Adaptive collaborative control method for heterogeneous unmanned swarm systems under weak information interaction

Abstract

The present invention discloses an adaptive collaborative control method for heterogeneous unmanned cluster systems under weak information interaction, and relates to the technical field of unmanned cluster systems. A heterogeneous unmanned cluster system model including a leader and a plurality of followers' respective dynamic models is established in advance, and a collaborative control method is designed based on the follower state and local error; for each follower, in a non-triggering period, the follower state is estimated by an open-loop estimator to obtain a state estimation error and a local estimation error, and when the state estimation error and the local estimation error meet an event triggering condition, data transmission is triggered; an adaptive law is designed based on the follower state, local tracking error, and local estimation error, and the feedback gain and coupling gain of the controller are dynamically adjusted according to the adaptive law. The present invention realizes collaborative control and discrete communication through an event triggering mechanism, and adopts an adaptive control method to adjust the feedback and coupling gain online, effectively coping with real-time changes in system parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned cluster systems, and in particular to an adaptive collaborative control method for heterogeneous unmanned cluster systems under weak information interaction. Background Art

[0002] The unmanned swarm system is a large and complex system composed of multiple intelligent unmanned platforms such as drones, unmanned vehicles, and unmanned boats. Multiple platforms perform special tasks through collaborative cooperation. It can complete complex tasks that are difficult or even impossible for a single intelligent agent to complete. It has the advantages of stronger adaptability, better fault tolerance, and better parallelism. It has broad application prospects in civil and military fields.

[0003] Unmanned cluster collaborative control algorithms usually work on a continuous communication framework. However, in reality, the resources of each unmanned platform are limited, and the communication network is often restricted by limited communication bandwidth. Continuous information interaction between unmanned platforms will lead to higher network traffic congestion and larger transmission delays, which in turn affects the stability of the entire system.

[0004] In addition, the existing design of collaborative control methods for multi-agent systems has a strong dependence on accurate system models. However, in practical applications, affected by factors such as external disturbances, the unmanned cluster system model may be difficult to accurately model and obtain, and has high uncertainty, which poses severe challenges to the implementation of collaborative control.

[0005] In general, the existing collaborative control methods for multi-agent systems have a heavy communication burden and are difficult to cope with the uncertainty and changes of the system.

[0006] Therefore, the existing technology still needs to be improved and developed. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide an adaptive collaborative control method for heterogeneous unmanned cluster systems under weak information interaction in response to the above-mentioned defects of the prior art, aiming to solve the problems in the prior art that the collaborative control method of multi-agent systems has a heavy communication burden and is difficult to cope with the uncertainty and changes of the system.

[0008] The technical solution adopted by the present invention to solve the problem is as follows:

[0009] In a first aspect, an embodiment of the present invention provides a method for adaptive collaborative control of heterogeneous unmanned cluster systems under weak information interaction, the method comprising:

[0010] Pre-establishing a heterogeneous unmanned swarm system model, wherein the heterogeneous unmanned swarm system model includes dynamic models corresponding to a leader and a plurality of followers, and designing a collaborative control method based on follower states and local errors;

[0011] For each of the followers, in a non-triggering period, an open-loop estimator is used to estimate the follower state to obtain a state estimation error and a local estimation error; when the state estimation error and the local estimation error meet an event triggering condition, data transmission is triggered;

[0012] A local tracking error of the follower is obtained, an adaptive law is designed according to the follower state, the local tracking error and the local estimation error, and a feedback gain and a coupling gain of a controller are dynamically adjusted according to the adaptive law.

[0013] In one implementation, the calculation formula corresponding to the controller is:

[0014] ;

[0015] in, Indicates Control input for each follower; It is Follower status; For the The local estimation error of followers satisfies:

[0016] ;

[0017] in, Indicates the event trigger mechanism The estimated states of the followers; the controller parameters is the adaptive feedback gain of the system, is the adaptive coupling gain of the system; the state error feedback matrix satisfy is the Hurwitz matrix; Nodes in the communication topology diagram representing an unmanned system Neighborhood of;

[0018] Choose the right matrix , And parameters , so that the following linear matrix inequality conditions hold:

[0019] ;

[0020] in, Leader-Follower Topology for the System The inverse matrix The characteristic value of is a symmetric positive definite matrix that satisfies , is a positive definite matrix, with parameters .

[0021] In one implementation, the calculation formula corresponding to the open-loop estimator is:

[0022] ;

[0023] in, The dimension is The Leader Systems Matrix; Indicates The estimated state of the followers, The dimension is The real number space of ; Indicates Follower's The triggering moment; Indicates The triggering time sequence of a follower; assuming that the followers with a communication link to the leader can continuously receive the status information transmitted by the leader, that is , Indicates the status of the leader. Represents the estimated state of the leader.

[0024] In one implementation, the calculation formula corresponding to the event triggering condition is:

[0025] ;

[0026] in, Indicates Follower's The triggering moment; represents the state estimation error; represents the local estimation error; Indicates trigger parameters;

[0027] satisfy:

[0028] ;

[0029] in, , , is the design variable, , express dimensional identity matrix, matrix ,in is the design parameter, so Zhengding, express dimensional identity matrix, is the selected positive definite matrix, Represents the smallest characteristic root of a matrix; , represents the topological matrix of leaders and followers, Represents the largest singular value of the matrix; ,and represents the transpose of a matrix, represents the Lyapunov matrix, The dimension is The expected input matrix is represents the state error feedback matrix; , represents the gain parameter; , Represents the gain parameter.

[0030] In one implementation, the calculation formula corresponding to the adaptive law is:

[0031] ;

[0032] in, represents the ideal feedback gain matrix, represents the ideal coupling gain; represents the feedback gain adjusted based on the adaptive law; represents the coupling gain adjusted based on the adaptive law; represents the adaptive parameter, ; represents a symmetric positive definite Lyapunov matrix; represents the expected input matrix, represents the state error feedback matrix; Indicates The status of a follower; represents the local estimation error. Indicates The local state error of the followers satisfies .

[0033] In one embodiment, the method further comprises:

[0034] For each of the followers, a feedback gain deviation is calculated according to an ideal feedback gain and a feedback gain adjusted based on the adaptive law; and a coupling gain deviation is calculated according to an ideal coupling gain and a coupling gain adjusted based on the adaptive law;

[0035] Calculating a Lyapunov function value according to the feedback gain deviation, the coupling gain deviation and the local state error respectively corresponding to all the followers;

[0036] The behavioral coordination of heterogeneous unmanned cluster systems is analyzed based on the Lyapunov function value.

[0037] In one embodiment, the method further comprises:

[0038] For each of the followers, an interval evaluation index is calculated in advance according to an upper bound of the norm of the model state, an upper bound of the norm of the local estimation error, an upper bound of the feedback gain, and an upper bound of the coupling gain;

[0039] Calculate the time interval between adjacent triggers;

[0040] The Zeno behavior of the follower is analyzed according to the adjacent trigger time intervals and the interval evaluation index.

[0041] In a second aspect, an embodiment of the present invention further provides a heterogeneous unmanned cluster system, which includes dynamic models corresponding to a leader and a plurality of followers, each of which is controlled through any of the steps of the above-mentioned adaptive collaborative control method for heterogeneous unmanned cluster systems under weak information interaction.

[0042] Beneficial effects of the present invention: The embodiment of the present invention adopts an event-triggered control mechanism, which can realize discrete communication while performing collaborative control, effectively avoiding continuous communication in a multi-agent system. In addition, the adaptive control method can adjust the feedback and coupling gain online, effectively coping with real-time changes in system parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a flow chart of the adaptive collaborative control method of heterogeneous unmanned cluster systems under weak information interaction provided by an embodiment of the present invention.

[0045] Figure 2 It is a module schematic diagram of an unmanned system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention discloses an adaptive collaborative control method for heterogeneous unmanned cluster systems under weak information interaction. In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0048] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as herein.

[0049] In view of the above-mentioned defects of the prior art, the present invention provides an adaptive collaborative control method for heterogeneous unmanned cluster systems under weak information interaction. The method pre-establishes a heterogeneous unmanned cluster system model, which includes dynamic models corresponding to a leader and several followers, and designs a collaborative control method based on the follower state and local error; for each follower, in a non-trigger period, the follower state is estimated by an open-loop estimator to obtain a state estimation error and a local estimation error; when the state estimation error and the local estimation error meet the event triggering condition, data transmission is triggered; the local tracking error of the follower is obtained, and an adaptive law is designed according to the follower state, the local tracking error and the local estimation error, and the feedback gain and coupling gain of the controller are dynamically adjusted according to the adaptive law. The present invention can realize discrete communication while performing collaborative control through an event triggering mechanism, effectively avoiding continuous communication in a multi-agent system. In addition, an adaptive control method is adopted, which can adjust the feedback and coupling gain online, effectively coping with real-time changes in system parameters.

[0050] like Figure 1 As shown, the method includes:

[0051] Step S100: pre-establishing a heterogeneous unmanned swarm system model, wherein the heterogeneous unmanned swarm system model includes dynamic models corresponding to a leader and a plurality of followers, and designs a collaborative control method based on follower states and local errors.

[0052] Specifically, the method of this embodiment is applicable to a heterogeneous unmanned swarm system to realize the coordinated control of various intelligent agents, each of which has a certain degree of autonomy and autonomy, such as an unmanned spacecraft or an underwater unmanned submarine, etc. The heterogeneous unmanned swarm system includes two roles, a leader and a follower, and the followers track the position and direction of the leader with a certain offset.

[0053] For example, a heterogeneous unmanned cluster system model consisting of N followers and 1 leader is pre-established, and they communicate through a topology graph.

[0054] The system model of the follower is established as:

[0055] , (1);

[0056] in, , It is Follower status, Indicates Follower Status The dimension of Indicates The derivative of the follower state; It is Control input for each follower; It is The output of each follower, Indicates Follower output The dimension of The dimension is The system matrix, The dimension is The input matrix is The dimension is The system output matrix.

[0057] The leader's system model is built as:

[0058] , (2);

[0059] in, It indicates the state of the leader and also the desired state that the followers need to track; Indicates leader status The dimension of represents the derivative of the leader's state; represents the output of the leader, Represents the leader output The dimension of The dimension is The Leader System Matrix, The dimension is The leader output matrix.

[0060] For the above heterogeneous unmanned cluster system, assume that there is a vector With scalar , so that the system matrix With the input matrix satisfy:

[0061] , (3);

[0062] Among them, the matrix pair Known, The dimension is The expected system matrix, The dimension is The expected input matrix of .

[0063] The controller is designed to:

[0064] , (4);

[0065] in, Indicates Follower status, Indicates Control input for each follower; is the feedback gain adjusted based on the adaptive law; is the coupling gain adjusted based on the adaptive law; represents the state error feedback matrix; represents the local estimation error.

[0066] Step S200: for each follower, in a non-triggering period, the follower state is estimated by an open-loop estimator to obtain a state estimation error and a local estimation error; when the state estimation error and the local estimation error meet an event triggering condition, data transmission is triggered.

[0067] Specifically, considering that the continuous exchange of explicit information between unmanned platforms will generate a large communication burden, this embodiment pre-configures an open-loop estimator for each follower in the unmanned system. By designing an event trigger mechanism, the system model of the follower only transmits state information when the event trigger condition is met. During the non-trigger period, the state information of the intelligent agent is estimated by the open-loop estimator, thereby effectively reducing the communication burden of the unmanned system.

[0068] In one implementation, the calculation formula corresponding to the open-loop estimator is:

[0069] , (5);

[0070] in, The dimension is The Leader System Matrix, Indicates The estimated state of the followers, The dimension is The real number space of ; Indicates Follower's The triggering moment; Indicates The triggering time sequence of a follower; assuming that the followers with a communication link to the leader can continuously receive the status information transmitted by the leader, that is Indicates the status of the leader. represents the estimated state of the leader; Nodes in the communication topology diagram representing an unmanned system 's neighborhood.

[0071] In one implementation, the calculation formula corresponding to the event triggering condition is:

[0072] , (6);

[0073] in, Indicates Follower's The triggering moment; represents the state estimation error; represents the local estimation error; Indicates trigger parameters. This embodiment defines The state estimation error of the followers (or agents) , Indicates The estimated state of the followers, Indicates The state of the follower, the initial state estimation error ; Local estimation error , Indicates the status of the leader. represents the weighted adjacency matrix Line elements, Nodes in the communication topology diagram representing an unmanned system Neighborhood of Indicates followers There is a communication link with the leader, Indicates followers There is no communication link between the leader and the server.

[0074] Trigger parameters Need to meet:

[0075] , (7);

[0076] Among them, among them, , , is the design variable, , express dimensional identity matrix, matrix ,in is the design parameter, so Zhengding, express dimensional identity matrix, is the selected positive definite matrix, Represents the smallest characteristic root of a matrix; , represents the topological matrix of leaders and followers, Represents the largest singular value of the matrix; ,and represents the transpose of a matrix, represents the Lyapunov matrix, The dimension is The expected input matrix is represents the state error feedback matrix; , represents the gain parameter; , Represents the gain parameter.

[0077] Step S300, obtaining the local tracking error of the follower, designing an adaptive law according to the follower state, the local tracking error and the local estimation error, and dynamically adjusting the feedback gain and coupling gain of the controller according to the adaptive law.

[0078] Specifically, the ideal control gain of the heterogeneous unmanned cluster system includes the ideal feedback gain and the ideal coupling gain. In actual application scenarios, due to the interference factors such as external disturbances in the heterogeneous unmanned cluster system, the system model (i.e., the system matrix With the input matrix ) has large uncertainty, so it is difficult to directly obtain the ideal control gain. In order to cope with the complex situation of unknown or time-varying system model, this embodiment realizes real-time adjustment of control feedback gain and coupling gain parameters, and adopts an adaptive method to design a dynamic event triggered controller.

[0079] Specifically, the following is the design reasoning process of the controller:

[0080] First, in order to achieve the state tracking goal of the heterogeneous unmanned swarm system, an ideal collaborative controller is designed based on state feedback. The design process uses the following formula:

[0081] , (8);

[0082] in, Indicates Control input for each follower; It is Follower status; Indicates The local state error of the follower; the controller parameters is the ideal feedback gain matrix, is the ideal coupling gain, the state error feedback matrix satisfy is the Hurwitz matrix;

[0083] Local state error satisfy:

[0084] , (9);

[0085] in, represents the weighted adjacency matrix Line elements; Nodes in the communication topology diagram representing an unmanned system Neighborhood of; Indicates The status of a follower; Indicates the status of the leader.

[0086] Choose the right matrix , And parameters , so that the following linear matrix inequality conditions (LMI conditions) hold:

[0087] , (10);

[0088] in, Leader-Follower Topology for the System The inverse matrix The characteristic value of is a symmetric positive definite matrix that satisfies , is a positive definite matrix, with parameters , The dimension is The expected input matrix of represents the state error feedback matrix; The dimension is The leader systems matrix.

[0089] Then, combined with the event triggering mechanism designed above, an adaptive method is used to design a dynamic event-triggered controller, and the above formula (4) is obtained.

[0090] In one implementation, the calculation formula corresponding to the adaptive law is:

[0091] , (11);

[0092] in, is the ideal feedback gain matrix, is the ideal coupling gain; is the feedback gain adjusted based on the adaptive law; is the coupling gain adjusted based on the adaptive law; For the The local state error of each follower; represents the adaptive parameter, ; represents a symmetric positive definite Lyapunov matrix; represents the expected input matrix; represents the state error feedback matrix; Indicates the The status of a follower; Indicates the status of the leader. represents the local estimation error.

[0093] In one implementation, the method further includes:

[0094] For each of the followers, a feedback gain deviation is calculated according to an ideal feedback gain and a feedback gain adjusted based on the adaptive law; and a coupling gain deviation is calculated according to an ideal coupling gain and a coupling gain adjusted based on the adaptive law;

[0095] Calculating a Lyapunov function value according to the feedback gain deviation, the coupling gain deviation and the local state error respectively corresponding to all the followers;

[0096] The behavioral coordination of heterogeneous unmanned cluster systems is analyzed based on the Lyapunov function value.

[0097] Specifically, this embodiment uses the Lyapunov stability analysis method to analyze the cooperative behavior of the unmanned system to verify the effectiveness of this method. When the first-order derivative of the calculated Lyapunov function value is less than or equal to zero, it means that the unmanned system can achieve the cooperative control goal.

[0098] In one implementation, the Lyapunov function is designed using the following formula:

[0099] , (12);

[0100] in, is the feedback gain deviation matrix, is the coupling gain deviation. Under the designed dynamic adaptive event-triggered controller, , it means that the unmanned system can achieve the collaborative control goal.

[0101] In one implementation, the method further includes:

[0102] For each of the followers, an interval evaluation index is calculated in advance according to an upper bound of the norm of the model state, an upper bound of the norm of the local estimation error, an upper bound of the feedback gain, and an upper bound of the coupling gain;

[0103] Calculate the time interval between adjacent triggers;

[0104] The Zeno behavior of the follower is analyzed according to the adjacent trigger time intervals and the interval evaluation index.

[0105] Specifically, this embodiment also verifies the effectiveness of this method by eliminating the Zeno phenomenon.

[0106] In one implementation, the adjacent triggering time interval of each follower system satisfies:

[0107] , (13);

[0108] in, It is The first follower (or agent) The triggering moment; and Respectively in The local estimation error norm within and state norm The upper bound of is the feedback gain data matrix norm The upper bound of is the coupling gain data matrix norm Under the event triggering mechanism designed in this embodiment, the triggering time interval is strictly positive, that is, no Zeno behavior exists in all intelligent agents in any finite time.

[0109] The advantages of the present invention are:

[0110] 1. The present invention can avoid continuous information interaction between followers through event triggering mechanism, thereby realizing collaborative control and discrete communication at the same time, getting rid of the limitation of limited communication bandwidth in actual situations, reducing the communication burden of the system, and effectively eliminating Zeno behavior.

[0111] 2. The present invention realizes state tracking by combining the event trigger mechanism with the adaptive method, reduces the communication burden of the actual system, and adjusts the feedback gain and coupling gain online when the system model is uncertain, responds to the uncertainty and changes of the system in time, and provides a new and effective idea for solving the state tracking problem.

[0112] Based on the above embodiments, the present invention also provides a heterogeneous unmanned cluster system, the system includes a leader and a plurality of followers respectively corresponding to the dynamic model, each of the followers is controlled by any of the steps of the above-mentioned heterogeneous unmanned cluster system adaptive collaborative control method under weak information interaction. Taking a leader and four followers as an example, the communication topology diagram of the heterogeneous unmanned cluster system is as follows: Figure 2 shown.

[0113] In summary, the present invention discloses an adaptive collaborative control method for heterogeneous unmanned cluster systems under weak information interaction. The method pre-establishes a heterogeneous unmanned cluster system model, which includes dynamic models corresponding to a leader and several followers, and designs a collaborative control method based on the follower state and local error; for each follower, in a non-trigger period, an open-loop estimator is used to estimate the follower state to obtain a state estimation error and a local estimation error; when the state estimation error and the local estimation error meet an event triggering condition, data transmission is triggered; the local tracking error of the follower is obtained, and an adaptive law is designed according to the follower state, the local tracking error and the local estimation error, and the feedback gain and coupling gain of the controller are dynamically adjusted according to the adaptive law. The present invention can realize discrete communication while performing collaborative control through an event triggering mechanism, effectively avoiding continuous communication in a multi-agent system. And an adaptive control method is adopted, which can adjust the feedback and coupling gain online, effectively coping with real-time changes in system parameters.

[0114] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. An adaptive collaborative control method for heterogeneous unmanned cluster systems under weak information interaction, characterized in that: The method comprises: Pre-establishing a heterogeneous unmanned swarm system model, wherein the heterogeneous unmanned swarm system model includes dynamic models corresponding to a leader and a plurality of followers, and designing a collaborative control method based on follower states and local errors; For each of the followers, in a non-triggering period, an open-loop estimator is used to estimate the follower state to obtain a state estimation error and a local estimation error; when the state estimation error and the local estimation error meet an event triggering condition, data transmission is triggered; Obtaining a local tracking error of the follower, designing an adaptive law according to the follower state, the local tracking error and the local estimation error, and dynamically adjusting a feedback gain and a coupling gain of a controller according to the adaptive law; The calculation formula corresponding to the controller is: ； in, Indicates Control input for each follower; It is Follower status; For the The local estimation error of followers satisfies: ； in, Indicates the event trigger mechanism The estimated states of the followers; the controller parameters is the adaptive feedback gain of the system, is the adaptive coupling gain of the system; the state error feedback matrix satisfy is the Hurwitz matrix; Nodes in the communication topology diagram representing an unmanned system Neighborhood of; Choose the right matrix , And parameters , so that the following linear matrix inequality conditions hold: ； in, Leader-Follower Topology for the System The inverse matrix The characteristic value of is a symmetric positive definite matrix that satisfies , is a positive definite matrix, with parameters ; The calculation formula corresponding to the open-loop estimator is: ； in, The dimension is The Leader Systems Matrix; Indicates The estimated state of the followers, The dimension is The real number space of ; Indicates Follower's The triggering moment; Indicates The triggering time sequence of a follower; assuming that the followers with a communication link to the leader can continuously receive the status information transmitted by the leader, that is , Indicates the status of the leader. represents the estimated state of the leader; The calculation formula corresponding to the event triggering condition is: ； in, Indicates Follower's The triggering moment; represents the state estimation error; represents the local estimation error; Indicates trigger parameters; satisfy: ； in, , , is the design variable, , express dimensional identity matrix, matrix ,in is the design parameter, so Zhengding, express dimensional identity matrix, is the selected positive definite matrix, Represents the smallest characteristic root of a matrix; , represents the topological matrix of leaders and followers, Represents the largest singular value of the matrix; ,and , represents the transpose of a matrix, represents the Lyapunov matrix, The dimension is The expected input matrix is represents the state error feedback matrix; , represents the gain parameter; , represents the gain parameter; The calculation formula corresponding to the adaptive law is: ； in, represents the ideal feedback gain matrix, represents the ideal coupling gain; represents the feedback gain adjusted based on the adaptive law; represents the coupling gain adjusted based on the adaptive law; Indicates The local state error of each follower; represents the adaptive parameter, ; represents a symmetric positive definite Lyapunov matrix; represents the expected input matrix; represents the state error feedback matrix; Indicates The status of a follower; represents the local estimation error.

2. The adaptive collaborative control method for heterogeneous unmanned cluster systems under weak information interaction according to claim 1 is characterized in that: The method further comprises: For each of the followers, a feedback gain deviation is calculated according to an ideal feedback gain and a feedback gain adjusted based on the adaptive law; and a coupling gain deviation is calculated according to an ideal coupling gain and a coupling gain adjusted based on the adaptive law; Calculating a Lyapunov function value according to the feedback gain deviation, the coupling gain deviation and the local state error respectively corresponding to all the followers; The behavioral coordination of heterogeneous unmanned cluster systems is analyzed based on the Lyapunov function value.

3. The adaptive collaborative control method for heterogeneous unmanned cluster systems under weak information interaction according to claim 1 is characterized in that: The method further comprises: For each of the followers, an interval evaluation index is calculated in advance according to an upper bound of the norm of the model state, an upper bound of the norm of the local estimation error, an upper bound of the feedback gain, and an upper bound of the coupling gain; Calculate the time interval between adjacent triggers; The Zeno behavior of the follower is analyzed according to the adjacent trigger time intervals and the interval evaluation index.

4. A heterogeneous unmanned cluster system, characterized in that: The system includes dynamic models corresponding to a leader and a plurality of followers, and each follower is controlled by the steps of the adaptive collaborative control method of heterogeneous unmanned cluster systems under weak information interaction as described in any one of claims 1 to 3 above.

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