Self-adaptive event triggering cooperative tracking control method for unmanned cluster system under resource limitation

Abstract

The invention discloses a self-adaptive event triggering cooperative tracking control method for an unmanned cluster system under resource limitation. The method comprises the following steps: establishing a dynamic model of a leader and a follower for a heterogeneous unmanned cluster system; constructing a distributed observer for the follower; determining a state measurement error and an internal dynamic variable of the follower, determining a dynamic event triggering condition of the follower, and constructing a self-adaptive event triggering controller of the follower; when the state measurement error meets or does not meet the dynamic event triggering condition, the adaptive event triggering controller adopts different means to obtain the system observation state of the neighbor node and updates the adaptive event triggering controller; and the follower realizes cooperative tracking control on the leader based on a system observation state. According to the method, the communication burden is effectively reduced, and an efficient and reliable solution is provided for cooperative control of the heterogeneous unmanned cluster system in a complex environment.

Description

Technical Field

[0001] This invention relates to the field of unmanned swarm technology, and more specifically to an adaptive event-triggered collaborative tracking control method for unmanned swarm systems under resource constraints. Background Technology

[0002] In recent years, intelligent unmanned systems, represented by drones, unmanned vehicles, and unmanned surface vessels (USVs), have been widely applied in various practical scenarios such as disaster emergency response, regional continuous monitoring, on-orbit space operations, and urban logistics delivery, thanks to their advantages of convenient maintenance, controllable cost, flexible deployment, and high reliability. They have gradually become one of the research hotspots in the field of control theory and engineering. The foundation for collaborative operation in unmanned swarm systems lies in efficient and reliable tracking control. This involves designing a reasonable distributed control protocol that enables each subsystem in the swarm to move along a preset trajectory and maintain consistent state / output, relying only on local information. However, existing research often assumes that the swarm consists of homogeneous platforms with completely consistent dynamic behavior. While such systems are relatively simple in theoretical analysis and controller design, they also suffer from inherent limitations such as weak collaborative capabilities, poor task adaptability, and limited intelligent behavior. In contrast, heterogeneous swarm systems composed of various types of unmanned platforms can integrate complementary capabilities such as wide-area perception from drones and high-precision operation from USVs / USVs, achieving deep coupling at the functional and structural levels. This results in stronger robustness and adaptability in complex dynamic environments. Therefore, research on collaborative control of heterogeneous unmanned systems has become an important direction for promoting the intelligent evolution of unmanned swarms.

[0003] In practical applications of unmanned systems, due to the complex and ever-changing working environment and the time-varying physical characteristics of the platform itself, accurate dynamic models of the system are often difficult to obtain, resulting in problems such as parameter uncertainty, unmodeled dynamics, and external disturbances. For example, during flight, UAVs may experience model parameter drift due to load changes, structural vibrations, or airflow disturbances; unmanned vehicles and unmanned boats in complex terrain or aquatic environments may exhibit strong nonlinear and time-varying characteristics due to uncertainties such as ground friction coefficients and water flow velocities. These model uncertainties severely affect the performance of traditional control methods and may even lead to system instability. For example, although PID control has the advantages of simple structure and ease of implementation, its fixed parameters make it difficult to adapt to changes in the dynamic characteristics of the system; feedback linearization methods based on accurate models will significantly degrade control performance when the model mismatch occurs; robust control can handle bounded uncertainties, but often at the cost of sacrificing system dynamic performance.

[0004] On the other hand, with the rapid development of embedded computing, wireless communication, and intelligent control technologies, unmanned swarm systems are increasingly exhibiting highly networked characteristics. The key to swarm collaboration lies in the real-time exchange of system status / output information between subsystems through a networked structure, allowing them to autonomously adjust control strategies accordingly. However, in practical deployments, individual platforms typically carry resource-constrained embedded processors and communication modules. Faced with the complex information interaction needs within the swarm and limited network bandwidth, traditional periodic communication and control update mechanisms are prone to resource contention and transmission congestion, severely restricting the large-scale application of distributed control in real-world scenarios. Therefore, how to effectively reduce the communication and computing burden under the constraint of limited platform resources has become a core problem that urgently needs to be solved in the collaborative control of heterogeneous unmanned swarm systems. Summary of the Invention

[0005] In view of this, the present invention provides an adaptive event-triggered cooperative tracking control method for unmanned swarm systems under resource constraints, which can solve the technical problem of how to achieve intelligent and reliable cooperative tracking control of unknown heterogeneous unmanned swarm systems under conditions of unknown model and dynamic uncertainty, using limited communication resources.

[0006] To solve the above-mentioned technical problems, the present invention is implemented as follows.

[0007] An adaptive event-triggered cooperative tracking control method for a resource-constrained unmanned swarm system includes: Step S1: For an unmanned swarm system including one leader and multiple followers, establish dynamic models for the leader and followers respectively, and determine the output information of the unmanned swarm system; Step S2: Construct a distributed observer for the followers based on the output information; Step S3: Determine the follower's state measurement error and internal dynamic variables, determine the follower's dynamic event triggering conditions, and construct the follower's adaptive event triggering controller; When the state measurement error does not meet the dynamic event triggering conditions, the adaptive event triggering controller samples and transmits the current system observation state of the distributed observers of the neighboring followers, and updates the adaptive event triggering controller; the followers realize cooperative tracking control of the leader based on the system observation state transmitted by the neighboring followers; When the state measurement error meets the dynamic event triggering condition, the adaptive event triggering controller updates itself based on the previously sampled and transmitted system observation state and the neighbor follower system observation state predicted by the follower's open-loop estimator; the follower achieves cooperative tracking control of the leader based on the predicted neighbor follower system observation state. The adaptive event triggering controller is updated based on the feedback control gain and coupling control gain of the calculated adaptive event triggering controller.

[0008] Preferably, in step S1, for a heterogeneous unmanned swarm system including one leader and multiple followers, dynamic models of the leader and followers are established respectively, wherein: The dynamic model of the leader is as follows:

[0009] in, Indicates time, Indicates the system status of the leader. express The derivative of The system output represents the leader's output. The state transition matrix for the leader. The output control matrix for the leader; The dynamic model of the follower is:

[0010] in, Indicates the first The system state of a follower express The derivative of For the first A follower's control input, Indicates the first The system output of each follower For the first The state transition matrix of each follower For the first The control input matrix of each follower Indicates the first The output control matrix of each follower, and the triplet All are unknown real matrices. N The number of followers.

[0011] Preferably, in step S2, the distributed observer is:

[0012] in, For the first The observation status of a distributed observer that follows a group of followers for The derivative of For the first The control input matrix of a distributed observer with followers This is the coupling gain vector. For the first Local error signal of a distributed observer with a follower For the feedback gain of the distributed observer, For the first A distributed observer with followers observes the output. For the first The system output of each follower The output control matrix for the leader.

[0013] Preferably, in step S3, the follower's state measurement error and internal dynamic variables are determined, wherein: No. The state measurement error of each follower is:

[0014] In the formula, t Indicates time, For the first The state measurement error of each follower; For the first The predicted values ​​of the observed states by a distributed observer. For the first The observation status of a distributed observer , They represent the first The first follower and the The next trigger moment; The internal dynamic variables of the follower are as follows:

[0015] in, It is the first The internal dynamic variables of a follower yes The derivative of It is a positive positive number. It is an open interval positive numbers, For the first A static event triggering function for a follower. For the first The local state prediction error signals of a distributed observer, and:

[0016] in, This is the static threshold coefficient. This represents the 2-norm of a vector.

[0017] Preferably, in step S3, the dynamic event triggering conditions of the follower are determined, and an adaptive event triggering controller for the follower is constructed, wherein: The dynamic event triggering conditions for followers are as follows:

[0018] in, To obtain The maximum time when it is established, and the maximum time satisfies greater than , It is a dynamic trigger coefficient; No. The adaptive event-triggered controller and adaptive parameter update law for each follower are as follows:

[0019] in, Indicates the first The adaptive event-triggered feedback control gain of the follower. For the first Coupled control gain in an adaptive event-triggered controller for a follower; For the first Local state prediction error signal of a distributed observer; For the first The first follower and the first The weights among the followers, if the i-th... The first follower can receive the first The information of each follower Greater than zero, otherwise It equals zero; For the first The weight between followers and leaders, if the first A follower can receive information from the leader. Greater than zero, otherwise It equals zero; N For the number of followers, , They represent the first and the The predicted values ​​of the states of each distributed observer. This indicates the leader's status information; express The derivative of Represents a symbolic function. Indicates the first Ideal coupling control gain for a follower and They represent the first The adaptive feedback control gain learning rate and adaptive coupling control gain learning rate of the followers. This indicates local output error. express The derivative, sign Represents the transpose of a matrix or vector.

[0020] Preferably, in step S3, the formula for the follower open-loop estimator state prediction is as follows:

[0021] in, express The derivative of The result is the state prediction of the open-loop estimator for the follower.

[0022] Preferably, in step S3, the adaptive event triggering controller is updated based on the calculated feedback control gain and coupling control gain in the adaptive event triggering controller, wherein:

[0023] in, Indicates the first Feedback control gain in an adaptive event-triggered controller for a follower For the first Coupled control gain in an adaptive event-triggered controller for a follower Describes the differential operator. Independent variables that are time-related; and These represent the initial values ​​of the feedback control gain and the coupling control gain in the adaptive event-triggered controller, respectively.

[0024] An adaptive event-triggered cooperative tracking control device for a resource-constrained unmanned swarm system includes: Model building module: Configured to build dynamic models of the leader and followers respectively for an unmanned swarm system including one leader and multiple followers, and determine the output information of the unmanned swarm system; Distributed Observer Builder Module: Configured to build distributed observers for followers based on output information; Tracking control module: configured to determine the follower's state measurement error and internal dynamic variables, determine the follower's dynamic event triggering conditions, and build an adaptive event triggering controller for the follower; When the state measurement error does not meet the dynamic event triggering conditions, the adaptive event triggering controller samples and transmits the current system observation state of the distributed observers of the neighboring followers, and updates the adaptive event triggering controller; the followers realize cooperative tracking control of the leader based on the system observation state transmitted by the neighboring followers; When the state measurement error meets the dynamic event triggering condition, the adaptive event triggering controller updates itself based on the previously sampled and transmitted system observation state and the neighbor follower system observation state predicted by the follower's open-loop estimator; the follower achieves cooperative tracking control of the leader based on the predicted neighbor follower system observation state. The adaptive event triggering controller is updated based on the feedback control gain and coupling control gain of the calculated adaptive event triggering controller.

[0025] The present invention provides an electronic device, characterized in that the electronic device comprises: A processor is used to execute multiple instructions; Memory, used to store multiple instructions; The plurality of instructions are to be stored in the memory and loaded and executed by the processor as described above.

[0026] This invention achieves online estimation of the state of unmeasurable systems by constructing a distributed state observer based on a closed-loop reference model; it realizes distributed cooperative tracking control of multi-parameter uncertain systems by jointly designing the parameter update law of adaptive coupling-feedback control gain; and it effectively reduces unnecessary data transmission while ensuring system stability and tracking performance by introducing a dynamic event triggering mechanism. This method overcomes the dependence of traditional control methods on accurate models and solves the cooperative control problem of heterogeneous unmanned swarms under conditions of unknown models, system uncertainty, and limited communication. It achieves intelligent and reliable cooperative tracking control of unknown heterogeneous unmanned swarm systems, providing technical support for the reliable application of unmanned swarms in complex environments. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the adaptive event-triggered collaborative tracking control method for unmanned swarm systems under resource constraints, as described in this invention. Detailed Implementation

[0028] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] like Figure 1 As shown, this invention proposes an adaptive event-triggered cooperative tracking control method for unmanned swarm systems under resource constraints, comprising: Step S1: For an unmanned swarm system including one leader and multiple followers, establish dynamic models for the leader and followers respectively, and determine the output information of the unmanned swarm system; Step S2: Construct a distributed observer for the followers based on the output information; Step S3: Determine the follower's state measurement error and internal dynamic variables, determine the follower's dynamic event triggering conditions, and construct the follower's adaptive event triggering controller; When the state measurement error does not meet the dynamic event triggering conditions, the adaptive event triggering controller samples and transmits the current system observation state of the distributed observers of the neighboring followers, and updates the adaptive event triggering controller; the followers realize cooperative tracking control of the leader based on the system observation state transmitted by the neighboring followers; When the state measurement error meets the dynamic event triggering condition, the adaptive event triggering controller updates itself based on the previously sampled and transmitted system observation state and the neighbor follower system observation state predicted by the follower's open-loop estimator; the follower achieves cooperative tracking control of the leader based on the predicted neighbor follower system observation state. The adaptive event triggering controller is updated based on the feedback control gain and coupling control gain of the calculated adaptive event triggering controller.

[0030] Further, in step S1, for a heterogeneous unmanned swarm system including one leader and multiple followers, dynamic models of the leader and followers are established respectively, wherein: The dynamic model of the leader is as follows:

[0031] in, Indicates time, Indicates the system status of the leader. express The derivative of The system output represents the leader's output. The state transition matrix for the leader. The output control matrix for the leader; The dynamic model of the follower is:

[0032] in, Indicates the first The system state of a follower express The derivative of For the first A follower's control input, Indicates the first The system output of each follower For the first The state transition matrix of each follower For the first The control input matrix of each follower Indicates the first The output control matrix of each follower, and the triplet All are unknown real matrices. N The number of followers.

[0033] This invention designs a distributed observer based on a closed-loop reference model for each follower, utilizing the leader's state transition matrix, output control matrix, and system output information, to estimate the state of the unmeasurable follower system.

[0034] Furthermore, in step S2, the distributed observer is:

[0035] in, For the first The observation status of a distributed observer that follows a group of followers for The derivative of For the first The control input matrix of a distributed observer with followers This is the coupling gain vector. For the first Local error signal of a distributed observer with a follower For the feedback gain of the distributed observer, For the first A distributed observer with followers observes the output. For the first The system output of each follower The output control matrix for the leader.

[0036] In this invention, there exists an ideal feedback control gain matrix. and ideally coupled control gain matrix The following model matching conditions are met:

[0037] Further, in step S3, the follower's state measurement error and internal dynamic variables are determined, wherein: No. The state measurement error of each follower is:

[0038] In the formula, t Indicates time, For the first The state measurement error of each follower; For the first The predicted values ​​of the observed states by a distributed observer. For the first The observation status of a distributed observer , They represent the first The first follower and the The next trigger moment; The internal dynamic variables of the follower are as follows:

[0039] in, It is the first The internal dynamic variables of a follower yes The derivative of It is a positive positive number. It is an open interval positive numbers, For the first A static event triggering function for a follower. For the first The local state prediction error signals of a distributed observer, and:

[0040] in, This is the static threshold coefficient. This represents the 2-norm of a vector.

[0041] In step S3, the dynamic event triggering conditions of the follower are determined, and an adaptive event triggering controller for the follower is constructed, wherein: The dynamic event triggering conditions for followers are as follows:

[0042] in, To obtain The maximum time when it is established, and the maximum time satisfies greater than , It is a dynamic trigger coefficient; No. The adaptive event-triggered controller and adaptive parameter update law for each follower are as follows:

[0043] in, Indicates the first The adaptive event-triggered feedback control gain of the follower. For the first Coupled control gain in an adaptive event-triggered controller for a follower; For the first Local state prediction error signal of a distributed observer; For the first The first follower and the first The weights among the followers, if the i-th... The first follower can receive the first The information of each follower Greater than zero, otherwise It equals zero; For the first The weight between followers and leaders, if the first A follower can receive information from the leader. Greater than zero, otherwise It equals zero; N For the number of followers, , They represent the first and the The predicted values ​​of the states of each distributed observer. This indicates the leader's status information; express The derivative of Represents a symbolic function. Indicates the first Ideal coupling control gain for a follower and They represent the first The adaptive feedback control gain learning rate and adaptive coupling control gain learning rate of the followers. This indicates local output error. express The derivative, sign Represents the transpose of a matrix or vector.

[0044] In step S3, the formula for the follower open-loop estimator state prediction is as follows:

[0045] in, express The derivative of The result is the state prediction of the open-loop estimator for the follower.

[0046] In step S3, the adaptive event triggering controller is updated based on the calculated feedback control gain and coupling control gain in the adaptive event triggering controller, wherein:

[0047] in, Indicates the first Feedback control gain in an adaptive event-triggered controller for a follower For the first Coupled control gain in an adaptive event-triggered controller for a follower Describes the differential operator. Independent variables that are time-related; and These represent the initial values ​​of the feedback control gain and the coupling control gain in the adaptive event-triggered controller, respectively.

[0048] In this invention, state measurement error and node-based open-loop estimator are defined. Dynamic event triggering conditions are designed based on Lyapunov stability theory to clarify when the system performs a series of operations such as sampling, transmission and communication. Finally, an adaptive event-triggered controller is obtained, which reduces the communication frequency and number of information transmissions between unmanned swarm systems while ensuring system stability and control performance, thus saving limited communication resources.

[0049] In this invention, At that moment, the event was triggered, the first The open-loop estimator obtains the first... The real state of a distributed observer When the event is not triggered, i.e. At that time, using the first The estimated state of the open-loop estimator Instead of the By monitoring the observation status of individual observers, unnecessary data transmission can be reduced, and data collisions and network congestion between large-scale unmanned cluster systems can be avoided.

[0050] This invention calculates and updates the feedback control gain and coupled control gain in the adaptive event-triggered controller in real time and online, enabling the control system to adapt to unknown dynamics and ensuring that an unknown heterogeneous unmanned swarm system can achieve cooperative tracking control. Simultaneously, by integrating both sides of the adaptive coupled-feedback control parameter update law, the controller gain matrix can be obtained and dynamically adjusted online according to the above rules.

[0051] This invention enables heterogeneous unmanned swarm systems to achieve collaborative tracking and control under conditions of incomplete state availability, unknown system model, uncertain system parameters, and limited communication, while effectively reducing communication burden, providing an efficient and reliable solution for the application of unmanned swarms in complex environments.

[0052] This invention provides a specific embodiment of an adaptive event-triggered collaborative tracking control method for unmanned cluster systems under resource constraints.

[0053] S1. Establish a system that includes a leader and The dynamics model of a heterogeneous unmanned swarm system composed of followers, and the leader's dynamics model are expressed as follows:

[0054] in, Indicates time, Indicates the system status of the leader. express The derivative of The system output represents the leader's output. Given the known leader state transition matrix, Output the control matrix for the known leader.

[0055] No. The dynamic model of a single follower is represented as follows:

[0056] in, Indicates the first The system state of a follower express The derivative of For the first A follower's control input, Indicates the first The system output of each follower For the first The state transition matrix of each follower For the first The control input matrix of each follower Indicates the first The output control matrix of each follower, and the triplet All are unknown real matrices. N The number of followers.

[0057] S2. For each follower unmanned system, using the leader's state transition matrix information, output control matrix, and system output information, design a distributed observer based on a closed-loop reference model to estimate the state of the unmeasurable follower system. Wherein, the... A distributed observer with followers based on a closed-loop reference model is designed in the following form:

[0058] in, For the first The observation status of a distributed observer that follows a group of followers for The derivative of For the first The control input matrix of a distributed observer with followers This is the coupling gain vector. For the first Local error signal of a distributed observer with a follower For the feedback gain of the distributed observer, For the first A distributed observer with followers observes the output. For the first The system output of each follower The output control matrix for the leader.

[0059] S3. For unknown heterogeneous unmanned swarm systems where the system state is not fully measurable, an adaptive controller with adjustable parameters is designed for each follower based on the system state observed by a distributed observer, in order to track the leader's state. Specifically, the... The adaptive controller for each follower is designed as follows:

[0060] in, Indicates the first Adaptive feedback control gain for each follower Indicates the first Adaptive coupling control gain for each follower; , They represent the first The observation status of the distributed observer and the first The observation status of a distributed observer This indicates the leader's status information; For the first The first follower and the first The weights among the followers, if the i-th... The first follower can receive the first The information of each follower Greater than zero, otherwise It equals zero; For the first The weight between followers and leaders, if the first A follower can receive information from the leader. Greater than zero, otherwise It equals zero; N The number of followers.

[0061] Adaptive feedback control gain and adaptive coupling control gain It is designed as follows:

[0062] in, express The derivative of express The derivative of Represents a symbolic function. and They represent the first The adaptive feedback control gain learning rate and adaptive coupling control gain learning rate of the followers. Indicates local output error, symbol Represents the transpose of a matrix or vector. Indicates the first Ideal coupling control gain for a follower.

[0063] S4. Define the state measurement error and a node-based open-loop estimator. Design dynamic event triggering conditions based on Lyapunov stability theory, clarifying when the system performs a series of operations such as sampling, transmission, and communication. This ultimately yields an adaptive event-triggered controller, reducing the communication frequency and number of information transmissions between unmanned swarm systems while ensuring system stability and control performance, thus saving limited communication resources. The specific implementation process is as follows: First, define the state measurement error for each follower, where the first... The state measurement error of each follower is:

[0064] In the formula, t Indicates time, and , Indicates unmanned system The trigger sequence and . For the first The observation status of a distributed observer For the first The predicted values ​​of the observed states are obtained from the observations of a distributed observer, which estimates the observed states using the following node-based open-loop estimator:

[0065] in, express The derivative of , These represent unmanned systems. The and the The next trigger moment.

[0066] exist At that moment, the event was triggered, the first The open-loop estimator obtains the first... The real state of a distributed observer When the event is not triggered, i.e. At that time, using the first The state predicted by the open-loop estimator Instead of the By monitoring the observation status of individual observers, unnecessary data transmission can be reduced, and data collisions and network congestion between large-scale unmanned cluster systems can be avoided.

[0067] Secondly, based on the previously defined state measurement error, a static event trigger function can be designed for each follower:

[0068] in, For the first A static event triggering function for a follower. This is the static threshold coefficient. No. Local error prediction signals of a distributed observer For the first The state measurement error of each follower This represents the 2-norm of a vector.

[0069] Next, to facilitate the design of dynamic event triggering conditions, we will further design internal dynamic variables for each follower:

[0070] in, It is the first The internal dynamic variables of a follower yes The derivative of It is a positive positive number. It is an open interval The positive numbers above.

[0071] From this, we can obtain the... Dynamic event triggering conditions for each follower:

[0072] in, To obtain The largest moment at the time of establishment And at all times satisfy , , They represent the first The first follower and the Next trigger time It is a dynamic trigger coefficient. For the first The internal dynamic variables of a follower.

[0073] Finally, combining the dynamic event-triggered controller update rules and the adaptive controller obtained by S3, an adaptive cooperative tracking controller based on dynamic event triggering can be designed for unknown heterogeneous unmanned swarm systems. In this design, the first... The adaptive event-triggered controller and adaptive parameter update law of the follower unmanned system are designed as follows:

[0074] in, Indicates the first The adaptive event-triggered feedback control gain of the follower. For the first Adaptive event-triggered coupling control gain of a follower; For the first Local state prediction error signal of a distributed observer; , They represent the first and the The predicted values ​​of the states of each distributed observer. This indicates the leader's status information; For the first , The weight among followers, if followers Can receive followers Information with a weight greater than zero has a weight if it is positive, otherwise it has a weight of zero. For the first The weight between followers and leaders, if followers If a leader's information can be received, the weight is greater than zero; otherwise, it is equal to zero. express The derivative of express The derivative; Represents a symbolic function; and These represent the adaptive feedback gain learning rate and the adaptive coupling gain learning rate, respectively. Indicates local output error; symbol Represents the transpose of a matrix or vector.

[0075] S5. Real-time calculation and online updating of feedback control gain and coupling control gain in the adaptive event-triggered controller enable the control system to adapt to unknown dynamics, ensuring that the unknown heterogeneous unmanned swarm system can achieve cooperative tracking control.

[0076] By integrating both sides of the adaptive coupled-feedback control parameter update law simultaneously, the controller gain matrix can be obtained to be dynamically adjusted online according to the following rules:

[0077] in, Indicates the first The adaptive event-triggered feedback control gain of the follower. For the first The adaptive event-triggered coupling control gain of the follower Describes the differential operator. Independent variables that are time-related; and These represent the initial values ​​of the adaptive event-triggered feedback control gain and the adaptive event-triggered coupling control gain, respectively.

[0078] The present invention also provides an adaptive event-triggered collaborative tracking control device for a resource-constrained unmanned swarm system, comprising: Model building module: Configured to build dynamic models of the leader and followers respectively for an unmanned swarm system including one leader and multiple followers, and determine the output information of the unmanned swarm system; Distributed Observer Builder Module: Configured to build distributed observers for followers based on output information; Tracking control module: configured to determine the follower's state measurement error and internal dynamic variables, determine the follower's dynamic event triggering conditions, and build an adaptive event triggering controller for the follower; When the state measurement error does not meet the dynamic event triggering conditions, the adaptive event triggering controller samples and transmits the current system observation state of the distributed observers of the neighboring followers, and updates the adaptive event triggering controller; the followers realize cooperative tracking control of the leader based on the system observation state transmitted by the neighboring followers; When the state measurement error meets the dynamic event triggering condition, the adaptive event triggering controller updates itself based on the previously sampled and transmitted system observation state and the neighbor follower system observation state predicted by the follower's open-loop estimator; the follower achieves cooperative tracking control of the leader based on the predicted neighbor follower system observation state. The adaptive event triggering controller is updated based on the feedback control gain and coupling control gain of the calculated adaptive event triggering controller.

[0079] The specific embodiments described above only illustrate the design principles of the present invention. The shapes and names of the components in this description may differ and are not limited. Therefore, those skilled in the art can modify or make equivalent substitutions to the technical solutions described in the foregoing embodiments; and these modifications and substitutions do not depart from the inventive spirit and technical solutions of the present invention, and should all fall within the protection scope of the present invention.

Claims

1. An adaptive event-triggered cooperative tracking control method for an unmanned swarm system under resource constraints, characterized in that, include: Step S1: For an unmanned swarm system including one leader and multiple followers, establish dynamic models for the leader and followers respectively, and determine the output information of the unmanned swarm system; Step S2: Construct a distributed observer for the followers based on the output information; Step S3: Determine the follower's state measurement error and internal dynamic variables, determine the follower's dynamic event triggering conditions, and construct the follower's adaptive event triggering controller; When the state measurement error does not meet the dynamic event triggering conditions, the adaptive event triggering controller samples and transmits the current system observation state of the distributed observers of the neighboring followers, and updates the adaptive event triggering controller; the followers realize cooperative tracking control of the leader based on the system observation state transmitted by the neighboring followers; When the state measurement error meets the dynamic event triggering condition, the adaptive event triggering controller updates itself based on the previously sampled and transmitted system observation state and the neighbor follower system observation state predicted by the follower's open-loop estimator; the follower achieves cooperative tracking control of the leader based on the predicted neighbor follower system observation state. The adaptive event triggering controller is updated based on the feedback control gain and coupling control gain of the calculated adaptive event triggering controller.

2. The method as described in claim 1, characterized in that, In step S1, for an unmanned swarm system comprising one leader and multiple followers, dynamic models for the leader and followers are established respectively, wherein: The dynamic model of the leader is as follows: in, Indicates time, Indicates the system status of the leader. express The derivative of This represents the system output of the leader. The state transition matrix for the leader. The output control matrix for the leader; The dynamic model of the follower is: in, Indicates the first The system state of a follower express The derivative of For the first A follower's control input, Indicates the first The system output of each follower For the first The state transition matrix of each follower For the first The control input matrix of each follower Indicates the first The output control matrix of each follower, and the triplet All are unknown real matrices. N The number of followers.

3. The method as described in claim 2, characterized in that, In step S2, the distributed observer is: in, For the first The observation status of a distributed observer that follows a group of followers for The derivative of For the first The control input matrix of a distributed observer with followers This is the coupling gain vector. For the first Local error signal of a distributed observer with a follower For the feedback gain of the distributed observer, For the first A distributed observer with followers observes the output. For the first The system output of each follower The output control matrix for the leader.

4. The method as described in claim 3, characterized in that, In step S3, the follower's state measurement error and internal dynamic variables are determined, wherein: No. The state measurement error of each follower is: In the formula, t Indicates time, For the first The state measurement error of each follower; For the first The predicted values ​​of the observed states by a distributed observer. For the first The observation status of a distributed observer , They represent the first The first follower and the The next trigger moment; The internal dynamic variables of the follower are as follows: in, It is the first The internal dynamic variables of a follower yes The derivative of It is a positive positive number. It is an open interval positive numbers, For the first A static event triggering function for a follower. For the first The local state prediction error signals of a distributed observer, and: in, This is the static threshold coefficient. This represents the 2-norm of a vector.

5. The method as described in claim 4, characterized in that, In step S3, the dynamic event triggering conditions of the follower are determined, and an adaptive event triggering controller for the follower is constructed, wherein: The dynamic event triggering conditions for followers are as follows: in, To obtain The maximum time when it is established, and the maximum time satisfies greater than , It is a dynamic trigger coefficient; No. The adaptive event-triggered controller and adaptive parameter update law for each follower are as follows: in, Indicates the first The adaptive event-triggered feedback control gain of the follower. For the first Coupled control gain in an adaptive event-triggered controller for a follower; For the first Local state prediction error signal of a distributed observer; For the first The first follower and the first The weights among the followers, if the i-th follower The first follower can receive the first The information of each follower Greater than zero, otherwise It equals zero; For the first The weight between followers and leaders, if the first If a follower can receive information from the leader, then... Greater than zero, otherwise It equals zero; N For the number of followers, , They represent the first and the The predicted values ​​of the states of each distributed observer. This indicates the leader's status information; express The derivative of Represents a symbolic function. Indicates the first Ideal coupling control gain for a follower and They represent the first The adaptive feedback control gain learning rate and adaptive coupling control gain learning rate of the followers. This indicates local output error. express The derivative, sign Represents the transpose of a matrix or vector.

6. The method as described in claim 5, characterized in that, In step S3, the formula for the follower open-loop estimator state prediction is as follows: in, express The derivative of The result is the state prediction of the open-loop estimator for the follower.

7. The method as described in claim 6, characterized in that, In step S3, the adaptive event triggering controller is updated based on the calculated feedback control gain and coupling control gain in the adaptive event triggering controller, wherein: in, Indicates the first Feedback control gain in an adaptive event-triggered controller for a follower For the first Coupled control gain in an adaptive event-triggered controller for a follower Describes the differential operator. Represents independent variables that are time-related; and These represent the initial values ​​of the feedback control gain and the coupling control gain in the adaptive event-triggered controller, respectively.

8. An adaptive event-triggered cooperative tracking control device for a resource-constrained unmanned swarm system, characterized in that, include: Model building module: Configured to build dynamic models of the leader and followers respectively for an unmanned swarm system including one leader and multiple followers, and determine the output information of the unmanned swarm system; Distributed Observer Builder Module: Configured to build distributed observers for followers based on output information; Tracking control module: configured to determine the follower's state measurement error and internal dynamic variables, determine the follower's dynamic event triggering conditions, and build an adaptive event triggering controller for the follower; When the state measurement error does not meet the dynamic event triggering conditions, the adaptive event triggering controller samples and transmits the current system observation state of the distributed observers of the neighboring followers, and updates the adaptive event triggering controller; the followers realize cooperative tracking control of the leader based on the system observation state transmitted by the neighboring followers; When the state measurement error meets the dynamic event triggering condition, the adaptive event triggering controller updates itself based on the previously sampled and transmitted system observation state and the neighbor follower system observation state predicted by the follower's open-loop estimator; the follower achieves cooperative tracking control of the leader based on the predicted neighbor follower system observation state. The adaptive event triggering controller is updated based on the feedback control gain and coupling control gain of the calculated adaptive event triggering controller.

9. A computer-readable storage medium, characterized in that, The storage medium stores a plurality of instructions; the plurality of instructions are loaded by a processor and executed as described in any one of claims 1-7.

10. An electronic device, characterized in that, The electronic device includes: A processor is used to execute multiple instructions; Memory, used to store multiple instructions; The plurality of instructions are to be stored in the memory and loaded by the processor and executed as described in any one of claims 1-7.

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