status information from the j-th unmanned vehicle, otherwise a ij =0; T1 is a fixed time estimate that all unmanned vehicles achieve consistency. The fixed time estimate for the system to solve the optimization problem under the fixed time centralized optimization algorithm is T=T1+T2, where T2 is the estimated time for the unmanned vehicle to converge from a consistent state to the optimal solution of the global objective function after time t>T1.

3. A method for distributed optimization of multiple unmanned vehicles with fixed time and scheduled time according to claim 2, characterized in that: The scheduled time centralized optimization algorithm is: Among them, u i (t) is the control input of the i-th unmanned vehicle, q, and η are positive constants and satisfy 0 <q<1 / 2, 0<η<1; Undirected connected communication topology diagram for multiple unmanned vehicle systems The algebraic connectivity of Indicates that the base is e The logarithm of T p is the time it takes for the pre-set multi-autonomous vehicle system to solve the optimization problem, ηT p It is the time for the pre-set unmanned vehicle to achieve a consistent result, and φ is the convex parameter of the global objective function.

4. A method for distributed optimization of multiple unmanned vehicles with fixed time and scheduled time according to claim 3, characterized in that: The fixed-time distributed gradient estimator is: Among them, the parameter θ 11 ,θ 12 is a positive constant, and Where N is the number of unmanned vehicles, ‖ρ i (t)-ρ j (t)‖ ∞ Represents ρ i (t)-ρ j The infinite norm of (t), Indicates that in the time interval [t0,T e ] Upper pair ‖ρ i (t)-ρ j (t)‖ ∞ Take the maximum value, T e ω i (t) The fixed time required to converge to the average value of the global objective function gradient; is the local objective function f of the i-th unmanned vehicle i (x i ) with respect to the gradient of time t; ω i (t) is the estimate of the average value of the gradient of the global objective function by the i-th unmanned vehicle, v i (t) is an intermediate variable.

5. A method for distributed optimization of multiple unmanned vehicles with fixed time and scheduled time according to claim 4, characterized in that: The fixed time 6. A method for distributed optimization of multiple unmanned vehicles with fixed time and scheduled time according to claim 4, characterized in that: The predetermined time distributed gradient estimator is: In the formula, the parameters in ‖ρ i (t)-ρ j (t)‖ ∞ Represents ρ i (t)-ρ j The infinite norm of (t), Indicates that in the time interval [t0,ηT p ] Upper pair ‖ρ i (t)-ρ j (t)‖ ∞ Take the maximum value; 0<η<1,ηT p The preset ω i (t) The time to converge to the average value of the global objective function gradient.

7. A method for distributed optimization of multiple unmanned vehicles with fixed time and scheduled time according to claim 6, characterized in that: The ω i The update rate of (t) is: Among them, θ 1* ,θ 2* is the corresponding θ 11 ,θ 12 Or θ 21 ,θ 22 ,||ω i (t)-ω j (t)|| represents ω i (t)-ω j The Euclidean norm of (t).

8. A method for distributed optimization of multiple unmanned vehicles with fixed time and scheduled time according to claim 4, characterized in that: The fixed-time distributed optimization algorithm is: Among them, Nω i (t) is the estimate of the gradient of the global objective function of the system by the i-th unmanned vehicle at time t, ||Nω i (t)|| is Nω i The Euclidean norm of (t), T1'=max{T1,T e }, T1 is a consistent fixed time estimate for all unmanned vehicles, T e ω i (t) The fixed time required for the system to converge to the average value of the global objective function gradient. The fixed time required for the system to solve the optimization problem under the action of the fixed-time distributed optimization algorithm is estimated to be T=T'+T2, where T2 is the estimated time for the unmanned vehicle to converge from a consistent state to the optimal solution of the global objective function after time t>T1'.

9. A method for distributed optimization of multiple unmanned vehicles with fixed time and scheduled time according to claim 6, characterized in that: The predetermined time distributed optimization algorithm is: Among them, Nω i (t) is the gradient of the global objective function estimated by the i-th unmanned vehicle at time t.

10. A distributed optimization system for multiple unmanned vehicles with fixed time and scheduled time to implement any of the methods described in claims 1-9, characterized in that: include: Model building unit, used to build the dynamic model and communication topology diagram of the multi-unmanned vehicle system; A global objective function construction unit constructs a global objective function based on a dynamics model; A centralized optimization algorithm design unit, which designs fixed-time and scheduled-time centralized optimization algorithms based on the communication topology graph and the global objective function; A distributed gradient estimator design unit, used to design fixed-time and scheduled-time distributed gradient estimators; A distributed optimization algorithm design unit, based on a distributed gradient estimator and a centralized optimization algorithm, designs fixed-time and scheduled-time distributed optimization algorithms based on gradient estimation, respectively; The control unit uses a fixed-time and scheduled-time distributed optimization algorithm based on gradient estimation to control multiple unmanned vehicles and obtain the optimal solution of the global objective function.

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