CN116679573B - Consistency tracking control method and device, electronic equipment and storage medium - Google Patents
Consistency tracking control method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN116679573B CN116679573B CN202310973885.2A CN202310973885A CN116679573B CN 116679573 B CN116679573 B CN 116679573B CN 202310973885 A CN202310973885 A CN 202310973885A CN 116679573 B CN116679573 B CN 116679573B
- Authority
- CN
- China
- Prior art keywords
- determining
- following object
- state
- following
- agent system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000004891 communication Methods 0.000 claims abstract description 56
- 239000003795 chemical substances by application Substances 0.000 claims description 162
- 230000006870 function Effects 0.000 claims description 49
- 239000013598 vector Substances 0.000 claims description 26
- 238000004590 computer program Methods 0.000 claims description 16
- 230000008878 coupling Effects 0.000 claims description 10
- 238000010168 coupling process Methods 0.000 claims description 10
- 238000005859 coupling reaction Methods 0.000 claims description 10
- 238000001514 detection method Methods 0.000 claims description 6
- 230000000694 effects Effects 0.000 abstract description 10
- 238000005516 engineering process Methods 0.000 abstract description 2
- 239000011159 matrix material Substances 0.000 description 17
- 238000012545 processing Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 7
- 230000001133 acceleration Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- 230000003044 adaptive effect Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000002441 reversible effect Effects 0.000 description 2
- 230000036962 time dependent Effects 0.000 description 2
- 241001235534 Graphis <ascomycete fungus> Species 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The present invention relates to the field of multi-agent system consistency control technologies, and in particular, to a consistency tracking control method, a device, an electronic apparatus, and a storage medium. The method comprises the following steps: determining a target object in a multi-agent system, and at least one follower object associated with the target object; for each following object, determining a state to be determined corresponding to the current following object according to relative state information between the current following object and at least one associated object associated with the current following object; and when the relative state to be determined corresponding to each follow-up object meets preset state information, determining that each follow-up object in the multi-agent system achieves consistent tracking. The state information is transferred to a small number of following objects through the target object, and according to the communication information transfer among the following objects, on the premise that the following objects cannot acquire global state information, the effect of consistency tracking is achieved for all the following objects.
Description
Technical Field
The present invention relates to the field of multi-agent system consistency control technologies, and in particular, to a consistency tracking control method, a device, an electronic apparatus, and a storage medium.
Background
As a multi-intelligent system group behavior, consistency tracking is also known as leader-follower consistency, meaning tracking the state of a leader on the follower's state.
At present, the consistency tracking method for the multi-agent system is mostly carried out under a linear system, that is, the prior art mostly carries out consistency tracking control on the motion states of a leader and a following object under an ideal state. However, in practical applications, since the motion states of the leader and the following object in the multi-agent system are generally nonlinear, it is difficult to realize real consistency tracking in practical applications based on the consistency tracking method determined under the linear control system.
In order to solve the above-mentioned problems, a consistent tracking method of a nonlinear multi-agent system needs to be proposed.
Disclosure of Invention
The invention provides a consistency tracking control method, a device, electronic equipment and a storage medium, which are used for solving the problem that for each following object in a nonlinear multi-agent system, on the premise that global state information in the multi-agent system cannot be acquired, consistency tracking with a target object is difficult to accurately achieve.
In a first aspect, an embodiment of the present invention provides a method for controlling consistency tracking, including:
determining a target object in a multi-agent system, and at least one follower object associated with the target object; wherein the multi-agent system is a nonlinear multi-agent system;
for each following object, determining a state to be determined corresponding to the current following object according to relative state information between the current following object and at least one associated object associated with the current following object; wherein the relative state information comprises at least one of relative speed, relative acceleration and relative position;
and when the relative state to be determined corresponding to each follow-up object meets preset state information, determining that each follow-up object in the multi-agent system achieves consistent tracking.
In a second aspect, an embodiment of the present invention further provides a consistency tracking control apparatus, including:
a following object determination module for determining a target object in a multi-agent system, and at least one following object associated with the target object; wherein the multi-agent system is a nonlinear multi-agent system;
the to-be-determined state determining module is used for determining a to-be-determined state corresponding to the current following object according to relative state information between the current following object and at least one associated object associated with the current following object for each following object; wherein the relative state information comprises at least one of relative speed, relative acceleration and relative position;
And the consistency tracking determination module is used for determining that each following object in the multi-agent system achieves consistency tracking when the relative state to be determined corresponding to each following object meets preset state information.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the coherency trace control method of any one of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, where a computer instruction is stored, where the computer instruction is configured to cause a processor to execute the method for consistent tracking control according to any embodiment of the present invention.
According to the technical scheme, a target object in the multi-agent system and at least one following object associated with the target object are determined; for each following object, determining a state to be determined corresponding to the current following object according to relative state information between the current following object and at least one associated object; when the relative state to be determined corresponding to each following object meets preset state information, determining that each following object in the multi-agent system achieves consistent tracking. The method solves the problem that on the premise that global state information in a multi-agent system cannot be acquired, consistency tracking with a target object is difficult to achieve, the target object of the multi-agent system transmits state information to a small number of following objects and transmits communication information among the following objects, and on the premise that the following objects cannot acquire global state information, all the following objects in the multi-agent system are controlled to achieve the effect of consistency tracking with the target object based on the small number of following objects.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for consistent tracking control according to a first embodiment of the invention;
FIG. 2 is a schematic illustration of a directed graph provided in accordance with a first embodiment of the present invention;
fig. 3 is a schematic diagram showing a state of a coupling gain changing with time according to a second embodiment of the present invention;
fig. 4 is a schematic diagram of a state of tracking error changing with time according to the consistent tracking control method according to the second embodiment of the invention;
fig. 5 is a schematic structural diagram of a consistency tracking control device according to a third embodiment of the present invention;
Fig. 6 is a schematic structural diagram of an electronic device implementing a consistent tracking control method according to an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.
Before the technical scheme is elaborated, an application scene of the technical scheme is simply introduced so as to more clearly understand the technical scheme. Cooperative control is applied to fields such as consistency control, formation control, sensor networks, task robots, unmanned aerial vehicles, unmanned vehicles, underwater vehicles and the like, and is paid attention to by extensive scholars. The consistency control means that the state consistency of each agent is finally achieved by negotiating with neighbor agents by the agents in the multi-agent system. Existing coherence controls can be broadly divided into two categories, non-leadership coherence control and leadership coherence control, which is also known as coherence tracking control. The technical scheme is mainly used for improving a consistency tracking control method of a leader in an intelligent agent system.
It is to be understood that the consistency tracking control method refers to that one leader agent and N follower agents are included in one multi-agent system, and the states of the follower agents are adjusted and controlled according to the states of the leader agents so that the states of the follower agents are consistent with the states of the leader agents. It should be noted that, the consistency tracking control method provided in the technical scheme is mainly applied to a nonlinear multi-agent system, for example, the multi-agent system is taken as an unmanned aerial vehicle group attack as an example, each unmanned aerial vehicle in the multi-agent system can be affected by various factors in the flight process, for example, air resistance, air flow, meteorological factors and the like, so that the unmanned aerial vehicle presents a nonlinear state in the actual flight process, and the technical scheme is mainly used for realizing the state of each follower agent tracking the upper leader agent when the multi-agent system is the nonlinear multi-agent system.
Example 1
Fig. 1 is a flowchart of a consistent tracking control method provided in an embodiment of the present invention, where the present embodiment is applicable to a nonlinear multi-agent system, and on the premise that each following object cannot acquire global state information of the multi-agent system, a small number of following objects are controlled by a target object, so as to achieve an effect of consistent tracking based on all following objects and the target object in the small number of following objects multi-agent system.
As shown in fig. 1, the method includes:
s110, determining a target object in the multi-agent system and at least one following object associated with the target object.
The multi-agent system is composed of a series of interacting agents, and the internal agents can complete a large amount of complex work which can not be completed by a single agent through mutual communication, cooperation, competition and other modes. Currently, multi-agent systems are widely used in a variety of fields, such as formation of aircraft, sensor networks, data fusion, multi-robot cooperative equipment, parallel computing, multi-robot cooperative control, traffic vehicle control, resource allocation of networks, and the like.
It should be noted that, the multi-agent system includes a system without a leader agent and a system with a leader agent, and in this technical solution, the multi-agent system involved is an agent system including a leader agent, and the multi-agent system is a nonlinear multi-agent system. The nonlinear multi-agent system is a system in which each agent in the system can present nonlinear motion in the motion process due to external factors such as air resistance or weather factors in the actual use process.
In practical application, can set up the leader agent in the multi-agent system, namely, target object, simultaneously, set up other agent except that the leader agent as the follower agent, namely, follow the object in this technical scheme, further, make each follower agent realize unanimously with the leader agent through the system regulation and control.
Optionally, determining the target object in the multi-agent system and the at least one follower object associated with the target object includes: acquiring a directed graph corresponding to the multi-agent system; wherein the directed graph is a communication topological graph; a target object in the multi-agent system and at least one follower object associated with the target object are determined from the directed graph.
The directed graph is a communication topological graph and can be constructed based on each intelligent agent in the multi-intelligent agent system. The target object is taken as a central vertex of the directed graph, the directed graph comprises at least one associated vertex, each associated vertex corresponds to one agent (namely, a following object), and line segments with directions exist between the vertexes in the directed graph, wherein the agent (namely, the following object) corresponding to the associated vertex pointed by the arrow can acquire communication information from the agent corresponding to the vertex at the starting position of the arrow.
It should be noted that, in the present technical solution, the following objects are divided into two classes, specifically, a first following object that directly obtains the communication information from the target object, and a second following object that cannot directly obtain the communication information from the target object.
Illustratively, as shown in fig. 2, the number "0" represents a target object, the number "1" represents a first follower object, and the numbers "2", "3", "4" are all second follower objects. It is clear that in the so-called first follower object, the follower object can directly acquire communication information from the target object, and the second follower object is a follower object that cannot directly acquire communication information from the target object.
Taking the number "1" as an example, a directional line segment exists between the number "0" and the number "1", and the direction points from the number "0" to the number "1", wherein the number "0" corresponds to the target object, the number "1" corresponds to the first following object, which indicates that the first following object corresponding to the number "1" can directly obtain communication information, such as speed, acceleration, position information and the like, of the target object from the target object corresponding to the number "0".
Taking the number "4" as an example, there are directional line segments at the numbers "1" and "4", and the direction points from the number "1" to the number "4"; meanwhile, a directional line segment also exists between the number "3" and the number "4", and the direction points from the number "3" to the number "4". Wherein the number "1" corresponds to the first following object, and the numbers "3" and "4" each represent the second following object. It indicates that the second following object corresponding to the number "4" may obtain the communication information corresponding to the number "1" from the first following object corresponding to the number "1" or may obtain the communication information corresponding to the number "3" from the second following object corresponding to the number "3".
It should be noted that, taking the number "3" as an example, a directional line segment exists between the number "2" and the number "3", which indicates that the second following object corresponding to the number "3" may obtain the corresponding communication information from the second following object corresponding to the number "2". However, no directional line segment exists between the number "3" and the number "1" and between the number "3" and the number "0", that is, the second follower object corresponding to the number "3" cannot acquire corresponding communication information from the first follower object corresponding to the number "1" or the target object corresponding to the number "0".
That is, the communication flow direction information between the respective agents in the multi-agent system can be determined by the line segment pointing information in the directed graph corresponding to the multi-agent system. It is obvious that, in the multi-agent system, not all following objects can acquire communication information corresponding to the target object from the target object, but only from the upper-level agents directly associated with the following objects.
In order to facilitate understanding, in the technical scheme, an agent corresponding to an arrow start end in the directed graph is used as a neighbor agent of the arrow direction end, and if the state of each follower is kept consistent with that of the target object on the premise that each follower cannot directly acquire communication information corresponding to the target object, it is required to ensure that the following error between the state information of the follower and the state information of the neighbor agent associated with the follower is minimized as much as possible. When the following errors between all the agents and the neighbor agents are the smallest, for example, the following errors are all 0, it can be determined that each agent in the multi-agent system achieves consistent tracking.
S120, for each following object, determining a state to be determined corresponding to the current following object according to relative state information between the current following object and at least one associated object associated with the current following object.
Taking the current following object as an example, an agent in the multi-agent system, which can directly acquire communication information, can be used as an associated object associated with the current following object. The relative state information includes at least one of a relative velocity, a relative acceleration, and a relative position. It should be noted that the associated object of the first following object may be a target object, or may be another first following object other than itself. For the second following object, the associated object may be the first following object or the second following object.
In the multi-agent system provided by the technical scheme, taking the current following object as an example, if the current following object is the first following object, the first following object can directly acquire the state information of the target object from the target object so as to adjust the relative state information between the first following object and the target object, determine the state to be determined corresponding to the current following object, and determine whether the current following object achieves the effect of keeping consistent tracking with the target object according to the state to be determined. However, if the current following object is the second following object, since the second following object cannot directly obtain the state information of the target object, if the second following object and the target object are required to achieve the consistency tracking effect, the state to be determined corresponding to the current following object can be indirectly determined according to the relative state information between the second following object and at least one associated object associated with the second following object, and whether the current following object achieves the effect of keeping consistency tracking with the target object is determined according to the state to be determined.
Optionally, determining the state to be determined corresponding to the current following object according to the relative state information between the current following object and the associated at least one associated object includes: determining at least one associated object associated with the current following object according to the communication flow direction information in the directed graph; determining a state determination mode corresponding to the current following object according to the number of the associated objects corresponding to the at least one associated object; based on the state determining mode, determining relative state information between the corresponding associated object and the current following object, so as to determine a state to be determined corresponding to the current following object based on at least one piece of relative state information.
The communication flow information may be understood as flow information of communication data, for example, if the first following object obtains communication information from the target object, the communication flow information flows from the target object to the first following object. The number of associated objects is one or more. The state determining mode refers to a state processing mode corresponding to the current following object, which is determined according to the number of associated objects corresponding to the current following object.
For example, when the number of associated objects is one, the state to be determined of the current following object may be determined directly from the relative state information between the current following object and the associated object. When the number of the associated objects is plural, it is necessary to determine the relative state information between the current following object and each associated object, so as to comprehensively consider the relative state information between the current following object and each associated object, and determine the state to be determined corresponding to the current following object together according to each relative state information.
Specifically, the communication paths among the agents in the multi-agent system can be determined according to the arrow pointing information in the directed graph, and the neighbor agents associated with the corresponding agents are determined according to the communication flow direction information in the communication paths. That is, from the communication flow direction information in the directed graph, at least one associated object corresponding to each following object can be determined. Further, the number of associated objects corresponding to each following object is determined to invoke the state determination mode corresponding to the corresponding following object.
Optionally, determining the relative state information between the corresponding associated object and the current following object based on the state determination mode includes: determining whether the number of associated objects is one; if yes, determining a state determining mode as a first state determining mode, and determining an error to be determined between the associated object and the current following object based on the first state determining mode so as to determine relative state information between the current following object and the corresponding associated object based on the error to be determined; if not, determining the state determining mode as a second state determining mode, respectively determining errors to be superimposed between each associated object and the current following object based on the second state determining mode, and obtaining a target following error corresponding to the current following object based on the sum of the errors to be superimposed so as to determine relative state information corresponding to the current following object based on the target following error.
Specifically, the directed line segment in the directed graph may represent a communication path, and if the directed line segment exists between two vertices, it indicates that a communication path exists between the target objects or the following correspondence corresponding to the two vertices connected by the directed line segment. In the multi-agent system, for any one of the following objects 1, through a directed line segment in the directed graph that points to the following object 1, at least one communication path corresponding to the following object 1 can be determined, and the number of associated objects corresponding to the following object 1 is determined according to the number of paths of the at least one communication path. If the number of the associated objects is one, determining a state to be determined corresponding to the current following object based on the first state determining mode. And if the number of the associated objects is a plurality of, determining a state to be determined corresponding to the current following object based on the second state determining mode.
For the first state determining manner, after determining the associated object corresponding to the current following object, state information, such as position information, speed information and acceleration information, corresponding to the current following object and the associated object are respectively acquired. Further, the first dynamics model is called to calculate the state information corresponding to the current following object and the associated object, so that an error to be determined between the current following object and the associated object is obtained, and the relative state information corresponding to the current following object is determined based on the error to be determined. The first dynamics model may be understood as a model for calculating error information between a current following object and an associated object from state information when the number of associated objects is one.
For the second state determining manner, after determining a plurality of associated objects corresponding to the current following object, if the current following object reaches consistency tracking, state information of the current following object and state information of the plurality of associated objects need to be acquired. Further, processing each state information based on the second dynamics model to obtain errors to be superimposed between the current following object and each associated object, and further, performing superposition processing on each error to be superimposed to obtain a target following error corresponding to the current following object so as to determine relative state information corresponding to the current following object based on the target following error. Wherein the second dynamics model may be understood as a model for calculating error information between the current following object and each associated object from the state information when the number of associated objects is plural.
And S130, when the relative states to be determined corresponding to the following objects meet preset state information, determining that the following objects in the multi-agent system achieve consistent tracking.
In practical application, an error vector corresponding to a current following object is constructed according to error information corresponding to each relative state information; when the error vector is zero, each following object in the multi-agent system is determined to achieve consistent tracking.
The error information is an error to be determined or a target following error.
Specifically, if the associated object associated with the following object is one and the error to be determined corresponding to the following object is zero, it indicates that the relative states of the following object and the associated object are consistent. If the number of associated objects associated with the following object is plural and the target following error is zero, the relative states between the following object and the associated plural associated objects are consistent. Based on the above, an error vector can be constructed according to the error information in the relative state information, and if and only if the error vector is zero, the relative states between each following object in the multi-agent system and the associated object are consistent, then it can be determined that each following object in the multi-agent system achieves a consistent tracking effect.
On the basis of which. In order to verify whether the consistency tracking control method provided in the technical scheme can control each following object to achieve consistency tracking with the target following object, each following object in the multi-agent system can be verified by constructing a Lyapunov function. Specifically, based on the relative state information corresponding to each following object, constructing a consistency tracking control input and a relative state column vector; based on the relative state column vector, a time-varying coupling gain function and a smooth monotonic increasing function which are input in the consistency tracking controller, constructing a Lyapunov function; the consistency tracking controller is input into each following object in the multi-agent system; when the Lyapunov function meets a preset function detection condition and the relative state column vector is zero, determining that each following object in the multi-agent system achieves consistency tracking; the preset function detection condition comprises that the Lyapunov function is a continuous micro-radial unbounded function, and the derivative of the Lyapunov function is smaller than or equal to zero.
According to the technical scheme, a target object in the multi-agent system and at least one following object associated with the target object are determined; for each following object, determining a state to be determined corresponding to the current following object according to relative state information between the current following object and at least one associated object; when the relative state to be determined corresponding to each following object meets preset state information, determining that each following object in the multi-agent system achieves consistent tracking. The method solves the problem that on the premise that global state information in a multi-agent system cannot be acquired, consistency tracking with a target object is difficult to achieve, the target object of the multi-agent system transmits state information to a small number of following objects and transmits communication information among the following objects, and on the premise that the following objects cannot acquire global state information, all the following objects in the multi-agent system are controlled to achieve the effect of consistency tracking with the target object based on the small number of following objects.
Example two
In one specific example, in controlling the consistent tracking of individual agents in a multi-agent system, as is not specifically described,indicating all->A set of real matrices.Representing an n-dimensional identity matrix.An n-dimensional column vector representing all elements as 1.Represents the kronecker product (Kronecker Product) of matrices a and B. Vector pair，And->Representing the 2-norm (i.e., euclidean norm) and the infinity norm of x, respectively. For->Sgn (x) represents a sign function.Representing the element +.>A diagonal matrix is formed. For a given matrix A, A T Representing the transpose of a. For symmetric real matrices X and Y of the same dimension, matrix inequality X>Y (X.gtoreq.Y) means that the matrix X-Y is symmetrically positive (semi-positive). For->，Representing the real part of Z.
The dynamic model of constructing a leader (i.e., target object) and a follower (i.e., follower object) from the multi-agent system is specifically:
wherein,,for the state of the ith agent,is the control input of the ith agent. Subscript ofRepresenting the leader, subscripts 1,2, … …, N representing N followers, i.e., x 0 Status representing leaderRepresenting the state of the follower, u 0 Control input on behalf of a leaderRepresenting the control input of the follower. Wherein the nonlinear function Is a continuous micro-functional.
In the technical scheme, when the intelligent agents in the multi-intelligent agent system are controlled to carry out consistency tracking, corresponding directed graphs are needed to be constructed in advance, wherein the directed graphs are communication topological graphs.
In the technical proposal, a directed graph is usedRepresenting communication between 1 leader and N followers in a multi-agent system.
Directed graph for communication among N followersAnd (3) representing.
Directed graphThe method meets the following conditions:I.e. +.>Is a sub-graph of (c).
Directed graphAdjacency matrix of->Satisfy->If and only if agent i can receive agent j's information, otherwise +.>。
Directed graphIs a laplace matrix of (c):Wherein, the method comprises the steps of, wherein,
defining adjacency matrix for leadersIf the follower agent i, i=1, 2, … …, N is able to receive the status information x of the leader 0 Then a i0 >0, otherwise a i0 =0。
In the multi-agent system provided in the present embodiment, not all the followers can receive the status information of the leader, but only a part of the followers can receive the status information of the leader. In other words, in the technical solution, a following object that can directly acquire the state information of the target object from the target object is a first following object, and a following object that cannot directly acquire the state information of the target object is a second following object. In practical application, taking one of the following objects as a current following object as an example, determining a corresponding state determining mode according to the number of associated objects corresponding to the current following object, so as to determine relative state information corresponding to the current following object according to the corresponding state determining mode. Further, if the state of the current following object and the state of at least one associated object corresponding to the current following object are consistent, it can be determined that the state of the current following object and the state of the target object in the multi-agent system are consistent and tracked.
Based on this, a matrix can be definedThe following steps are:
lemma 1: if directed graphIncluding a directed spanning tree with a leader as the root node, then a matrixAnd is reversible. Definition matrix->The matrix G, then,are positive definite matrices.
And (4) lemma 2: continuous micro-funciton for arbitrary real valueThere is a smooth scalar function a (x) 1, b (y) 1, such that
If it isSatisfy->There is a smooth scalar function +.>So that
Specifically, the tracking error of each follower is defined as;define tracking error of multi-agent system as +.>. Specifically, when the consistent tracking state corresponding to each follower is determined, if the number of associated objects corresponding to the follower is one, the tracking error is referred to as an error to be determined, and if the number of associated objects corresponding to the follower is a plurality of, the tracking error is referred to as a target tracking error.
Wherein for each follower i, i=1, 2, … …, N in the multi-agent system, a distributed controller u is designed that uses only local relative information i So that for any initial state，
The multi-intelligent system in the technical scheme meets the following assumption:
suppose 1: the control input of the leader is continuous and bounded, i.e.:wherein D is a known positive constant; and the status of the leader is bounded, namely:Wherein->Is an unknown positive constant.
Suppose 2: directed graphComprising a directed node with leader 0 as the root nodeAnd (5) generating a tree.
On this basis, for multi-agent systems, the relative state information between the follower and its neighbors (i.e., associated objects) is defined, and the corresponding coherence-tracking controllers are designed based on the relative states.
Specifically, in the multi-agent system (1), the relative state z between the ith follower and its neighbors i (i.e., the state to be determined) is defined as:
according to formula (2),is an n-dimensional column vector. Definition of all z i The column vectors are the relative states of the multi-agent system->There is->Wherein the column vector e is the tracking error of the multi-agent system already defined above.
From lemma 1, under assumption 1, matrixAnd is reversible.
Therefore, it is desirable to design a controller to achieve consistency tracking, namely: tracking errors for multi-agent systemsAsymptotically converges to zero, which is equivalent to designing a distributed controller (i.e., a coherence tracking controller) So that the relative state z of the multi-agent system asymptotically converges to zero.
According to formulas (1) and (2), the firstRelative state of follower z i The kinetic model of (2) is:
definition of the definition
Wherein,,is to make polynomial +>Is a constant stable to Hurwitz. Then there are:
wherein the method comprises the steps of，
According to the assumption 1 and the average theorem, there is a smooth functionSo that
Due toObtained according to formula (3)
According to lemma 2, there is a smooth functionSo that
Wherein the constant isDependent on matrix H, unknown positive constant->And->。/>
Definition matrixSelect P>0 satisfies->And linear matrix inequality:
wherein->Representation matrix->Is a characteristic value of (a).
For positive definite matrix P, there is a real non-singular matrix S such that. Definitions->Combine->And formula (7), can be obtained:
wherein,,。
definition of the definition。
Then:
taking column vectors，
Definition of the definition
，
There is a smooth functionSo that
Wherein the constant is。/>
Based onThe following distributed adaptive nonlinear consistency tracking controller is designed:
wherein the controller parametersFor a normal number, D is the upper bound of the leader control input, given by assumption 1.Is a time-varying coupling gain (i.e. a coupling gain function), +.>。
Is a smooth monotonically increasing function.Is a smooth function, satisfying equation (9).
Theorem 1: under the condition that the assumption 1 and the assumption 2 are satisfied, the distributed adaptive controller (10) can realize the consistency tracking control of the multi-agent system (1), and in addition, the time-varying coupling gainRespectively converging to some constant.
And 4, by selecting a proper Lyapunov function, the designed distributed self-adaptive controller (10) can realize the consistency tracking control of the multi-agent system (1) by using LaSalle theorem (namely, laSell theorem).
And (3) proving: consider the following Lyapunov function (Lyapunov function):
wherein the method comprises the steps ofFor column vector->Is an element of the group.
Obviously, V1 is positive and Lyapunov function V1 is along the system (4)
Defining column vectors
. According to the quotation 1 and the designed controller (10), the formula (9) is combined, and the deduction calculation can be obtained: />
Consider the following Lyapunov function (i.e., lyapunov function):
wherein, h is a constant,. Lyapunov function->The derivatives along the system (4) are:
order theAccording to inequalities (12) and (13), there is +.>
Wherein the constant is. According to the controller (10), a control unit (A)>Is a smooth monotonically increasing function,then
Consider the following Lyapunov function
Selection constant. According to the controller (10), the controller parameter k is more than or equal to 8, the time-varying coupling gain is +. >Smooth monotonically increasing function->. According to inequality (14), the Lyapunov function V derivative along the system (4) is: />
When (when)Is a continuous microradial borderless function, +.>According to Lassel theorem, whenAsymptotically converges to zero,>is bounded. According to the formula (4) andthere is->。
Due toRespectively converging to some constant.
As a specific example:
step 1, constructing a multi-agent system consisting of one leader and 4 followers according to the multi-agent system, as shown in fig. 2. Each agent dynamics model is:
wherein the subscriptRepresenting the leader. Nonlinear functionThe global Lipschitz condition (i.e., lipschitz continuous condition) is not satisfied. Assume control input by the leaderInitial state of agentWhereinIn the intervalInternally randomly select, there are。
Step 2, defining a tracking error of each follower:
define tracking error for multi-agent systems:
step 3, defining relative states between the follower and its neighbors for the multi-agent system:
definition of the first embodimentRelative state between an individual follower and its neighborsThe definition is as follows:
according to the step 3 and the step 4, selectingThen->. Definitions->，According to (6)
Wherein, define:
Consider a nonlinear function:
wherein,,。
according to formula (8) and formula (9):
controller parameter selectionSelect->The distributed adaptive nonlinear consistency tracking controller is:
wherein,,。
FIG. 3 is a graph of coupling gainThe time-dependent curve from which the coupling gain can be seenRespectively converging to a determined constant. FIG. 4 is a tracking error of the followerFrom the time-dependent curve, it can be seen that the tracking error asymptotically converges to zero. This demonstrates the effectiveness of the fully distributed consistency tracking control approach presented in this solution.
According to the technical scheme, a target object in the multi-agent system and at least one following object associated with the target object are determined; for each following object, determining a state to be determined corresponding to the current following object according to relative state information between the current following object and at least one associated object; when the relative state to be determined corresponding to each following object meets preset state information, determining that each following object in the multi-agent system achieves consistent tracking. The method solves the problem that on the premise that global state information in a multi-agent system cannot be acquired, consistency tracking with a target object is difficult to achieve, the target object of the multi-agent system transmits state information to a small number of following objects and transmits communication information among the following objects, and on the premise that the following objects cannot acquire global state information, all the following objects in the multi-agent system are controlled to achieve the effect of consistency tracking with the target object based on the small number of following objects.
Example III
Fig. 5 is a schematic structural diagram of a consistency tracking control device according to a third embodiment of the present invention. As shown in fig. 5, the apparatus includes: a follow object determination module 210, a to-be-determined state determination module 220, and a consistency tracking determination module 230.
Wherein the following object determining module 210 is configured to determine a target object in the multi-agent system and at least one following object associated with the target object; wherein the multi-agent system is a nonlinear multi-agent system;
a to-be-determined state determining module 220, configured to determine, for each following object, a to-be-determined state corresponding to a current following object according to relative state information between the current following object and at least one associated object associated with the current following object; wherein the relative state information comprises at least one of relative speed, relative acceleration and relative position;
and the consistency tracking determination module 230 is configured to determine that each following object in the multi-agent system achieves consistency tracking when the relative state to be determined corresponding to each following object satisfies the preset state information.
According to the technical scheme, a target object in the multi-agent system and at least one following object associated with the target object are determined; for each following object, determining a state to be determined corresponding to the current following object according to relative state information between the current following object and at least one associated object; when the relative state to be determined corresponding to each following object meets preset state information, determining that each following object in the multi-agent system achieves consistent tracking. The method solves the problem that on the premise that global state information in a multi-agent system cannot be acquired, consistency tracking with a target object is difficult to achieve, the target object of the multi-agent system transmits state information to a small number of following objects and transmits communication information among the following objects, and on the premise that the following objects cannot acquire global state information, all the following objects in the multi-agent system are controlled to achieve the effect of consistency tracking with the target object based on the small number of following objects.
Optionally, the following object determining module includes: the directed graph determining submodule is used for acquiring a directed graph corresponding to the multi-agent system; wherein the directed graph is a communication topological graph;
and the following object determination submodule is used for determining at least one following object associated with the target object in the multi-agent system according to the directed graph.
Optionally, the at least one follower object includes a first follower object that directly obtains communication information from the target object, and/or a second follower object that cannot directly obtain communication information from the target object.
Optionally, the to-be-determined state determining module includes: an associated object determining unit, configured to determine at least one associated object associated with the current following object according to communication flow direction information in the directed graph;
a state determining mode determining unit, configured to determine a state determining mode corresponding to the current following object according to the number of associated objects corresponding to the at least one associated object; wherein the number of the associated objects is one or more;
and the state to be determined determining unit is used for determining relative state information between the corresponding associated object and the current following object based on the state determining mode so as to determine the state to be determined corresponding to the current following object based on at least one piece of relative state information.
Optionally, the to-be-determined state determining unit includes: a number judging subunit, configured to determine whether the number of associated objects is one;
the first subunit is configured to determine that the state determining manner is a first state determining manner if the state determining manner is positive, and determine an error to be determined between the associated object and the current following object based on the first state determining manner, so as to determine relative state information between the current following object and the corresponding associated object based on the error to be determined;
and the second subunit is used for determining that the state determination mode is a second state determination mode, respectively determining errors to be superimposed between each associated object and the current following object based on the second state determination mode, and obtaining a target following error corresponding to the current following object based on the sum of the errors to be superimposed so as to determine relative state information corresponding to the current following object based on the target following error.
Optionally, the consistency tracking determination module includes: the error vector determining unit is used for constructing an error vector corresponding to the current following object according to the error information corresponding to each piece of relative state information; wherein the error information is the error to be determined or the target following error;
And the consistency tracking determination unit is used for determining that each following object in the multi-agent system achieves consistency tracking when the error vector is zero.
Optionally, the consistency tracking control device further includes: the relative state column vector determining module is used for constructing a consistency tracking control input and a relative state column vector based on the relative state information corresponding to each following object;
the function construction module is used for constructing a Lyapunov function based on the relative state column vector, the time-varying coupling gain function and the smooth monotonic increasing function which are input in the consistency tracking controller; wherein the coherence tracking controller inputs are provided in each following object in the multi-agent system;
the consistency tracking verification module is used for determining that each following object in the multi-agent system achieves consistency tracking when the Lyapunov function meets a preset function detection condition and the relative state column vector is zero; the preset function detection condition comprises that the Lyapunov function is a continuous micro-radial non-boundary function, and the derivative of the Lyapunov function is smaller than or equal to zero.
The consistency tracking control device provided by the embodiment of the invention can execute the consistency tracking control method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 6 shows a schematic structural diagram of the electronic device 10 of the embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a consistency tracking control method.
In some embodiments, the consistency tracking control method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the consistency tracking control method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the coherency trace control method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
The computer program for implementing the coherency trace control method of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (7)
1. A consistency tracking control method, comprising:
acquiring a directed graph corresponding to the multi-agent system; wherein the directed graph is a communication topological graph;
determining a target object and at least one following object associated with the target object in the multi-agent system according to the directed graph;
for each following object, determining at least one associated object associated with the current following object according to the communication flow direction information in the directed graph;
Determining a state determination mode corresponding to the current following object according to the number of the associated objects corresponding to the at least one associated object; wherein the number of the associated objects is one or more;
based on the state determining mode, determining relative state information between the corresponding associated object and the current following object, so as to determine a state to be determined corresponding to the current following object based on at least one relative state information;
when the relative state to be determined corresponding to each following object meets preset state information, determining that each following object in the multi-agent system achieves consistent tracking;
wherein the determining, based on the state determining manner, relative state information between the corresponding associated object and the current following object includes: determining whether the number of associated objects is one; if yes, determining the state determination mode as a first state determination mode, and determining an error to be determined between the associated object and the current following object based on the first state determination mode so as to determine relative state information between the current following object and the corresponding associated object based on the error to be determined; if not, determining the state determination mode as a second state determination mode, respectively determining errors to be superimposed between each associated object and the current following object based on the second state determination mode, and obtaining a target following error corresponding to the current following object based on the sum of the errors to be superimposed so as to determine relative state information corresponding to the current following object based on the target following error.
2. The method of claim 1, wherein the at least one follower object comprises a first follower object that directly obtains communication information from the target object, and/or a second follower object that cannot directly obtain communication information from the target object.
3. The method according to claim 1, wherein determining that each following object in the multi-agent system achieves consistent tracking when the relative state to be determined corresponding to each following object satisfies preset state information, comprises:
constructing an error vector corresponding to the current following object according to error information corresponding to each piece of relative state information; wherein the error information is the error to be determined or the target following error;
and when the error vector is zero, determining that each following object in the multi-agent system achieves consistent tracking.
4. The method as recited in claim 1, further comprising:
based on the relative state information corresponding to each following object, constructing a consistency tracking control input and a relative state column vector;
constructing a Lyapunov function based on the relative state column vector, and a time-varying coupling gain function and a smooth monotonically increasing function input in a consistency tracking controller; wherein the coherence tracking controller inputs are provided in each following object in the multi-agent system;
When the Lyapunov function meets a preset function detection condition and the relative state column vector is zero, determining that each following object in the multi-agent system achieves consistency tracking; the preset function detection condition comprises that the Lyapunov function is a continuous micro-radial non-boundary function, and the derivative of the Lyapunov function is smaller than or equal to zero.
5. A consistency tracking control apparatus, comprising:
the directed graph determining submodule is used for acquiring a directed graph corresponding to the multi-agent system; wherein the directed graph is a communication topological graph;
a following object determination sub-module for determining a target object in the multi-agent system and at least one following object associated with the target object from the directed graph;
an associated object determining unit, configured to determine, for each following object, at least one associated object associated with the current following object according to the communication flow information in the directed graph;
a state determining mode determining unit, configured to determine a state determining mode corresponding to the current following object according to the number of associated objects corresponding to the at least one associated object; wherein the number of the associated objects is one or more;
The to-be-determined state determining unit is used for determining relative state information between the corresponding associated object and the current following object based on the state determining mode so as to determine a to-be-determined state corresponding to the current following object based on at least one piece of relative state information;
the consistency tracking determination module is used for determining that each following object in the multi-agent system achieves consistency tracking when the relative state to be determined corresponding to each following object meets preset state information;
wherein the to-be-determined state determining unit includes: a number judging subunit, configured to determine whether the number of associated objects is one; the first subunit is configured to determine that the state determining manner is a first state determining manner if the state determining manner is positive, and determine an error to be determined between the associated object and the current following object based on the first state determining manner, so as to determine relative state information between the current following object and the corresponding associated object based on the error to be determined; and the second subunit is used for determining that the state determination mode is a second state determination mode if not, respectively determining errors to be superimposed between each associated object and the current following object based on the second state determination mode, and obtaining a target following error corresponding to the current following object based on the sum of the errors to be superimposed so as to determine relative state information corresponding to the current following object based on the target following error.
6. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the coherency trace control method of any one of claims 1-4.
7. A computer readable storage medium storing computer instructions for causing a processor to implement the coherency trace control method of any one of claims 1-4 when executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310973885.2A CN116679573B (en) | 2023-08-04 | 2023-08-04 | Consistency tracking control method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310973885.2A CN116679573B (en) | 2023-08-04 | 2023-08-04 | Consistency tracking control method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116679573A CN116679573A (en) | 2023-09-01 |
| CN116679573B true CN116679573B (en) | 2023-10-20 |
Family
ID=87779541
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310973885.2A Active CN116679573B (en) | 2023-08-04 | 2023-08-04 | Consistency tracking control method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116679573B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2476904C1 (en) * | 2011-08-30 | 2013-02-27 | Открытое акционерное общество "Конструкторское бюро приборостроения" | Method of tracking object and method of generating signal for controlling beam position of transceiver antenna of object tracking system |
| CN109445447A (en) * | 2019-01-14 | 2019-03-08 | 北京航空航天大学 | A kind of multiple agent formation tracking and controlling method and system |
| CN114841074A (en) * | 2022-05-17 | 2022-08-02 | 重庆大学 | Nonlinear multi-agent consistency method based on state observation and experience pool |
| CN114851198A (en) * | 2022-05-17 | 2022-08-05 | 广州大学 | Consistent tracking fixed time stability control method for multi-single-link mechanical arm |
| CN116047984A (en) * | 2023-03-07 | 2023-05-02 | 北京全路通信信号研究设计院集团有限公司 | Consistency tracking control method, device, equipment and medium of multi-agent system |
-
2023
- 2023-08-04 CN CN202310973885.2A patent/CN116679573B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2476904C1 (en) * | 2011-08-30 | 2013-02-27 | Открытое акционерное общество "Конструкторское бюро приборостроения" | Method of tracking object and method of generating signal for controlling beam position of transceiver antenna of object tracking system |
| CN109445447A (en) * | 2019-01-14 | 2019-03-08 | 北京航空航天大学 | A kind of multiple agent formation tracking and controlling method and system |
| CN114841074A (en) * | 2022-05-17 | 2022-08-02 | 重庆大学 | Nonlinear multi-agent consistency method based on state observation and experience pool |
| CN114851198A (en) * | 2022-05-17 | 2022-08-05 | 广州大学 | Consistent tracking fixed time stability control method for multi-single-link mechanical arm |
| CN116047984A (en) * | 2023-03-07 | 2023-05-02 | 北京全路通信信号研究设计院集团有限公司 | Consistency tracking control method, device, equipment and medium of multi-agent system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116679573A (en) | 2023-09-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102170105B1 (en) | Method and apparatus for generating neural network structure, electronic device, storage medium | |
| CN116047984B (en) | Consistency tracking control method, device, equipment and medium of multi-agent system | |
| WO2020051270A1 (en) | Unsupervised depth prediction neural networks | |
| CN113139696B (en) | Trajectory prediction model construction method and trajectory prediction method and device | |
| CN114987546A (en) | Training method, device and equipment of trajectory prediction model and storage medium | |
| CN113705515B (en) | Training of semantic segmentation model and generation method and device of high-precision map lane line | |
| CN111833391B (en) | Image depth information estimation method and device | |
| CN114274148B (en) | Track planning method and device, electronic equipment and storage medium | |
| CN114312843B (en) | Method and device for determining information | |
| CN115958606A (en) | Mechanical arm obstacle avoidance track planning method and device and electronic equipment | |
| CN115344753A (en) | Federal diagram calculation method, federal diagram calculation configuration method, device and electronic equipment | |
| CN116679573B (en) | Consistency tracking control method and device, electronic equipment and storage medium | |
| JP7425169B2 (en) | Image processing method, device, electronic device, storage medium and computer program | |
| CN116543417A (en) | Human body posture estimation method, device, equipment and storage medium | |
| CN110610140A (en) | Training method, device and equipment of face recognition model and readable storage medium | |
| CN113516013B (en) | Target detection method, target detection device, electronic equipment, road side equipment and cloud control platform | |
| CN115447584A (en) | Method, device and equipment for determining lane center line and storage medium | |
| Li et al. | Coordinated control strategies for heterogeneous Multi-UUV recovery system in low-visibility underwater implicit interaction scenarios | |
| CN113869186A (en) | Model training method and device, electronic equipment and computer readable storage medium | |
| CN117589188B (en) | Driving path planning method, driving path planning device, electronic equipment and storage medium | |
| CN114771518B (en) | Lane center guide wire generation method and device, electronic equipment and medium | |
| CN117289686B (en) | Parameter calibration method and device, electronic equipment and storage medium | |
| CN114571457B (en) | Singular pose avoidance method, apparatus, device and storage medium | |
| CN117970817B (en) | Nonlinear electromechanical system on-line identification and robust control method and device | |
| CN117901118A (en) | Path planning method, device and equipment for robot mechanical arm and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |