Distributed dynamic following control system and method for intelligent platoon coverage tracking
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HANGZHOU NORMAL UNIVERSITY
- Filing Date
- 2026-04-13
- Publication Date
- 2026-07-21
AI Technical Summary
Existing drone formation control systems rely excessively on central nodes, leading to increased communication load and insufficient robustness and fault tolerance, making them unsuitable for complex application scenarios.
A distributed dynamic follow control system is adopted, which uses the DAT algorithm to realize local communication within the UAV formation. By transmitting signals between adjacent nodes, the dependence on the central node is reduced, thereby improving the robustness and reliability of the system.
It enables high-precision coverage tracking and dynamic response of UAV formations in complex environments, improves the robustness and fault tolerance of the system, and ensures the stability and coordination of the formation under communication constraints.
Smart Images
Figure CN122431406A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent control technology, specifically to a distributed dynamic following control system and method for intelligent formation coverage tracking. Background Technology
[0002] Multi-unmanned system collaborative technology, leveraging the unique advantages of its distributed architecture, achieves global mission objectives through local information exchange and collaborative cooperation among various intelligent control units. This not only significantly reduces communication burden but also substantially enhances system resilience, effectively preventing mission interruptions caused by single-point failures. This collaborative mode overcomes the inherent limitations of individual unmanned systems in terms of payload, endurance, and observation range, efficiently completing complex tasks such as wide-area coverage, high-precision operations, and dynamic target tracking. It significantly improves mission execution efficiency and reliability, and is of great significance for promoting the deep application of automation technology in various military and civilian fields.
[0003] With the continuous expansion of unmanned system application scenarios, such as unmanned aerial vehicle (UAV) swarms as a typical application of cooperative control, their advantages in joint surveillance and reconnaissance, emergency rescue, and regional patrol missions are becoming increasingly apparent due to their mobility, wide coverage, and strong mission adaptability. In complex mission scenarios, UAV swarms sometimes need to work in coordination with unmanned ground vehicles (UGVs). For example, in joint land-air reconnaissance and surveillance missions, UAVs need to provide aerial coverage and threat warnings for UGVs. This requires the UAV swarm to cover the entire UGV convoy, track it in real time, and maintain a preset formation. Due to the limited perception range of a single UAV and the communication constraints in mission scenarios, centralized control is difficult to meet the requirements of real-time performance and reliability. Therefore, it is urgent to develop distributed swarm cooperative control algorithms to achieve autonomous cooperation between UAV swarms and UGVs, thereby improving the mission capabilities of individual UAVs and effectively addressing diverse mission requirements in complex environments.
[0004] Current algorithms for drone formation, maintenance, coverage, and tracking still have significant limitations and are difficult to adapt to complex and ever-changing real-world application scenarios. Taking joint land-air reconnaissance missions as an example, traditional solutions typically require real-time acquisition of the geometric center of the ground drone convoy formation and global broadcast of this information to all airborne drones to achieve formation, maintenance, coverage, and target tracking functions. Such methods usually rely on at least one drone with global perception and communication capabilities as a central node, which collects the position information of all drones, calculates the geometric center, and distributes the results uniformly to other airborne drones. However, this architecture has obvious bottlenecks: the communication load increases dramatically with the scale of the drone system, and the overall system performance is constrained by the capabilities of the central node; once the central node fails or the communication link is interrupted, it will directly lead to the failure of formation control, seriously affecting the robustness and fault tolerance of the system.
[0005] How to overcome the over-reliance on the central node in formation joint operation scenarios, which makes the overall system integrity constrained by the capabilities of the central node, and solve the limitations of traditional formation control schemes in terms of system robustness and fault tolerance, based on the idea of local perception and communication of intelligent control units such as UAVs, and thus improve the applicability of formation collaboration in complex application scenarios, is an urgent problem to be solved in this field. Summary of the Invention
[0006] This invention provides a distributed dynamic following control system and method for intelligent formation coverage tracking, which solves the problems mentioned in the background art.
[0007] This invention provides the following technical solution: a distributed dynamic following control system for intelligent formation coverage tracking. The formation includes a first formation and a second formation. Both the first and second formations include an equal number of intelligent control units. 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 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.
[0008] As an optional solution of the distributed dynamic following control system for intelligent formation coverage tracking described in this invention, the DAT algorithm obtains the estimated value of the geometric center of the second formation in real time and adjusts the formation of the first formation in real time according to the estimated value. During the simultaneous dynamic movement of the first and second formations, the input signal of the DAT algorithm is the geometric center of the second formation, and the output signal of the DAT algorithm is the adjustment signal of the first formation. The adjustment signal first determines whether the geometric centers of the two formations are consistent. If they are inconsistent, the adjustment calculation value is output to move the first formation to the calculated position. If they are consistent, the input and output signals of the next dynamic period are cyclically calculated to achieve the purpose of complete coverage and dynamic tracking by adjusting the first formation in real time according to the movement of the second formation.
[0009] As an optional solution of the distributed dynamic following control system for intelligent formation coverage tracking described in this invention, the geometric center of the first formation and the geometric center of the second formation are vertically overlapped. When the geometric centers of the first formation and the second formation are correspondingly overlapped in the same vertical direction from the top-view angle, the dynamically moving first formation can achieve complete coverage and tracking of the second formation. This realizes the range coverage and real-time tracking function of the first formation cluster over multiple moving targets in the second formation, effectively solving the lag problem of traditional algorithms when tracking multiple highly maneuverable targets.
[0010] As an optional solution for the distributed dynamic following control system for intelligent platooning coverage tracking described in this invention, the intelligent control unit of the first platoon is a UAV, and the intelligent control unit of the second platoon is a UGV. The first platoon is located above the second platoon, and the number of intelligent control units is N. Each UAV in the first platoon receives the position information of the UGV in the second platoon corresponding to it in real time, and each UAV also receives the position information of the UAV adjacent to it. The motion trajectory of each UGV is represented as follows: ,in, It is the first UGV in The position at any given moment; the trajectory of each UAV is represented as: , It is the first UAV in Position at any given moment. The first formation of N UAVs are at the same altitude, and each UAV, while in flight, is always aligned with the geometric center of the second formation. Maintain a certain relative distance The preset relative position of any UAV driven to the geometric center of the UGV is: ,in, This refers to the preset relative position that each UAV needs to be driven to the geometric center of the UGV. By using several UAVs at the same flight altitude and several UGVs at the same horizontal altitude, the target coverage and tracking capabilities of the formation are applied to UAV-UGV joint reconnaissance missions. This allows each UAV in this scenario to quickly respond and adjust to the received signals from the UGVs via the DAT algorithm, thus ensuring that the moving UAV formation always covers and tracks the moving UGV formation. By using preset UAV offset vectors, any desired UAV formation can be flexibly configured, ensuring the accuracy and stability of the UAV formation. Furthermore, for any node in the network topology formed by N UAVs... , and its derivative There are positive numbers and , so that: By modeling and assuming that the UAVs only have local perception and communication capabilities and that all UAVs fly at the same altitude, the control of the UAV formation only needs to focus on the planar coordinates of each UAV. This enables the formation, maintenance, and tracking of the global formation to be achieved through local radio communication within the UAV formation, thus simultaneously achieving the dual tasks of UAV formation coverage and target tracking. By assuming boundary conditions, the established model is made closer to the actual physical system conditions, while ensuring the rigor of the DAT algorithm.
[0011] As an optional solution for the distributed dynamic following control system for intelligent formation coverage tracking described in this invention, the data processing of the 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 To compensate for the insufficient communication load of existing central node drones that need to collect all information and uniformly distribute the calculation results to each drone, the DAT algorithm is implemented. This allows multiple drones in a formation to form a multi-node communication network, reducing the possibility of formation control failure due to central node failure. The DAT algorithm calculates the tracking error of each drone relative to the preset formation and determines the degree of convergence through an error threshold, ensuring the overall cooperative stability of the drone formation. In the DAT algorithm, >0, ,and , Let represent the network connectivity. By selecting control gain parameters that satisfy the above theorem conditions, the proposed DAT algorithm can guarantee the convergence of the UAV formation, thereby achieving the overall cooperative stability of the UAV formation.
[0012] This application also provides a distributed dynamic following control method for intelligent formation coverage tracking, including:
[0013] S1: Each intelligent control unit in the first formation initializes its position state. and internal state The intelligent control unit of the second formation synchronously initializes its position status and sets its movement trajectory. The movement trajectory of the intelligent control unit of the second formation is represented as follows: Where N is the number of intelligent control units. It is the second formation Each intelligent control unit The position of the time; where the initialization time is... Execute S2-S4 sequentially;
[0014] S2: 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, It is the first formation Each intelligent control unit Location at any given moment 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;
[0015] 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; position state updates include: ;
[0016] S4: Update iteration time, calculate tracking error and algorithm termination judgment. If the tracking error is less than or equal to a given threshold... If the algorithm succeeds, the algorithm terminates; otherwise, continue executing S2-S4 until the algorithm terminates. The update iteration time is... ; Calculate tracking error as follows: .
[0017] Through the above algorithm steps, the DAT algorithm can achieve a robust and accurate coverage and tracking effect for drone formations under a distributed, lightweight information exchange mechanism, while maintaining the preset formation of drone formations.
[0018] The present invention has the following beneficial effects:
[0019] 1. This intelligent formation tracking distributed dynamic following control system uses a distributed lightweight information exchange mechanism: the formation can achieve global formation, maintenance and tracking functions through local radio communication only. It is used to solve the problem of over-reliance on the central node in multi-unmanned system collaborative operation scenarios, and to solve the problem of the limitations of traditional unmanned system formation control schemes in terms of system robustness and fault tolerance, which makes unmanned system formation collaboration unsuitable for complex application scenarios.
[0020] 2. This intelligent formation coverage and tracking distributed dynamic following control system can achieve simultaneous "formation coverage + target tracking" dual tasks: based on the position status of the intelligent control unit of the first formation, the real-time position of the intelligent control unit of the corresponding communication second formation, and the preset formation offset vector, it can simultaneously achieve the dual task requirements of "maintaining the preset formation of the first formation" and "covering and tracking the second formation".
[0021] 3. The distributed dynamic following control method for intelligent formation coverage tracking has a convergence guarantee mechanism for the first formation: calculate the tracking error of the intelligent control unit of the first formation relative to the preset formation, determine the degree of convergence through the error threshold, and ensure the overall cooperative stability of the first formation. Attached Figure Description
[0022] Figure 1 This is a schematic diagram of the UAV-UGV joint reconnaissance topology in Embodiment 1 of the present invention.
[0023] Figure 2 This is a schematic diagram showing the relative distance between the geometric centers of the drone formation and the unmanned vehicle formation in Embodiment 1 of the present invention.
[0024] Figure 3 This is a flowchart of the DAT algorithm in Embodiment 2 of the present invention. Detailed Implementation
[0025] 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.
[0026] Example 1
[0027] Please see Figure 1 and Figure 2 One type of intelligent formation coverage tracking distributed dynamic following control system includes:
[0028] The first formation and the second formation are used. The first formation covers and tracks the second formation using the DAT algorithm. The first formation consists of four drones, and the second formation consists of four unmanned vehicles.
[0029] In this embodiment, the first formation is located above the second formation, and the signal transmission distance between the first formation and the second formation remains equal during the movement process, so as to achieve complete coverage of the second formation by the first formation.
[0030] The DAT algorithm obtains a real-time estimate of the geometric center of the second formation, and then adjusts the formation of the first formation based on the real-time estimate.
[0031] In this embodiment, the DAT algorithm enables all UAVs in the first formation to transmit signals without relying on centralized control. Instead, they transmit signals to each other through adjacent UAVs, which greatly reduces the latency and poor robustness caused by centralized control signals in the first formation in existing algorithms. This allows the first formation to maintain high real-time performance and strong stability in tracking the second formation.
[0032] The geometric centers of the first formation and the second formation are vertically aligned.
[0033] In this embodiment, the geometric center is the average spatial position calculated with equal weights when all UAVs in the formation are considered as a set of spatial points. It is the core reference point describing the overall position, attitude, and trajectory of the UAV formation. When the geometric centers of the first formation and the second formation overlap vertically, it means that in terms of the overall position and attitude of the UAVs, the first formation is always directly above the second formation.
[0034] Each drone receives the status signal from the unmanned vehicle and the status signals transmitted between adjacent drones. The position information of each drone is calculated using the DAT algorithm.
[0035] Besides this embodiment, the intelligent control unit can also be different units. For example, the intelligent control unit of the first formation can be a combination of various types of flight units, or a combination of various types of flight units and rail-mounted mobile units. The intelligent control unit of the second formation can be a combination of various types of ground units, or a combination of various types of ground units and underground rail-mounted mobile units, and so on. By adjusting the control input of each intelligent control unit of the first formation through the DAT algorithm and calculating the position information of each intelligent control unit of the first formation, the first formation can always maintain synchronous movement and stable signal transmission with the second formation, thereby improving dynamic response speed and robustness.
[0036] like Figure 1As shown, the first formation consists of four UAVs, and the second formation consists of four UGVs. The UAV formation is located above the UGV formation. Each UAV receives the vehicle position status signal from its corresponding UGV and the flight position status signal from its neighboring UAV. The position information of each UAV is calculated using the DAT algorithm.
[0037] In this embodiment, as Figure 1 As shown, the four UAVs are respectively , , , All were flying at the same altitude, and the four UGVs were respectively , , , All vehicles move on the same plane. In joint battlefield reconnaissance scenarios, the UAV formation collects aerial information from above, while the unmanned vehicle formation determines its route based on the ground terrain below. At the same time, each UGV in the unmanned vehicle formation continuously sends a corresponding stable signal to each UAV in the UAV formation, keeping the overall route of the UAV formation and the flight position of each UAV controllable.
[0038] To establish a joint land-air reconnaissance system consisting of 4 UAVs and 4 UGVs, the modeling steps include:
[0039] The trajectory of each UGV is represented as follows: Each UAV in The position of time is represented as Each UGV can continuously send its own location information to its corresponding UAV, and each UAV receives the location information of its neighboring UAVs. .
[0040] All UAVs fly at the same altitude, and each UAV remains aligned with the geometric center of the UGV formation throughout its flight. Maintain a relative distance That is, each UAV must drive itself to the following position: .
[0041] The formation of drones and unmanned vehicles forms a joint UAV-UGV reconnaissance formation, further enabling:
[0042] The network topology consisting of four nodes is bidirectional and interconnected, ensuring that local information can achieve global consensus through neighbor interactions; due to the boundedness of the reference signal, 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: .
[0043] In this embodiment, in a real physical system, 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 computational delays, each operator requires more time to achieve accurate tracking, which means the time-varying reference signal cannot change too rapidly. It is bounded.
[0044] The Distributed Average Tracking (DAT) algorithm proposed in this embodiment is designed as follows: 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 It is every one Adjacent Need to be driven The preset relative position of the geometric center.
[0045] In this embodiment, the DAT algorithm can achieve its goal if the following theorem is satisfied: Selecting the control gain. >0, robustness parameter And the control gain parameter satisfies: , This represents the network connectivity.
[0046] In summary, this system overcomes the over-reliance on the central node in UAV-UGV joint operation scenarios, which limits the overall system performance to the capabilities of the central node. It also addresses the limitations of traditional UAV formation control schemes in terms of system robustness and fault tolerance, thereby improving the applicability of UAV formation collaboration in complex application scenarios. It enables the formation, maintenance, and tracking of the entire UAV formation through only local radio communication, and simultaneously achieves the dual task requirements of maintaining the preset formation of the UAV formation and covering and tracking the ground unmanned vehicle formation, ensuring the overall collaborative stability of the UAV formation.
[0047] Example 2
[0048] like Figure 3 As shown, a distributed dynamic following control method for intelligent formation coverage tracking is applied to the distributed dynamic following control system for intelligent formation coverage tracking in Embodiment 1, comprising:
[0049] Step 1: Initialization Operation
[0050] Each drone Initialize its own position state and internal state ;
[0051] Each vehicle Initialize its own position state And set your own movement trajectory: ;
[0052] Step 2: Initialize time Repeat the following steps;
[0053] Step 3: The control input is calculated based on its own location status and the received location status of its neighbors as follows:
[0054] ;
[0055] Step 4: Selecting the iteration step size Then, based on its own state and control input, it performs the following state update.
[0056] Update internal status as follows: ;
[0057] calculate Position coordinates at the next moment as follows: ;
[0058] Step 5: Update the algorithm iteration time, calculate the tracking error and the algorithm termination criteria. The specific operations are as follows:
[0059] Update iteration time ;
[0060] Calculate tracking error as follows: ;
[0061] Determine if the iteration has ended: If the tracking error is less than or equal to a given threshold. ,Right now If the algorithm succeeds, then the algorithm ends; otherwise, continue executing steps 3-5 until the algorithm ends.
[0062] This embodiment addresses the UAV-UGV joint reconnaissance mission scenario, enabling UAVs to achieve global formation, maintenance, coverage, and tracking through internal local radio communication. The proposed DAT algorithm is not only applicable to the specific scenario of UAV formations tracking UAV formations, but its core control concept and technical framework also possess good versatility, allowing it to be extended to other multi-UAV control systems requiring coverage and tracking of multiple highly maneuverable targets. It simultaneously solves the problems of traditional algorithms, such as lag, system uncertainty, low accuracy, and low robustness caused by excessive communication load on the central node when tracking multiple highly maneuverable targets. Based on distributed average following technology, it achieves range coverage and real-time tracking of multiple moving targets on the ground by UAV swarms, demonstrating broad application prospects and potential value, thus reflecting its wide applicability.
[0063] 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.
[0064] 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. A distributed dynamic following control system for intelligent formation coverage tracking, characterized in that, The formation consists of a first formation and a second formation. The first formation covers and tracks the second formation using the DAT algorithm. The first and second formations each include an equal number of intelligent control units. Each intelligent control unit in one formation has a corresponding position and signal exchange relationship with one of the intelligent control units in the other formation in order to obtain external communication signals of the formation. 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 through external and internal communication signals.
2. The distributed dynamic following control system for intelligent formation coverage tracking according to claim 1, characterized in that, The DAT algorithm obtains the estimated geometric center of the second formation in real time and adjusts the formation of the first formation in real time based on the estimated value.
3. The distributed dynamic following control system for intelligent formation coverage tracking according to claim 1, characterized in that, The geometric centers of the first formation and the second formation overlap vertically.
4. The distributed dynamic following control system for intelligent formation coverage tracking according to any one of claims 1 to 3, characterized in that, The first formation's intelligent control unit is a UAV, and the second formation's intelligent control unit is a UGV. The first formation is located above the second formation.
5. The distributed dynamic following control system for intelligent formation coverage tracking according to claim 4, characterized in that, The number of intelligent control units is N. Any UAV in the first formation receives the position information of the UGV in the second formation corresponding to it in real time, and any UAV receives the position information of the UAV next to it. The trajectory of each UGV is represented as follows: ,in, It is the first UGV in The position at any given moment; the trajectory of each UAV is represented as: , It is the first UAV in The location at any given moment.
6. The distributed dynamic following control system for intelligent formation coverage tracking according to claim 5, characterized in that, The first formation consists of N UAVs at the same altitude, and each UAV remains aligned with the geometric center of the second formation while in flight. Maintain a certain relative distance The preset relative position of any UAV driven to the geometric center of the UGV is: ,in, It is the preset relative position that each UAV needs to be driven to the geometric center of the UGV.
7. The distributed dynamic following control system for intelligent formation coverage tracking according to claim 6, characterized in that, For any node in the network topology formed by N UAVs , and its derivative There are positive numbers and , so that: .
8. The distributed dynamic following control system for intelligent formation coverage tracking according to claim 1, 2, 3, or 5, characterized in that, Data processing in the 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 .
9. The distributed dynamic following control system for intelligent formation coverage tracking according to claim 8, characterized in that, In the DAT algorithm >0, ,and , This represents the network connectivity.
10. A distributed dynamic following control method for intelligent formation coverage tracking, applied to the distributed dynamic following control system for intelligent formation coverage tracking as described in any one of claims 1-9, characterized in that, include: S1: Each intelligent control unit in the first formation initializes its position state. and internal state The intelligent control unit of the second formation synchronously initializes its position status and sets its movement trajectory. The movement trajectory of the intelligent control unit of the second formation is represented as follows: Where N is the number of intelligent control units. It is the second formation Each intelligent control unit The position of the time; where the initialization time is... Execute S2-S4 sequentially; S2: 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, It is the first formation Each intelligent control unit Location at any given moment 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; 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; position state updates include: ; S4: Update iteration time, calculate tracking error and algorithm termination judgment. If the tracking error is less than or equal to a given threshold... If the algorithm succeeds, the algorithm terminates; otherwise, continue executing S2-S4 until the algorithm terminates. The update iteration time is... ; Calculate tracking error as follows: .