Multi-uav proportional cooperative control method based on preset time and performance constraints

Abstract

The application discloses a kind of multi-unmanned aerial vehicle proportion collaborative control method based on preset time and performance constraint, to solve the problems of limited control target adaptability, slow convergence speed, insufficient accuracy and weak fault tolerance of prior art.The scheme includes:1) according to external disturbance, model uncertainty and actuator failure, establish unmanned aerial vehicle attitude dynamics system, and use directed graph to describe the communication relationship between unmanned aerial vehicles;2) introduce proportion collaborative control method to construct proportion collaborative error, convert the control problem into the preset time stable control problem of the error;3) design preset performance constraint mechanism and command filter, combined with neural network to approximate unknown nonlinear dynamics, adaptive law and controller are designed by backstepping recursive algorithm;4) the stability of the system is verified by Lyapunov function.The application can realize multi-unmanned aerial vehicle collaborative control within a preset time, with high convergence accuracy and fault tolerance, suitable for multi-unmanned aerial vehicle transportation, exploration, disaster relief and other operation scenarios.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) control technology, and further relates to multi-UAV collaborative control technology. Specifically, it is a multi-UAV proportional collaborative control method based on preset time and performance constraints, which can be used to realize multi-UAV collaborative operations in scenarios such as transportation, exploration, disaster relief, surveillance, and reconnaissance. Background Technology

[0002] With the rapid development of unmanned aerial vehicle (UAV) technology, multi-UAV collaborative systems, as the core carrier for the future cluster application of unmanned equipment, have been widely used in complex scenarios such as reconnaissance and monitoring, disaster relief, and material delivery. These systems often need to complete diverse collaborative tasks such as tracking, formation flying, and encirclement and interception. In some adversarial and complex scenarios, UAVs exhibit a dynamic and complex relationship of "cooperation and confrontation coexisting." Furthermore, actual mission scenarios place higher performance demands on collaborative control technology, including fast convergence speed, high convergence accuracy, and strong robustness. This further increases the technical difficulty of implementing multi-UAV collaborative control.

[0003] Existing cooperative control schemes are mostly designed for single-type control objectives such as consistency control, formation control, and inclusion control. For example, patent application CN120161772A proposes an event-triggered cooperative control method and system for multi-quadrotor formation maneuvering flight. This scheme designs a multi-UAV formation flight control strategy based on an event-triggered method, but it is only applicable to formation control scenarios, and its versatility is significantly limited. Furthermore, existing schemes generally lack consideration for special requirements such as "proportional cooperative control," making it difficult to meet the complex flight mission requirements in multi-tasking scenarios.

[0004] Regarding convergence performance, most existing cooperative control methods can only achieve "asymptotic convergence" or "finite-time convergence." For example, patent document CN120371009A discloses a finite-time UAV cooperative control method based on UWB. However, the convergence speed of this finite-time scheme heavily depends on the initial state of the system, making it impossible to precisely control the convergence speed. In scenarios with stringent timeliness requirements (such as emergency fire reconnaissance and sudden disaster relief), the uncertainty of the convergence speed will directly affect the mission execution efficiency and may even lead to mission failure due to response delays.

[0005] Furthermore, traditional control methods largely fail to effectively constrain errors during system operation. When facing complex conditions such as high-speed flight and rapid attitude adjustments, accumulated errors can easily cause the UAV to deviate from its preset trajectory or cooperative state, significantly reducing control accuracy. Simultaneously, in real-world flight environments, multi-UAV systems are susceptible to external disturbances such as strong airflow and electromagnetic interference, as well as unmodeled dynamic effects such as fuselage vibration and aerodynamic parameter changes, and actuator failures such as motor malfunctions. Existing control methods are poorly adaptable to these unexpected situations, making it difficult to quickly adjust control strategies to maintain system stability, severely limiting the reliability of multi-UAV systems in practical applications.

[0006] In summary, existing multi-UAV cooperative control technologies still have many problems that need to be solved, and there is an urgent need to propose a cooperative control scheme that combines high versatility, controllable convergence speed, strong error constraints, and robustness. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies by proposing a multi-UAV proportional cooperative control method based on preset time and performance constraints. This method aims to solve the technical problem of difficulty in controlling multiple UAVs under conditions of unknown nonlinear dynamics, external disturbances, and actuator failures. By modeling UAV dynamics and communication, a proportional cooperative control research framework is constructed; and a preset performance constraint mechanism and a preset time distributed control scheme are designed. This invention can achieve proportional cooperative control of multiple UAVs within a preset time and possesses good convergence accuracy and fault tolerance.

[0008] To achieve the above objectives, the technical solution of the present invention includes the following steps:

[0009] (1) For quadcopter drones, in scenarios including external disturbances, model uncertainties and actuator failures, establish a drone attitude dynamics model; take each drone as a node and construct a directed spanning tree topology with at least one root node to ensure that each follower drone can receive information from at least one leader drone, forming a reliable multi-drone communication network.

[0010] (2) Introduce a multi-UAV proportional cooperative control method. Based on the given proportional parameters and formation function, construct the proportional cooperative error of the multi-UAV and transform the proportional cooperative control problem into a preset time stable control problem of the error. The preset time is flexibly set according to the task requirements and does not depend on the initial state of the system.

[0011] (3) The transient and steady-state performance of the proportional coordination error is constrained by a preset performance function, and the proportional coordination error is transformed by an error transformation function to ensure that the coordination error converges within a preset range;

[0012] (4) Construct a preset time command filter and dynamically estimate the upper bound of the derivative of the filter input to improve the system convergence performance;

[0013] (5) By approximating the unknown nonlinear dynamics through neural networks, combined with the backstepping recursive algorithm, and using the transformed proportional cooperative error information and the constructed command filter, the parameter adaptive law and controller are designed.

[0014] (6) By constructing the Lyapunov function, the convergence accuracy and fault tolerance of the multi-UAV system in achieving proportional cooperative control of the target within a preset time are verified.

[0015] Compared with the prior art, the present invention has the following advantages:

[0016] First, it has a wider range of applications and significantly improved versatility.

[0017] Because this invention introduces a proportional cooperative control method, by flexibly adjusting the proportional parameters and formation functions, it can simultaneously adapt to traditional cooperative control scenarios such as consistency control, formation control, and inclusion control, as well as the special requirement of proportional cooperative control. This design breaks through the limitation of existing solutions that are only designed for a single control objective, eliminating the need to develop independent control logic for different tasks, and significantly improving the adaptability and engineering practicality of multi-UAV cooperative control.

[0018] Secondly, the convergence time is controllable, significantly improving response speed and task reliability.

[0019] This invention employs a preset time control technology, which can ensure that multiple UAVs complete the collaborative control objective within a pre-set precise time. Its convergence time does not depend on the initial state and design parameters of the system, completely solving the pain point of uncontrollable convergence speed in finite time control schemes. In scenarios with stringent time requirements, such as emergency fire reconnaissance and sudden disaster relief, it can effectively guarantee mission execution efficiency and avoid mission failure due to response delays.

[0020] Third, it offers higher control precision and stronger error constraint capabilities.

[0021] This invention employs a novel preset performance control strategy, which not only strictly guarantees the transient and steady-state performance of proportional coordination error, but also breaks through the limitation on the initial value of proportional coordination error. This strategy eliminates the impact of error accumulation on control accuracy from the root, and can ensure that the UAV swarm maintains a high-precision coordination state even under complex conditions such as high-speed flight and rapid attitude adjustment, avoiding trajectory deviation or formation collapse.

[0022] Fourth, it has stronger fault tolerance, and its system robustness and environmental adaptability are significantly enhanced.

[0023] This invention combines neural network approximation technology with adaptive control methods to dynamically compensate for the effects of unknown nonlinear dynamics, external disturbances (such as strong airflow and electromagnetic interference), and actuator failures (such as motor failure). This design significantly improves the stability and reliability of the system in complex environments, enabling UAV swarms to autonomously adjust their control strategies to maintain a coordinated state when encountering emergencies. It ensures continuous mission execution without human intervention, significantly improving the survivability and mission success rate of multi-UAV systems in practical applications. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the overall implementation of the method of the present invention;

[0025] Figure 2 This is a schematic diagram of the UAV structure provided in this invention;

[0026] Figure 3 This is a schematic diagram of the UAV communication network provided in this invention;

[0027] Figure 4 This is a diagram showing the results of applying the present invention to a multi-UAV control scenario;

[0028] Figure 5 This is a diagram showing the results of applying the present invention to a multi-UAV consistency control scenario;

[0029] Figure 6 This is a diagram showing the results of applying the present invention to a multi-UAV binary consensus control scenario;

[0030] Figure 7 This is a diagram showing the results of applying the present invention to a multi-UAV formation control scenario. Detailed Implementation

[0031] The present invention will now be further described with reference to the accompanying drawings.

[0032] Example 1: Refer to Appendix Figure 1-3 The present invention proposes a multi-UAV proportional cooperative control method based on preset time and performance constraints, which specifically includes the following steps:

[0033] Step 1. Targeting Figure 2 The quadcopter UAV shown here takes into account external disturbances (such as wind field interference and environmental noise), model uncertainties (such as rotor parameter deviation and changes in fuselage mass distribution) and actuator failures (such as rotor thrust loss and control surface jamming). An attitude dynamics model is established, that is, the dynamic equations of the follower UAV are modeled, and a directed graph is used to describe the communication network between follower UAVs and between follower UAVs and leader UAVs in the multi-UAV system, and a communication topology is constructed.

[0034] The dynamic equations of the UAV described in this embodiment are expressed as follows:

[0035] ,

[0036] in, , , These are the angular accelerations for the roll, pitch, and yaw channels, respectively. , , This represents the angular velocity of the corresponding channel; The moment of inertia of the UAV in the roll, pitch, and yaw directions represents the magnitude of the inertia of the UAV rotating about the corresponding axis. This is the damping coefficient for the corresponding channel, used to quantify the drag effect during rotation. The control inputs for roll, pitch, and yaw channels are typically provided by torque commands from the control surfaces or motors. The unknown bounded external disturbances corresponding to the corresponding channels cover uncertainties such as wind interference and model errors; the cross-coupling terms in the equations reflect the inertial coupling effect between the various rotation channels of the UAV.

[0037] To simplify the attitude controller design process and consider model uncertainties and non-affine actuator faults, the UAV dynamic equations are transformed into:

[0038] ,

[0039] in, , , , Indicates the output; , , Unknown nonlinear smooth function and This indicates unmodeled dynamics. For non-affine actuator failure, among which Represents a nonlinear fault function. The fault activation function has the following piecewise form:

[0040] ,

[0041] in For fault parameters, representing the rate of fault evolution.

[0042] The communication topology between drones is implemented as follows:

[0043] Each drone is abstracted as a node, and a directed spanning tree topology is constructed. The root node corresponds to the leader drone, and the remaining nodes are follower drones. It is ensured that each follower drone has at least one directed path from a leader drone to that follower drone, thus guaranteeing the connectivity of information transmission. For example, in this system containing 6 drones (e.g., ... Figure 3 As shown), drones 5 and 6 serve as the leading drones. Drones 1, 2, 3, and 4 act as follower drones. The communication topology ensures that drones 1, 2, 3, and 4 can all receive commands from drones 5 and 6, thereby improving communication redundancy. If the... One drone can be obtained directly. Information about each drone, ,otherwise .

[0044] Step 2. Introduce a multi-UAV proportional cooperative control method. Based on the actual task requirements, determine the preset time value, proportional parameters, and formation function. Based on the given proportional parameters and formation function, construct the proportional cooperative error of the multi-UAVs. Transform the proportional cooperative control problem into a preset time stable control problem of this error. The preset time is flexibly set according to the task requirements and does not depend on the initial state of the system.

[0045] Order No. The following drone was in The proportional coordination error of each attitude channel is: ,in, Indicates the index that follows the drone. This indicates the corresponding roll, pitch, and yaw flight attitudes; the error consists of two parts: the first part is the error between the current follower drone and other follower drones. The relative error between them is expressed by the proportional parameter. and formation functions Quantify the relative attitude relationship of the expected value. and The second part is about current follower drones and leader drones. The tracking error between the two drones is used to ensure that the follower drone tracks the desired attitude of the leader drone. , , To determine the number of drones in command; the entire error definition, through the introduction of proportional parameters and formation functions, achieves flexible control over formation, with the ultimate goal of minimizing the error. At the preset time Convergence is used to ensure the coordinated stability of multi-drone formations.

[0046] In this embodiment, the proportional cooperative error is constructed as follows: :

[0047] Let the first The attitude of the leader drone is Given the proportional parameters and formation functions as follows: and (This can be dynamically adjusted according to task requirements), then the first... The proportional coordination error of a drone is defined as:

[0048] ;

[0049] This error transforms the proportional control problem into an error. The problem of stable control of preset time, preset time It can be flexibly configured according to mission requirements and is independent of the system's initial state. By constructing a proportional coordination error, the original multi-UAV cooperative control problem is transformed into... At the preset time This simplifies the formulation and realization of the control objective by addressing the stable control problem that converges to near zero.

[0050] The aforementioned proportional parameters are dynamically adjusted according to the formation task requirements to adapt to different collaborative control objectives. These collaborative control objectives include consistency control, formation control, inclusion control, and corresponding binary control tasks, as well as composite objectives combining multiple tasks to meet complex practical control needs.

[0051] Step 3. After determining the proportional coordination error based on the information from Steps 1 and 2, design a preset performance function and an error transformation function to construct a preset performance mechanism, transforming the constrained proportional coordination error into an unconstrained error signal. This includes using the preset performance function to constrain the transient and steady-state performance of the proportional coordination error, and using the error transformation function to transform the proportional coordination error to ensure that the coordination error converges within a preset range; the specific implementation is as follows:

[0052] (3.1) Select the preset performance function:

[0053] ,

[0054] in, As the initial performance boundary, For steady-state performance boundary, For decay rate, A preset time is used to ensure coordination error. The convergence time.

[0055] (3.2) Transform the original error using the error transformation function. Transform into new error ,make sure It always converges within the preset performance boundaries to avoid problems such as excessive overshoot or slow convergence.

[0056] The error transformation function is expressed as follows:

[0057] .

[0058] Step 4. Construct a preset time command filter, and at the same time, reduce the computational complexity and avoid chattering caused by sudden changes in the filter input by dynamically estimating the upper bound of the derivative of the filter input, thereby improving the system convergence.

[0059] The preset time command filter constructed in this embodiment is represented as follows:

[0060] ,

[0061] in For filtering error, and Let these represent the output and input of the filter, respectively, and they satisfy... Unknown constants express The upper realm, Yes The estimate, and satisfy , This indicates the estimation error. ,in and They represent positive even numbers and positive odd numbers, respectively. . , , .

[0062] Step 5. Use neural networks to approximate the unknown nonlinear dynamics in the multi-UAV system, including unmodeled dynamics and nonlinear terms related to actuator faults. Combine the backstepping recursive algorithm and use the transformed proportional cooperative error information and the constructed command filter to design the parameter adaptive law and controller.

[0063] In this embodiment, the aforementioned neural network approximation uses a radial basis function (RBF) neural network to approximate unknown nonlinear dynamics. :

[0064] ,

[0065] in, For the neural network weight vector, For the basis function vector, This is the approximation error.

[0066] The aforementioned backstepping recursive algorithm specifically utilizes the transformed proportional cooperative error. and command filter output The adaptive law for design parameters is expressed as follows:

[0067]

[0068] in, , , These are design parameters.

[0069] The aforementioned controller is a virtual controller. and actual controller The components are represented as follows:

[0070] ,

[0071] in, , , These are design parameters.

[0072] ,

[0073] in, These are design parameters.

[0074] Using the designed virtual controller The input is the filter input, and the output is obtained by filtering via command filtering. And then in An actual controller was designed based on this. Under this control scheme, the conversion error is achieved. The preset time convergence ensures that the original error is guaranteed under the designed preset performance mechanism. The system achieves convergence within a preset time and satisfies given preset performance constraints. The parameters include an adaptive rate and a controller, where the controller comprises a virtual controller and an actual controller. Combined with the preset performance function and command filtering, they form a unified control scheme to perform attitude coordination control of multiple UAVs within a preset time, satisfying preset performance constraints, including overshoot, convergence speed, and steady-state error.

[0075] Step 6. By constructing a Lyapunov function, verify the convergence accuracy and fault tolerance of the multi-UAV system in achieving proportional cooperative control of the target within a preset time, as follows:

[0076] (6.1) Constructing the Lyapunov function:

[0077] ;

[0078] (6.2) Performance Analysis:

[0079] By calculating the derivative of V, we can derive...

[0080] ,

[0081] in Since t is a constant, according to the presupposed time stability theory, it can be known that when time t satisfies At that time, error , , , , All converge to the following compact set:

[0082] ;

[0083] This indicates that the system converges within the preset time T, meets the given preset performance, and has good convergence accuracy and fault tolerance.

[0084] The effects of the present invention will be further explained below with reference to simulation experiments.

[0085] 1. Simulation conditions:

[0086] The simulation experiments of this invention were conducted in a hardware environment with an AMD Ryzen 7 PRO processor and 8GB of memory, and in a MATLAB software environment.

[0087] 2. Simulation content:

[0088] The simulation system comprises four follower drones (labeled 1, 2, 3, and 4) and two leader drones (labeled 5 and 6). Using the design scheme of this invention, simulation experiments are conducted under four scenarios: control, consensus control, binary consensus control, and formation control. The control objective requires achieving the target within a preset time. Implemented within s.

[0089] In step 1, in the dynamic equations of the follower drone, , , , , Their initial states are randomly selected as

[0090] ;

[0091] ;

[0092] ;

[0093] .

[0094] Actuator in s malfunctioned, and the fault parameters Communication networks between drones, such as Figure 3 As shown.

[0095] In step 2, given the output of the leader drone By selecting different scaling parameters and formation functions To achieve the control objectives in four different scenarios, as detailed below:

[0096] (1) Includes control: , ,

[0097] , , ;

[0098] (2) Consistency control: , , , ;

[0099] (3) Binary Consistency Control: , , , , ;

[0100] (4) Formation control: , , , , , .

[0101] In step 3, the preset performance function parameters are selected as follows: , , , .

[0102] In step 4, the command filter parameters are selected as follows: , , , And variables The initial value is randomly selected as follows:

[0103] , ,

[0104] , .

[0105] In step 5, the basis function is chosen as a Gaussian function, containing 9 nodes with a width of [missing information]. The center is evenly distributed in the interval In the parameter adaptive rate, the parameter selection is as follows: The parameters for the virtual controller and the actual controller are selected as follows: ,variable The initial value is randomly selected as follows:

[0106] , ,

[0107] , .

[0108] 3. Simulation results:

[0109] Figures 4-7 show the results of multi-UAV control, consensus control, binary consensus control, and formation control, respectively. As can be seen from the figures, in all four control scenarios, the cooperative control objective was achieved within a preset time of 5 seconds, exhibiting high control accuracy and good fault tolerance. This demonstrates the effectiveness of the control scheme provided in this invention.

[0110] To highlight the beneficial effects of this invention, the following description, in conjunction with the invention patents CN120161772A and CN120371009A mentioned in the background section, is further provided using the table below:

[0111] Table 1. Comparison of the effects of the present invention and existing methods

[0112]

[0113] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0114] The above simulation analysis proves the correctness and effectiveness of the method proposed in this invention.

[0115] The parts of this invention not described in detail are common knowledge to those skilled in the art.

[0116] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Obviously, those skilled in the art, after understanding the content and principle of the present invention, may make various modifications and changes in form and detail without departing from the principle and structure of the present invention. However, these modifications and changes based on the concept of the present invention are still within the scope of protection of the claims of the present invention.

Claims

1. A multi-UAV proportional cooperative control method based on preset time and performance constraints, characterized in that, Includes the following steps: (1) For quadcopter drones, in scenarios including external disturbances, model uncertainties and actuator failures, establish a drone attitude dynamics model; take each drone as a node and construct a directed spanning tree topology with at least one root node to ensure that each follower drone can receive information from at least one leader drone, forming a reliable multi-drone communication network. (2) Introduce a multi-UAV proportional cooperative control method. Based on the given proportional parameters and formation function, construct the proportional cooperative error of the multi-UAV and transform the proportional cooperative control problem into a preset time stable control problem of the error. The preset time is flexibly set according to the task requirements and does not depend on the initial state of the system. (3) The transient and steady-state performance of the proportional coordination error is constrained by a preset performance function, and the proportional coordination error is transformed by an error transformation function to ensure that the coordination error converges within a preset range; (4) Construct a preset time command filter and dynamically estimate the upper bound of the derivative of the filter input to improve the system convergence performance; (5) By approximating the unknown nonlinear dynamics through neural networks, combined with the backstepping recursive algorithm, and using the transformed proportional cooperative error information and the constructed command filter, the parameter adaptive law and controller are designed. (6) By constructing the Lyapunov function, the convergence accuracy and fault tolerance of the multi-UAV system in achieving proportional cooperative control of the target within a preset time are verified.

2. The multi-UAV proportional cooperative control method according to claim 1, characterized in that: In the directed spanning tree topology described in step (1), the root node corresponds to the leader drone, and the remaining nodes correspond to the follower drones, and there are one or more leader drones.

3. The multi-UAV proportional cooperative control method according to claim 1, characterized in that: The proportional coordination error of the multiple UAVs mentioned in step (2) is constructed as follows: Order No. The following drone was in The proportional coordination error of each attitude channel is: ,in, Indicates the index that follows the drone. This indicates the corresponding roll, pitch, and yaw flight attitudes; the error consists of two parts: the first part is the error between the current follower drone and other follower drones. The relative error between them is expressed by the proportional parameter. and formation functions Quantify the relative attitude relationship of the expected value. and The second part is about current follower drones and leader drones. The tracking error between the two drones is used to ensure that the follower drone tracks the desired attitude of the leader drone. , , To determine the number of drones in command; the entire error definition, through the introduction of proportional parameters and formation functions, achieves flexible control over formation, with the ultimate goal of minimizing the error. At the preset time Convergence is used to ensure the coordinated stability of multi-drone formations.

4. The multi-UAV proportional cooperative control method according to claim 3, characterized in that: The proportional parameters and formation functions are dynamically adjusted according to the requirements of the collaborative task to adapt to different collaborative control objectives.

5. The multi-UAV proportional cooperative control method according to claim 4, characterized in that: The collaborative control objectives include consistency control, formation control, inclusion control and corresponding binary control tasks, as well as composite objectives of the above multiple tasks, to meet complex practical control needs.

6. The multi-UAV proportional cooperative control method according to claim 3, characterized in that: The preset performance function mentioned in step (3) is used to ensure that the dynamic performance of the system meets the task requirements, and is specifically expressed as follows: ， in, As the initial performance boundary, For steady-state performance boundary, For decay rate, A preset time is used to ensure coordination error. The convergence time.

7. The multi-UAV proportional cooperative control method according to claim 6, characterized in that: The step (3) described above involves transforming the proportional coordination error using an error transformation function. Specifically, this transformation is achieved by using the nonlinear characteristics of the tangent function to transform the original error... The transformed error is obtained by mapping to a bounded interval. To ensure It always converges within the preset performance boundaries; the error transformation function is expressed as follows: 。 8. The multi-UAV proportional cooperative control method according to claim 7, characterized in that: The command filtering described in step (4) is used to eliminate the computational complexity problem in the traditional backstepping method, and at the same time dynamically estimates the upper bound of the derivative of the filter input to improve the system convergence performance.

9. The multi-UAV proportional cooperative control method according to claim 8, characterized in that: The neural network described in step (5) is used to approximate the unknown nonlinear terms in the UAV attitude dynamics model to improve the robustness of the controller; specifically, a radial basis function (RBF) neural network is used to approximate the unknown nonlinear dynamics. : ， in, For the neural network weight vector, For radial basis functions, The error is a bounded approximation; the backstepping recursive algorithm utilizes the transformed proportional cooperative error. The output of the command filter is used to design an adaptive law of parameters to compensate for the effects of unknown nonlinearity and external disturbances.

10. The multi-UAV proportional cooperative control method according to claim 9, characterized in that: Step (5) describes the parameter adaptive rate and controller, where the controller includes a virtual controller and an actual controller; combined with the preset performance function and command filtering, they together form a control scheme to complete the attitude coordination control task of multiple UAVs within a preset time and meet the preset performance constraints, which include overshoot, convergence speed and steady-state error.

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