CN114545773A - Heterogeneous multi-intelligent robot system modeling and distributed consistency control method - Google Patents

Heterogeneous multi-intelligent robot system modeling and distributed consistency control method Download PDF

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CN114545773A
CN114545773A CN202210156622.8A CN202210156622A CN114545773A CN 114545773 A CN114545773 A CN 114545773A CN 202210156622 A CN202210156622 A CN 202210156622A CN 114545773 A CN114545773 A CN 114545773A
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黄毅
张世豪
王佩良
许文韬
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Shandong New Generation Information Industry Technology Research Institute Co Ltd
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Abstract

The invention provides a heterogeneous multi-intelligent robot system modeling and distributed consistency control method, which comprises the following steps: step 1: a plurality of intelligent robots with dynamics of different orders arranged in the same working environment are used as research objects and are numbered; step 2: establishing a discrete time dynamic model according to information characteristics of the heterogeneous multi-intelligent robot that the running state and the speed are subjected to non-convex constraint and the like; and step 3: and designing and executing a corresponding distributed consistency control algorithm according to the dynamic model, so that the heterogeneous multi-intelligent robot system realizes distributed consistency motion. The invention is based on a heterogeneous multi-intelligent-robot system with non-convex constrained speed, and ensures that the running states of all intelligent robots are consistent by utilizing the position state information of the neighboring intelligent robots.

Description

Heterogeneous multi-intelligent robot system modeling and distributed consistency control method
Technical Field
The invention relates to a heterogeneous multi-intelligent-robot discrete-time system modeling and distributed consistency control method with speed subjected to non-convex constraint, and belongs to the technical field of intelligent robot control.
Background
In recent years, with the progress of science and technology and the development of society, people hope to be more free from complicated daily affairs, and the intelligent robot as an intelligent product capable of replacing labor force attracts people's extensive research interest, and the market development of the intelligent robot is promoted. In addition, the development of the intelligent robot industry as an important mark for measuring the level of technological innovation and high-end manufacturing industry of a country is receiving high attention from all countries of the world. Many problems faced by people today are very complex, multiple disciplines are required to be crossed, multiple systems are required to be cooperated to be properly solved, in the field of robots, along with continuous extension and expansion of service scenes of the robots, intelligent robots of single systems cannot meet all requirements of human social life, and efficient control of the robots is required to be achieved through a mode of modeling heterogeneous robot systems. The heterogeneous multi-intelligent robot system has a very wide application prospect, can be applied to various important aspects such as satellite formation control, sensor networks, target tracking, cooperative control of unmanned spacecrafts and distributed robots, and the like, and in recent years, a plurality of students are interested in the contents, but most research results are concentrated on the consistency of homogeneous or low-order heterogeneous multi-intelligent robot systems, and the research on the distributed consistency of heterogeneous multi-intelligent robots under high-order non-convex constraint is very lacking. Different from the practical situation of many applications, the intelligent robots are set to have the same system in many scenes, and the problem of distributed consistency of the heterogeneous multi-robot system still needs to be solved urgently, so that research and discussion in wider engineering application are needed. For example, the heterogeneous multi-intelligent robot system includes a plurality of information processing subsystems, such as planning decision, information fusion, automatic driving, two-dimensional or three-dimensional visual processing, and the like, it is emphasized that the subsystems are inseparable, and they must be mutually coordinated and information shared, the subsystems respectively and independently complete a large number of tasks and effectively form a whole to complete the total tasks, and their technical cooperation can ensure stable and efficient operation of the heterogeneous multi-intelligent robot system. In addition, the robot receives various constraint conditions in actual operation, so that the operation state of the robot is constrained in a closed set. Therefore, the research of the heterogeneous multi-robot system under the non-convex constraint and the distributed consistency control method has important theoretical and practical significance. Meanwhile, a system model of the heterogeneous robot system has high-order heterogeneous dynamics characteristics, model conversion is complex, and results prove that the corresponding control protocol has robustness on any bounded communication delay and a directed spanning tree under each specific time interval. On the basis, the distributed consistency of the high-order heterogeneous multi-intelligent robot system is obtained. The patent mainly provides a system modeling and control algorithm which is not convexly constrained and can realize distributed consistency from the control angle of a heterogeneous multi-intelligent robot system. The main research content comprises three aspects of modeling of a heterogeneous multi-intelligent robot system, design of a distributed consistency control algorithm and selection of control parameters.
The existing constrained heterogeneous multi-intelligent-robot system modeling is mainly focused on low-order modeling, and model construction is not carried out on a high-order multi-intelligent-robot system. To this end, the patent is based on modeling heterogeneous multi-robot systems without convex velocity constraints.
The distributed consistency control algorithm is mainly designed to keep consistency of each system of the heterogeneous multi-intelligent robot in position and speed, so that influence of external interference is counteracted, and the operation burden of a computer is reduced; the second part is provided with adjustable control parameters, and the stable and efficient operation of engineering projects is ensured by using the information interaction of intelligent robots under different systems.
Control parameter selection is an important step in the control field, and an appropriate controller is designed aiming at the uncertainty of the internal structure and the external disturbance of a system, so that a certain index reaches and keeps the optimal or approximately optimal. By adjusting the control parameters, the control parameters can be adjusted in real time under the condition of giving initial values, so that the aim of optimizing a control result is fulfilled.
In summary, the heterogeneous multi-intelligent-robot system modeling and distributed consistency control method based on the non-convex constraint achieves the purpose of controlling the cooperative operation of the multi-intelligent robots by modeling the heterogeneous multi-intelligent robots in the study object and selecting appropriate parameters by adopting a distributed consistency control algorithm.
Disclosure of Invention
The invention aims to provide a non-convex constrained modeling and distributed consistency control method for a heterogeneous multi-intelligent robot system, which is used for carrying out distributed consistency control on a plurality of intelligent robot systems in the same working environment on the premise that each intelligent robot normally operates, so that the aim that the multi-system robot can efficiently work is fulfilled.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a heterogeneous multi-intelligent-robot-system modeling and distributed consistency control method subject to non-convex constraint comprises the following steps:
the method comprises the following steps: a plurality of robots operating in the same working environment and having dynamics of different orders are taken as research objects, the robots of each order are numbered, the ith robot is represented by a symbol i, and the symbol i1,i3,...,ilRepresenting first, second to l order kinetic robot indices, n1,n2,...,σlRespectively representing the number of first, second to l order robots.
Step two: establishing a discrete time dynamic model according to information characteristics such as the dynamic state and non-convex speed constraint of the operation of the heterogeneous multi-intelligent robot system;
step three: according to the heterogeneous robot system and the built model, a consistency control target is determined, namely the positions and the speeds of all heterogeneous intelligent robots are consistent:
Figure BDA0003512472190000031
Figure BDA0003512472190000032
where k is the time series index, xi(k) Is the position vector, x, of the ith robotj(k) Is the position vector of the jth robot, vi(k) For the ith robotVelocity vector, vj(k) Is the velocity vector of the jth robot;
step four: considering that a heterogeneous multi-intelligent robot system comprises a first order, a second order and a third order … l; designing a corresponding control algorithm for each robot according to the non-convex constraint condition and the distributed consistency control method of each robot;
step five: satisfying a non-convex constraint set according to the speed of each actual heterogeneous multi-intelligent robot system
Figure BDA0003512472190000033
Writing the designed control method into a control program of the corresponding robot;
step six: when consistency control needs to be executed, the automatic circulation control program acquires the position state information of the neighbor robot in real time through a wireless communication network established by the robot system, and automatically adjusts the position state until a consistency control target is reached.
Preferably, in the second step, a discrete time dynamical model is established according to the dynamic state of the heterogeneous multi-intelligent robot system and the non-convex speed constraint information as follows:
Figure BDA0003512472190000041
in the formula: by symbols
Figure BDA0003512472190000042
Respectively representing the position state variables and symbols of the first-order, second-order to l-order robots
Figure BDA0003512472190000043
Is a parameter variable of order q, a sign
Figure BDA0003512472190000044
Control input of ith robot representing dynamics of order l, symbol n1,n2,...,σlRespectively representing first, second to l order dynamic machinesThe number of people, denoted by the symbol k as a discrete time sequence, the symbol T as a system sample time,
Figure BDA0003512472190000045
expressed as a non-convex constraint operator,
Figure BDA0003512472190000046
expressed as a non-convex constrained set of velocities for the ith robot of the m-order dynamics,
Figure BDA0003512472190000047
is a set of r-dimensional real column vectors, and the initial state of the system is
Figure BDA0003512472190000048
And
Figure BDA0003512472190000049
and the l-order variable of each heterogeneous multi-robot system
Figure BDA00035124721900000410
Are all in a non-empty set
Figure BDA00035124721900000411
And (4) the following steps.
Preferably, the distributed consistency control of the multiple intelligent robots in step three is that the control of the intelligent robot system with the dynamics of order l-1 is in the following form:
Figure BDA0003512472190000051
wherein
Figure BDA0003512472190000052
In order to control the feedback coefficient(s),
Figure BDA0003512472190000053
representing the connectivity of the im < th > m-th order agent to the neighbor j,
Figure BDA0003512472190000054
the neighbor robot set of the im-th intelligent robot of the m-th order is shown,
Figure BDA0003512472190000055
which is indicative of a control feedback factor,
Figure BDA0003512472190000056
a position state variable representing a neighboring smart robot,
Figure BDA0003512472190000057
denotes the ithmAnd (4) position state variables of the m-order intelligent robot.
Preferably, the
Figure BDA0003512472190000058
It is also to satisfy: for all of the i's, the average value of i,
Figure BDA0003512472190000059
Figure BDA00035124721900000510
wherein
Figure BDA00035124721900000511
Andρ iare two constants greater than zero;
preferably, the designed control method is written into a control program of the corresponding robot, and the values of the control parameters are required to meet the following requirements:
Figure BDA00035124721900000512
the invention has the advantages that:
1. heterogeneous dynamic models of different robots in actual operating environments are established;
2. according to the established system model, a distributed consistency control method of the heterogeneous system is provided, and a system parameter selection and adjustment scheme is given, so that the states of the heterogeneous system are consistent finally.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a schematic view of the flow structure of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A heterogeneous multi-intelligent-robot-system modeling and distributed consistency control method subject to non-convex constraint comprises the following steps:
the method comprises the following steps: a plurality of robots operating in the same working environment and having dynamics of different orders are taken as research objects, the robots of each order are numbered, the ith robot is represented by a symbol i, and the symbol i1,i3,...,ilRepresenting first, second to l order kinetic robot indices, n1,n2,...,σlRespectively representing the number of first, second to l order robots.
Step two: establishing a discrete time dynamic model according to information characteristics such as the dynamic state of the operation of the heterogeneous multi-intelligent robot system, non-convex speed constraint and the like as follows:
Figure BDA0003512472190000071
in the formula: by symbols
Figure BDA0003512472190000072
Respectively representing the position state variables and symbols of the first-order, second-order to l-order robots
Figure BDA0003512472190000073
Is a parameter variable of order q, a sign
Figure BDA0003512472190000074
Control input of ith robot representing dynamics of order l, symbol n1,n2,...,σlRespectively representing the number of first-order, second-order to l-order dynamic robots, representing a discrete time sequence by a symbol k, representing a system sampling time by a symbol T,
Figure BDA0003512472190000075
expressed as a non-convex constraint operator,
Figure BDA0003512472190000076
expressed as a non-convex constrained set of velocities for the ith robot of the m-order dynamics,
Figure BDA0003512472190000077
is a set of r-dimensional real column vectors, and the initial state of the system is
Figure BDA0003512472190000078
And
Figure BDA0003512472190000079
and the l-order variable of each heterogeneous multi-robot system
Figure BDA00035124721900000710
Are all in a non-empty set
Figure BDA00035124721900000711
And (4) the following steps.
Step three: according to the heterogeneous robot system and the built model, a consistency control target is determined, namely the positions and the speeds of all heterogeneous intelligent robots are consistent:
Figure BDA0003512472190000081
Figure BDA0003512472190000082
where k is the time series index, xi(k) Is the position vector, x, of the ith robotj(k) Is the position vector of the jth robot, vi(k) Is the velocity vector of the i-th robot, vj(k) Is the velocity vector of the jth robot;
and designing a corresponding distributed control method according to the consistency control target. Firstly, the control law of the multi-intelligent robot system with first-order dynamics is designed as follows:
Figure BDA0003512472190000083
Figure BDA0003512472190000084
is a weight, represents the ith1The connectivity of the first-order agent to neighbor j,
Figure BDA0003512472190000085
is the ith1The intelligent robots are adjacent to the robot set,
Figure BDA0003512472190000086
a location state variable representing a neighbor agent j.
Then, by using a control law design method of the first-order kinetic intelligent robot, the control law of the second-order kinetic intelligent robot is designed:
Figure BDA0003512472190000087
wherein the time index k is not less than 0, and
Figure BDA0003512472190000088
in order to control the feedback coefficient(s),
Figure BDA0003512472190000089
denotes the ith2The connectivity of each second-order agent to neighbor j,
Figure BDA00035124721900000810
is represented by the ith2A set of neighboring intelligent robots of an individual intelligent robot.
The control law of the intelligent robot system with the dynamics of m-3, 4, …, l-1 order is designed according to the control laws of the first-order and second-order intelligent robots, and the like, and the control laws of the intelligent robot system with the dynamics of m-3, 4, … and l-1 order have the following forms:
Figure BDA00035124721900000811
wherein
Figure BDA0003512472190000091
In order to control the feedback coefficient(s),
Figure BDA0003512472190000092
denotes the ithmThe connectivity of each m-th order agent to neighbor j,
Figure BDA0003512472190000093
is represented by the imA set of neighbor robots of the m-order intelligent robots,
Figure BDA0003512472190000094
which is indicative of a control feedback factor,
Figure BDA0003512472190000095
a position state variable representing a neighboring smart robot,
Figure BDA0003512472190000096
denotes the ithmIntelligent machine of m rankA position state variable of the person.
Preferably, the
Figure BDA0003512472190000097
It is also to satisfy: for all of the i's, the average value of i,
Figure BDA0003512472190000098
Figure BDA0003512472190000099
wherein
Figure BDA00035124721900000910
Andρ iare two constants greater than zero;
step four: considering a heterogeneous multi-intelligent robot system comprising a first order, a second order and a third order … l; designing a corresponding control algorithm for each robot according to the non-convex constraint condition and the distributed consistency control method of each robot;
step five: satisfying a non-convex constraint set according to the speed of each actual heterogeneous multi-intelligent robot system
Figure BDA00035124721900000911
The described
Figure BDA00035124721900000912
It is also to satisfy: for all of the i's, the average value of i,
Figure BDA00035124721900000913
wherein
Figure BDA00035124721900000914
Andρ itwo constants larger than zero are used for writing the designed control method into the control program of the corresponding robot, and the values of the control parameters are required to meet the following conditions:
Figure BDA00035124721900000915
step six: when consistency control needs to be executed, the automatic circulation control program is used for acquiring the position state information of the neighbor robot in real time and automatically adjusting the position state until a consistency control target is reached through a wireless communication network established by the robot system, wherein the communication condition of the wireless communication network meets the condition of the combined communication directed spanning tree.

Claims (5)

1. A heterogeneous multi-intelligent robot system modeling and distributed consistency control method is characterized by comprising the following steps:
the method comprises the following steps: a plurality of robots operating in the same working environment and having dynamics of different orders are taken as research objects, the robots of each order are numbered, the ith robot is represented by a symbol i, and the symbol i1,i3,...,ilRepresenting first, second to l order kinetic robot indices, n1,n2,...,σlRespectively representing the number of first, second to l order robots.
Step two: establishing a discrete time dynamic model according to information characteristics such as the dynamic state and non-convex speed constraint of the operation of the heterogeneous multi-intelligent robot system;
step three: according to the heterogeneous robot system and the built model, a consistency control target is determined, namely the positions and the speeds of all heterogeneous intelligent robots are consistent:
Figure FDA0003512472180000011
Figure FDA0003512472180000012
where k is the time series index, xi(k) Is the position vector, x, of the ith robotj(k) Is the position vector of the j-th robot, vi(k) Is the velocity vector of the i-th robot, vj(k) Is the velocity vector of the jth robot;
step four: considering that a heterogeneous multi-intelligent robot system comprises a first order, a second order and a third order … l; designing a corresponding control algorithm for each robot according to the non-convex constraint condition and the distributed consistency control method of each robot;
step five: satisfying a non-convex constraint set according to the speed of each actual heterogeneous multi-intelligent robot system
Figure FDA0003512472180000013
Writing the designed control method into a control program of the corresponding robot;
step six: when consistency control needs to be executed, the automatic circulation control program acquires the position state information of the neighbor robot in real time through a wireless communication network established by the robot system, and automatically adjusts the position state until a consistency control target is reached.
2. The modeling and distributed consistency control method for the heterogeneous multi-intelligent robot system according to claim 1, wherein in the second step, a discrete time dynamics model is established according to the dynamic state and the non-convex speed constraint information of the operation of the heterogeneous multi-intelligent robot system as follows:
Figure FDA0003512472180000021
in the formula: by symbols
Figure FDA0003512472180000022
Respectively representing the position state variables and symbols of the first-order, second-order to l-order robots
Figure FDA0003512472180000023
Is a parameter variable of order q, a sign
Figure FDA0003512472180000024
Express the first order kineticsControl input of i robots, symbol n1,n2,...,σlRespectively representing the number of first-order, second-order to l-order dynamic robots, representing a discrete time sequence by a symbol k, representing a system sampling time by a symbol T,
Figure FDA0003512472180000025
expressed as a non-convex constraint operator,
Figure FDA0003512472180000026
expressed as a non-convex constrained set of velocities for the ith robot of the m-order dynamics,
Figure FDA0003512472180000027
is a set of r-dimensional real column vectors, and the initial state of the system is
Figure FDA0003512472180000028
And
Figure FDA0003512472180000029
and the l-order variable of each heterogeneous multi-robot system
Figure FDA00035124721800000210
Are all in a non-empty set
Figure FDA00035124721800000211
And (4) inside.
3. The modeling and distributed consistency control method for the heterogeneous multi-intelligent-robot system according to claim 2, wherein the control law of the distributed consistency control for the multi-intelligent-robot in the third step is in the following form for the intelligent robot system with m-2, 3.
Figure FDA0003512472180000031
Wherein
Figure FDA0003512472180000032
In order to control the feedback coefficient(s),
Figure FDA0003512472180000033
denotes the ithmThe connectivity of each m-th order agent to neighbor j,
Figure FDA0003512472180000034
is represented by the imA set of neighbor robots of the m-order intelligent robots,
Figure FDA0003512472180000035
a position state variable representing a neighboring smart robot,
Figure FDA0003512472180000036
denotes the ithmAnd (4) position state variables of the m-order intelligent robot.
4. The heterogeneous multi-intelligent-robot-system modeling and distributed consistency control method according to claim 3, wherein the method comprises
Figure FDA0003512472180000037
It is also to satisfy: for all of the i's, the average value of i,
Figure FDA0003512472180000038
Figure FDA0003512472180000039
wherein
Figure FDA00035124721800000310
Andρ iare two constants greater than zero;
5. the heterogeneous multi-intelligent-robot-system modeling and distributed consistency control method according to claim 4, wherein the designed control method is written into a control program of a corresponding robot, and values of control parameters are required to satisfy:
T＜1,p(1)＝2,p(2)＝p(3)＝...＝p(l)＝3,
Figure FDA00035124721800000311
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115185189A (en) * 2022-09-06 2022-10-14 人工智能与数字经济广东省实验室(广州) Consistency optimal control method, system, device and medium with privacy protection

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115185189A (en) * 2022-09-06 2022-10-14 人工智能与数字经济广东省实验室(广州) Consistency optimal control method, system, device and medium with privacy protection
CN115185189B (en) * 2022-09-06 2023-09-05 人工智能与数字经济广东省实验室(广州) Consistency optimal control method, system, equipment and medium with privacy protection

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