Air-sea heterogeneous unmanned swarm collaborative formation control method based on sliding mode consistency

Abstract

The present invention relates to the technical field of collaborative formation control, and discloses a method for collaborative formation control of air-sea heterogeneous unmanned clusters based on sliding mode consistency. The method comprises constructing a kinematic model of an air-sea heterogeneous unmanned cluster including multiple types of intelligent agents, and constructing a leader-follower model according to a set virtual navigator; constructing an air-sea heterogeneous unmanned cluster formation tracking error model according to the leader-follower model; and establishing an air-sea heterogeneous unmanned cluster fixed-time formation control law based on sliding mode consistency according to the air-sea heterogeneous unmanned cluster formation tracking error model to perform fixed-time collaborative formation control. The present invention can improve the formation control efficiency and environmental adaptability of heterogeneous unmanned clusters.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative formation control, and in particular to an air-sea heterogeneous unmanned cluster collaborative formation control method based on sliding mode consistency. Background Art

[0002] In the context of the rapid development of science and technology, with the continuous development of unmanned system technology, the performance of unmanned swarms has gradually increased, and its application fields have involved many fields such as civil, national defense, and scientific research. At present, unmanned swarm technology has been put into use in many fields, such as intelligent transportation of unmanned vehicles, detection tasks of unmanned ships at sea, and aerial combat of unmanned aerial vehicle swarms. However, as the tasks become more and more complex, the limitations of unmanned systems composed of a single type of intelligent agent become more and more obvious, and they will be subject to many restrictions when facing complex and changing environments. While heterogeneous unmanned swarms have greatly improved the functions of unmanned swarm systems, they have also continuously expanded the scope of application of unmanned swarms, which enables heterogeneous unmanned swarms to perform more diverse tasks in more complex environments. It has great development potential and has become one of the important development directions of future intelligent systems. There are many problems in the research of heterogeneous unmanned swarms, such as task allocation, path planning, collaborative formation control, and situational awareness. Among them, collaborative formation control is one of the most important research issues. For heterogeneous unmanned swarms, it can improve task execution efficiency, enhance system adaptability and flexibility, improve system reliability and stability, and expand application fields.

[0003] Prior art 1 discloses an affine formation control method for a fixed-wing UAV cluster based on a leader-follower mode. In this method, the limitations of an unmanned system composed of a single type of intelligent agent become increasingly obvious, and it is subject to many restrictions when facing a complex and changing environment. The followers are highly dependent on the leader. If the leader fails or loses communication, the entire formation may be affected. Moreover, it is impossible to give an accurate time for the formation to converge, and it is impossible to complete the formation change within a certain time limit.

[0004] Prior art 2 discloses a distributed fixed-time collaborative control of spacecraft formation flying. This method only considers the formation control of homogeneous spacecraft, has a small application scenario, and the executable tasks are relatively single. It also adopts sliding mode control, which is insensitive to system model uncertainty and external disturbances compared to sliding mode consistency control. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides an air-sea heterogeneous unmanned swarm collaborative formation control method based on sliding mode consistency.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0007] A method for controlling air-sea heterogeneous unmanned swarm collaborative formation based on sliding mode consistency comprises the following steps:

[0008] Construct a kinematic model of heterogeneous unmanned swarms in the air and sea, including multiple types of intelligent agents, and build a leader-follower model based on the set virtual navigator;

[0009] Based on the leader-follower model, a tracking error model for heterogeneous unmanned swarm formation in air and sea is constructed;

[0010] According to the tracking error model of air-sea heterogeneous unmanned swarm formation, a fixed-time formation control law of air-sea heterogeneous unmanned swarm based on sliding mode consistency is established to carry out fixed-time collaborative formation control.

[0011] The present invention has the following beneficial effects:

[0012] The present invention takes the air-sea heterogeneous unmanned swarm composed of unmanned aerial vehicles, unmanned ships and unmanned submarines as the research object, and designs a collaborative formation control method for the swarm system under different environmental conditions, which can improve the formation control efficiency and environmental adaptability of the heterogeneous unmanned swarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The figure is a flow chart of a method for controlling air-sea heterogeneous unmanned swarm collaborative formation based on sliding mode consistency;

[0014] Figure 2 It is a schematic diagram of the three-dimensional motion trajectory of the air-sea heterogeneous unmanned swarm during a fixed-time static formation mission;

[0015] Figure 3a For the fixed-time static formation mission of the air-sea heterogeneous unmanned swarm Schematic diagram of the three-dimensional spatial position of the formation members at all times;

[0016] Figure 3b For the fixed-time static formation mission of the air-sea heterogeneous unmanned swarm Schematic diagram of the three-dimensional spatial position of the formation members at all times;

[0017] Figure 3c For the fixed-time static formation mission of the air-sea heterogeneous unmanned swarm Schematic diagram of the three-dimensional spatial position of the formation members at all times;

[0018] Figure 3d For the fixed-time static formation mission of the air-sea heterogeneous unmanned swarm Schematic diagram of the three-dimensional spatial position of the formation members at all times;

[0019] Figure 4 It is a schematic diagram of the actual control command change curve during the fixed-time static formation mission of the air-sea heterogeneous unmanned swarm;

[0020] Figure 5a Schematic diagram of the position tracking error of UAV formation members during the fixed-time static formation mission of air-sea heterogeneous unmanned swarm;

[0021] Figure 5b Schematic diagram of the velocity tracking error of UAV formation members during a fixed-time static formation mission of an air-sea heterogeneous unmanned swarm;

[0022] Figure 6a Schematic diagram of the position tracking error of USV formation members during the fixed-time static formation mission of air-sea heterogeneous unmanned swarm;

[0023] Figure 6b Schematic diagram of the speed tracking error of USV formation members during the fixed-time static formation mission of air-sea heterogeneous unmanned swarm;

[0024] Figure 7a Schematic diagram of the position tracking error of UUV formation members during the fixed-time static formation mission of air-sea heterogeneous unmanned swarm;

[0025] Figure 7b Schematic diagram of the velocity tracking error of UUV formation members during a fixed-time static formation mission of an air-sea heterogeneous unmanned swarm;

[0026] Figure 8 It is a schematic diagram of the three-dimensional motion trajectory of the air-sea heterogeneous unmanned swarm during the fixed-time time-varying formation mission;

[0027] Figure 9a Fixed-time time-varying formation mission for heterogeneous unmanned swarms in the air and sea Schematic diagram of the three-dimensional spatial position of the formation members at all times;

[0028] Figure 9b Fixed-time time-varying formation mission for heterogeneous unmanned swarms in the air and sea Schematic diagram of the three-dimensional spatial position of the formation members at all times;

[0029] Fig.9c Fixed-time time-varying formation mission for heterogeneous unmanned swarms in the air and sea Schematic diagram of the three-dimensional spatial position of the formation members at all times;

[0030] Figure 9d Fixed-time time-varying formation mission for heterogeneous unmanned swarms in the air and sea Schematic diagram of the three-dimensional spatial position of the formation members at all times;

[0031] Fig.10 It is a schematic diagram of the actual control command change curve during the fixed-time time-varying formation mission of the air-sea heterogeneous unmanned swarm;

[0032] Fig.11aSchematic diagram of the position tracking error of UAV formation members during the fixed-time time-varying formation mission of the air-sea heterogeneous unmanned swarm;

[0033] Fig.11b Schematic diagram of the velocity tracking error of UAV formation members during the fixed-time time-varying formation mission of the air-sea heterogeneous unmanned swarm;

[0034] Fig.12a Schematic diagram of the position tracking error of USV formation members during the fixed-time time-varying formation mission of the air-sea heterogeneous unmanned swarm;

[0035] Figure 12b Schematic diagram of the speed tracking error of USV formation members during the fixed-time time-varying formation mission of the air-sea heterogeneous unmanned swarm;

[0036] Fig.13a Schematic diagram of the position tracking error of UUV formation members during the fixed-time time-varying formation mission of the air-sea heterogeneous unmanned swarm;

[0037] Fig.13b Schematic diagram of the velocity tracking error of UUV formation members during the fixed-time time-varying formation mission of heterogeneous unmanned swarm in the air and sea. DETAILED DESCRIPTION

[0038] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0039] like Figure 1 As shown, an embodiment of the present invention provides an air-sea heterogeneous unmanned swarm collaborative formation control method based on sliding mode consistency, comprising the following steps S1 to S3:

[0040] S1. Construct a kinematic model of heterogeneous unmanned swarm in air and sea including multiple types of intelligent agents, and construct a leader-follower model based on the set virtual navigator;

[0041] In an optional embodiment of the present invention, step S1 takes a group of air-sea heterogeneous unmanned swarms including N intelligent agents as the research object, where the categories of formation members include drones, unmanned ships and unmanned underwater vehicles. There are various types of drones, and the endurance problem needs to be considered when flying at sea. Therefore, the drones in this embodiment are all selected as fixed-wing drones. In the northeast sky coordinate system, the three-degree-of-freedom particle model of the fixed-wing drone can be described as:

[0042]

[0043] in, . Respectively represent A drone in Moment Direction coordinates, Direction coordinates, Direction coordinates. , , Represent the speed, heading angle and pitch angle respectively, , , Represent the control input of speed, heading angle and pitch angle respectively, , , are the time constants corresponding to speed, heading angle and pitch angle respectively.

[0044] Without considering the influence of sea level fluctuations, the movement of the unmanned ship on the water surface can be regarded as movement on a two-dimensional plane, and the particle model of the unmanned ship can be constructed as follows:

[0045]

[0046] in, , Respectively represent An unmanned ship Moment Direction coordinates, Direction coordinates. , Represent the speed and heading angle respectively. , Respectively represent the control input of speed and heading angle, , are the time constants corresponding to the speed and heading angle respectively.

[0047] Based on the six-degree-of-freedom dynamics model of the unmanned underwater vehicle, the three-degree-of-freedom particle kinematics model of the unmanned underwater vehicle is constructed as follows:

[0048]

[0049] in, , , , , respectively representing the An unmanned underwater vehicle Moment Direction coordinates, Direction coordinates, Direction coordinates. , , Respectively represent speed, heading angle, and longitudinal inclination angle. , , Respectively represent the control input of speed, heading angle, and longitudinal inclination angle, , , are the time constants corresponding to speed, heading angle, and longitudinal inclination angle respectively.

[0050] Among the members of the air-sea heterogeneous unmanned cluster formation, the kinematic models of drones, unmanned ships, and unmanned submarines are different in that the unmanned ship is a two-dimensional kinematic model, and the actual control inputs and time constants of different types of intelligent agents are different, which reflects the heterogeneity of the cluster formation.

[0051] In order to facilitate the design of subsequent control algorithms, the kinematic model of the air-sea heterogeneous unmanned swarm is simplified to the following second-order integrator form:

[0052]

[0053] in, Representing an Agent i exist t The three-dimensional spatial position coordinates at the moment, , ; Representing an Agent i exist t The speed of time, ; Representing an Agent i exist t The acceleration input at the moment is the virtual control input of the agent. In particular, for the two-dimensional particle kinematic model of the unmanned ship, we have , , .

[0054] There is a corresponding conversion relationship between the virtual control input and the actual control input of the intelligent body. For the kinematic model of the drone, its actual control input is , , With virtual control input The conversion relationship is:

[0055]

[0056] in, Indicates drone n exist t The actual control input of the speed at the moment, Indicates dronen exist t The actual control input of the heading angle at the moment, Indicates drone n exist t The actual control input of the pitch angle at the moment, represents the time constant corresponding to the speed, represents the time constant corresponding to the heading angle, represents the time constant corresponding to the pitch angle, Indicates drone n exist t Moment x Direction virtual control input, Indicates drone n exist t Moment y Direction virtual control input, Indicates drone n exist t Moment z Direction virtual control input, Indicates drone n exist t The speed of time, Indicates drone n exist t The heading angle at the moment, Indicates drone n exist t The pitch angle at the moment.

[0057] For the kinematic model of the unmanned ship, its actual control input , , and the conversion relationship with the virtual control input is:

[0058]

[0059] in, Unmanned ship l exist t The actual control input of the speed at the moment, Unmanned ship l exist t The actual control input of the heading angle at the moment, represents the time constant corresponding to the speed, represents the time constant corresponding to the heading angle, Unmanned ship l exist t Moment x Direction virtual control input, Unmanned ship l exist t Moment yDirection virtual control input, Unmanned ship l exist t The speed of time, Unmanned ship l exist t The heading angle at the moment.

[0060] For the kinematic model of the unmanned underwater vehicle, its actual control input , , , and the conversion relationship with the virtual control input is:

[0061]

[0062] in, Unmanned underwater vehicle k exist t The actual control input of the speed at the moment, Unmanned underwater vehicle k exist t The actual control input of the heading angle at the moment, Unmanned underwater vehicle k exist t The actual control input of the longitudinal inclination angle at the moment, represents the time constant corresponding to the speed, represents the time constant corresponding to the heading angle, represents the time constant corresponding to the longitudinal inclination angle, Unmanned underwater vehicle k exist t Moment x Direction virtual control input, Unmanned underwater vehicle k exist t Moment y Direction virtual control input, Unmanned underwater vehicle k exist t Moment z Direction virtual control input, Unmanned underwater vehicle k exist t The speed of time, Unmanned underwater vehicle k exist t The heading angle at the moment, Unmanned underwater vehicle k exist t The longitudinal inclination at the moment.

[0063] Therefore, the control law can be designed according to the kinematic model of the air-sea heterogeneous unmanned swarm to complete the formation control task of the kinematic model of the intelligent body.

[0064] In actual application environments, air-sea heterogeneous unmanned swarms need to track reference trajectories in a certain formation configuration. This embodiment constructs a virtual navigator and establishes a leader-follower model. By controlling the movement of the virtual navigator, the swarm formation can track the reference trajectory and form a formation configuration. The model of the virtual navigator is designed as follows:

[0065]

[0066] in, Indicates that the virtual pilot is t The three-dimensional spatial position coordinates at the moment, Indicates that the virtual navigator is t The speed of time, Indicates that the virtual pilot is t The acceleration input at the time.

[0067] S2. Based on the leader-follower model, a tracking error model for heterogeneous unmanned swarm formation in air and sea is constructed;

[0068] In an optional embodiment of the present invention, step S2 defines the desired formation position configuration and speed configuration to be maintained as: and ,in , , and satisfy , .

[0069] The first The state tracking error of an agent is defined as:

[0070]

[0071] The above formula can be used to convert the kinematic model of the air-sea heterogeneous unmanned swarm into i The second-order integrator model of an agent is transformed into a state tracking error system:

[0072]

[0073] in, represents the position tracking error, represents the speed tracking error, represents the acceleration input of the agent, represents the expected acceleration of the agent.

[0074] definition , , , The state tracking error system of the unmanned cluster formation can be obtained as follows:

[0075]

[0076] Define the first The consistent state tracking error variable of each agent is:

[0077]

[0078] in, is the adjacency matrix The elements in is the pilot adjacency matrix Elements in . Definition , , using the properties of the Kronecker product and combining the variables in the formula, we can get the overall consistency state tracking error variable of the unmanned cluster formation system as follows:

[0079]

[0080] Since the communication topology between unmanned clusters is an undirected connected graph, is a positive definite matrix and non-singular, so , Can be equivalent to , .

[0081] The heterogeneous unmanned swarm composed of drones, unmanned ships, and unmanned submarines is taken as the research object. The communication topology of the unmanned swarm formation is an undirected connected graph, and the motion reference information of the virtual navigator and the expected formation configuration meet assumptions 3.1 and 3.2. For the state tracking error system corresponding to the above system, a unified controller is designed for each member in the formation, so that the state tracking error of each formation member converges to zero within a fixed time.

[0082] According to the above content, the state tracking error and Equivalent to and , which means that each formation member in the unmanned swarm has achieved the desired formation configuration. Furthermore, due to , Equivalent to , ,Right now , Equivalent to , , so the designed controller makes the mixed state tracking error variable of each formation member and Being equal to zero means that the heterogeneous unmanned swarm has completed the desired formation configuration.

[0083] S3. According to the tracking error model of air-sea heterogeneous unmanned cluster formation, a fixed-time formation control law of air-sea heterogeneous unmanned cluster based on sliding mode consistency is established to perform fixed-time collaborative formation control.

[0084] In an optional embodiment of the present invention, step S3 considers the following form of sliding mode:

[0085]

[0086] in, is a continuous variable, The form is as follows:

[0087]

[0088]

[0089] in, , , , are the design parameters of the sliding surface, satisfying , , , , is a normal number, , When the sliding surface satisfies When, status It can converge to the equilibrium point in a fixed time, and the upper bound of the convergence time is for:

[0090]

[0091] Based on the fixed time sliding surface, the design of the first Sliding surface of an agent for:

[0092]

[0093] because , so we have:

[0094]

[0095]

[0096]

[0097] in .definition , , using the properties of the Kronecker product, the sliding surface can be written as:

[0098]

[0099] definition , then the sliding surface can be expressed as:

[0100]

[0101] Based on the fixed time sliding surface, the design of the first The control law of an agent is as follows:

[0102]

[0103] in, Representing an Agent i The control output, represents the arrival law parameter, satisfying , ; Denotes the control law parameters, satisfying , ; represents the fixed-time sliding surface, represents the control compensation term, ; express.

[0104]

[0105] In the formula, , , and Satisfy , , , control compensation term With sliding surface Satisfies the following relationship:

[0106]

[0107] in, for The derivative of , in order to derive The expression of , first derive the function The derivative of expression.

[0108] when hour , at this time:

[0109]

[0110] when hour, , at this time:

[0111]

[0112] Therefore, for , The derivative of The expression is:

[0113]

[0114] because , we can get The expression is:

[0115]

[0116]

[0117] The following is an analysis and explanation of the above-mentioned air-sea heterogeneous unmanned cluster collaborative formation control method based on sliding mode consistency provided by the present invention in combination with simulation experiments.

[0118] The simulation results are as follows Figure 2 As shown in Figure 7. Figure 2 This is the three-dimensional motion trajectory of the air-sea heterogeneous unmanned swarm. Different graphics are used to represent different types of intelligent agents. The dots represent unmanned aerial vehicles, the diamonds represent unmanned ships, the triangles represent unmanned submarines, and the five-pointed stars represent virtual navigators. The curves in the figure are their corresponding three-dimensional motion trajectories. From the motion trajectory in the figure, we can see that the air-sea heterogeneous unmanned swarm can quickly converge from the initial position to the desired position at the beginning, complete the configuration of the desired formation, and After switching the formation configuration, the unmanned cluster can quickly complete the configuration of the new formation.

[0119] Figure 3 shows the positions of the members of the air-sea heterogeneous unmanned swarm in three-dimensional space at different times, which can more clearly show the process of formation convergence. When , the formation members are randomly distributed at the initial position, and when The unmanned swarm has completed the task of the desired formation configuration 1. The desired formation configuration of the unmanned cluster was switched at the same time, so the unmanned cluster needed to make quick adjustments to complete the configuration of the new formation. Figure 3cThe transformation process of the unmanned swarm from the desired formation configuration 1 to the desired formation configuration 2 is demonstrated. The swarm quickly completes the transformation to the desired formation and keeps moving forward in the desired formation configuration 2 until the end of the simulation.

[0120] Figure 4 It is the change curve of the actual control command of the air-sea heterogeneous unmanned cluster, which includes the speed change curve, yaw angle change curve and pitch angle change curve of all formation members of the cluster. It can be seen from the figure that in the process of the unmanned cluster converging to the expected formation configuration 1, the speed, yaw angle and pitch angle quickly converge to the expected value. In particular, for the unmanned ship (USV), it has no change in pitch angle, so it remains zero. For the unmanned underwater vehicle (UUV), the pitch angle in the figure here represents its longitudinal inclination angle.

[0121] Figure 4 is the change curve of the actual control command of each formation member of the air-sea heterogeneous unmanned swarm. When the formation members switch to the new formation configuration, the speed, yaw angle and pitch angle of the formation members change dramatically. This is caused by the switching of the desired configuration of the formation. Under the action of the designed controller, the unmanned cluster quickly converges to the new formation configuration, and the control input instructions can also quickly converge to the expected values.

[0122] Figures 5 to 7 show the state tracking errors of each member of the unmanned swarm, where the state tracking error includes position tracking error and velocity tracking error. From these figures, we can see that when the mission starts, the position tracking error and velocity tracking error of each member in the swarm are converges to zero, which means that the unmanned cluster is From the initial state of random distribution, it converges to the desired formation configuration 1. When the desired formation changes, the state tracking error fluctuates greatly, and the greater the difference between the desired state and the current state, the greater the fluctuation. From these figures, we can see that no matter how large the state tracking error caused by the formation change is, the cluster can always This also verifies that the designed controller can enable the air-sea heterogeneous unmanned swarm to complete the desired formation configuration within a fixed time, no matter how big the difference between the current state and the expected state is.

[0123] In particular, since the kinematic model of the unmanned ship (USV) is constructed as a two-dimensional plane model, its The position tracking error and velocity tracking error in the direction are always kept zero, so the USV is not shown in Figure 6. The state of the directional tracking error transform image.

[0124] The above experimental results show that the air-sea heterogeneous unmanned swarm fixed-time formation control algorithm designed by the present invention based on sliding mode consistency can better complete the control tasks of fixed formation and formation configuration switching, and can enable the swarm to complete the desired formation configuration within a fixed time limit regardless of the initial state of the unmanned swarm. Therefore, the present invention verifies the effectiveness of the designed algorithm through simulation experiments.

[0125] The simulation results of the time-varying formation control task are shown in Figure 8 to Figure 1 3. Among them, Figure 8 This is a three-dimensional motion trajectory diagram of a heterogeneous unmanned swarm in the air and sea under the time-varying formation control task. Different graphics are used in the figure to represent different types of intelligent agents, where dots represent unmanned aerial vehicles, diamonds represent unmanned ships, triangles represent unmanned underwater vehicles, and five-pointed stars represent virtual navigators. From this figure, we can see that the unmanned swarm can better complete the configuration of the time-varying formation while tracking the trajectory of the virtual navigator. The unmanned aerial vehicle (UAV) and unmanned underwater vehicle (UUV) are in the control task of the time-varying formation. The time-varying value of the desired formation is added in the axis direction, while the unmanned ship (USV) is in The time-varying variables of the desired formation are added to the plane.

[0126] Figure 9 shows the positions of each formation member of the air-sea heterogeneous unmanned cluster in three-dimensional space at different times. It can be seen from the figure that each formation member in the unmanned cluster can quickly converge from the randomly distributed initial position to the desired time-varying formation configuration and keep moving forward.

[0127] Fig.10 This is the changing curve of the actual control input instructions of the air-sea heterogeneous unmanned swarm during the execution of the time-varying formation control task. Due to the influence of the time-varying formation control task, each formation member in the swarm needs to continuously change and adjust its own control instructions to complete the time-varying formation configuration, so their speed, yaw angle, and pitch angle transformation curves show periodic changes.

[0128] Figures 11 to 13 show the state tracking errors of each member of the air-sea heterogeneous unmanned swarm, where the state tracking error includes position tracking error and velocity tracking error. From these figures, we can see that the state tracking error of each member of the swarm quickly converges to near zero at the beginning, and the convergence time is This means that the unmanned swarm has completed the desired formation configuration within a fixed time limit. However, in the subsequent tracking process, due to the influence of the time-varying formation configuration, the state tracking errors of each formation member fluctuate in a region close to zero, so the control effect of the time-varying formation will be affected by the desired configuration of the time-varying formation.

[0129] Due to the addition of unmanned ships (USV) The time-varying variables of the expected formation configuration in the plane are much larger than the variables added to the unmanned aerial vehicles (UAVs) and unmanned underwater vehicles (UUVs), so the state tracking errors of the USV formation members are more obvious than the fluctuations of the UAVs and UUVs, as shown in Figure 12.

[0130] Through the above simulation experimental analysis, it can be seen that the fixed-time formation control algorithm based on sliding mode consistency designed by the present invention also has good performance for the control task of time-varying formation, and can make the formation state tracking error of the unmanned cluster converge to a region close to zero within a fixed time.

[0131] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0132] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for controlling air-sea heterogeneous unmanned swarm collaborative formation based on sliding mode consistency, characterized in that: The following steps are involved: Construct a kinematic model of heterogeneous unmanned swarms in the air and sea, including multiple types of intelligent agents, and build a leader-follower model based on the set virtual navigator; According to the leader-follower model, a tracking error model for heterogeneous unmanned swarm formation in air and sea is constructed, specifically: in, represents the position tracking error, represents the speed tracking error, represents the acceleration input of the agent, represents the expected acceleration of the agent; According to the tracking error model of air-sea heterogeneous unmanned swarm formation, a fixed-time formation control law of air-sea heterogeneous unmanned swarm based on sliding mode consistency is established to carry out fixed-time collaborative formation control. Specifically: in, Representing an Agent i The control output, represents the arrival law parameter, represents the control law parameters, represents the fixed-time sliding surface, represents the control compensation term, Representing an Agent i The sliding mode function of .

2. The air-sea heterogeneous unmanned swarm collaborative formation control method based on sliding mode consistency according to claim 1 is characterized in that: The specific kinematic model of the air-sea heterogeneous unmanned swarm is: in, Representing an Agent i exist t The three-dimensional spatial position coordinates at the moment, Representing an Agent i exist t The speed of time, Representing an Agent i exist t The acceleration input at the time.

3. The air-sea heterogeneous unmanned swarm collaborative formation control method based on sliding mode consistency according to claim 1 is characterized in that: The air and sea heterogeneous unmanned swarm includes drones, unmanned ships and unmanned submarines.

4. The air-sea heterogeneous unmanned swarm collaborative formation control method based on sliding mode consistency according to claim 3 is characterized in that: The conversion relationship between virtual control input and actual control input in the kinematic model of the UAV is as follows: in, Indicates drone n exist t The actual control input of the speed at the moment, Indicates drone n exist t The actual control input of the heading angle at the moment, Indicates drone n exist t The actual control input of the pitch angle at the moment, represents the time constant corresponding to the speed, represents the time constant corresponding to the heading angle, represents the time constant corresponding to the pitch angle, Indicates drone n exist t Moment x Direction virtual control input, Indicates drone n exist t Moment y Direction virtual control input, Indicates drone n exist t Moment z Direction virtual control input, Indicates drone n exist t The speed of time, Indicates drone n exist t The heading angle at the moment, Indicates drone n exist t The pitch angle at the moment.

5. The air-sea heterogeneous unmanned swarm collaborative formation control method based on sliding mode consistency according to claim 3 is characterized in that: The conversion relationship between the virtual control input and the actual control input in the kinematic model of the unmanned ship is as follows: in, Unmanned ship l exist t The actual control input of the speed at the moment, Unmanned ship l exist t The actual control input of the heading angle at the moment, represents the time constant corresponding to the speed, represents the time constant corresponding to the heading angle, Unmanned ship l exist t Moment x Direction virtual control input, Unmanned ship l exist t Moment y Direction virtual control input, Unmanned ship l exist t The speed of time, Unmanned ship l exist t The heading angle at the moment.

6. The air-sea heterogeneous unmanned swarm collaborative formation control method based on sliding mode consistency according to claim 3 is characterized in that: The conversion relationship between virtual control input and actual control input in the kinematic model of the unmanned underwater vehicle is as follows: in, Unmanned underwater vehicle k exist t The actual control input of the speed at the moment, Unmanned underwater vehicle k exist t The actual control input of the heading angle at the moment, Unmanned underwater vehicle k exist t The actual control input of the longitudinal inclination angle at the moment, represents the time constant corresponding to the speed, represents the time constant corresponding to the heading angle, represents the time constant corresponding to the longitudinal inclination angle, Unmanned underwater vehicle k exist t Moment x Direction virtual control input, Unmanned underwater vehicle k exist t Moment y Direction virtual control input, Unmanned underwater vehicle k exist t Moment z Direction virtual control input, Unmanned underwater vehicle k exist t The speed of time, Unmanned underwater vehicle k exist t The heading angle at the moment, Unmanned underwater vehicle k exist t The longitudinal inclination at the moment.

7. The air-sea heterogeneous unmanned swarm collaborative formation control method based on sliding mode consistency according to claim 1 is characterized in that: The leadership model of the virtual navigator is as follows: in, Indicates that the virtual navigator is t The three-dimensional spatial position coordinates at the moment, Indicates that the virtual navigator is t The speed of time, Indicates that the virtual navigator is t The acceleration input at the time.

Images