CN108255169B - Vehicle and coordination control method of multiple vehicle networks - Google Patents
Vehicle and coordination control method of multiple vehicle networks Download PDFInfo
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Abstract
The invention provides a vehicle and a coordination control method of a multi-vehicle network, wherein the multi-vehicle network comprises a plurality of individual vehicles, and the coordination control method executed for each individual vehicle comprises the following steps: acquiring the current time position of an individual vehicle and the current time position of a neighbor vehicle of the individual vehicle; determining a symbol parameter of the individual vehicle and a symbol parameter of a neighbor vehicle of the individual vehicle; determining the sampling step length of the individual vehicle according to the symbol parameters of the individual vehicle, the symbol parameters of the neighbor vehicles of the individual vehicle and the weighted adjacency matrix of the multi-vehicle network; and controlling the individual vehicle according to the current time position of the individual vehicle, the current time position of the neighbor vehicle of the individual vehicle, the symbol parameter of the neighbor vehicle of the individual vehicle and the sampling step length. The invention can ensure that all vehicles reach two symmetrical bidirectional expected positions which are randomly appointed in two groups, and has high control precision.
Description
Technical Field
The invention relates to the technical field of control, in particular to a vehicle and a coordination control method of a multi-vehicle network.
Background
At present, a coordination control method of a multi-vehicle network is more and more widely applied to practical problems. This is mainly due to the fact that more and more actual tasks are complex and often difficult to accomplish by a single vehicle, but can be accomplished by cooperation between multiple vehicles. In addition, the efficiency of the vehicle system in the operation process can be improved through cooperation among the multiple vehicles, and further, when the working environment changes or the vehicle system is partially failed, the multiple vehicle system can still complete the preset task through the cooperation relationship of the multiple vehicle system. However, the prior art has the disadvantage that all vehicles can only reach the same designated position, and the coordination control task of different vehicles reaching different designated positions cannot be realized according to actual needs. In particular, when dealing with cross-scale coordinated control issues of multiple vehicles at different specified scales, it is often difficult to make effective use of the issues.
Disclosure of Invention
The object of the present invention is to solve at least one of the above technical drawbacks.
In order to achieve the above object, an aspect of the present invention provides a coordinated control method for a multi-vehicle network, wherein the multi-vehicle network includes a plurality of individual vehicles. For each individual vehicle, performing the steps of: a: acquiring the current time position of an individual vehicle and the current time position of a neighbor vehicle of the individual vehicle; b: determining a symbol parameter of the individual vehicle and a symbol parameter of a neighbor vehicle of the individual vehicle; c: determining a sampling step size of the individual vehicle according to the symbol parameters of the individual vehicle, the symbol parameters of the neighbor vehicles of the individual vehicle and the weighted adjacency matrix of the multi-vehicle network, wherein the sampling step size is non-negative and meets a preset weight rule condition; and D: controlling the individual vehicle according to the current time position of the individual vehicle, the current time position of the neighbor vehicle of the individual vehicle, the symbol parameter of the neighbor vehicle of the individual vehicle and the sampling step length.
In one embodiment of the present invention, the predetermined weight rule condition is:
wherein γ is the sampling step, aijIs an element of the ith row and jth column of the weighted adjacency matrix of the multi-vehicle network, NiN is the total number of vehicles in the multi-vehicle network.
In an embodiment of the present invention, the step D specifically includes: d1: according to the current time position of the individual vehicle, the current time position of the neighbor vehicle of the individual vehicle, the symbol parameter of the individual vehicle and the symbol parameter of the neighbor vehicle of the individual vehicle, through a formula
Determining a control quantity of an individual vehicle, wherein xi(t) is the position of the individual vehicle i at the current time t, xj(t) is the position of the neighbor vehicle j of the individual vehicle i at the current time t, σiIs a symbolic parameter, σ, of an individual vehiclejIs a symbolic parameter, u, of an individual vehicle ji(t) is a control quantity of the individual vehicle i at the current time t; and D2: according to the control quantity, the current time position of the individual vehicle and the sampling step length, the formula x is usedi(t+1)＝xi(t)+γui(t) determining a position of the individual vehicle at a next instant in time, wherein xi(t +1) is the position of the individual vehicle i at the next time t + 1.
According to the coordination control method of the multi-vehicle network, the coordination control task that different vehicles reach different specified positions can be realized according to actual needs by introducing the symbolic parameters, cross-scale coordination control of the multiple vehicles under two different specified scales is realized, and the control precision is high.
In another aspect of the present invention, a vehicle is provided, including: an acquisition module, configured to acquire a current-time position of the vehicle and a current-time position of a neighboring vehicle of the vehicle; a first determination module for determining a symbol parameter of the individual vehicle and a symbol parameter of a neighboring vehicle of the individual vehicle; the second determination module is used for determining the sampling step length of the individual vehicle according to the symbol parameters of the vehicle, the symbol parameters of the neighbor vehicles of the vehicle and the weighted adjacency matrix of the multi-vehicle network where the vehicle is located, wherein the sampling step length is non-negative and meets the preset weight rule condition; and the control module is used for controlling the vehicle according to the current time position of the vehicle, the current time positions of the neighbor vehicles of the vehicle, the symbol parameters of the neighbor vehicles of the vehicle and the sampling step length.
In one embodiment of the present invention, the predetermined weight rule condition is:
wherein γ is the sampling step, aijIs an element of the ith row and jth column of the weighted adjacency matrix of the multi-vehicle network, NiN is the total number of vehicles in the multi-vehicle network.
In one embodiment of the inventionThe control module specifically includes: a control amount determination unit configured to determine a control amount of the vehicle by the following formula according to a current-time position of the vehicle, a current-time position of a neighboring vehicle of the vehicle, a symbol parameter of the vehicle, and a symbol parameter of the neighboring vehicle of the vehicle:
wherein x isi(t) is the position of the vehicle i at the current time t, xj(t) is the current time position, σ, of the neighboring vehicle j of vehicle iiIs a symbolic parameter, σ, of vehicle ijIs a symbolic parameter of the vehicle j, ui(t) is a control quantity of the vehicle i at the current time t; and a position determination unit for determining a position of the vehicle at a next time by the following formula according to the control amount of the vehicle, the position of the vehicle at the current time, and the sampling step length: x is the number ofi(t+1)＝xi(t)+γui(t) wherein xi(t +1) is the position of the vehicle i at the next time t + 1.
According to the vehicle provided by the embodiment of the invention, through the introduction of the symbolic parameters, the coordination control task that different vehicles arrive at different specified positions can be realized according to actual needs, the cross-scale coordination control of a plurality of vehicles under two different specified scales is realized, and the control precision is high.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a coordinated control method of a multi-vehicle network according to one embodiment of the invention;
FIG. 2 is a schematic diagram of a multi-vehicle network in accordance with one embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a vehicle according to an embodiment of the present invention; and
fig. 4 is a schematic structural diagram of a control module according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative only and should not be construed as limiting the invention.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "connected" and "connected" are to be interpreted broadly, e.g., as being fixed or detachable or integrally connected; can be mechanically or electrically connected; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Fig. 1 is a flowchart of a coordinated control method of a multi-vehicle network according to an embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:
step S101, the position of the current time of the individual vehicle and the position of the current time of the neighbor vehicle of the individual vehicle are obtained.
Where a neighbor vehicle of an individual vehicle refers to the set of all other vehicles to which information flows. FIG. 2 is a schematic diagram of a multi-vehicle network, as shown in FIG. 2, with individual vehicles 2 and 4 being neighbors of an individual vehicle 5, and individual vehicles 1, 3, and 6 not being neighbors of an individual vehicle 5, in accordance with one embodiment of the present invention.
Specifically, the current-time positions of the own vehicle and its neighboring vehicles can be acquired by the camera device mounted on the individual vehicle.
Step S102, determining the symbol parameter of the individual vehicle and the symbol parameter of the neighbor vehicle of the individual vehicle.
Specifically, the symbol parameter of each individual vehicle may be specified in advance. Wherein individual vehicles in the multi-vehicle network are divided into two groups. The symbol parameter of an individual vehicle in one group is designated as +1 and the symbol parameter of an individual vehicle in the other group is designated as-1.
Step S103, determining the learning parameters of the individual vehicle according to the symbol parameters of the individual vehicle, the symbol parameters of the neighbor vehicles of the individual vehicle and the weighted adjacency matrix of the multi-vehicle network, wherein the learning parameters are non-negative and meet the preset weight rule condition.
In one embodiment of the invention, the predetermined weight rule condition is:
wherein γ is the sampling step, aijIs an element of the ith row and jth column of the weighted adjacency matrix of the multi-vehicle network, NiN is the total number of vehicles in the multi-vehicle network.
And step S104, controlling the individual vehicle according to the current time position of the individual vehicle, the current time position of the neighbor vehicle of the individual vehicle, the symbol parameter of the neighbor vehicle of the individual vehicle and the sampling step length.
Specifically, first, the control amount of the individual vehicle is determined by the following formula, based on the position of the individual vehicle at the present time, the positions of the neighboring vehicles of the individual vehicle at the present time, the symbol parameter of the individual vehicle, and the symbol parameters of the neighboring vehicles of the individual vehicle:
wherein x isi(t) is the position of the vehicle i at the current time t, xj(t) is the position of the neighbor vehicle j of vehicle i at the current time t, σiIs a symbolic parameter, σ, of an individual vehicle ijIs a symbolic parameter, u, of an individual vehicle ji(t) is a control amount of the vehicle i at the current time t.
Then, based on the control amount, the current time position of the individual vehicle, and the sampling step length, the position of the individual vehicle at the next time is determined by the following formula:
xi(t+1)＝xi(t)+γui(t)，
wherein x isi(t +1) is the position of the individual vehicle i at the next time t + 1.
According to the coordination control method of the multi-vehicle network, the coordination control task that different vehicles reach different specified positions can be realized according to actual needs by introducing the symbolic parameters, cross-scale coordination control of the multiple vehicles under two different specified scales is realized, and the control precision is high.
The invention further provides a vehicle.
Fig. 3 is a schematic structural view of a vehicle according to an embodiment of the present invention. As shown in fig. 3, the vehicle includes: the device comprises an acquisition module 10, a first determination module 20, a second determination module 30 and a control module 40.
The acquisition module 10 is configured to acquire a current time position of the vehicle and a current time position of a neighboring vehicle of the vehicle. For example, the acquisition module 10 may be a camera device mounted on a vehicle.
The first determination module 20 is used to determine the symbol parameters of the individual vehicle and the symbol parameters of the neighbour vehicles of the individual vehicle. Specifically, the symbol parameter of each individual vehicle may be specified in advance. Wherein individual vehicles in the multi-vehicle network are divided into two groups. The symbol parameter of an individual vehicle in one group is designated as +1 and the symbol parameter of an individual vehicle in the other group is designated as-1.
The second determination module 30 is configured to determine a sampling step size of the vehicle according to the symbol parameter of the individual vehicle, the symbol parameter of the neighboring vehicle of the individual vehicle, and the weighted adjacency matrix of the multi-vehicle network in which the vehicle is located, where the sampling step size is non-negative and satisfies a predetermined weight rule condition.
In one embodiment of the invention, the predetermined weight rule condition is:
wherein γ is the sampling step, aijIs an element of the ith row and jth column of the weighted adjacency matrix of the multi-vehicle network, NiN is the total number of vehicles in the multi-vehicle network.
The control module 40 is configured to control the vehicle according to a current time position of the vehicle, a current time position of a neighboring vehicle of the vehicle, a symbol parameter of the individual vehicle, a symbol parameter of the neighboring vehicle of the individual vehicle, and a sampling step size.
Fig. 4 is a schematic structural diagram of the control module 40 according to an embodiment of the present invention. As shown in fig. 4, the control module 40 includes a control amount determining unit 410 and a position determining unit 420.
The control amount determining unit 410 is configured to determine the control amount of the vehicle according to the current time position of the vehicle, the current time position of the neighboring vehicle of the vehicle, the symbol parameter of the individual vehicle, the symbol parameter of the neighboring vehicle of the individual vehicle, and the sampling step size, by the following formula:
wherein x isi(t) is the position of the vehicle i at the current time t, xj(t) is the position of the neighbor vehicle j of vehicle i at the current time t, σiIs a symbolic parameter, σ, of an individual vehicle ijIs a symbolic parameter, u, of an individual vehicle ji(t) is a control amount of the vehicle i at the current time t.
The position determination unit 420 is configured to determine the position of the individual vehicle at the next time by the following formula according to the control amount of the vehicle determined by the control amount determination unit 410 and the position of the vehicle at the current time:
xi(t+1)＝xi(t)+γui(t)，
wherein x isi(t +1) is the position of the vehicle i at the next time t + 1.
According to the vehicle provided by the embodiment of the invention, through the introduction of the symbolic parameters, the coordination control task that different vehicles arrive at different specified positions can be realized according to actual needs, the cross-scale coordination control of a plurality of vehicles under two different specified scales is realized, and the control precision is high.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (2)
1. A coordinated control method of a multi-vehicle network, characterized in that the multi-vehicle network comprises a plurality of individual vehicles, for each individual vehicle the following steps are performed:
a: acquiring the current time position of an individual vehicle and the current time position of a neighbor vehicle of the individual vehicle;
b: determining symbol parameters of the individual vehicles and symbol parameters of neighbor vehicles of the individual vehicles, wherein the symbol parameters of the individual vehicles and the symbol parameters of the neighbor vehicles of the individual vehicles are pre-specified, and the value of the symbol parameters is 1 or-1, so that the individual vehicles are divided into two groups, wherein the symbol parameters of the vehicles in one group of vehicles are 1, and the symbol parameters of the vehicles in the other group of vehicles are-1;
c: determining a sampling step size of the individual vehicle according to the symbol parameter of the individual vehicle, the symbol parameter of the neighbor vehicle of the individual vehicle and the weighted adjacency matrix of the multi-vehicle network, wherein the sampling step size is non-negative and meets a predetermined weight rule condition, and the predetermined weight rule condition is as follows:
wherein γ is the sampling step, aijIs an element of the ith row and jth column of the weighted adjacency matrix of the multi-vehicle network, NiThe vehicle is a neighbor vehicle set of the individual vehicle i, and n is the total number of vehicles in the multi-vehicle network; and
d: controlling the individual vehicle according to the current-time position of the individual vehicle, the current-time positions of the neighbor vehicles of the individual vehicle, the symbol parameters of the neighbor vehicles of the individual vehicle and the sampling step length;
wherein, the step D specifically comprises:
d1: determining a control quantity of the individual vehicle according to the current-time position of the individual vehicle, the current-time positions of the neighboring vehicles of the individual vehicle, the symbol parameter of the individual vehicle, and the symbol parameter of the neighboring vehicles of the individual vehicle by the following formula:
wherein x isi(t) is the position of the individual vehicle i at the current time t, xj(t) is the position of the neighbor vehicle j of the individual vehicle i at the current time t, σiIs a symbolic parameter, σ, of an individual vehicle ijIs a symbolic parameter, u, of an individual vehicle ji(t) is a control quantity of the individual vehicle i at the current time t, aijIs an element of the ith row and jth column of the weighted adjacency matrix of the multi-vehicle network, NiA set of neighbor vehicles that are the individual vehicle i; and
d2: determining the position of the individual vehicle at the next moment according to the control quantity, the position of the individual vehicle at the current moment and the sampling step length by the following formula: x is the number ofi(t+1)＝xi(t)+γui(t) wherein xi(t +1) is the position of the individual vehicle i at the next time t +1, and γ is the sampling step.
2. A vehicle, characterized by comprising:
an acquisition module, configured to acquire a current-time position of the vehicle and a current-time position of a neighboring vehicle of the vehicle;
the vehicle-mounted device comprises a first determination module, a second determination module and a control module, wherein the first determination module is used for determining a symbol parameter of the vehicle and a symbol parameter of a neighbor vehicle of the vehicle, the symbol parameters of the vehicle and the symbol parameters of the neighbor vehicle of the vehicle are pre-specified, the value of the symbol parameter is 1 or-1, so that a plurality of individual vehicles are divided into two groups, the symbol parameter of the vehicle in one group of vehicles is 1, and the symbol parameter of the vehicle in the other group of vehicles is-1;
a second determining module, configured to determine a sampling step length of the vehicle according to the symbol parameter of the vehicle, the symbol parameter of a neighboring vehicle of the vehicle, and a weighted adjacency matrix of a multi-vehicle network in which the vehicle is located, where the sampling step length is non-negative and meets a predetermined weight rule condition, and the predetermined weight rule condition is:
wherein γ is the sampling step, aijIs an element of the ith row and jth column of the weighted adjacency matrix of the multi-vehicle network, NiThe vehicle is a neighbor vehicle set of the individual vehicle i, and n is the total number of vehicles in the multi-vehicle network; and
the control module is used for controlling the vehicle according to the current time position of the vehicle, the current time positions of the neighbor vehicles of the vehicle, the symbol parameters of the neighbor vehicles of the vehicle and the sampling step length;
wherein, the control module specifically includes:
a control amount determination unit configured to determine a control amount of the vehicle by the following formula according to a current-time position of the vehicle, a current-time position of a neighboring vehicle of the vehicle, a symbol parameter of the vehicle, and a symbol parameter of the neighboring vehicle of the vehicle:
wherein x isi(t) is the position of the individual vehicle i at the current time t, xj(t) is the position of the neighbor vehicle j of the individual vehicle i at the current time t, σiIs a symbolic parameter, σ, of an individual vehicle ijIs a symbolic parameter, u, of an individual vehicle ji(t) is a control quantity of the individual vehicle i at the current time t, aijIs an element of the ith row and jth column of the weighted adjacency matrix of the multi-vehicle network, NiA set of neighbor vehicles that are the individual vehicle i; and
a position determination unit for determining a position of the vehicle at a next time by the following formula according to the control amount of the vehicle and the position of the vehicle at the current time:
xi(t+1)＝xi(t)+γui(t),
wherein x isi(t +1) is the position of the individual vehicle i at the next time t +1, and γ is the sampling step.
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