CN112394719A - Multi-moving-body system formation control device and method based on sampling data - Google Patents

Multi-moving-body system formation control device and method based on sampling data Download PDF

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CN112394719A
CN112394719A CN201910702090.1A CN201910702090A CN112394719A CN 112394719 A CN112394719 A CN 112394719A CN 201910702090 A CN201910702090 A CN 201910702090A CN 112394719 A CN112394719 A CN 112394719A
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CN112394719B (en
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孙雅妮
邹文成
向峥嵘
周超
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Nanjing University of Science and Technology
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0242—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using non-visible light signals, e.g. IR or UV signals
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
    • G05D1/0253—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means extracting relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
    • G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
    • 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]

Abstract

The invention discloses a device and a method for controlling formation of a multi-moving-body system based on sampling data. The device comprises an image acquisition processing module, a sensor module, a wireless communication module and a control module. The method comprises the following steps: firstly, arranging a plurality of moving bodies in a preset multi-moving body network, numbering the moving bodies, and setting a system communication topology; then establishing a motion model of the moving body, giving formation information and establishing a control target; then, a formation control algorithm based on the sampling data and the observer is constructed; and finally, periodically acquiring the position information of neighbors or leaders by each moving body through a wireless communication network, fusing the acquired data, and executing a formation algorithm to realize formation control. The invention is used for measuring the speed information of the moving body, is convenient and easy to operate, has accurate measuring result, realizes formation control by sampling data, and saves communication cost and network resources.

Description

Multi-moving-body system formation control device and method based on sampling data
Technical Field
The invention relates to the technical field of multi-moving body formation control, in particular to a multi-moving body system formation control device and method based on sampling data.
Background
The multi-moving body system is an important branch in the field of artificial intelligence and is an emerging leading-edge system science with extremely strong comprehensiveness. The multi-moving body system is a system which is composed of a plurality of moving bodies with certain sensing and communication capabilities, and the moving bodies carry out information interaction through a wireless communication network and coordinate with each other to complete a given task. The concept of the multiple moving body system originates from the group behaviors of many animals in nature, such as birds flying in a formation, fish rotating around a certain center, and the like, and the animal groups all have huge numbers, can form certain coordinated motions by depending on the connection and instinct among individuals, and are orderly, distributed and coordinated motions and intelligent behaviors. Multiple moving body systems are widely used in industry, military, traffic, etc., and the formation control of multiple moving bodies is a typical application.
Distributed control is mostly adopted for multi-moving body formation control at present, and the core problem is consistency. Consistency means that as time increases, the members in the multi-body moving system make the states of each member consistent according to a certain control law through mutual action and information interaction. In the design of a control algorithm, communication among multiple moving body systems needs to be abstracted into a topological structure, the state change rule of the topological structure is abstracted into a control law, and the control target is realized by controlling the speed or the acceleration of a moving body. At present, most of multi-moving body systems have real-time continuous internal information interaction, have the problems of difficult moving body speed information measurement and inaccurate measurement results, and occupy a large amount of communication cost and network resources.
Disclosure of Invention
The invention aims to provide a multi-moving-body system formation control device and a multi-moving-body system formation control method based on sampling data, which have the advantages of high accuracy, low communication cost and network resource saving.
The technical solution for realizing the purpose of the invention is as follows: a multi-moving body system formation control device based on sampling data comprises an image acquisition processing module, a sensor module, a wireless communication module and a control module;
the image acquisition processing module is used for shooting moving images of moving bodies, calculating actual position coordinates by a computer, feeding back the actual position coordinates to the corresponding moving bodies, acquiring information of surrounding environment and dynamically adjusting formation information;
the sensor module is arranged on the moving body and used for measuring the distance between the moving body and a neighbor or the distance between the moving body and a leader;
the wireless communication module is used for communication between moving bodies and between the moving bodies and a computer;
the control module is arranged on the moving body and used for fusing the collected data and executing a formation control algorithm.
A multi-moving body system formation control method based on sampling data comprises the following steps:
step 1: arranging a plurality of moving bodies in a preset multi-moving-body network, numbering the moving bodies, and setting a system communication topology;
step 2: establishing a motion model of a moving body, giving formation information and establishing a control target;
and step 3: constructing a formation control algorithm based on the sampling data and the observer;
and 4, step 4: each moving body periodically acquires the position information of neighbors or leaders through a wireless communication network, fuses the acquired data, executes a formation algorithm and realizes formation control.
Further, in step 1, a plurality of moving objects are arranged in a preset multi-moving-object network and numbered, and a system communication topology is set, specifically as follows:
arranging a plurality of moving bodies in a preset multi-moving body network and numbering the moving bodies, wherein the number of a leader is 0, the number of followers is i, i is 1,2,3, the. The interaction between the followers and the leader is unidirectional, only the leader is allowed to send information to the followers, and at least one follower can obtain the information of the leader; the follower will be described using the graph GCommunication topology between A ═ aij]N×NIs an adjacent matrix, L is a Laplacian matrix; definition biRepresenting the communication weight between the ith follower and the leader, b if the follower can receive the information of the leaderi> 0, otherwise bi0, B ═ diag { B1,…,bN}。
Further, the establishing of the motion model of the moving body in the step 2 is specifically as follows:
the multi-motion system is a second-order nonlinear system, wherein the dynamic model of the follower is as follows:
Figure BDA0002151112210000021
Figure BDA0002151112210000022
the leader's kinetic model was:
Figure BDA0002151112210000023
Figure BDA0002151112210000024
wherein ,
Figure BDA0002151112210000025
xi,vie R represents the position and speed state of the ith moving body, f (t, x)i,vi) Is a non-linear term of a moving body, ui,die.R denotes the control input and external disturbance of the ith follower, x0、v0Respectively representing the position, speed state, f (t, x) of the leader0,v0) Is a non-linear term of the leader.
Further, the establishment of the control target in step 2 is specifically as follows:
to pair
Figure BDA0002151112210000031
Epsilon > 0 and nu > 0 are present so that
Figure BDA0002151112210000032
And
Figure BDA0002151112210000033
is formed, wherein hiAnd
Figure BDA0002151112210000034
position information and velocity formation information, h, of the ith moving body, respectively0、
Figure BDA0002151112210000035
Respectively, position information and speed formation information of the leader.
Further, the construction of the formation control algorithm based on the sampled data and the observer in step 3 is specifically as follows:
aiming at the system model and the control target, the following formation control algorithm is constructed:
Figure BDA0002151112210000036
Figure BDA0002151112210000037
Figure BDA0002151112210000038
wherein ,
Figure BDA0002151112210000039
and
Figure BDA00021511122100000310
respectively represent xii,1 and ξi,2Is determined by the estimated value of (c),
Figure BDA00021511122100000311
Figure BDA00021511122100000312
wi＝ui/μ2，μ,k1,k2,a1,a2is a constant; t is tkkT denotes the time at which the data sample is taken, k 0,1, …, T > 0 denotes the sampling period and satisfies
Figure BDA00021511122100000313
χ,
Figure BDA00021511122100000314
σ is all constants greater than 0.
Further, each moving body described in step 4 periodically acquires the position information of the neighbors or the leaders through the wireless communication network, fuses the acquired data, executes a formation algorithm, and realizes formation control, specifically as follows:
step 4.1, according to the designed sampling period, the moving body receives self position information and formation information sent by a computer every other corresponding time, and the distance between the moving body and a neighbor or the distance between the moving body and a leader, which is measured by a built-in sensor, is collected;
4.2, fusing the received data;
and 4.3, executing a formation control algorithm to update the acceleration of the moving body, so that the moving body adjusts the self motion track according to the formation requirement.
Compared with the prior art, the invention has the following remarkable advantages: (1) the established dynamic model of the moving body considers nonlinear terms and external interference and can better accord with practical application; (2) based on a distributed consistency protocol, without depending on global information, each moving body can realize formation control only by using relative position information of a neighbor moving body or a leader as control input, and the method has the advantage of easy realization; (3) a control protocol is constructed based on the sampling data, and the moving body only needs to periodically collect data and update control input, so that the communication cost and network resources are saved; (4) the observer is used for estimating the speed of the moving body, the sensor module arranged in the moving body is provided with a sensor for measuring the distance, so that the speed information of the moving body is measured simply, conveniently and accurately, the formation information can be adjusted, the moving track of the moving body is adjusted, and the formation changing or obstacle avoiding capability is improved.
Drawings
Fig. 1 is a schematic structural diagram of a multi-moving-body system formation control device based on sampling data.
FIG. 2 is a flow chart of the multi-body moving system formation control method based on the sampling data.
Fig. 3 is a communication topology diagram of the multi-motor system of the present invention.
Detailed Description
The invention relates to a multi-moving body system formation control device based on sampling data, which comprises an image acquisition processing module, a sensor module, a wireless communication module and a control module;
the image acquisition processing module is used for shooting moving images of moving bodies, calculating actual position coordinates by a computer, feeding back the actual position coordinates to the corresponding moving bodies, acquiring information of surrounding environment and dynamically adjusting formation information;
the sensor module is arranged on the moving body and used for measuring the distance between the moving body and a neighbor or the distance between the moving body and a leader;
the wireless communication module is used for communication between moving bodies and between the moving bodies and a computer;
the control module is arranged on the moving body and used for fusing the collected data and executing a formation control algorithm.
A multi-moving body system formation control method based on sampling data comprises the following steps:
step 1: arranging a plurality of moving bodies in a preset multi-moving-body network, numbering the moving bodies, and setting a system communication topology;
step 2: establishing a motion model of a moving body, giving formation information and establishing a control target;
and step 3: constructing a formation control algorithm based on the sampling data and the observer;
and 4, step 4: each moving body periodically acquires the position information of neighbors or leaders through a wireless communication network, fuses the acquired data, executes a formation algorithm and realizes formation control.
Further, in step 1, a plurality of moving objects are arranged in a preset multi-moving-object network and numbered, and a system communication topology is set, specifically as follows:
arranging a plurality of moving bodies in a preset multi-moving body network and numbering the moving bodies, wherein the number of a leader is 0, the number of followers is i, i is 1,2,3, the. The interaction between the followers and the leader is unidirectional, only the leader is allowed to send information to the followers, and at least one follower can obtain the information of the leader; the communication topology between followers is described using fig. G, where a ═ aij]N×NIs an adjacent matrix, L is a Laplacian matrix; definition biRepresenting the communication weight between the ith follower and the leader, b if the follower can receive the information of the leaderi> 0, otherwise bi0, B ═ diag { B1,…,bN}。
Further, the establishing of the motion model of the moving body in the step 2 is specifically as follows:
the multi-motion system is a second-order nonlinear system, wherein the dynamic model of the follower is as follows:
Figure BDA0002151112210000051
Figure BDA0002151112210000052
the leader's kinetic model was:
Figure BDA0002151112210000053
Figure BDA0002151112210000054
wherein ,
Figure BDA0002151112210000055
xi,vie R represents the position and speed state of the ith moving body, f (t, x)i,vi) Is a non-linear term of a moving body, ui,die.R denotes the control input and external disturbance of the ith follower, x0、v0Respectively representing the position, speed state, f (t, x) of the leader0,v0) Is a non-linear term of the leader.
Further, the establishment of the control target in step 2 is specifically as follows:
to pair
Figure BDA0002151112210000056
Epsilon > 0 and nu > 0 are present so that
Figure BDA0002151112210000057
And
Figure BDA0002151112210000058
is formed, wherein hiAnd
Figure BDA0002151112210000059
position information and velocity formation information, h, of the ith moving body, respectively0、
Figure BDA00021511122100000510
Respectively, position information and speed formation information of the leader.
Further, the construction of the formation control algorithm based on the sampled data and the observer in step 3 is specifically as follows:
aiming at the system model and the control target, the following formation control algorithm is constructed:
Figure BDA00021511122100000511
Figure BDA00021511122100000512
Figure BDA00021511122100000513
wherein ,
Figure BDA0002151112210000061
and
Figure BDA0002151112210000062
respectively represent xii,1 and ξi,2Is determined by the estimated value of (c),
Figure BDA0002151112210000063
Figure BDA0002151112210000064
wi＝ui/μ2，μ,k1,k2,a1,a2is a constant to be constructed; t is tkkT denotes the time at which the data sample is taken, k 0,1, …, T > 0 denotes the sampling period and satisfies
Figure BDA0002151112210000065
χ,
Figure BDA0002151112210000066
σ is all constants greater than 0. The constant to be constructed can be calculated according to a system dynamics model and a Lyapunov stability criterion.
Further, each moving body described in step 4 periodically acquires the position information of the neighbors or the leaders through the wireless communication network, fuses the acquired data, executes a formation algorithm, and realizes formation control, specifically as follows:
step 4.1, according to the designed sampling period, the moving body receives self position information and formation information sent by a computer every other corresponding time, and the distance between the moving body and a neighbor or the distance between the moving body and a leader, which is measured by a built-in sensor, is collected;
4.2, fusing the received data;
and 4.3, executing a formation control algorithm to update the acceleration of the moving body, so that the moving body adjusts the self motion track according to the formation requirement.
The invention is described in further detail below with reference to the figures and specific examples.
Examples
With reference to fig. 1, the device for controlling formation of a multi-moving-body system based on sampled data according to the present invention comprises an image acquisition and processing module, a sensor module, a wireless communication module and a control module;
the image acquisition processing module comprises a camera and a computer, wherein the camera is fixed at a certain height and mainly completes two tasks: firstly, shooting a moving image of a moving body by a camera, transmitting the moving image to a computer for processing, calculating an actual position coordinate, and feeding the actual position coordinate back to the corresponding moving body; secondly, the system is also responsible for collecting information of surrounding environment, such as barrier information, so as to dynamically adjust formation information and feed back the formation information to a corresponding moving body;
the sensor module is arranged on the moving body, comprises an ultrasonic sensor and an infrared sensor and is used for measuring the distance between the sensor module and a neighbor or a leader;
the wireless communication module is used for communication between the moving bodies and the computer; the moving bodies and the neighbors are communicated to acquire distance information measured by the sensors, and the moving bodies and the computer are communicated to transmit the position coordinates of the calculated moving bodies to the moving bodies;
the control module is arranged on the moving body and used for fusing the collected data and executing a formation control algorithm.
The dynamic model of the moving body established by the invention considers the nonlinear term and the external interference and can better accord with the practical application;
with reference to fig. 2, a method for controlling formation of a multi-moving body system based on sampled data includes the following steps:
step 1: arranging a plurality of moving bodies in a preset multi-moving-body network, numbering the moving bodies, and setting a system communication topology, wherein the method specifically comprises the following steps:
arranging a plurality of moving bodies and numbering in a given horizontal plane moving area, wherein the number of a leader is 0, the number of followers is i, i is 1,2,3,.., N, N is more than or equal to 3, each follower can communicate with the moving bodies adjacent to the follower to acquire the position information of the follower, and the interaction is bidirectional; the interaction between the followers and the leader is unidirectional, only the leader is allowed to send information to the followers, and at least one follower can obtain the information of the leader; the communication topology between the followers is described using fig. G, where a ═ aij]N×NIs an adjacent matrix, L is a Laplacian matrix; definition biRepresenting the communication weight between the ith follower and the leader, b if the follower can receive the information of the leaderi> 0, otherwise bi0, and B ═ diag { B1,…,bN}. The communication topology of the moving body is shown in fig. 3.
Step 2: establishing a motion model of a moving body, giving formation information and establishing a control target, wherein the specific steps are as follows:
establishing a motion model for a single moving body, and modeling by using a second-order nonlinear band interference system, wherein the dynamic model of a follower is as follows:
Figure BDA0002151112210000071
the leader's kinetic model was:
Figure BDA0002151112210000072
wherein, it is to
Figure BDA0002151112210000073
xi,vi,yie.R represents the ith moving body position, speed and output, f (t, x)i,vi) Is a non-linear part of a moving body, ui,diE R, i e Γ represent the ith follower's control input and bounded external disturbance, i.e., | di|＜D,D＞0。
According to the established dynamic model, the formation control targets to be realized by the system are as follows:
Figure BDA0002151112210000074
epsilon > 0 and nu > 0 exist so that the formula (3) holds
Figure BDA0002151112210000081
wherein hiAnd
Figure BDA0002151112210000082
respectively, the position information and the speed formation information of the ith moving body, and the formation information can be adjusted in real time according to the surrounding environment or formation requirement of the moving body.
And step 3: constructing a formation control algorithm based on the sampling data and the observer, and specifically comprising the following steps:
according to given hardware conditions, the sensor module of each moving body can only measure and acquire position information, and speed information cannot be acquired, so an observer needs to be added to estimate the information which cannot be acquired when control input is constructed, and a specific algorithm is as follows:
Figure BDA0002151112210000083
wherein ,
Figure BDA0002151112210000084
and
Figure BDA0002151112210000085
respectively represent xii,1 and ξi,2Is determined by the estimated value of (c),
Figure BDA0002151112210000086
Figure BDA0002151112210000087
wi＝ui/μ2，μ,k1,k2is a constant to be constructed, and1,a2is that make
Figure BDA0002151112210000088
Constants for the Hurwitz matrix; t is tkkT denotes the sampling time, k 0,1, … T > 0 denotes the sampling period and satisfies
Figure BDA0002151112210000089
χ,
Figure BDA00021511122100000810
σ is all constants greater than 0. The constant to be constructed can be calculated according to a system dynamics model and a Lyapunov stability criterion.
Construction error System ei＝[ei,1,ei,2]T, wherein
Figure BDA00021511122100000811
Order to
Figure BDA00021511122100000812
Constructing a lyapunov function as shown in (5):
Figure BDA00021511122100000813
wherein
Figure BDA00021511122100000814
P is a positive definite symmetric matrix and satisfies
Figure BDA00021511122100000815
Has STP+PS≤-τINIt is true that the first and second sensors,
Figure BDA0002151112210000091
according to the Lyapunov stability criterion, backstepping method can be adopted to prove that omega is more than 0 under the control protocol (4), so that
Figure BDA0002151112210000092
This shows that the system can realize substantial consistency, further shows that the control target (3) can be realized, and in addition, a proper value of the parameter to be constructed can be calculated according to the inequality and is brought into the control algorithm (4) to obtain a final actual control algorithm. For example, case of fig. 3: n is 5, if f (t, x)i,vi)＝0.1cosxi+0.1sinvi，di0.01i +0.03cos (t), i belongs to {1, …,5}, and the parameter mu of the control protocol can be calculated to be 4, k according to the Lyapunov stability criterion1＝4，k2T is 2.01. The distributed consistency protocol does not depend on global information, each moving body can realize formation control only by using the relative position information of the neighbor moving body or the leader as control input, and the distributed consistency protocol has the advantage of easy realization.
And 4, step 4: each moving body periodically acquires the position information of a neighbor or a leader through a wireless communication network, fuses the acquired data, executes a formation algorithm, and realizes formation control, which specifically comprises the following steps:
step 4.1, according to the constructed sampling period, the moving body receives self position information and formation information sent by a computer every other corresponding time, and collects the distance between the moving body and a neighbor or a leader, which is measured by a built-in sensor;
4.2, fusing the received data;
and 4.3, executing a formation control algorithm to update the acceleration of the moving body, so that the moving body adjusts the self motion track according to the formation requirement.
The invention constructs a control protocol based on the sampling data, and the moving body only needs to periodically collect data and update the control input, thereby saving the communication cost and the network resource; the observer is used for estimating the speed of the moving body, the sensor module arranged in the moving body is provided with a sensor for measuring the distance, so that the speed information of the moving body is measured simply, conveniently and accurately, the formation information can be adjusted, the moving track of the moving body is adjusted, and the formation changing or obstacle avoiding capability is improved.

Claims (7)

1. A multi-moving body system formation control device based on sampling data is characterized by comprising an image acquisition processing module, a sensor module, a wireless communication module and a control module;
the image acquisition processing module is used for shooting moving images of moving bodies, calculating actual position coordinates by a computer, feeding back the actual position coordinates to the corresponding moving bodies, acquiring information of surrounding environment and dynamically adjusting formation information;
the sensor module is arranged on the moving body and used for measuring the distance between the moving body and a neighbor or the distance between the moving body and a leader;
the wireless communication module is used for communication between moving bodies and between the moving bodies and a computer;
the control module is arranged on the moving body and used for fusing the collected data and executing a formation control algorithm.
2. A multi-moving body system formation control method based on sampling data is characterized by comprising the following steps:
step 1: arranging a plurality of moving bodies in a preset multi-moving-body network, numbering the moving bodies, and setting a system communication topology;
step 2: establishing a motion model of a moving body, giving formation information and establishing a control target;
and step 3: constructing a formation control algorithm based on the sampling data and the observer;
and 4, step 4: each moving body periodically acquires the position information of neighbors or leaders through a wireless communication network, fuses the acquired data, executes a formation algorithm and realizes formation control.
3. The sampled data-based multi-moving body system formation control method according to claim 2, wherein the step 1 arranges a plurality of moving bodies in a preset multi-moving body network and numbers the moving bodies, and sets a system communication topology, specifically as follows:
arranging a plurality of moving bodies in a preset multi-moving body network and numbering the moving bodies, wherein the number of a leader is 0, the number of followers is i, i is 1,2,3, the. The interaction between the followers and the leader is unidirectional, only the leader is allowed to send information to the followers, and at least one follower can obtain the information of the leader; the communication topology between followers is described using fig. G, where a ═ aij]N×NIs an adjacent matrix, L is a Laplacian matrix; definition biRepresenting the communication weight between the ith follower and the leader, b if the follower can receive the information of the leaderi> 0, otherwise bi0, B ═ diag { B1,…,bN}。
4. The sampled-data-based multi-moving-body-system formation control method according to claim 2 or 3, wherein the motion model of the moving body is established in step 2, specifically as follows:
the multi-motion system is a second-order nonlinear system, wherein the dynamic model of the follower is as follows:
Figure FDA0002151112200000021
Figure FDA0002151112200000022
the leader's kinetic model was:
Figure FDA0002151112200000023
Figure FDA0002151112200000024
wherein ,
Figure FDA0002151112200000025
xi,vie R represents the position and speed state of the ith moving body, f (t, x)i,vi) Is a non-linear term of a moving body, ui,die.R denotes the control input and external disturbance of the ith follower, x0、v0Respectively representing the position, speed state, f (t, x) of the leader0,v0) Is a non-linear term of the leader.
5. The method for controlling formation of a multi-body moving system based on sampled data as claimed in claim 4, wherein the establishment of the control objective in step 2 is as follows:
to pair
Figure FDA0002151112200000026
Epsilon > 0 and nu > 0 are present so that
Figure FDA0002151112200000027
And
Figure FDA0002151112200000028
is formed, wherein hiAnd
Figure FDA0002151112200000029
position information and velocity formation information, h, of the ith moving body, respectively0、
Figure FDA00021511122000000210
Respectively, position information and speed formation information of the leader.
6. The sampled data-based multi-body system formation control method according to claim 5, wherein the formation control algorithm based on the sampled data and the observer is constructed in step 3, and specifically comprises the following steps:
aiming at the system model and the control target, the following formation control algorithm is constructed:
Figure FDA00021511122000000211
Figure FDA00021511122000000212
Figure FDA00021511122000000213
wherein ,
Figure FDA00021511122000000214
and
Figure FDA00021511122000000215
respectively represent xii,1 and ξi,2Is determined by the estimated value of (c),
Figure FDA00021511122000000216
Figure FDA00021511122000000217
wi＝ui/μ2，μ,k1,k2,a1,a2is a constant; t is tkkT denotes the time at which the data sample is taken, k 0,1, …, T > 0 denotes the sampling period and satisfies
Figure FDA0002151112200000031
χ,
Figure FDA0002151112200000032
σ is all constants greater than 0.
7. The multi-moving body system queuing control method based on sampled data as claimed in claim 2 or 6, wherein each moving body in step 4 periodically acquires the position information of the neighbors or leaders through the wireless communication network, fuses the acquired data, executes the queuing algorithm, and realizes the queuing control, specifically as follows:
step 4.1, according to the designed sampling period, the moving body receives self position information and formation information sent by a computer every other corresponding time, and the distance between the moving body and a neighbor or the distance between the moving body and a leader, which is measured by a built-in sensor, is collected;
4.2, fusing the received data;
and 4.3, executing a formation control algorithm to update the acceleration of the moving body, so that the moving body adjusts the self motion track according to the formation requirement.
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