Anti-interference distributed dynamic following control system and method for intelligent platoon

Abstract

The application discloses an anti-interference distributed dynamic following control system and method for intelligent formation, comprising a first formation and a second formation, the first formation covering and tracking the second formation through a DAT algorithm, any intelligent control unit of any formation having a corresponding position and signal mutual transmission relationship with one intelligent control unit of another formation; adjacent intelligent control units in the first formation mutually transmit signals, the position of any intelligent control unit in the first formation being calculated based on the DAT algorithm and through external communication signals of the formation and internal communication signals of the formation, the DAT algorithm being an AD-DAT algorithm with anti-interference capability, and the AD-DAT algorithm actively compensating an interference estimation value to control input of the first formation in a feedforward mode to solve the low robustness problem of the intelligent formation when covering and tracking multiple intelligent control units through the DAT cooperative control algorithm, which is caused by external interference from the external environment.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically to an anti-interference distributed dynamic following control system and method for intelligent formation. Background Technology

[0002] Currently, unmanned systems are demonstrating increasingly significant advantages in joint surveillance and reconnaissance, emergency rescue, and regional patrol missions. However, current algorithms for the formation, maintenance, coverage, and tracking of UAV formations still have significant limitations, making them difficult to adapt to complex and ever-changing real-world application scenarios. To address the issue of local perception and communication in intelligent control units such as UAVs, overcome the over-reliance on a central node in coordinated formation operations, which restricts the overall system performance due to the capabilities of the central node, and resolve the limitations of traditional formation control schemes in terms of system robustness and fault tolerance, thereby improving the applicability of formation collaboration in complex application scenarios, the current distributed dynamic following control system for intelligent formations adopts the following technical solution: The formation includes a first formation and a second formation, each containing an equal number of intelligent control units. Any intelligent control unit in any formation has a corresponding position and signal exchange relationship with one intelligent control unit in the other formation to obtain external communication signals. Adjacent intelligent control units within the first formation exchange signals to obtain internal communication signals. The position of any intelligent control unit within the first formation is calculated based on the DAT algorithm and using both external and internal communication signals. The first formation covers and tracks the second formation using the DAT algorithm. The first formation consists of several identical first units, and the second formation consists of several identical second units. Each first unit calculates and adjusts its real-time position information based on the state signals of its corresponding second unit and the state signals of its neighboring first units, improving the accuracy of the first unit's position information. This further enhances the tracking accuracy, dynamic response speed, and robustness of the first formation over the second formation. A distributed control architecture is adopted, where each first unit only needs to communicate with its neighboring nodes, significantly improving the system's robustness and reliability under non-ideal environments such as communication constraints. The DAT algorithm controls multiple nodes in the first formation to transmit signals one-to-one with multiple nodes in the second formation, thereby reducing the formation's dependence on the central node and improving the robustness and fault tolerance of the formation control system.

[0003] However, the first unit of the first formation can typically only obtain the position information (i.e., local reference signal) of the second unit of its corresponding second formation. To obtain the geometric center of the entire second formation, each first unit inevitably needs to exchange information with its neighboring first units via a wireless channel. Therefore, in a real physical environment, formation coordination tasks inevitably face the problem of external environmental interference. For example, common external environmental interferences such as geographical, meteorological, electromagnetic, aerodynamic, and servo-driven interferences, if injected into the sensors, control inputs, or actuators of the control system, will inevitably affect the final control performance, eventually leading to gradual degradation. This makes it difficult for the first formation to accurately estimate and track the geometric center of the second formation, thus affecting the overall performance of formation formation, maintenance, coverage, and tracking. Despite this, most existing works presuppose an "ideal environment," resulting in insufficient research on the anti-interference capabilities of the DAT problem. This is not only determined by the inherent research challenges of the control problem itself, but also related to the fact that existing anti-interference schemes cannot be directly applied.

[0004] Overcoming the major drawback of poor control performance and inability to achieve accurate tracking caused by external environmental interference when the control system controls the first formation and tracks the second formation in a joint operation scenario of unmanned systems using the DAT cooperative control algorithm is a problem that urgently needs to be solved in this field. Summary of the Invention

[0005] This invention provides an anti-interference distributed dynamic following control system and method for intelligent formation, which solves the problems mentioned in the background art.

[0006] This invention provides the following technical solution: an anti-interference distributed dynamic following control system for intelligent formations, the formations including a first formation and a second formation, the first formation covering and tracking the second formation through a DAT algorithm, any intelligent control unit in any formation having a corresponding position and signal exchange relationship with one of the intelligent control units in the other formation; adjacent intelligent control units within the first formation exchange signals, the position of any intelligent control unit within the first formation is calculated based on the DAT algorithm and through external and internal communication signals of the formation, wherein the DAT algorithm is an AD-DAT algorithm with anti-interference capability, after the second formation is subjected to external interference, the AD-DAT algorithm actively compensates the control input of the first formation with the interference estimate in a feedforward manner.

[0007] By treating external environmental interference, including geographical, meteorological, and electromagnetic interference, as well as aerodynamic and servo drive interference, as harmonic interference, real-time and accurate online estimation of harmonic interference is performed. Then, the control input of the DAT algorithm is redesigned using the compensation and suppression approach. The estimated value is then actively compensated into the control input in a feedforward manner, thereby eliminating the impact of harmonic interference injected by the external environment on the DAT algorithm in advance. This gives the AD-DAT algorithm anti-interference capability, ensuring that the first formation can still achieve robust and accurate tracking of the second formation in complex interference environments, thus guaranteeing the smooth completion of the collaborative reconnaissance and surveillance mission between the first and second formations.

[0008] As an optional solution to the anti-interference distributed dynamic following control system for intelligent formation of this invention, it also includes an interference state observer. The interference state observer utilizes a known dynamic model of the internal state of the first formation and the external interference to estimate the external interference in real time. The external interference is harmonic interference with a known dynamic model that affects the stability of electromagnetic signals. The interference observer control technology enables real-time and accurate online estimation of harmonic interference, and then the control input of the DAT algorithm is redesigned using a compensation and suppression approach.

[0009] As an optional scheme of the anti-interference distributed dynamic following control system of the intelligent formation of the present invention, the first formation includes several first units and the second formation includes several second units. Each first unit receives the status signal of the second unit in a one-to-one correspondence, and the first units adjacent to each first unit transmit status signals to each other. The control input signal of each first unit is adjusted by the AD-DAT algorithm, and the position information of each first unit is calculated.

[0010] As an optional scheme of the anti-interference distributed dynamic following control system for intelligent formation of the present invention, the number of intelligent control units is N. The intelligent control units of the first formation are UAVs, and the intelligent control units of the second formation are UGVs. The first formation is located above the second formation. For any node in the network topology formed by N UAVs... Its time-varying reference signal and its derivative There are positive numbers and , so that: Furthermore, the external interference is a type of harmonic interference, generated by the following linear external system. ,in It is the internal state. and It is a constant matrix used to represent frequency and observable characteristics. This represents the external disturbance acting on the control input. By assuming boundary conditions, the established model is made closer to the actual physical system condition, while ensuring the rigor of the proposed algorithm. In actual physical systems, due to mechanical and electrical limitations, the reference signal and its derivative cannot become infinitely large, meaning they are always bounded. Furthermore, due to communication and computation delays, each intelligent control unit in the first formation requires more time to achieve accurate tracking, which means the time-varying reference signal cannot change too rapidly. It is bounded. It is worth noting that harmonic interference is a type of interference widely present in engineering fields; for a specific environment, its interference frequency and observability characteristic matrix... and This knowledge can usually be gained through practical experience. Data processing for the AD-DAT algorithm includes: in, It is the first formation Each intelligent control unit The internal state at any given moment yes The derivative, yes Time-based control input, These are robust parameters. It is about controlling the gain. It is the first formation Each intelligent control unit Location at any given moment This refers to the fact that each intelligent control unit in the first formation receives the position information of its neighboring intelligent control units. Each intelligent control unit in the first formation needs to be driven to a preset relative position at the geometric center of the second formation. The first formation's intelligent control units, adjacent to each other, need to be driven to a preset relative position at the geometric center of the second formation. It is the second formation Each intelligent control unit Location at any given moment yes Moment , This refers to external disturbances acting on the control input. Indicates to The estimated value is used to compensate for external disturbances. The disturbance state observer includes:

[0011]

[0012] in and It is a constant matrix used to represent frequency and observable characteristics. It refers to the internal state of the observer. It is the internal state. and They are respectively for and The estimated value, This is the gain matrix. For a class of harmonic interferences with known dynamic models, the control method based on the interference state observer provides robust estimation of external interferences. Furthermore, the system model and the interference state observer are designed independently, facilitating the practical implementation of the algorithm. To overcome the shortcomings of existing DAT algorithms, by implementing the AD-DAT algorithm, the first formation can maintain accurate and stable tracking performance even when subjected to interference injected from the external environment while covering and tracking the second formation. Moreover, the parameters... and satisfy:

[0013]

[0014] in , , ,and

[0015] Ξ, Ω, and Θ are all operational symbols, A1...A N C1...C1 are constant matrices representing the frequencies of each intelligent control unit. N K1...K are constant matrices representing the observable characteristics of each intelligent control unit. N Let represent the gain matrices of each intelligent control unit, and T be the matrix transpose. For network connectivity, It is the identity matrix. This refers to the variable symbol. To address existing external disturbances, by selecting control parameters that satisfy the aforementioned theorem conditions, the proposed AD-DAT algorithm can ensure that the intelligent control unit of the first formation in the air can dynamically follow the intelligent control unit of the second formation in real time according to the given formation.

[0016] This application also provides an anti-interference distributed dynamic following control method for intelligent formation, including:

[0017] S1: Each intelligent control unit in the first formation initializes its position state. and internal state , It is the first formation, the [number]th Intelligent control unit in The internal state at any given moment It is the first formation Each intelligent control unit Position at time, set parameters for the disturbance state observer and , It is the internal state. This refers to external disturbances acting on the control input. Indicates to The estimated value is used to compensate for external interference. The intelligent control unit of the second formation synchronously initializes its position state and sets its movement trajectory. The movement trajectory of the intelligent control unit of the second formation is represented as: Where N is the number of intelligent control units. It is the second formation Each intelligent control unit The position at that moment;

[0018] S2: Initialization time S2-S5 are executed sequentially. Each intelligent control unit in the first formation calculates the control input based on its own position status and the position status of its adjacent intelligent control units. The calculation method is as follows: ,in, yes Time-based control input, Each intelligent control unit in the first formation needs to be driven to a preset relative position at the geometric center of the second formation. This refers to any intelligent control unit in the first formation receiving the position information of its adjacent intelligent control units. The first formation's intelligent control units, adjacent to each other, need to be driven to a preset relative position at the geometric center of the second formation. It controls the gain;

[0019] S3: Each intelligent control unit in the first formation selects the iteration step size. The state is updated in the next time step based on its position state, internal state, and control input. The internal state update includes: ,in, These are robust parameters used to calculate the position coordinates of each intelligent control unit in the first formation at the next moment. include: ;

[0020] S4: Design an interference state observer to estimate external interference. Each intelligent control unit in the first formation selects an iteration step size. The observer's internal state is estimated based on its own state and control input. Update Update Get , and ;in, , and It is a constant matrix used to represent frequency and observable characteristics. It is the gain matrix; based on the internal state of the disturbance state observer. and system internal state Estimating the internal state of external disturbances Estimate the external disturbance state value ;

[0021] S5: Update iteration time, used to determine if the algorithm iteration has ended. The update iteration time is... ; Calculate tracking error as follows: N represents the number of intelligent control units. If the tracking error is less than a given threshold... ,Right now If the error is less than or equal to the given threshold, the algorithm ends; otherwise, continue executing S2-S5 until the tracking error is less than or equal to the given threshold.

[0022] This invention offers the following advantages: The intelligent formation's anti-interference distributed dynamic following control system first estimates the external interference by using interference observer control technology, then redesigns the control input of the DAT algorithm using a compensation and suppression approach, eliminating the impact of external interference on the DAT algorithm and enabling the AD-DAT algorithm to possess anti-interference capabilities. By introducing an interference state observer to each intelligent control unit of the first formation, it can perform real-time and accurate online estimation of a type of harmonic interference, compensating the estimated value to the original system's control input via feedforward. This ensures that the first formation can still achieve highly robust and accurate coverage and tracking of the second formation even in complex interference environments, guaranteeing the successful completion of unmanned system collaborative reconnaissance and surveillance missions. The proposed AD-DAT algorithm can be widely applied to multiple unmanned system distributed collaborative control scenarios. Attached Figure Description

[0023] Figure 1 This is a flowchart of the AD-DAT algorithm in Embodiment 2 of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1

[0026] In modern automated and intelligent military and civilian fields, unmanned aerial vehicles (UAVs) are widely used in environmental monitoring, communication relay, search and rescue, and collaborative reconnaissance missions due to their high mobility, flexibility, and scalability. Compared to single-UAV operations, multiple UAVs working together can accomplish complex tasks far exceeding the capabilities of a single aircraft, achieving a highly efficient swarm effect. In joint land-air reconnaissance and surveillance missions, the land-air collaborative operation of UAVs and unmanned ground vehicles (UGVs) is particularly crucial. In this collaborative mode, UAV formations typically fly in the air, providing a wide-area perspective and early warning, which requires UAVs to dynamically track the real-time positions of multiple UGVs on the ground.

[0027] To achieve the above functions, the first approach that comes to mind and is implemented is the traditional centralized control method. Although this method is simple and easy to implement, it can bring a heavy communication burden to large-scale systems and also poses the risk of single point of failure (especially the central node), which seriously limits the reliability and scalability of the system.

[0028] To overcome the limitations of centralized control, distributed control has become key to solving the problem of multi-UAV cooperative control. Distributed Average Tracking (DAT) technology, developed in recent years, is particularly suitable for UAV swarm cooperative scenarios. The core idea of ​​the DAT algorithm is that each agent in the system (such as a UAV) can ultimately calculate and track the average value of multiple time-varying reference signals for all agents by communicating and exchanging information locally with neighboring agents. This perfectly aligns with the needs of UAV-UGV land-air cooperative applications: each UAV can only know the position of its corresponding UGV (i.e., a local reference signal), while the global task of the UAV swarm is to maintain a specific formation coverage and track all ground UGVs (i.e., ensuring the UAV swarm center coincides with the geometric center of the UGV swarm). Therefore, under distributed, communication-constrained, and incomplete information conditions, the DAT algorithm provides the core theoretical foundation for realizing UAV-UGV land-air cooperative operations.

[0029] In UAV-UGV air-ground cooperative application scenarios, each UAV can typically only acquire the position information of its corresponding UGV. To obtain the geometric center of the entire UGV formation, each UAV needs to exchange information with its neighboring UAVs via wireless channels. In real physical environments, air-ground cooperative missions inevitably face the problem of external environmental interference. For example, common external environmental interferences include geographical, meteorological, and electromagnetic interference, aerodynamic interference, and servo drive interference. If these interferences are injected into the sensors, control inputs, or actuators of the control system, the final control performance will inevitably be affected by the interference, ultimately making it difficult for the UAV formation to accurately estimate and track the geometric center of the UGV formation, thus affecting the overall performance of formation formation, maintenance, coverage, and tracking. Despite this, most existing works assume an "ideal environment," resulting in insufficient research on the anti-interference capabilities of the DAT problem. This is not only determined by the inherent research challenges of the control problem itself, but also related to the fact that existing anti-interference schemes cannot be directly applied.

[0030] Overcoming the major drawback of poor control performance and inability to achieve accurate tracking caused by external environmental interference when controlling multiple UAV formations to cover and track multiple unmanned vehicle formations in UAV-UGV joint operation scenarios using the AD-DAT collaborative control algorithm is a problem that urgently needs to be solved in this field.

[0031] This application proposes an anti-interference distributed dynamic following control system for intelligent formations, comprising: a first formation and a second formation, with a total of four intelligent control units (ICUs). The IUs of the first formation are UAVs, and the IUs of the second formation are UGVs. The first formation is positioned above the second formation. The first formation covers and tracks the second formation using a DAT algorithm. Each IU in any formation has a corresponding position and signal exchange relationship with one IU in the other formation. Adjacent IUs within the first formation exchange signals. The position of any IU in the first formation is calculated based on the DAT algorithm and through external and internal communication signals. The DAT algorithm is an AD-DAT algorithm with anti-interference capabilities. When the second formation is subjected to external interference, the AD-DAT algorithm actively compensates the control input of the first formation with the interference estimate via a feedforward method. The system also includes an interference state observer, which uses a known dynamic model of the internal state of the first formation and the external interference to estimate the external interference in real time. The external interference is harmonic interference with a known dynamic model that affects the stability of electromagnetic signals.

[0032] In this embodiment, for each drone An external disturbance state observer is introduced to perform real-time and accurate online estimation of injected harmonic interference from the external environment. The estimated value of the injected harmonic interference from the external disturbance state observer is then fed forward to compensate the control input. This "active compensation" mechanism can cancel out the injected harmonic interference before it affects the system output, thereby greatly improving the system's dynamic response speed and tracking accuracy.

[0033] In this embodiment, the proposed AD-DAT algorithm has better anti-interference capabilities compared with the traditional DAT algorithm.

[0034] The AD-DAT algorithm modeling steps include:

[0035] S1. Each vehicle The trajectory of the motion is represented as Each exist The position of time is represented as Each one Can be directed to the corresponding Continuously transmit its own location information, and each Adjacent to it They can exchange each other's position coordinates;

[0036] S2. Assume all Design each aircraft to fly at the same altitude. Always in the air Geometric center of formation Maintain a relative distance That is, each To motivate yourself to reach the following position

[0037] ;

[0038] S3. Assuming that the UAV formation and the unmanned vehicle formation form a joint UAV-UGV reconnaissance formation, the following assumptions are further proposed:

[0039] Assumption 1 (Connectivity): Assume that the network topology consisting of N nodes is bidirectional and connected;

[0040] Assumption 2 (Boundedness): For any node in the network topology Its time-varying reference signal and its derivative All are bounded, meaning they have positive constants. and Make

[0041] ;

[0042] Assumption 3 (Boundedness of External Interference): Assume that the external interference under consideration is a type of harmonic interference, and is generated by the following linear external system.

[0043]

[0044] in It is the internal state. and It is a constant matrix used to represent frequency and observable characteristics.

[0045] In this embodiment, we consider that the harmonic interference from the external environment gradually tends to a constant over time or ends within a finite time. In actual physical systems, due to mechanical and electrical limitations, the reference signal and its derivative cannot become infinite, meaning they are always bounded. Furthermore, due to communication and computational delays, each operator requires more time to achieve accurate tracking, meaning the time-varying reference signal cannot change too rapidly. It is bounded. It is worth noting that harmonic interference is a type of interference widely present in engineering fields; for a specific environment, its interference frequency and observability characteristic matrix... and This knowledge can usually be gained through practical experience.

[0046] The Anti-Disturbances Distributed Average Tracking (AD-DAT) algorithm proposed in this embodiment is designed as follows:

[0047]

[0048] in, yes The internal state, yes The derivative of the internal state, It is a control input. It is about controlling the gain. yes Location, yes Location, It is every one Need to be driven The preset relative position of the geometric center Indicates the action on control input External interference, yes The estimated value is used to compensate for external disturbances;

[0049] To achieve external interference To achieve accurate estimation, we design the disturbance state observer as follows:

[0050]

[0051] in It refers to the internal state of the observer. and They are respectively for and The estimated value, It is the gain matrix. For a class of harmonic disturbances with known dynamic models, the control method based on the disturbance state observer provides a robust estimate of external disturbances, and the system model and the disturbance state observer are designed independently, which facilitates the implementation of the algorithm in practice.

[0052] In this embodiment, the AD-DAT algorithm can achieve its goal if the theorem is satisfied, and the parameters in the AD-DAT algorithm and the disturbance state observer are... and satisfy

[0053]

[0054] in ,and

[0055] .

[0056] If the control parameters satisfy the above theorem conditions, then the AD-DAT algorithm proposed in this embodiment can ensure that the airborne UAVs can achieve real-time coverage and tracking of the land-based UAVs according to the given formation.

[0057] Example 2

[0058] like Figure 1 As shown, an anti-interference distributed dynamic following control method for intelligent formations, applying the anti-interference distributed dynamic following control system for intelligent formations in Embodiment 1, includes:

[0059] Step 1: Initialization Operation

[0060] 1.1 Each drone Initialize its own position state and internal state And set the parameters of the interference state observer. and ;

[0061] 1.2 per vehicle Initialize its own position state And set your own movement trajectory ;

[0062] Step 2: Initialize time Repeat the following steps;

[0063] Step 3: The control input is calculated based on its own location status and the received location status of its neighbors as follows:

[0064]

[0065] Step 4: Selecting the iteration step size Then, based on its own state and control input, it performs the following state update.

[0066] 4.1 Update internal status as follows

[0067]

[0068] 4.2 Calculation Position coordinates at the next moment as follows

[0069]

[0070] Step 5: Design an interference state observer to estimate external interference and update the results. , and ;

[0071] 5.1 Select step size as Then based on The internal state of the observer is determined by its own state and control input. Update

[0072]

[0073] 5.2 Internal State Based on Disturbance State Observer and system internal state Estimating the internal state of external disturbances

[0074]

[0075] 5.3 Estimation of external disturbance state values

[0076]

[0077] Step 6: Update the algorithm iteration time and determine whether the algorithm iteration has ended. The specific operations are as follows:

[0078] 6.1 Update and iteration time ;

[0079] 6.2 Calculate tracking error as follows:

[0080]

[0081] 6.3 Determine if the iteration has ended: If the tracking error is less than a given threshold. ,Right now If the algorithm succeeds, then the algorithm ends; otherwise, continue executing steps 3-6 until the algorithm ends.

[0082] This embodiment addresses the scenario of joint UAV-UGV reconnaissance missions, enabling UAVs to form, maintain, cover, and follow ground-based unmanned vehicle formations even under external interference. The proposed method is not only applicable to the specific scenario of UAV formations tracking unmanned vehicle formations, but its core control concepts and technical framework also possess good versatility. It can be extended to other multi-unmanned systems requiring distributed collaborative control, facing external interference, and demanding high tracking accuracy and robustness, demonstrating broad application prospects and potential value, thus reflecting its wide applicability.

[0083] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0084] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An anti-interference distributed dynamic following control system for intelligent formations, wherein the formation includes a first formation and a second formation, the first formation covers and tracks the second formation using a DAT algorithm, and any intelligent control unit in any formation has a corresponding position and signal exchange relationship with one of the intelligent control units in the other formation; adjacent intelligent control units within the first formation exchange signals, and the position of any intelligent control unit within the first formation is calculated based on the DAT algorithm and through external and internal communication signals of the formation, characterized in that... The DAT algorithm is an AD-DAT algorithm with anti-interference capability. After the second formation is subjected to external interference, the AD-DAT algorithm actively compensates the control input of the first formation with the interference estimate in a feedforward manner.

2. The intelligent formation anti-interference distributed dynamic following control system according to claim 1, characterized in that, It also includes a disturbance state observer, which uses known dynamic models of the internal state of the first formation and external disturbances to estimate external disturbances in real time.

3. The intelligent formation anti-interference distributed dynamic following control system according to claim 1, characterized in that, External interference refers to harmonic interference from known dynamic models that affect the stability of electromagnetic signals.

4. The intelligent formation anti-interference distributed dynamic following control system according to claim 1, characterized in that, The first formation includes several first units, and the second formation includes several second units. Each first unit receives the status signal from the second unit in a one-to-one correspondence, and the first units adjacent to each other transmit status signals to each other. The control input signal of each first unit is adjusted by the AD-DAT algorithm, and the position information of each first unit is calculated.

5. The intelligent formation anti-interference distributed dynamic following control system according to any one of claims 1-4, characterized in that, The number of intelligent control units is N. The intelligent control units in the first formation are UAVs, and the intelligent control units in the second formation are UGVs. The first formation is located above the second formation. For any node in the network topology formed by N UAVs... Its time-varying reference signal and its derivative There are positive numbers and , so that: Furthermore, the external interference is a type of harmonic interference, generated by the following linear external system. ,in It is the internal state. and It is a constant matrix used to represent frequency and observable characteristics. This refers to external disturbances acting on the control input.

6. The intelligent formation anti-interference distributed dynamic following control system according to claim 5, characterized in that, Data processing for the AD-DAT algorithm includes: in, It is the first formation Each intelligent control unit The internal state at any given moment yes The derivative, yes Time-based control input, These are robust parameters. It is about controlling the gain. It is the first formation Each intelligent control unit Location at any given moment This refers to the fact that each intelligent control unit in the first formation receives the position information of its neighboring intelligent control units. Each intelligent control unit in the first formation needs to be driven to a preset relative position at the geometric center of the second formation. The first formation's intelligent control units, adjacent to each other, need to be driven to a preset relative position at the geometric center of the second formation. It is the second formation Each intelligent control unit Location at any given moment yes Moment , This refers to external disturbances acting on the control input. Indicates to The estimated value is used to compensate for external disturbances.

7. The intelligent formation anti-interference distributed dynamic following control system according to claim 5, characterized in that, The disturbance state observer includes: ， in and It is a constant matrix used to represent frequency and observable characteristics. It is the internal state of the observer. It is the internal state. and They are respectively for and The estimated value, It is the gain matrix.

8. The intelligent formation anti-interference distributed dynamic following control system according to claim 7, characterized in that, parameter and satisfy: ， in , , ,and Ξ, Ω, and Θ are all operational symbols, A1...A N C1...C1 are constant matrices representing the frequencies of each intelligent control unit. N K1...K are constant matrices representing the observable characteristics of each intelligent control unit. N Let represent the gain matrices of each intelligent control unit, and T be the matrix transpose. For network connectivity, It is the identity matrix. It is a variable symbol.

9. A method for anti-interference distributed dynamic following control of intelligent formations, applied to the anti-interference distributed dynamic following control system of intelligent formations as described in any one of claims 1-8, characterized in that, include: S1: Each intelligent control unit in the first formation initializes its position state. and internal state , It is the first formation, the [number]th Intelligent control unit in The internal state at any given moment It is the first formation Each intelligent control unit Position at time, set parameters for the disturbance state observer and , It is the internal state. This refers to external disturbances acting on the control input. Indicates to The estimated value is used to compensate for external interference. The intelligent control unit of the second formation synchronously initializes its position state and sets its movement trajectory. The movement trajectory of the intelligent control unit of the second formation is represented as: Where N is the number of intelligent control units. It is the second formation Each intelligent control unit The position at that moment; S2: Initialization time S2-S5 are executed sequentially. Each intelligent control unit in the first formation calculates the control input based on its own position status and the position status of its adjacent intelligent control units. The calculation method is as follows: ,in, yes Time-based control input, Each intelligent control unit in the first formation needs to be driven to a preset relative position at the geometric center of the second formation. This refers to any intelligent control unit in the first formation receiving the position information of its adjacent intelligent control units. The first formation's intelligent control units, adjacent to each other, need to be driven to a preset relative position at the geometric center of the second formation. It controls the gain; S3: Each intelligent control unit in the first formation selects the iteration step size. The state is updated in the next time step based on its position state, internal state, and control input. The internal state update includes: ,in, These are robust parameters used to calculate the position coordinates of each intelligent control unit in the first formation at the next moment. include: ; S4: Design an interference state observer to estimate external interference. Each intelligent control unit in the first formation selects an iteration step size. The observer's internal state is estimated based on its own state and control input. Update Update Get , and ;in, , and It is a constant matrix used to represent frequency and observable characteristics. It is the gain matrix; based on the internal state of the disturbance state observer. and system internal state Estimate the internal state of external disturbances Estimate the state values ​​of external disturbances ; S5: Update iteration time, used to determine if the algorithm iteration has ended. The update iteration time is... ; Calculate tracking error as follows: N represents the number of intelligent control units. If the tracking error is less than a given threshold... ,Right now If the error is less than or equal to the given threshold, the algorithm ends; otherwise, continue executing S2-S5 until the tracking error is less than or equal to the given threshold.

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