CN116700300A - A Robust Adaptive Consistency Control Method for Multi-AUV Distributed Cluster - Google Patents

Abstract

The application discloses a robust self-adaptive consistency control method of a multi-AUV distributed cluster, which comprises the following steps: establishing a motion mathematical model consisting of a virtual leader and a follower in the multi-AUV cluster system, and rewriting the model into a standard second-order system suitable for the design of a consistency control protocol; determining a communication topological structure of the multi-AUV cluster system; calculating the consistency error and the filtering consistency error of each AUV; constructing a robust neural damping term, and designing a distributed consistency control protocol according to the robust neural damping term and the self-adaptive control gain; designing an adaptive law of adaptive parameters of a robust damping term and an adaptive law of a time-varying control gain; and adjusting the parameters related to the consistency control protocol and the self-adaption law, and applying the parameters to the multi-AUV cluster consistency control. The application can realize progressive consistency control under the condition that the AUV model has any nonlinear model uncertain item and unknown leader dynamics, and can expand formation control, inclusion control and cooperative target tracking of AUV clusters.

Description

Robust self-adaptive consistency control method for multi-AUV distributed cluster

Technical Field

The application relates to a distributed control technology of a multi-underwater autonomous vehicle, in particular to a robust self-adaptive consistency control method of a multi-AUV distributed cluster.

Background

Cooperative cluster control of multi-underwater autonomous vehicles (AUV) has received wide attention in recent years from expert students and engineering personnel in the field of marine control theory due to its high efficiency and low power consumption. Multiple aircraft cluster modes can be divided into two types, centralized and distributed. The centralized mode depends on a specific central node, so that the states of all aircrafts in the group are mastered to make decisions and control, and the centralized mode has high requirements on communication and calculation resources and has poor expansibility. Meanwhile, the centralized model has the problem of single point failure, namely once a central node is hit and broken, the whole cluster system is difficult to normally operate. In contrast, in the distributed mode, each individual only grasps the information of partial nodes adjacent to the individual through local information exchange, so that the requirements on communication and computing capacity are greatly reduced, better flexibility is achieved, and the distributed network communication system is suitable for the underwater cluster environment with limited communication. The consistency problem is a basic problem of the multi-agent system, and refers to that the states of all agents are consistent through local information interaction by designing a distributed consistency control protocol. The consistency control method can be expanded to the problems of collaborative formation, collaborative target tracking, collaborative target inclusion and the like. The research on the distributed consistency control method of the multi-underwater vehicle not only has important theoretical significance, but also has good reference value for engineering problems such as collaborative exploration and collaborative combat of the underwater cluster system.

The motion model of the underwater vehicle has high nonlinearity and uncertainty, is influenced by unknown ocean environment disturbance, and is difficult to establish an accurate mathematical model. The existing control method can not effectively solve the problem of multi-underwater vehicle consistency considering model uncertainty. The control scheme of approximators based on a neural network, a fuzzy system and the like requires enough weight parameters to perform online learning, so that a large calculation burden is caused. Furthermore, in order to solve for the control gain parameters that meet the stability conditions, it must be assumed that certain topology information, such as the minimum eigenvalues of the laplace matrix, are globally known, have large limitations, and generally only guarantee that the consistency error is ultimately bounded. Therefore, there is a need to study a multi-underwater vehicle progressive consistency control protocol with model uncertainty, independent of global information, and with better robustness, adaptability and lower computational burden.

Disclosure of Invention

The application aims at solving the technical problems in the prior art, and provides a robust self-adaptive consistency control method for a multi-AUV distributed cluster.

The technical scheme adopted for solving the technical problems is as follows:

the application provides a robust self-adaptive consistency control method of a multi-AUV distributed cluster, which comprises the following steps:

step 1: establishing a motion mathematical model consisting of a virtual leader and a plurality of followers in the multi-AUV cluster system, and rewriting the model into a standard second-order system suitable for the design of a consistency control protocol;

step 2: determining a communication topological structure of the multi-AUV cluster system;

step 3: calculating the consistency error and the filtering consistency error of each AUV;

step 4: constructing a robust neural damping term, and designing a distributed consistency control protocol according to the robust neural damping term and the self-adaptive control gain;

step 5: according to the consistency error of each AUV obtained in the step 3, designing an adaptive law of adaptive parameters of a robust damping item and an adaptive law of a time-varying control gain;

step 6: and adjusting the parameters related to the consistency control protocol and the self-adaption law, and applying the parameters to the multi-AUV cluster consistency control.

Further, the method for establishing a motion mathematical model composed of 1 virtual leader and a plurality of followers in the multi-AUV cluster system in the step 1 of the present application is as follows:

the multi-AUV cluster system comprises 1 virtual leader and n follower aircraft; the motion model of the ith AUV may be described as follows:

in the formula ,the position and the course of the aircraft under the geodetic coordinate system are given; />Longitudinal, lateral linear velocity and yaw rate of the aircraft; />A state transition matrix from a carrier coordinate system to a geodetic coordinate system; />For the coriolis force and centripetal force matrix, +.>Is a damping force matrix; />For controlling the input vector, i.e. the force and moment.

Further, the method for rewriting the standard second-order system in the step 1 of the present application is as follows:

the motion model of the AUV is converted into a normalized second order nonlinear system as follows:

in the formula ,，/>as a nonlinear function of aircraft speed and heading; />Is the converted control input vector; similarly, the motion model of the virtual leader is described as:

wherein the heading and speed of the virtual leader are specified by the user according to the cluster task.

Further, the method of the step 2 of the present application is as follows:

the communication structure of the multi-AUV cluster system is as follows: only a portion of the aircraft needs to know the status information of the virtual leader; each follower aircraft only carries out two-way communication with other aircraft in the communication range of the follower aircraft to form a communication undirected graph; there is a directed path from the virtual leader to any follower, i.e., there is a spanning tree with the virtual leader as the root node.

Further, the calculation method of the step 3 of the application is as follows:

the consistency error and the filtering consistency error of each AUV are calculated, and specifically described as follows:

wherein ,representing the weight of communication between aircraft i and aircraft j, if communication between the two is possible +.>On the contrary, the->；/>Representing the communication weight between the aircraft i and the virtual leader, if the aircraft i can obtain information of the virtual leader +.>On the contrary, the->The method comprises the steps of carrying out a first treatment on the surface of the On this basis, the filter consistency error for each follower aircraft is defined as:

。

wherein ,is an adjustment parameter greater than 0.

Further, the method of the step 4 of the present application is as follows:

the form of the robust damping term is:

wherein ,is an adaptive parameter vector; />，/>For radial basis function vectors, each radial basis function +.>The form of (c) is described as:

in the formula ,input vector for radial basis function, +.> and />Center vector and width as radial basis functions; />For integrating a bounded function for ensuring that a multi-AUV system achieves progressive consistency,/> and />An adjustment parameter greater than 0;

based on the constructed robust adaptive damping term, the following robust adaptive consistency control protocol is designed as follows:

in the formula ,is a time-varying control gain vector; according to the robust self-adaptive consistency protocol, obtaining an actual control input vector of each aircraft before coordinate transformation:

。

further, the method of the step 5 of the present application is as follows:

the specific form of the adaptive law of control gain is as follows:

the adaptive law of the adaptive parameters in the robust adaptive damping term is as follows:

in the formula , and />An adaptive law adjustment parameter greater than 0, specified by a user; /> and />The parameters are adjusted for an adaptive law greater than 0, specified by the user.

Further, the method for controlling the consistency of the multi-AUV cluster in the step 6 of the application comprises the following steps:

at each sampling moment, each AUV collects the state of the AUV, performs information interaction with the neighbor AUV according to a preset communication topology, calculates the actual control input of the AUV, updates the control gain and the self-adaptive parameters according to the parameter self-adaptive law, and finally realizes progressive consistency of the multi-AUV system.

The application has the beneficial effects that:

the method provided by the application can realize progressive consistency control under the condition that the AUV model has any nonlinear model uncertainty item and unknown leader dynamics. The method can be expanded to formation control, inclusion control and collaborative target tracking of multiple AUV clusters. Compared with the prior art, the technical scheme of the application has the following beneficial effects: 1. the method uses the robust neural damping term to process the model uncertainty term of the AUV and the unknown dynamics of the leader, only 3 self-adaptive parameters are required to be updated online to calm the composite uncertainty term, and the method has small online calculation burden and is suitable for engineering practice. The method comprises the steps of carrying out a first treatment on the surface of the 2. The control gain updated on line is used for carrying out consistency protocol design, and an integral bounded function is introduced into a parameter self-adaptive law, so that progressive consistency control is realized under the condition that global information is not needed, and compared with a traditional control method which only ensures that consistency errors are ultimately bounded, the control method has higher accuracy.

Drawings

The application will be further described with reference to the accompanying drawings and examples, in which:

FIG. 1 is a logical block diagram of an algorithm execution of the present application;

FIG. 2 is a communication topology of a multiple AUV cluster system;

FIG. 3 is a graph of 3 AUV horizontal trajectories;

FIG. 4 is a graph showing the results of a simulation of the consistency of 3 AUVs in the x-direction;

FIG. 5 is a graph showing the results of a simulation of the consistency of 3 AUVs in the y-direction;

fig. 6 is a simulation result of the consistency of 3 AUV course angles.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

In the example, the robust self-adaptive consistency control method of the application is utilized to realize consistency control of a cluster system consisting of 3 AUVs.

Step 1: and establishing a motion mathematical model of a virtual leader and a follower in the multi-AUV cluster system, and rewriting the motion mathematical model into a standard second-order system suitable for the design of a consistency control protocol.

Specifically, the motion models of the three AUVs in this embodiment are described as follows:

（1）

in the formula ,is the position and heading of the aircraft in the geodetic coordinate system. />Is the longitudinal, lateral linear velocity and yaw rate of the aircraft. />Is a state transition matrix from the carrier coordinate system to the geodetic coordinate system. />For the coriolis force and centripetal force matrix, +.>Is a damping force matrix. />For controlling the input vector, i.e. the force and moment. Without loss of generality, three aircraft are given the same model parameters. The specific forms of the mass matrix and the damping force matrix are as follows: />，/>. Further, the AUV motion model is rewritten into a standard second-order system form suitable for the design of the coherence control protocol.

（2）

in the formula ,，/>is a nonlinear function of the speed and heading of the aircraft. />Is the converted control input vector. Similarly, the motion model of the virtual leader can be described as:

（3）

wherein ,satisfy->. Specifically, in this example +.>Is a periodic reference signal.

Step 2: and determining the communication topology structure of the multi-AUV cluster system.

Specifically, only AUV No. 1 may receive the status information of the virtual leader in this example. And the AUV 1 and the AUV 2 are in bidirectional communication, and the AUV 2 and the AUV 3 are in bidirectional communication. Based on this, it can be determined that there is one spanning tree with the virtual leader as the root node. Further, the laplace matrix and the leader information matrix of the cluster system may be written as follows:

（4）

in addition, a matrix can be definedThe H matrix may prove positive in the presence of a spanning tree with the leader as the root node.

Step 3: and calculating the consistency error and the filtering consistency error of each AUV.

Specifically, the consistency error for each aircraft in this example can be calculated as:

（5）

wherein ,，/>. Further, the filter consistency error for each aircraft may be calculated as:

（6）

wherein ,is an adjustment parameter greater than 0.

Step 4: and constructing a robust neural damping term, and designing a distributed consistency control protocol according to the robust neural damping term and the self-adaptive control gain.

Specifically, in this example, a robust neural damping term vector is constructed for each follower aircraft based on the filter consistency error, which can be obtained

（7）

wherein ,is an adaptive parameter vector. />，/>For radial basis function vectors, each radial basis function +.>Can be described as

（8）

in the formula ,input vector for radial basis function, +.> and />Is the center vector and width of the radial basis function. />For integrating a bounded function for ensuring that a multi-AUV system achieves progressive consistency,/> and />Is an adjustment parameter greater than 0.

Based on the constructed robust adaptive damping term, the following robust adaptive consistency control protocol may be designed as follows:

（9）

in the formula ,is a time-varying control gain vector.

According to the robust self-adaptive consistency protocol, the actual control input vector of each aircraft before coordinate transformation can be obtained:

（10）

step 5: and (3) designing an adaptive law of adaptive parameters of the robust damping term according to the consistency error of each aircraft obtained in the step (3), and controlling the adaptive law of the gain in time variation.

Specifically, in this example, the adaptive law of the adaptive parameters of the robust adaptive damping term is designed as:

（11）

the adaptive law of time-varying control gain in the coherence protocol is designed as:

（12）

in the formula , and />The parameters are adjusted for an adaptive law greater than 0, specified by the user.

Step 6: and adjusting the relevant parameters of each consistency control protocol and the self-adaptive law, and applying the parameters to multi-AUV cluster consistency control.

Specifically, in this example, each control parameter is selected as follows:

initial value of control gain:

initial value of adaptive parameter:

adjustment parameters of the adaptive law:

other adjustment parameters:

at each sampling moment, each AUV collects the state of the AUV, performs information interaction with the neighbor AUV according to a preset communication topology, calculates the actual control input of the AUV, updates the control gain and the self-adaptive parameters according to the parameter self-adaptive law, and finally realizes progressive consistency of the multi-AUV system, and simulation results are shown in figures 3-6.

Fig. 3-6 show the horizontal plane trace of the 3 AUVs, the consistency control results in the X-direction and the consistency control results in the Y-direction, respectively. From simulation results, the method provided by the application can enable the multi-AUV cluster system to accurately and rapidly realize the consistency of each state under the condition of model uncertainty and unknown leader dynamics, and has better robustness and adaptability.

The robust self-adaptive consistency control method of the multi-AUV distributed cluster mainly has the following two aspects:

(1) The uncertainty of an AUV model and unknown dynamics of a leader are processed by using a radial basis function neural network, a minimum parameterization method is introduced, the number of adaptive parameters is compressed by using a robust neural damping technology, and only 3 adaptive parameters are needed to learn online to calm a composite uncertainty item. Therefore, the application further reduces the calculation burden on the premise of better robustness and adaptability, and is more suitable for engineering practice.

(2) The design of the consistency protocol is carried out by using the time-varying control gain updated online, so that the complete distributed design without global information is realized, and an integral bounded function is introduced into the self-adaptive law, so that the consistency error gradually converges to 0, and the method has better control precision compared with the traditional method capable of only ensuring the bounded consistency error.

It should be noted that, as described in step 2, the method of the present application requires that the communication topology of the AUV cluster system is bidirectional communication between AUVs, and a spanning tree with a virtual leader as a root node exists. On the basis of meeting the requirements, the AUV cluster system is designed according to the steps in the specification, the adjusting parameters in the steps are reasonably adjusted, and the same or similar control effect can be still realized for the AUV cluster system with different model parameters as the AUV cluster system. In addition, only the interaction location and speed information between AUVs is required in the present application. For consistency control problems in specific degrees of freedom, such as heading consistency problems and forward speed consistency problems, only the mathematical model of the aircraft in the step 1 is required to be reduced, and the design is carried out according to the rest steps, at the moment, only state information in the specific degrees of freedom is required to be interacted between the aircraft, and the requirement of the technical scheme of the application on information transmission can be met under the condition that the speed of a underwater acoustic communication system is not less than 100 bps.

It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of the processes should be determined by the functions and the internal logic, and should not be construed as limiting the implementation process of the embodiments of the present application.

It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims.

Claims (8)

1. A robust self-adaptive consistency control method of a multi-AUV distributed cluster is characterized by comprising the following steps:

step 1: establishing a motion mathematical model consisting of a virtual leader and a plurality of followers in the multi-AUV cluster system, and rewriting the model into a standard second-order system suitable for the design of a consistency control protocol;

step 2: determining a communication topological structure of the multi-AUV cluster system;

step 3: calculating the consistency error and the filtering consistency error of each AUV;

step 4: constructing a robust neural damping term, and designing a distributed consistency control protocol according to the robust neural damping term and the self-adaptive control gain;

step 5: according to the consistency error of each AUV obtained in the step 3, designing an adaptive law of adaptive parameters of a robust damping item and an adaptive law of a time-varying control gain;

step 6: and adjusting the parameters related to the consistency control protocol and the self-adaption law, and applying the parameters to the multi-AUV cluster consistency control.

2. The robust adaptive consistency control method of multi-AUV distributed cluster according to claim 1, wherein the method for establishing a motion mathematical model composed of 1 virtual leader and a plurality of followers in the multi-AUV cluster system in step 1 is as follows:

the multi-AUV cluster system comprises 1 virtual leader and n follower aircraft; the motion model of the ith AUV may be described as follows:

in the formula ,the position and the course of the aircraft under the geodetic coordinate system are given; />Longitudinal, lateral linear velocity and yaw rate of the aircraft; />A state transition matrix from a carrier coordinate system to a geodetic coordinate system;for the coriolis force and centripetal force matrix, +.>Is a damping force matrix; />For controlling the input vector, i.e. the force and moment.

3. The robust adaptive consistency control method of the multi-AUV distributed cluster according to claim 2, wherein the method of rewriting the standard second-order system in the step 1 is as follows:

the motion model of the AUV is converted into a normalized second order nonlinear system as follows:

in the formula ,，/>as a nonlinear function of aircraft speed and heading; />Is the converted control input vector; similarly, the motion model of the virtual leader is described as:

wherein the heading and speed of the virtual leader are specified by the user according to the cluster task.

4. The robust adaptive consistency control method of a multi-AUV distributed cluster according to claim 1, wherein the method of step 2 is:

the communication structure of the multi-AUV cluster system is as follows: only a portion of the aircraft needs to know the status information of the virtual leader; each follower aircraft only carries out two-way communication with other aircraft in the communication range of the follower aircraft to form a communication undirected graph; there is a directed path from the virtual leader to any follower, i.e., there is a spanning tree with the virtual leader as the root node.

5. The robust adaptive consistency control method of a multi-AUV distributed cluster according to claim 1, wherein the calculating method of step 3 is as follows:

the consistency error and the filtering consistency error of each AUV are calculated, and specifically described as follows:

wherein ,representing the weight of communication between aircraft i and aircraft j, if communication between the two is possible +.>Reverse, oppositeIn the case of a combination of the above-mentioned materials,；/>representing the communication weight between the aircraft i and the virtual leader, if the aircraft i can obtain information of the virtual leader +.>On the contrary, the->The method comprises the steps of carrying out a first treatment on the surface of the On this basis, the filter consistency error for each follower aircraft is defined as:

。

wherein ,is an adjustment parameter greater than 0.

6. The robust adaptive consistency control method of a multi-AUV distributed cluster according to claim 1, wherein the method of step 4 is:

the form of the robust damping term is:

wherein ,is an adaptive parameter vector; />，/>For radial basis function vectors, each radial basis function +.>The form of (c) is described as:

in the formula ,input vector for radial basis function, +.> and />Center vector and width as radial basis functions; />For integrating a bounded function for ensuring that a multi-AUV system achieves progressive consistency,/> and />An adjustment parameter greater than 0;

based on the constructed robust adaptive damping term, the following robust adaptive consistency control protocol is designed as follows:

in the formula ,is a time-varying control gain vector; according to the robust adaptive consistency protocol,obtaining an actual control input vector of each aircraft before coordinate transformation:

。

7. the robust adaptive consistency control method of a multi-AUV distributed cluster according to claim 1, wherein the method of step 5 is:

the specific form of the adaptive law of control gain is as follows:

the adaptive law of the adaptive parameters in the robust adaptive damping term is as follows:

in the formula , and />An adaptive law adjustment parameter greater than 0, specified by a user; /> and />The parameters are adjusted for an adaptive law greater than 0, specified by the user.

8. The robust adaptive consistency control method of a multi-AUV distributed cluster according to claim 1, wherein the method of multi-AUV cluster consistency control in step 6 is as follows:

at each sampling moment, each AUV collects the state of the AUV, performs information interaction with the neighbor AUV according to a preset communication topology, calculates the actual control input of the AUV, updates the control gain and the self-adaptive parameters according to the parameter self-adaptive law, and finally realizes progressive consistency of the multi-AUV system.