Communication constraint-oriented time-varying coupling unmanned system multi-formation cooperative control method

Abstract

The invention discloses a time-varying coupling unmanned system multi-formation cooperative control method for communication constraints. Firstly, a discrete time dynamic model and a controller input model of the networked unmanned multi-formation system are established, and formation division and formation reference trajectory definition are carried out at the same time. Secondly, under the condition that the communication bandwidth is limited, a synchronization error dynamic model is constructed, a hierarchical bit rate constraint relation of a system layer, a formation layer and a node layer is established, a uniform quantization coding-decoding mechanism is introduced to quantize a node state, and augmented modeling of an error system is realized by using a Kronecker product. And then, designing a multi-formation cooperative control strategy with fault-tolerant capability, solving controller gain through a linear matrix inequality, and ensuring bounded convergence of synchronization errors. Furthermore, a dynamic bit rate allocation mechanism based on a phototropic growth optimization algorithm is designed, and optimal configuration of communication resources is realized, so that the collaborative consistency and robustness of a multi-formation system under communication limited and complex working conditions are improved.

Description

Technical Field

[0001] This invention relates to multi-formation control technology for unmanned systems, and in particular to a time-varying coupled multi-formation cooperative control method for unmanned systems oriented towards communication constraints. Background Technology

[0002] With the development of distributed networked systems such as unmanned systems and intelligent swarms, achieving coordinated control of multiple formations has become a core issue in the field of automation. These unmanned systems typically consist of a large number of intelligent nodes connected via communication networks, and their control performance is highly dependent on the quality of information exchange between nodes. However, in practical applications, limitations in communication bandwidth, dynamic changes in network topology, and the reliability of the equipment itself severely restrict the performance of existing control methods.

[0003] Existing technologies suffer from the following problems: In terms of communication resource management, current methods lack a mechanism for dynamically and optimally allocating the total system bit rate among nodes or formations. In networked collaborative control, the total communication bandwidth of the system is a limited and valuable shared resource. Traditional control schemes typically employ static or average bit rate allocation strategies, allocating the same communication resources to all nodes or formations. This "one-size-fits-all" allocation method ignores the differentiated communication accuracy requirements of different nodes due to their dynamic characteristics, positions in the topology, and the tasks they undertake. It cannot dynamically allocate more bit rate resources to nodes that have a greater impact on system synchronization performance or whose states are more uncertain, resulting in the inefficient use of valuable bandwidth resources and thus limiting the optimal control performance that the system can achieve under strict bandwidth constraints.

[0004] In terms of cooperative control architecture, existing methods struggle to effectively address the challenges posed by time-varying network coupling and heterogeneous node dynamics. Many multi-squad cooperative control studies are based on fixed or simple communication topologies and assume that all nodes possess identical dynamic characteristics. However, in real-world unmanned systems, nodes may exhibit heterogeneous dynamic characteristics due to task grouping, and the coupling relationships between nodes can dynamically shift depending on environmental or task requirements. This makes traditional consensus control protocols based on fixed topologies and homogeneous nodes difficult to apply directly, and may even lead to system instability.

[0005] Furthermore, in real-world systems, critical components such as actuators may experience random failures, further amplifying system uncertainty and even compromising the stability and synchronization performance of the closed-loop system. While some current fault-tolerant control methods can address these issues, most are based on the assumption of ideal communication.

[0006] Therefore, in order to address the issues of communication resource constraints and time-varying coupled networks in unmanned systems, it is urgent to design a reliable control strategy to improve the multi-squad collaboration capability of networked unmanned systems. Summary of the Invention

[0007] To address the aforementioned shortcomings, the present invention aims to improve the consistency and robustness of networked unmanned systems in multi-formation motion under complex conditions by designing a dynamic bit rate allocation mechanism based on a light-growing optimization algorithm and a fault-tolerant synchronization controller that considers random actuator failures.

[0008] To achieve this objective, the present invention adopts the following technical solution:

[0009] One of the above technical solutions includes the following beneficial effects: The dynamic bit rate allocation mechanism based on the Phototropic Growth Optimization (PGA) algorithm proposed in this patent can intelligently and non-uniformly allocate the total bit rate of the system according to the dynamic characteristics and synchronization requirements of different nodes or formations in the network. Compared with existing average or fixed allocation strategies, this method can achieve optimal utilization of limited channel resources, allocate more resources to key nodes that have a greater impact on system stability, and thus significantly improve the synchronization performance and control accuracy of multi-formation unmanned systems overall. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of the framework of the method steps of the present invention;

[0011] Figure 2 and Figure 3 This refers to the tracking performance of nodes in the two formations for their respective targets after adopting the control method proposed in this scheme.

[0012] Figure 4 It is the upper bound of the synchronization error of the networked unmanned system, the L2 norm of the synchronization error when it is under control, and the L2 norm of the synchronization error when it is uncontrolled;

[0013] Figure 5 This is a performance comparison chart of average allocation and optimized allocation under the constraint of total system bit rate. Detailed Implementation

[0014] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0015] like Figure 1 As shown, a multi-formation cooperative control method for time-varying coupled unmanned systems oriented towards communication constraints includes the following steps:

[0016] S100, Modeling of Networked Unmanned Multi-Squad System: For unmanned systems formed by multiple intelligent nodes interconnected through a communication network, a unified discrete-time dynamic model is established, taking into account the nonlinear characteristics of nodes, the time-varying nature of network topology, random failures of actuators, and the requirements of multi-squad tasks. The modeling includes: describing the dynamic behavior of each node in discrete-time state-space form; modeling the network coupling structure as a convex multicellular time-varying coupling model and transforming it into a linear combination of multiple fixed coupling modes through convex hull decomposition; modeling the control input as a form jointly determined by the controller gain and the operating state variables of the actuators; dividing N intelligent nodes into m squads and defining the reference trajectory or control target dynamic model for each squad, providing a mathematical description basis for subsequent cooperative control and communication resource optimization.

[0017] S200, Synchronization Error Construction under Communication Constraints: Addressing the issues of limited communication bandwidth and the need for quantized transmission of state information in networked unmanned multi-formation systems, a state quantization modeling method based on bit rate constraints is introduced to construct a dynamic synchronization error model incorporating communication constraints, time-varying coupling, and actuator random failures. This construction includes: establishing a formation and node bit rate allocation model under total system bit rate constraints; quantifying node states and introducing quantization errors; defining the synchronization error of a node relative to its formation target; and using the Kronecker product to augment the error system using the convex hull decomposition method, laying the foundation for stability analysis and controller design.

[0018] S300, a fault-tolerant multi-squad cooperative control strategy: Addressing system stability issues caused by actuator random failures, time-varying network topology, and the superposition of communication errors, a fault-tolerant cooperative control method is designed. The method combines a quadratic Lyapunov function with a convex multicellular time-varying coupled model, transforming stability conditions into linear matrix inequality constraints. Auxiliary matrix variables and relaxation factors are introduced to define nonlinear terms, random failure terms, and quantization error terms. The gain matrix of the fault-tolerant cooperative controller for each intelligent node is obtained through the LMI toolbox, ensuring that the multi-squad synchronization error is bounded and converges within a preset range.

[0019] S400, a dynamic bit rate allocation mechanism based on the light-growing optimization algorithm: Addressing the issues of limited total communication bandwidth and inconsistent node communication accuracy requirements, an adaptive dynamic optimization method for communication resources is designed. This method includes: using the upper bound of the synchronization error obtained in S300 as a performance indicator, constructing a comprehensive optimization model containing total bit rate constraints and intra-formation bit rate constraints; searching for the optimal bit rate allocation scheme through population initialization, fitness evaluation, population grouping, mutation update, and global update operations of the light-growing optimization algorithm; and applying the optimal allocation result to the system communication layer to dynamically configure the communication resources of each formation and node, thereby improving the overall synchronization performance of the system.

[0020] This patent proposes a dynamic bit rate allocation mechanism based on the Phototropic Growth Optimization (PGA) algorithm. This mechanism intelligently distributes the total bit rate of the system non-uniformly according to the dynamic characteristics and synchronization requirements of different nodes or formations in the network. Compared with existing average or fixed allocation strategies, this method achieves optimal utilization of limited channel resources, allocating more resources to key nodes that have a greater impact on system stability, thereby significantly improving the overall synchronization performance and control accuracy of multi-formation unmanned systems. This patent fully considers and addresses the time-varying nature of the communication coupling relationship between different formations in the unmanned system. Compared with many existing cooperative control methods based on fixed topology assumptions, this patent establishes a convex multicellular time-varying coupling model based on switching topology and uses the convex hull decomposition formula to transform the time-varying parameters into constraints of linear matrix inequalities. This enables the designed controller to adaptively switch topology, ensuring the cooperative stability of the system under dynamic connectivity. The fault-tolerant synchronization controller designed in this patent considers random actuator failures and simulates random on / off failures of actuators through a Bernoulli process. Compared to controllers that do not adequately account for actuator failures or assume complete reliability, this design effectively compensates for performance losses caused by equipment failures. It ensures that the entire multi-formation system can maintain a stable formation and cooperative trajectory under complex operating conditions with communication constraints and time-varying coupled networks, guaranteeing its consistency and robustness.

[0021] Specifically, step S100 is as follows:

[0022] S110 will have Each intelligent node has [number] intelligent nodes, and each intelligent node has [number] intelligent nodes. The dynamic model of a networked unmanned system in each state is established in the following form:

[0023] ;

[0024] in, Indicates the first Each intelligent node The state variable at any given time; Indicates the first Each intelligent node The specific form of the controller input at any given time will be given in later steps; Represents a nonlinear function; Indicates the first The state parameter matrix of each intelligent node; Represents the parameter matrix of a nonlinear function; Indicates the global coupling strength of the network. The coupling configuration matrix represents the time-varying network topology. If the first... The first intelligent node can receive the first Information from each smart node, ,otherwise , Represents the time-varying internal coupling matrix;

[0025] The three parameters describing a convex multicellular time-varying coupled structure satisfy the following convex hull decomposition formula:

[0026] ;

[0027] in, , Using the above formula, time-varying coupling can be transformed into... A linear combination of fixed-mode coupling;

[0028] S120, considering the combined effects of long-term operating losses and complex external operating environment, the actuator will inevitably experience random failures, therefore the controller input... It can be represented in the following form:

[0029] ;

[0030] in, Indicates the first The controller gain of each node (which will be determined later); It is a Bernoulli random process used to represent whether a fault occurs; if a fault occurs... ,otherwise Its expectation and variance can be expressed in the following form:

[0031] ;

[0032] S130, based on the requirements of the task being performed, divides the intelligent nodes of the networked unmanned system into... Each formation , ... The format is as follows:

[0033] ;

[0034] Among them, 1, 2, ... For the intelligent node number; for the first Formation, in At any given time, its control objective can be expressed in the following form:

[0035] ;

[0036] in Indicates the first The state parameter matrix of the formation control target; the state parameter matrix of the intelligent nodes in the same formation is the same as the state parameter matrix of the formation control target, that is, if ,but = .

[0037] By constructing a unified discrete-time dynamics model that incorporates node nonlinearity, network topology time-varying characteristics, actuator random failures, and multi-team task requirements, the time-varying coupled structure is transformed into a linear combination of fixed coupling modes. Actuator failures are characterized by Bernoulli stochastic processes, and the dynamics models of control targets for each team are clearly defined. This provides a precise and practical mathematical foundation for subsequent synchronization error construction, fault-tolerant controller design, and communication resource optimization. It can also flexibly adapt to the differentiated operational requirements of multiple teams, ensure consistency within teams, and incorporate key characteristics of complex operating conditions in advance to support the implementation of subsequent core functions. This ensures that the entire collaborative control system is logically coherent and adaptable to actual engineering scenarios.

[0038] Specifically, step S200 is as follows:

[0039] S210. To characterize the bandwidth-constrained nature of unmanned system communication networks, this scheme uses bit rate as a quantitative indicator and establishes the following bit rate constraint model for multi-swarm communication networks:

[0040] ;

[0041] ;

[0042] in, The available bit rate for the entire unmanned system communication network. For the first Available bit rate for formation For the first The first in the formation The available bit rate of each node; for ease of description, use... Represents a node The allocated bit rate;

[0043] The state variable, after a uniform quantization encoding-decoding process, can be represented as: ,make For quantization error, the L2 norm of this error satisfies: ,in It is the first The quantization region of each intelligent node;

[0044] S220, for the first The nodes at Synchronization error at time It can be obtained by subtracting the current state variable from its objective function, i.e. = - Furthermore, it can be expanded into the following form:

[0045] ;

[0046] in, ;

[0047] To facilitate the subsequent construction of linear matrix inequalities, the above error expression is augmented using the Kronecker product, based on the convex hull decomposition formula, and takes the following form:

[0048] ;

[0049] in , ;

[0050] ;

[0051] , express 3D identity matrix It represents the Kronecker product.

[0052] By constructing a dynamic model of synchronization error that incorporates communication constraints, time-varying coupling, and random actuator failures, a hierarchical bit rate constraint relationship is established at the system, formation, and node levels. A uniform quantization encoding-decoding mechanism is introduced to quantize node states, and the encoding-decoding errors generated by quantization are incorporated into the synchronization error system. Simultaneously, the Kronecker product is used to vectorize and reconstruct the node-level error model, achieving an augmented expression of the synchronization error system. This allows the constructed error model to simultaneously reflect actual operating conditions such as communication constraints, time-varying network topology, and uncertain actuator reliability. Based on this, the augmented error model provides a unified and analytical modeling foundation for subsequent synchronization stability analysis and fault-tolerant controller design. It also facilitates integration with linear matrix inequalities, ensuring the logical coherence and engineering practicality of the entire control system from error analysis to controller solution.

[0053] Furthermore, step S300 specifically includes:

[0054] To obtain the fault-tolerant control gain matrix of the multi-formation cooperative controller for the unmanned system, this scheme uses the LMI toolbox to solve a set of linear matrix inequalities to obtain the parameters of the matrix. The specific steps are as follows:

[0055] S310, initialize the positive integer variables to be calculated using the sdpvar function. and and symmetric positive definite matrix ,in ;

[0056] S320, based on the parameters defined above and the parameters of the previously defined augmented form of the synchronization error, construct the following linear matrix inequality:

[0057] ;

[0058] in, ;

[0059] ;

[0060] ;

[0061] ;

[0062] in, and They represent the first Upper and lower bounds of the nonlinear function of a smart node. , For an identity matrix of appropriate dimension, for 3D identity matrix Represents symmetric matrix elements;

[0063] The S330 uses the solver provided in the LMI toolbox to solve for the variables and matrices to be determined, and the controller gain. = The solution can be obtained using the following formula: Furthermore, the L2 norm of the synchronization error can be obtained to satisfy:

[0064] ;

[0065] in, Representation matrix The smallest eigenvalue, This represents the upper bound of the synchronization error.

[0066] By employing a core technology that combines quadratic Lyapunov functions with a convex multicellular time-varying coupling model, the system stability condition is transformed into a linear matrix inequality constraint. Simultaneously, auxiliary matrix variables and relaxation factors are introduced to effectively define nonlinear terms, random fault terms, and quantization error terms. The gain matrix of the fault-tolerant collaborative controller is then precisely solved using the LMI toolbox. This ensures that the synchronization error of multiple formations is bounded and converges within a preset range under complex operating conditions involving actuator random faults, time-varying network topology, and superimposed communication errors. This improves the stability, fault tolerance, and robustness of the collaborative control of networked unmanned multi-formation systems, providing reliable performance support for subsequent optimized allocation of communication resources.

[0067] Furthermore, step S400 specifically involves: to achieve optimal allocation of communication resources and thereby improve the multi-formation cooperative control performance of the unmanned system, a bit rate allocation mechanism based on a light-growing optimization algorithm is proposed, with the specific steps as follows:

[0068] S410, construct the optimization objective function based on the system preset parameters and synchronization error;

[0069] S420, bit rate optimization allocation based on the light-growing optimization algorithm;

[0070] S430, based on the output results in step S420, set the available bit rate parameters for each node in the system.

[0071] By constructing an optimization objective function with the upper bound of S300 synchronization error as the core performance indicator, including dual constraints on total bit rate and intra-formation bit rate, and a penalty function, and relying on the population initialization, group update, dynamic mutation, and global search mechanism of the light-growing optimization algorithm, the optimal bit rate allocation scheme that adapts to the differentiated communication accuracy requirements of nodes and formations is efficiently solved. This scheme is then dynamically configured in the system communication layer, achieving precise and efficient utilization of limited communication resources and effectively reducing the impact of communication errors on collaborative control. This further improves the synchronization accuracy, stability, and robustness of the networked unmanned multi-formation system under complex operating conditions.

[0072] In addition, in step S410:

[0073] Construct the following optimization objective function:

[0074] ;

[0075] in:

[0076] and For the penalty function, , , and This is the penalty coefficient.

[0077] By constructing an optimization objective function that includes dual constraints on the total system bit rate and the bit rate within the formation, along with corresponding penalty functions, and using the upper bound of the synchronization error obtained from S300 as the core performance guide, the constraint boundary of bit rate allocation is accurately quantified. Furthermore, the penalty function effectively avoids resource allocation imbalances. This provides a scientific, rigorous, and practically applicable objective guide for subsequent optical growth optimization algorithms to search for the optimal bit rate allocation scheme. It ensures that the optimization process focuses on improving system synchronization performance, achieving precise adaptation and efficient utilization of limited communication resources, and further supporting the stability and robustness of collaborative control in networked unmanned multi-formation systems.

[0078] In addition, in step S420:

[0079] S421: Initialize algorithm parameters, setting the basic parameters of the phototropic growth optimization algorithm, including population size. and maximum number of iterations Randomly generate the initial cell population. =[ Each cell corresponds to a set of feasible bit rate allocation schemes, which are used to represent the communication bit rate configuration of each formation and each node;

[0080] S422: Initial fitness assessment, using the constructed optimization objective function to assess the initial cell population. Each individual cell Calculate fitness And select the individual with the best fitness from them as the current fully initial optimal solution. This is used to guide the direction of subsequent searches;

[0081] S423: Population partitioning, randomly dividing the current cell population into two population types with a total number of individuals. and The subpopulation is denoted as and Each subpopulation contains a set of cells corresponding to a different search strategy, and the best individual in each subpopulation is recorded. and To enhance the algorithm's ability to balance global and local search;

[0082] S424: Set the iteration counter, initialize the iteration counter , that is to say This is used to record the current iteration number of the algorithm and serve as the basis for dynamically adjusting the search parameters in the future;

[0083] S425: Calculate the dynamic adjustment factor. Based on the current iteration number and the maximum iteration number, calculate the search adjustment factor. This is used to dynamically control the algorithm's global exploration capability in the early stages of iteration and its local development capability in the later stages of iteration; the calculation formula is as follows: ;in, It is a natural constant; This represents the current iteration number. This represents the maximum number of iterations.

[0084] S426: First subpopulation ( The update operation, for each cell in the first subpopulation, involves introducing random cells and the best individual in that subpopulation, and performing mutation operations based on random perturbation and optimal guidance respectively to generate new candidate cells. These generated cells are then incorporated into the current population to expand the search space. The specific formula is as follows:

[0085]

[0086] in, and For subpopulation The Middle New cell individuals are generated from individual cells based on mutation operations; For cell population The first in Individual cells; A random cell individual within a population; For cell population The current optimal individual; For cell population A cell randomly selected from the data; This refers to the search adjustment factor calculated in step S5; It follows a Bernoulli distribution with a probability of 0.5; , , for Random variables between;

[0087] S427: Second subpopulation ( The update operation, for each individual cell in the second type of cell population, performs a mutation operation using the current individual, a randomly selected individual, and the best individual from another subpopulation to generate new candidate cells, thereby enhancing the algorithm's ability to escape local optima within a local region; the specific formula is as follows:

[0088]

[0089] in, and For subpopulation The Middle New cell individuals are generated from individual cells based on mutation operations; For cell population The first in Individual cells; A random cell individual within a population; and These are a random cell and the optimal cell from the first type of cell population after step S6; This refers to the search adjustment factor calculated in step S5; It follows a Bernoulli distribution with a probability of 0.5; and yes Random variables between;

[0090] S428: Global update operation. Based on the information of the initial cell, the globally optimal cell, and neighboring cells, new candidate cells are constructed and incorporated into the cell population. This global update mechanism guides the entire population to evolve towards a better bit rate allocation. The specific formula is as follows:

[0091]

[0092] in, and The initial cell populations are respectively The Middle The and the first Individual cells; , For cell population The average fitness The optimal fitness of the cell population; This is the current globally optimal solution; This refers to the search adjustment factor calculated in step S5; It follows a Bernoulli distribution with a probability of 0.5; and yes Random variables between;

[0093] S429: Fitness reassessment and screening: The fitness values ​​of the updated cell population are recalculated, and individual cells are sorted according to their fitness, retaining those with the highest fitness. Individuals, as a new generation of the population, simultaneously update the current global optimal solution;

[0094] S4210: Boundary and constraint processing. Perform boundary condition checks on the screened cell individuals to ensure that all bit rate allocation schemes meet the system's total bit rate constraint and the bit rate constraint within the formation. Individuals that do not meet the constraints are corrected or removed.

[0095] S4211: Iteration update judgment, determine whether the maximum number of iterations has been reached; if not, update the iteration counter. Then return to step S5 to continue execution; if the maximum number of iterations is reached, the iteration process ends.

[0096] S4212: Outputs the optimal bit rate allocation result, outputs the final globally optimal solution as the optimal current bit rate allocation scheme, and sets the communication bit rate parameters of each formation and each intelligent node in the system accordingly.

[0097] Through a series of refined operations in the light-growing optimization algorithm, including population initialization, fitness evaluation, population grouping, dynamic adjustment factor calculation, differential mutation update of two types of subpopulations, and global update, combined with fitness re-evaluation screening and constraint boundary handling mechanisms, a highly efficient balance between global exploration and local development is achieved, effectively avoiding the algorithm from getting trapped in local optima. At the same time, it can quickly search for the optimal bit rate allocation scheme that meets the dual constraints of the total system bit rate and the bit rate within the formation, ensuring that limited communication resources are accurately adapted to the differentiated communication accuracy requirements of each formation and node, and further improving the synchronization performance, control accuracy, and robustness of the networked unmanned multi-formation system.

[0098] To verify the effectiveness of the method proposed in this patent, simulation verification was conducted.

[0099] Consider a networked unmanned system with 5 nodes, divided into two formations with 3 and 2 nodes respectively.

[0100] Figure 2 and Figure 3 The images show the tracking performance of nodes in two formations for their respective targets after adopting the control method proposed in the patent. This demonstrates that each node's state can quickly and accurately track the trajectory of its formation target within a finite time, verifying the effectiveness of the controller in a time-varying coupled topology.

[0101] Figure 4 These are the upper bound of the synchronization error of the networked unmanned system, the L2 norm of the synchronization error under controlled conditions, and the L2 norm of the synchronization error under uncontrolled conditions. Therefore, compared to the error that oscillates continuously and significantly under uncontrolled conditions, this method stabilizes the system's synchronization error within a preset range, significantly improving the system's cooperative control accuracy and robustness.

[0102] To verify the effectiveness of the proposed bit rate allocation method, such as Figure 5 As shown, under the constraints of a total system bit rate of 16 bit / s and 32 bit / s, the performance of two strategies—average allocation and optimized allocation based on the method of this patent—was compared. Simulation data shows that, under both total bandwidth conditions, the upper bound of the system's synchronization error after optimized allocation using the method of this patent is significantly lower than that of the average allocation strategy.

[0103] Therefore, compared to uncontrolled systems, the controller designed in this scheme can significantly improve the performance of formation cooperative control, and the proposed dynamic bit rate allocation mechanism has advantages in improving the accuracy of system cooperative control and resource utilization efficiency.

[0104] The technical principles of the present invention have been described above with reference to specific embodiments. These descriptions are merely for explaining the principles of the invention and should not be construed as limiting the scope of protection of the invention in any way. Based on this explanation, those skilled in the art can readily conceive of other specific embodiments of the invention without inventive effort, and these embodiments will all fall within the scope of protection of the present invention.

Claims

1. A multi-formation cooperative control method for time-varying coupled unmanned systems oriented towards communication constraints, characterized in that, Includes the following steps: S100, Modeling of Networked Unmanned Multi-Squad System: For unmanned systems formed by multiple intelligent nodes interconnected through a communication network, a unified discrete-time dynamic model is established, taking into account the nonlinear characteristics of nodes, the time-varying nature of network topology, random failures of actuators, and the requirements of multi-squad tasks. The modeling includes: describing the dynamic behavior of each node in discrete-time state-space form; modeling the network coupling structure as a convex multicellular time-varying coupling model and transforming it into a linear combination of multiple fixed coupling modes through convex hull decomposition; modeling the control input as a form jointly determined by the controller gain and the operating state variables of the actuators; dividing N intelligent nodes into m squads and defining the reference trajectory or control target dynamic model for each squad, providing a mathematical description basis for subsequent cooperative control and communication resource optimization. S200, Synchronization Error Construction under Communication Constraints: Addressing the issues of limited communication bandwidth and the need for quantized transmission of state information in networked unmanned multi-formation systems, a state quantization modeling method based on bit rate constraints is introduced to construct a dynamic synchronization error model incorporating communication constraints, time-varying coupling, and actuator random failures. This construction includes: establishing a formation and node bit rate allocation model under total system bit rate constraints; quantifying node states and introducing quantization errors; defining the synchronization error of a node relative to its formation target; and using the Kronecker product to augment the error system using the convex hull decomposition method, laying the foundation for stability analysis and controller design. S300, a fault-tolerant multi-squad cooperative control strategy: Addressing system stability issues caused by actuator random failures, time-varying network topology, and the superposition of communication errors, a fault-tolerant cooperative control method is designed. The method combines a quadratic Lyapunov function with a convex multicellular time-varying coupled model, transforming stability conditions into linear matrix inequality constraints. Auxiliary matrix variables and relaxation factors are introduced to define nonlinear terms, random failure terms, and quantization error terms. The gain matrix of the fault-tolerant cooperative controller for each intelligent node is obtained through the LMI toolbox, ensuring that the multi-squad synchronization error is bounded and converges within a preset range. S400, a dynamic bit rate allocation mechanism based on the light-growing optimization algorithm: Addressing the issues of limited total communication bandwidth and inconsistent node communication accuracy requirements, an adaptive dynamic optimization method for communication resources is designed. This method includes: using the upper bound of the synchronization error obtained in S300 as a performance indicator, constructing a comprehensive optimization model containing total bit rate constraints and intra-formation bit rate constraints; searching for the optimal bit rate allocation scheme through population initialization, fitness evaluation, population grouping, mutation update, and global update operations of the light-growing optimization algorithm; and applying the optimal allocation result to the system communication layer to dynamically configure the communication resources of each formation and node, thereby improving the overall synchronization performance of the system.

2. The multi-formation cooperative control method for time-varying coupled unmanned systems oriented towards communication constraints as described in claim 1, characterized in that, The specific steps of S100 are as follows: S110 will have Each intelligent node has [number] intelligent nodes, and each intelligent node has [number] intelligent nodes. The dynamic model of a networked unmanned system in each state is established in the following form: ； in, Indicates the first Each intelligent node The state variable at any given time; Indicates the first Each intelligent node The specific form of the controller input at any given time will be given in later steps; Represents a nonlinear function; Indicates the first The state parameter matrix of each intelligent node; Represents the parameter matrix of a nonlinear function; Indicates the global coupling strength of the network. The coupling configuration matrix represents the time-varying network topology. If the first... The first intelligent node can receive the first Information from each smart node, ,otherwise , Represents the time-varying internal coupling matrix; The three parameters describing a convex multicellular time-varying coupled structure satisfy the following convex hull decomposition formula: ； in, , Using the above formula, time-varying coupling can be transformed into... A linear combination of fixed-mode coupling; S120, considering the combined effects of long-term operating losses and complex external operating environment, the actuator will inevitably experience random failures, therefore the controller input... It can be represented in the following form: ； in, Indicates the first The controller gain of each node (which will be determined later); It is a Bernoulli random process used to represent whether a fault occurs; if a fault occurs... ,otherwise Its expectation and variance can be expressed in the following form: ； S130, based on the requirements of the task being performed, divides the intelligent nodes of the networked unmanned system into... Each formation , ... The format is as follows: ； Among them, 1, 2, ... For the intelligent node number; for the first Formation, in At any given time, its control objective can be expressed in the following form: ； in Indicates the first The state parameter matrix of the formation control target; the state parameter matrix of the intelligent nodes in the same formation is the same as the state parameter matrix of the formation control target, that is, if ,but = .

3. The multi-formation cooperative control method for time-varying coupled unmanned systems oriented towards communication constraints according to claim 2, characterized in that, The specific steps of S200 are as follows: S210. To characterize the bandwidth-constrained nature of unmanned system communication networks, this scheme uses bit rate as a quantitative indicator and establishes the following bit rate constraint model for multi-swarm communication networks: ； in, The available bit rate for the entire unmanned system communication network. For the first Available bit rate for formation For the first The first in the formation The available bit rate of each node; for ease of description, use... Represents a node The allocated bit rate; The state variable, after a uniform quantization encoding-decoding process, can be represented as: ;make For quantization error, the L2 norm of this error satisfies: ,in It is the first The quantization region of each intelligent node; S220, for the first The nodes at Synchronization error at time It can be obtained by subtracting the current state variable from its objective function, i.e. = - Furthermore, it can be expanded into the following form: ； in, ; To facilitate the subsequent construction of linear matrix inequalities, the above error expression is augmented using the Kronecker product, based on the convex hull decomposition formula, and takes the following form: ； in , ; ； , express 3D identity matrix It represents the Kronecker product.

4. The multi-formation cooperative control method for time-varying coupled unmanned systems oriented towards communication constraints according to claim 3, characterized in that, The specific steps of S300 are as follows: To obtain the fault-tolerant control gain matrix of the multi-formation cooperative controller for the unmanned system, this scheme uses the LMI toolbox to solve a set of linear matrix inequalities to obtain the parameters of the matrix. The specific steps are as follows: S310, initialize the positive integer variables to be calculated using the sdpvar function. and and symmetric positive definite matrix ,in ; S320, based on the parameters defined above and the parameters of the previously defined augmented form of the synchronization error, construct the following linear matrix inequality: ； in, ; ； ； ； in, and They represent the first Upper and lower bounds of the nonlinear function of a smart node. , For an identity matrix of appropriate dimension, for 3D identity matrix Represents symmetric matrix elements; The S330 uses the solver provided in the LMI toolbox to solve for the variables and matrices to be determined, and the controller gain. = The solution can be obtained using the following formula: Furthermore, the L2 norm of the synchronization error can be obtained to satisfy: ； in, Representation matrix The smallest eigenvalue, This represents the upper bound of the synchronization error.

5. The multi-formation cooperative control method for time-varying coupled unmanned systems oriented towards communication constraints according to claim 4, characterized in that, Step S400 specifically involves: To achieve optimal allocation of communication resources and thereby improve the multi-formation cooperative control performance of unmanned systems, a bit rate allocation mechanism based on a light-growing optimization algorithm is proposed, with the following specific steps: S410, construct the optimization objective function based on the system preset parameters and synchronization error; S420, bit rate optimization allocation based on the light-growing optimization algorithm; S430, based on the output results in step S420, set the available bit rate parameters for each node in the system.

6. The multi-formation cooperative control method for time-varying coupled unmanned systems oriented towards communication constraints according to claim 5, characterized in that, In step S410: Construct the following optimization objective function: ； in: and For the penalty function, , , and This is the penalty coefficient.

7. The multi-formation cooperative control method for time-varying coupled unmanned systems oriented towards communication constraints according to claim 6, characterized in that, In step S420: S421: Initialize algorithm parameters, setting the basic parameters of the phototropic growth optimization algorithm, including population size. and maximum number of iterations ; Randomly generate the initial cell population =[ Each cell corresponds to a set of feasible bit rate allocation schemes, which are used to represent the communication bit rate configuration of each formation and each node; S422: Initial fitness assessment, using the constructed optimization objective function to assess the initial cell population. Each individual cell Calculate fitness And select the individual with the best fitness from them as the current fully initial optimal solution. This is used to guide the direction of subsequent searches; S423: Population partitioning, randomly dividing the current cell population into two population types with a total number of individuals. and The subpopulation is denoted as and Each subpopulation contains a set of cells corresponding to a different search strategy, and the best individual in each subpopulation is recorded. and To enhance the algorithm's ability to balance global and local search; S424: Set the iteration counter, initialize the iteration counter , that is to say This is used to record the current iteration number of the algorithm and serve as the basis for dynamically adjusting the search parameters in the future; S425: Calculate the dynamic adjustment factor. Based on the current iteration number and the maximum iteration number, calculate the search adjustment factor. This is used to dynamically control the algorithm's global exploration capability in the early stages of iteration and its local development capability in the later stages of iteration; the calculation formula is as follows: ;in, It is a natural constant; This represents the current iteration number. This represents the maximum number of iterations. S426: First subpopulation ( The update operation, for each cell in the first subpopulation, involves introducing random cells and the best individual in that subpopulation, and performing mutation operations based on random perturbation and optimal guidance respectively to generate new candidate cells. These generated cells are then incorporated into the current population to expand the search space. The specific formula is as follows: ； in, and For subpopulation The Middle New cell individuals are generated from individual cells based on mutation operations; For cell population The first in Individual cells; A random cell individual within a population; For cell population The current optimal individual; For cell population A cell randomly selected from the data; This refers to the search adjustment factor calculated in step S5; It follows a Bernoulli distribution with a probability of 0.5; , , for Random variables between; S427: Second subpopulation ( The update operation, for each individual cell in the second type of cell population, performs a mutation operation using the current individual, a randomly selected individual, and the best individual from another subpopulation to generate new candidate cells, thereby enhancing the algorithm's ability to escape local optima within a local region; the specific formula is as follows: ； in, and For subpopulation The Middle New cell individuals are generated from individual cells based on mutation operations; For cell population The first in Individual cells; A random cell individual within a population; and These are a random cell and the optimal cell from the first type of cell population after step S6; This refers to the search adjustment factor calculated in step S5; It follows a Bernoulli distribution with a probability of 0.5; and yes Random variables between; S428: Global update operation. Based on the information of the initial cell, the globally optimal cell, and neighboring cells, new candidate cells are constructed and incorporated into the cell population. This global update mechanism guides the entire population to evolve towards a better bit rate allocation. The specific formula is as follows: ； in, and The initial cell populations are respectively The Middle The and the first Individual cells; , For cell population The average fitness The optimal fitness of the cell population; This is the current globally optimal solution; This refers to the search adjustment factor calculated in step S5; It follows a Bernoulli distribution with a probability of 0.5; and yes Random variables between; S429: Fitness reassessment and screening: The fitness values ​​of the updated cell population are recalculated, and individual cells are sorted according to their fitness, retaining those with the highest fitness. Individuals, as a new generation of the population, simultaneously update the current global optimal solution; S4210: Boundary and constraint processing. Perform boundary condition checks on the screened cell individuals to ensure that all bit rate allocation schemes meet the system's total bit rate constraint and the bit rate constraint within the formation. Individuals that do not meet the constraints are corrected or removed. S4211: Iteration update judgment, determine whether the maximum number of iterations has been reached; if not, update the iteration counter. Then return to step S5 to continue execution; if the maximum number of iterations is reached, the iteration process ends. S4212: Output the optimal bit rate allocation result, output the final global optimal solution as the optimal current bit rate allocation scheme, and set the communication bit rate parameters of each formation and each intelligent node in the system accordingly.

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