A disturbed multi-uav system active anti-jamming consistent tracking control method with "intelligent" pilot

Abstract

The application discloses a disturbed multi-unmanned aerial vehicle system active anti-interference consistency tracking control method with a "wise" leader. Firstly, a linear extended state observer is constructed for the case of unknown external disturbance and system state, which lays a foundation for subsequent controller design and external disturbance processing. Then, a controller is designed for the leader and the follower unmanned aerial vehicle. The designed controller can make the leader switch to the "wise" leader at the pulse moment, that is, the leader can obtain the feedback information of the follower unmanned aerial vehicle at the pulse moment, leading the motion trend of the whole system. Then, a self-triggered mechanism function based on the error between the follower unmanned aerial vehicle and the leader is designed to determine the self-triggered pulse moment when the "wise" leader needs to play a role, and it is strictly proved that there is no Zeno phenomenon. Finally, under the action of disturbance, the disturbed multi-unmanned aerial vehicle system active anti-interference consistency tracking control with the "wise" leader is realized, and the error between the follower unmanned aerial vehicle and the "wise" leader is reduced compared with the ordinary leader, and the "wise" leader makes a certain contribution to the stability of the whole system.

Description

Technical Field

[0001] This invention relates to the field of multi-UAV system control technology, specifically to an active anti-interference consistency tracking control method for disturbed multi-UAV systems with an "intelligent" navigator. Background Technology

[0002] A multi-agent system (MAS) is a complex, large-scale system composed of a large number of distributed, autonomous or semi-autonomous subsystems (agents) interconnected by a network; it is a "system of systems." Therefore, MAS systems possess characteristics such as autonomy, distribution, and coordination, and can solve complex problems that are difficult for individual systems to address. They have wide applications in sensor networks, unmanned vehicle / drone swarms, multi-missile attacks, and intelligent transportation systems.

[0003] In recent years, leader-follower consistency in multi-drone systems has become a hot topic. Leader-follower consistency control aims to achieve consistency across the entire system by enabling follower drones to adjust their behavior based on the leader's actions. Under this control framework, there are typically one or more leaders whose behavior is considered a reference signal, and other follower drones achieve different mission objectives by maintaining consistency with the leaders.

[0004] However, it is not difficult to see from the above research framework that in the traditional leader-follower consistency control framework, the navigator is always independent of the following drones and is not affected by them. Therefore, inspired by the literature [1], the attributes of the navigator are modified to enable it to obtain feedback information from its neighboring following drones within a specific time period, and such a navigator is called a "smart" navigator. By introducing a self-triggering mechanism and pulse control, the "smart" navigator only plays a role when needed, avoiding the frequent use of the "smart" navigator and maintaining the leadership-follower framework of the entire system. At the same time, the "smart" navigator contributes to the cohesion of the entire team. Summary of the Invention

[0005] The purpose of this invention is to propose an active anti-interference consistency tracking control method for a disturbed multi-UAV system with an "intelligent" navigator, which can effectively reduce the error between the following UAV and the navigator, and improve the stability and cohesion of the entire system.

[0006] The specific technical solution of the present invention is as follows: A method for active anti-interference consistency tracking control of a disturbed multi-UAV system with an "intelligent" navigator, comprising the following steps:

[0007] Considering the various interferences in the external environment, based on reference [2], the following dynamic models of the disturbed following UAV system and the "intelligent" navigation UAV are established:

[0008]

[0009]

[0010] In the above formula, i = {0, 1…N} represents the number of agents, θ i (t), i = {1, ..., N} represents the external disturbances of the following UAV system. i = {1, ..., N} represents the state of the following drone, and x0(t), i = 0 represents the state of the navigator. i = {1, ..., N} represents the continuous part of the controller to be designed for the drone. i = {1, ..., N} represents the discrete part of the controller to be designed for the UAV. i=0 indicates the continuous part of the controller to be designed by the navigator. i = 0 indicates that the discrete part of the controller to be designed by the navigator, A, B, and C are known system matrices with suitable dimensions, and D satisfies the matching condition D = B.

[0011] First, to handle the impact of external disturbances on the drone, we select extended state variables. And construct the following augmented new multi-drone system:

[0012]

[0013] In the formula,

[0014] For the augmented new multi-agent system, the following linear extended state observer is designed:

[0015]

[0016] In the formula, This represents an estimate of the state of the following drone system. This represents the estimated value of the external disturbance. This represents the gain matrix of the linearly extended state observer.

[0017] Furthermore, by selecting an appropriate observer gain, the linearly extended state observer provides accurate estimates. Based on the observer output values, a navigator controller is designed.

[0018]

[0019] In the formula, g j This indicates that when the follower drone is connected to the navigator, g j =1, otherwise g j =0;l k >0 indicates the time of the self-triggered pulse. The pulse gain, where K represents the local feedback gain. Iterative updates are performed using the following formula:

[0020]

[0021] In the formula, This indicates a self-triggering mechanism function. This represents the neighborhood error between the navigator and the follower drones. Let α represent the triggering error, α > 0, β > 0, λ > 0, and i′ = {0, 1, 2, ..., N}.

[0022] Through the design of the navigator controller, it can be seen that the "intelligent" navigator only functions at the self-triggered pulse moment. Next, it is demonstrated that the absence of the Zeno phenomenon indicates that the "intelligent" navigator does not function frequently, thus maintaining the overall leader-follower organizational framework of the system.

[0023]

[0024]

[0025]

[0026] therefore This proves that there is no Zeno phenomenon.

[0027] Based on the output value of the linearly extended state observer, the following follower controller is constructed:

[0028]

[0029] Next, we set up tracking errors.

[0030] Consider t∈(t k-1 , t k ),

[0031] Construct the following Lyapunov function:

[0032]

[0033] In the formula, The variable representing the tracking error in compact set form is e(t) = [e x T (t), e θ T (t)] T Let P1 represent the observer's state and the perturbation estimation error in a compact set form, and let P2 represent a positive definite and symmetric matrix.

[0034] Differentiating the constructed Lyapunov function yields the following equation:

[0035]

[0036] The following expressions are treated separately:

[0037]

[0038]

[0039] In the formula, To represent a scalar, Represents the tensor product.

[0040] Based on this, we can obtain the following formula:

[0041]

[0042] In the formula,

[0043]

[0044]

[0045] Further calculations yield the following formula:

[0046]

[0047] Solving the above differential inequality equation using the GronWall-Bellman inequality, we obtain:

[0048]

[0049] In the formula,

[0050] For the self-triggered pulse timing, consider t = t k And construct the following Lyapunov function:

[0051]

[0052] Assuming the system variables are left-continuous at the time of the self-triggering impulse, we can obtain the following equation:

[0053]

[0054] Passing conditions We can further obtain the following formula:

[0055] V(t k +)≤η(k)V(t k - )=η(k)V(t k ),

[0056] In the formula, 0 < η(k) < 1 indicates that it is a constant between 0 and 1.

[0057] In summary, for any time t∈(t k , t k+1 After scaling and rearranging, we have the following formula:

[0058]

[0059] In the formula, Let represent a continuous function whose value approaches negative infinity as time approaches infinity, and its boundary.

[0060] Therefore, an active anti-interference consistency tracking control for a disturbed multi-UAV system with an "intelligent" navigator is achieved under the control law designed in this invention. Attached Figure Description

[0061] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0062] Figure 2 This is a diagram of the system communication topology.

[0063] Figure 3 The example uses the estimated state and estimated perturbation diagram of the linear extended state observer under the method proposed in this invention;

[0064] Figure 4 This is a diagram illustrating the bounded consistency state of the system under the method proposed in this invention, as shown in the example.

[0065] Figure 5 The following is a diagram illustrating the system tracking error effect using the method proposed in this invention as an example.

[0066] Figure 6 The accompanying diagram shows a comparison between the "intelligent" navigator and the ordinary navigator using the method proposed in this invention, as an example.

[0067] Figure 7 This embodiment uses a self-triggering diagram based on the method proposed in this invention. Detailed Implementation

[0068] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading the present invention, any modifications of the present invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0069] Consider a multi-UAV system consisting of four follower UAVs, labeled 1, 2, 3, and 4, and a navigator UAV labeled 0. Their communication topology is as follows: Figure 2 As shown;

[0070] like Figure 1 As shown, a method for active anti-interference consistency tracking control of a disturbed multi-UAV system with an "intelligent" navigator includes the following steps:

[0071] Step 1: Set initial values ​​for various parameters and control gain;

[0072] Step 2: Update the dynamic variables within the system using the designed controller;

[0073] Step 3: Utilize the set self-trigger function f i′ (t), when the internal dynamic table variable of the system meets the trigger condition, switch the controller input;

[0074] Step 4: Repeat steps 2 and 3 until the runtime ends.

[0075] Figure 3 A linearly extended state observer is presented, which enables real-time and effective estimation of the state and external disturbances of a follow-up UAV system under a given observer gain.

[0076] Figure 4 A multi-UAV system with an "intelligent" navigator is presented, demonstrating that bounded consistency is achieved under the designed controller.

[0077] Figure 5 The error between the following drone and the navigator was shown to have achieved bounded convergence.

[0078] Figure 6 The study compared the tracking error trajectories under the influence of a "smart" navigator with those under the influence of a regular navigator. This showed that the "smart" navigator helped reduce tracking errors and contributed to the cohesion of the entire team.

[0079] Figure 7 The self-triggered effect diagram shows that the Zeno phenomenon is eliminated in the whole system, the "intelligent" navigator does not play a role frequently, and the leader-follower organizational framework of the whole system is still maintained.

[0080] References

[0081] [1]Nagy M, Z,Biro D,et al.Hierarchical group dynamics in pigeonflockS[J].Nature,2010,464(7290)∶890-893.

[0082] [2]Zhang B,Sun X,Liu S,et al. Adaptive model predictive control withextended state observer for multi-UAV formatjon flight[J].IntemationalJournal of Adaptive Control and Signal Processing,2020,34(10):1341-1358。

Claims

1. A method for active anti-interference consistency tracking control of a disturbed multi-UAV system with an "intelligent" navigator, characterized in that, The entire multi-drone system includes an "intelligent" navigator, capable of acquiring feedback information from follower drones. The control method includes the following steps: Step 1: Considering the various disturbances in the actual environment, establish a dynamic model of the disturbed multi-UAV system with external disturbances; Step 2: For unknown but bounded external disturbances and situations where the state of the UAV system cannot be directly measured, firstly, the external disturbances in the UAV system are regarded as new system state variables. Secondly, a new augmented multi-UAV system is established and a linear extended state observer is constructed. Finally, the state estimate and external disturbance estimate of the UAV system are output simultaneously. Step 3: Introduce a self-triggering mechanism to determine when the "intelligent" navigator needs to take action. When the system meets the self-triggering conditions, at the self-triggering pulse moment, design a pulse control law for the navigator that can obtain the state information of its neighboring followers, causing the navigator to switch to the "intelligent" navigator, and updating the "intelligent" navigator's own state information under the action of the designed control law. This indicates a self-triggering mechanism function. Indicates tracking error. Indicates triggering error. It is a positive number. Indicates the agent's ID; Step 4: For the following UAV, construct an interference compensator based on a linear extended state observer during the non-pulse time interval; at the self-trigger pulse time that satisfies the self-triggering mechanism designed in Step 3, construct a distributed pulse control law based on a linear extended state observer. Step 5: Combining steps 2, 3, and 4, and utilizing Lyapunov stability theory and impulse control theory, active anti-interference consistency tracking control of a disturbed multi-UAV system with an "intelligent" navigator was achieved.

2. The active anti-interference consistency tracking control method for a disturbed multi-UAV system with an "intelligent" navigator as described in claim 1, taking into account the various types of interference in the actual environment, establishes the following dynamic model of the disturbed multi-UAV system with external disturbances: The dynamic model of the drone is shown below: ， in, Indicate the agent number; by using feedback linearization, the nonlinear UAV system is transformed into the following linear time-invariant double integrator chain model: ， Based on this, when Let the time be represented as following the drone, and considering external disturbances, the following drone can be transformed into the form of the following state-space equation: ， when If we denote time as the navigator, then the navigator dynamics model is as follows: In the above formula, This indicates following external disturbances to the drone. Indicates the status of following the drone. Indicates the navigator status. This indicates the continuous part of the controller to be designed for the drone. This indicates the discrete components of the controller to be designed for the drone. This indicates the continuous portion of the controller to be designed for the navigator. This represents the discrete component of the controller to be designed for the Navigator. , , It is a known system matrix with suitable dimensions. Meets matching conditions .

3. The active anti-interference consistency tracking control method for a disturbed multi-UAV system with an "intelligent" navigator as described in claim 2, for cases where the external disturbance is unknown but bounded and the UAV system state cannot be directly measured, firstly, the external disturbance in the UAV system is regarded as a new system state variable; secondly, a new augmented multi-UAV system is established and a linear extended state observer is constructed; finally, the state estimate and external disturbance estimate of the UAV system are output simultaneously. The specific steps are as follows: First, choose the extended state variables. And construct the following augmented new multi-drone system: In the formula, , , , , ; For the augmented new multi-UAV system, the following linearly extended state observer is designed: In the formula, , This represents an estimate of the state of the following drone system. This represents the estimated value of the external disturbance. This represents the gain matrix of the linearly extended state observer.

4. The active anti-interference consistency tracking control method for a disturbed multi-UAV system with a "smart" navigator as described in claim 3 introduces a self-triggering mechanism to determine the timing when the "smart" navigator needs to take action. When the system meets the self-triggering condition, at the self-triggering pulse moment, a pulse control law is designed for the navigator to obtain the state information of its neighboring followers, causing the navigator to switch to the "smart" navigator, and updating the state information of the "smart" navigator itself under the action of the designed control law. The specific steps are as follows: Design the leader controller as follows: In the formula, This indicates that when followers are associated with leaders... ,otherwise ; Indicates at the pulse moment pulse gain, Indicates local feedback gain. Iterative updates are performed using the following formula: ； Next, we will prove that there is no Zeno phenomenon: therefore This proves that there is no Zeno phenomenon.

5. The active anti-interference consistency tracking control method for a disturbed multi-UAV system with an "intelligent" navigator as described in claim 4, for the following UAV, constructs an interference compensator based on a linear extended state observer during the non-pulse time interval; at the self-trigger pulse time satisfying the self-triggering mechanism designed in step three, constructs a distributed pulse control law based on a linear extended state observer, the specific steps of which are as follows: Design the following drone controller: 。 6. The active anti-interference consistency tracking control method for a disturbed multi-UAV system with an "intelligent" navigator as described in claim 5, combined with steps two, three, and four, utilizes Lyapunov stability theory and impulse control theory to achieve self-disturbing bounded consistency control of the disturbed multi-UAV system with an "intelligent" navigator. The specific steps are as follows: B001: Set tracking error ,consider And construct the Lyapunov function: B002: In the formula, , It is the observer estimation error variable. Denotes a positive definite and symmetric matrix. Denotes a positive definite and symmetric matrix. B003: Calculation The derivative: B004: In the formula, To represent a scalar, Represents the tensor product. B005: Based on this, we can obtain the following formula: B006: In the formula, , , , , B007: Further calculations yield the following formula: B008: In the formula, , , , , B009: Consider The variable is left-continuous at the self-triggering pulse moment, and the Lyapunov function is constructed as follows: B0010: Passing Conditions We can further obtain the following formula: B0011: In the formula, This indicates that it is a constant between 0 and 1; B0012: In summary, for any We have the following formula: B0013: In the formula, Let represent a continuous function whose value approaches negative infinity as time approaches infinity, and its boundary. , B0014: Therefore, the active anti-interference consistency tracking control objective of a disturbed multi-UAV system with an "intelligent" navigator is achieved under the designed control law.

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