CN114509937A - Stable cooperative control method and device for multi-vehicle queue system by taking energy conservation as guidance - Google Patents

Stable cooperative control method and device for multi-vehicle queue system by taking energy conservation as guidance Download PDF

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CN114509937A
CN114509937A CN202210009076.5A CN202210009076A CN114509937A CN 114509937 A CN114509937 A CN 114509937A CN 202210009076 A CN202210009076 A CN 202210009076A CN 114509937 A CN114509937 A CN 114509937A
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徐利伟
徐明诚
殷国栋
刘赢
采国顺
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Southeast University
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Abstract

The invention discloses a stable cooperative control method and a device of a multi-vehicle queue system by taking energy conservation as guidance, wherein the method comprises the following steps: 1) the controlled vehicle adopts real-time state information of a pilot vehicle, an adjacent vehicle and the controlled vehicle; 2) establishing an energy optimal speed planning problem according to the collected energy efficiency and road information of the hub motor, and solving the problem through a dynamic planning method to obtain an energy optimal speed; 3) after the energy optimal speed of each following vehicle in the queue is collected, an energy optimal vehicle distance strategy is established, and the queue is controlled to run under the vehicle distance strategy; 4) and according to the energy optimal inter-vehicle distance strategy, the stability of the queue system is realized by applying a queue stability robust cooperative control algorithm. The control method designs a novel energy optimal inter-vehicle distance strategy, can realize the consistency of the speed of each following vehicle in space, and breaks through the problem that the fixed time distance and fixed inter-vehicle distance strategy can only ensure the consistency of the speed of the following vehicle and the speed of a pilot vehicle in a time domain.

Description

Stable cooperative control method and device for multi-vehicle queue system by taking energy conservation as guidance
Technical Field
The invention relates to a stable cooperative control method and device of a multi-vehicle queue system by taking energy conservation as guidance, belonging to the field of multi-vehicle queue control.
Background
Energy conservation is an important mark of modern automobiles and is also an important index for measuring the comprehensive performance of the automobiles. For modern vehicles, besides adopting novel energy driving by changing the structure of a vehicle chassis, another way for effectively reducing the energy consumption of the vehicles is to form a multi-vehicle queue system. In the multi-vehicle queue system, vehicles can reduce the following distance between the vehicles to form continuous traffic flow so as to reduce air resistance and achieve the purpose of reducing energy consumption.
If the application number is: CN201810178137.4, application name: in a patent application of an electric vehicle energy management and driving method based on a dynamic programming algorithm, an electric vehicle energy management and driving method based on the dynamic programming algorithm is disclosed, and the method comprises the steps of measuring power of a hub motor of a distributed electric vehicle; acquiring information by using a geographic information system and a vehicle positioning device to obtain road gradient information; acquiring the state of the vehicle and the state of the front vehicle by using a vehicle condition monitoring device; the global optimal distributed torque and the running method in the running process of the electric automobile are recursively calculated based on a dynamic programming algorithm, but the method is only suitable for a single electric automobile and cannot be applied to a multi-automobile queue system.
At the same time, from the standpoint of queue energy conservation, it is far from sufficient to reduce the windage by maintaining a compact geometry. In order to further improve the energy saving performance of the queuing system, many scholars research the multi-vehicle queuing system from the perspective of achieving the best energy efficiency of vehicles, but most of the scholars do not consider the problem of queue stability.
Ignoring the queue stability may degrade vehicle tracking of optimal energy speed when external disturbances and communication lags are present in the queue, thereby additionally increasing energy consumption.
In addition, the existing inter-vehicle distance strategies, including the fixed inter-vehicle distance strategy and the fixed time distance strategy, ultimately drive the speed of the following vehicle to be consistent with that of the pilot vehicle in the time domain, and it is essentially difficult to achieve the optimal energy efficiency of the vehicle. For example, when the speed of the pilot vehicle is reduced when the pilot vehicle is on an uphill slope, the follow-up vehicle running on a flat road can also reduce the speed of the pilot vehicle, and thus the energy loss in the queue is increased.
The invention provides a variable-pitch strategy which can keep the running speed of a vehicle consistent in a spatial domain, can realize optimal vehicle energy and reduce energy loss caused by unnecessary acceleration and deceleration.
However, running the queue at the energy-optimal speed may affect the stability of the queue, which may cause serious safety problems.
Therefore, the stable cooperative control method and device of the multi-vehicle queue system taking energy conservation as guidance are designed, and have important significance for the existing multi-vehicle queue control and the future unmanned field.
Disclosure of Invention
The invention aims to provide a stable cooperative control method of a multi-vehicle queue system and a device adapting the method, which can enable a vehicle queue to run at an energy optimal speed without influencing the stability of the queue.
The invention discloses a stable cooperative control method of a multi-vehicle queue system by taking energy conservation as guidance, which is used for realizing the best energy efficiency of vehicles, keeping the stability of queue running and providing technical support for intelligent networked automobiles; the method comprises a queue system and a queue cooperative control method, wherein the queue system is composed of intelligent networked automobiles under an intelligent traffic system and has a certain geometric configuration; the queue system comprises a pilot vehicle and a following vehicle;
the queue cooperative control method comprises the following steps:
step one, in the queue system, all vehicles acquire the real-time state information of the current vehicle and acquire the real-time state information of a pilot vehicle and a vehicle in front of the current vehicle;
step two, establishing an energy optimal speed planning problem by the current vehicle according to the collected energy efficiency of the hub motor and road gradient information, and solving the problem by a dynamic planning method to obtain an energy optimal speed;
step three, after collecting the optimal speed of each following vehicle energy in the queue, establishing an optimal energy inter-vehicle distance strategy which can effectively avoid vehicle collision, and controlling the queue to run under the inter-vehicle distance strategy;
and step four, giving a control law according to an energy optimal inter-vehicle distance strategy, establishing a discrete homogeneous queue model, and obtaining a new control law by applying a queue stability robust cooperative control algorithm, so that the queue system realizes the stability of the queue system under the new control law.
The queue system is stable, namely the convergence of the distance error and the speed error under the interference is realized from front to back of the whole queue.
Further, in the second step, the speed planning problem is only solved for the situation that the single vehicle system runs on the free road, and the energy optimal speed planning problem is as follows:
Figure BDA0003458232500000021
wherein, FXA resultant longitudinal force to maintain vehicle motion; numbering the vehicles in the queue, assuming that the pilot vehicle number is 0, the following vehicles along the queue increase in sequence according to the numbers 1, 2 and …, Jv，iPlanning a problem for the speed of the ith following vehicle in the queue; let the total distance that the queue needs to travel be D, assuming it can be equally divided into nDParts, and each part is marked as delta D;
Figure BDA0003458232500000022
Pio，f/rand Pii，f/rF represents the front wheel and r represents the rear wheel for the power of the hub motor in driving and braking modes respectively;
Λi＝diag{sign(1+Id，f)，sign(1+Id，r)，sign(1-Id，f)，sign(1-Id，r) Sign (■) is a sign function, Id，f/rIndicating the operating state of the motor, Id，f/r1 is in a driving state, Id，f/r-1 is the braking state;
Figure BDA0003458232500000031
Vmin≤Vi(j)≤Vmax，
Figure BDA0003458232500000032
amax，Vminand VmaxRespectively the maximum and minimum vehicle speed allowed for the vehicle, aminAnd amaxThe minimum and maximum acceleration allowed for the vehicle.
Further, in the third step, the energy-optimal inter-vehicle distance policy is:
Figure BDA0003458232500000034
in the formula
Figure BDA0003458232500000035
Is greater than zero and is greater than zero,
Figure BDA0003458232500000036
Figure BDA0003458232500000037
respectively the ideal displacement of the ith-1 th following vehicle and the ith following vehicle at the moment k under the fixed inter-vehicle distance strategy,
Figure BDA0003458232500000038
the displacement is the ideal displacement under the energy-optimal vehicle speed; didThe ideal inter-vehicle distance between adjacent vehicles under the fixed inter-vehicle distance strategy;
Figure BDA0003458232500000039
further, in the fourth step, the control rate expression equation is:
ui(k)＝λ1(ξi(k)-ξi-1(k))+λ2(ζi(k)-ζi-1(k))+λ3ξi(k-εt(k))+λ4ζi(k-εt(k))
wherein ξi(k) Is the distance error xi between the following vehicle and the pilot vehicle at the moment ki(k)＝S0(k)-Si(k)-κi(k)+iDid，S0/i(k) Actual displacement of the pilot vehicle and the ith follow-up vehicle at the moment k respectively; zetai(k) To follow the speed error between the car and the lead car at time k,
Figure BDA00034582325000000310
V0/i(k) respectively the longitudinal speed of the pilot vehicle and the ith follow-up vehicle at the moment k,
Figure BDA00034582325000000311
respectively obtaining ideal speeds of a pilot vehicle and an ith following vehicle under an energy optimal inter-vehicle distance strategy; h is1≤εt(k)＝τ(k)+η(k)δt≤h2τ (k) and η (k) are the information time lag and the packet loss number in the vehicle-to-vehicle communication, h1And h2Respectively is the minimum value and the maximum value of the equivalent time lag, and satisfies that h is more than or equal to 01＜h2。
Further, in the fourth step, the discrete homogeneous queue model is:
Figure BDA00034582325000000312
wherein χ (k) [ ×1(k)，…，χn(k)]T，χi(k)＝[ξi(k)，ζi(k)]TIs the state quantity of the system; w (k) ═ w1(k)，…，wn(k)]T，
Figure BDA00034582325000000313
For the amount of interference, V, of the queuing systemAIs the speed of the wind from the outside world,
Figure BDA00034582325000000314
acceleration of the vehicle under an optimal energy inter-vehicle distance strategy is usually unknown;
Figure BDA00034582325000000315
is the output of the system, and is,
Figure BDA00034582325000000316
Figure BDA00034582325000000317
CAis the number of equivalent air resistance lines, MiThe mass of the whole vehicle is; xi (k) is the system measurement output, phi (k) is the system state equation;
Figure BDA00034582325000000318
Cc、Coand
Figure BDA00034582325000000319
is a coefficient matrix, and K is a control gain matrix to be determined, expressed as:
Figure BDA00034582325000000320
Figure BDA0003458232500000041
in the formula I2Is a second order unit matrix;
Figure BDA0003458232500000042
further, in the robust cooperative control algorithm, the performance index of robust control is as follows:
Figure BDA0003458232500000043
where γ is a constant and is greater than zero.
Further, the control gain matrix K to be determined is obtained by a Lyapunov-Clarsofsky functional method and an H-infinity robust control theory1And K2Comprises the following steps:
Figure BDA0003458232500000044
wherein the content of the first and second substances,
Figure BDA0003458232500000045
and P is a positive definite matrix, Cv -1And Cl -1Are respectively CvAnd ClThe pseudo-inverse matrix of (2).
The invention also discloses a stable cooperative control device of the multi-vehicle queue system by taking energy conservation as guidance, which comprises the following components:
the information acquisition device is used for acquiring the real-time state information of the vehicle and acquiring the real-time state information of the pilot vehicle and the vehicles adjacent to the vehicle;
an energy optimal speed planning means for obtaining an energy optimal speed of each following vehicle;
and the robust control device is used for realizing the stable control of the queue system through a queue robust cooperative control algorithm.
Further, the energy optimal speed planning device is connected with the information acquisition device;
the optimal speed planning device comprises an algorithm module a and an execution module a; after the algorithm module a receives the relevant data information transmitted by the information acquisition device, establishing a speed planning problem, solving the problem to calculate to obtain an energy optimal speed, and establishing a variable vehicle spacing strategy according to the energy optimal speed;
the execution module a controls the whole queue to run at the distance between the vehicles; the executing module a controls according to the distance between the vehicle and the front vehicle measured by the external environment sensing module and a control distance program stored in the energy optimal speed planning device.
Further, the robust control device comprises an algorithm module b and an execution module b; after receiving the real-time state information of a pilot vehicle and an adjacent vehicle in the queue system, the algorithm module b calculates a required control law according to a queue stable robust cooperative control algorithm and controls the execution module b according to the control law; and the execution module b controls the queue system to stably run longitudinally.
The invention has the beneficial effects that:
aiming at a multi-vehicle queue system, a brand-new energy-saving-oriented multi-vehicle queue system stable cooperative control method for a fleet is established, and an energy optimal inter-vehicle distance strategy for ensuring consistent vehicle speeds of all vehicles in the fleet in a spatial domain is ensured to realize optimal queue energy;
secondly, based on the theory of Lyapunov-Clarsofsky stability and H∞The robust control theory can effectively control the queue to realize the stability of the queue.
Therefore, the invention can give consideration to the optimal energy and the driving stability of the multi-vehicle queue and simultaneously guarantee the economic efficiency and the safety.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a schematic view of the control arrangement of the present invention;
FIG. 3 is a schematic view of the control device connection of the present invention;
FIG. 4 is an energy optimal vehicle spacing strategy diagram of the present invention;
FIG. 5 is a schematic diagram of the energy-saving oriented discrete queue system stability controller of the present invention;
the reference numbers in the figures are: 1-external environment perception module, 2-self state acquisition module, 3-wireless communication equipment, 4-energy optimal speed planning device and 5-robust control device.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
Fig. 1 is a general block diagram of a stable cooperative control method and device for a multi-vehicle queue system guided by energy saving, and a system used in the cooperative control method for the queue system provided by the embodiment of the invention comprises any controlled vehicle in a queue. Each vehicle is an intelligent networked vehicle, and the state of each vehicle is completely observable and controllable.
As shown in fig. 2, each vehicle is equipped with an information acquisition device, an energy optimal speed planning device 4 and a robust control device 5, and the connection manner of the devices is shown in the general block diagram of fig. 1 and fig. 3, which will be specifically explained in the following steps.
The following process of ensuring energy conservation and stability is specifically explained by combining a multi-vehicle queue system cooperative control method and device:
the method comprises the following steps: the controlled vehicle adopts real-time state information of a pilot vehicle, an adjacent vehicle and the vehicle through the information acquisition device.
The first step is described in detail below with reference to the accompanying drawings:
as shown in fig. 2 and 3, the information acquisition apparatus specifically includes a wireless communication device 3, an external environment sensing module 1, and a self-state acquisition module 2. The wireless communication equipment 3 consists of a vehicle-to-vehicle communication (DSRC) based on a short-range communication technology and a wireless information transmission module (WIFI) and is responsible for collecting the position and speed information of a pilot vehicle; the external environment sensing module 1 consists of a laser radar and a camera and is responsible for collecting the position and speed information of a front vehicle; the self-state acquisition module 2 comprises a GPS, a GIS, a wheel speed encoder and the like and is responsible for acquiring the position information of the vehicle, the road information in front of the position of the vehicle, the longitudinal speed of the vehicle and the like.
Step two: according to the energy efficiency and road information of the hub motor collected by the information acquisition device, an energy optimal speed planning problem is established in the energy optimal speed planning device 4, and the problem is solved through a dynamic planning method to obtain the energy optimal speed.
The second step is described in detail below with reference to the accompanying drawings:
as shown in fig. 3, the energy-optimized speed planning apparatus 4 is connected to the information acquisition apparatus, and includes an algorithm module a and an execution module a.
The energy optimal speed planning problem established in the algorithm module a is as follows:
Figure BDA0003458232500000061
wherein, FXA resultant longitudinal force to maintain vehicle motion; the vehicles in the queue are numbered, and the number of the pilot vehicle is assumed to be 0, and the numbers of the following vehicles along the queue are increased in sequence according to 1, 2 and …，Jv，iPlanning a problem for the speed of the ith following vehicle in the queue; let the total distance that the queue needs to travel be D, assuming it can be equally divided into nDParts, and each part is marked as delta D;
Figure BDA0003458232500000062
Figure BDA0003458232500000063
Pio，f/rand Pii，f/rF represents the front wheel and r represents the rear wheel for the power of the hub motor in driving and braking modes respectively; lambdai＝diag{sign(1+Id，f)，sign(1+Id，r)，sign(1-Id，f)，sign(1-Id，r) Sign (■) is a sign function, Id，f/rIndicating the operating state of the motor, Id，f/r1 is in a driving state, Id，f/r-1 is the braking state;
Figure BDA0003458232500000064
Vmin≤Vi(j)≤Vmax，
Figure BDA0003458232500000065
Figure BDA0003458232500000066
Vminand VmaxMaximum and minimum vehicle speeds permitted for the vehicle, amin and a, respectivelymaxThe minimum and maximum acceleration allowed for the vehicle.
As shown in fig. 5, solving the energy-optimal speed planning problem by the existing dynamic planning method can obtain the energy-optimal speed of each controlled vehicle offline.
In order to minimize the energy consumption of the whole train system, it is assumed that the pilot vehicle is already running at the optimal energy speed, and therefore the controlled vehicle is the following vehicle in the train.
It should be noted that, in order to facilitate establishment of the above planning problem, it is assumed that all following vehicles are driven by hub motors, and since the torque response speed of the motors is high, the time lag inside the following vehicle system is ignored.
Step three: and after the energy optimal speed of each following vehicle in the queue is collected, establishing an energy optimal vehicle distance strategy, and controlling the queue to run under the vehicle distance strategy.
The third step is specifically explained below with reference to the accompanying drawings:
the energy-optimal inter-vehicle distance strategy is explained in conjunction with a schematic diagram thereof. As shown in FIG. 4, assuming that the queue is running on a road with a known start point and end point, a coordinate system is established with the start point as the origin, and S is assignedi(k) Is the actual displacement of the ith following vehicle at time k relative to the origin of coordinates.
The ideal inter-vehicle distance under the optimal energy inter-vehicle distance strategy established in the algorithm module a is as follows:
Figure BDA0003458232500000067
wherein the content of the first and second substances,
Figure BDA0003458232500000071
Figure BDA0003458232500000072
respectively the ideal displacement of the ith-1 th following vehicle and the ith following vehicle at the moment k under the fixed inter-vehicle distance strategy,
Figure BDA0003458232500000073
the displacement is the ideal displacement under the energy-optimal vehicle speed; didThe ideal inter-vehicle distance between adjacent vehicles under the fixed inter-vehicle distance strategy.
It should be noted that, in order to prevent collision of adjacent vehicles,
Figure BDA0003458232500000074
should be greater than zero. Redesign of
Figure BDA0003458232500000075
And the vehicle is ensured to have enough safety distance with the vehicles in front of and behind the vehicle.
Figure BDA0003458232500000076
The design is as follows:
Figure BDA0003458232500000077
the algorithm module a controls the execution module a. And the execution module a controls the whole queue to run at the energy optimal inter-vehicle distance. The control of the execution module a is based on the distance between the external environment sensing module 1 and the front vehicle and the control distance program stored in the energy optimal speed planning device 4.
Step four: and according to the energy optimal inter-vehicle distance strategy, the stability of the queue system is realized by applying a queue stability robust cooperative control algorithm.
The following will specifically describe step four with reference to the accompanying drawings:
as shown in fig. 3, the robust control device 5 is connected to the information acquisition device and the energy-optimized speed planning device 4, and includes an algorithm module b and an execution module b.
The queue stability robust cooperative control algorithm is executed in the algorithm module b, and the in-queue system stability and the system stability of the queue under the conditions of time lag, interference and parameter uncertainty can be realized. The core idea is as follows: giving a control law according to an energy optimal inter-vehicle distance strategy, and searching for corresponding control gains to enable the queue system to meet the following control targets under the new control law:
(1) when the external interference is zero, the established discrete homogeneous queue system is gradually stable;
(2) the robust cooperative control algorithm meets performance indexes under certain interference;
(3) the system transient error is non-increasing along the queue direction;
the requirements (1) and (2) can realize the internal stability of the queue, namely the distance error and the speed error between the front vehicle and the rear vehicle in the queue tend to zero; and (3) the stability of the queue system can be realized, namely the convergence of the distance error and the speed error under the interference is realized from front to back of the whole queue.
Defining the control law of the ith following vehicle as follows:
ui(k)＝λ1(ξi(k)-ξi-1(k))+λ2(ζi(k)-ζi-1(k))+λ3ξi(k-εt(k))+λ4ζi(k-εt(k))
wherein ξi(k) Is the distance error xi between the following vehicle and the pilot vehicle at the moment ki(k)＝S0(k)-Si(k)-κi(k)+iDid，S0/i(k) Actual displacement of the pilot vehicle and the ith follow-up vehicle at the moment k respectively; zetai(k) To follow the speed error between the car and the lead car at time k,
Figure BDA0003458232500000078
V0/i(k) respectively the longitudinal speed of the pilot vehicle and the ith follow-up vehicle at the moment k,
Figure BDA0003458232500000079
respectively the ideal speed h of the pilot vehicle and the ith following vehicle under the energy optimal inter-vehicle distance strategy1≤εt(k)＝τ(k)+η(k)δt≤h2τ (k) and η (k) are the information time lag and the packet loss number in the vehicle-to-vehicle communication, h1And h2Respectively is the minimum value and the maximum value of the equivalent time lag, and satisfies that h is more than or equal to 01＜h2。
Further, the establishment of the discrete homogeneous multi-vehicle queue system comprises the following steps:
Figure BDA0003458232500000081
wherein, χ (k) ═ χ1(k)，…，χn(k)]T，χi(k)＝[ξi(k)，ζi(k)]TIs the state quantity of the system; w (k) ═ w1(k)，…，wn(k)]T，
Figure BDA0003458232500000082
For the amount of interference, V, of the queuing systemAIs the speed of the wind from the outside world,
Figure BDA0003458232500000083
acceleration of the vehicle under an optimal energy inter-vehicle distance strategy is usually unknown;
Figure BDA0003458232500000084
is the output of the system, and is,
Figure BDA0003458232500000085
Figure BDA0003458232500000086
CAfor equivalent air resistance coefficient, MiThe mass of the whole vehicle is; xi (k) is the system measurement output and phi (k) is the system state equation.
Figure BDA0003458232500000087
Cc、CoAnd
Figure BDA00034582325000000814
is a coefficient matrix, and K is a control gain matrix to be determined, expressed as:
Figure BDA0003458232500000088
Figure BDA0003458232500000089
in the formula I2Is a second order unit matrix;
Figure BDA00034582325000000810
it should be noted that the reason why the queue system is considered to be homogeneous is: on one hand, the heterogeneous queue modeling process can be greatly simplified; on the other hand, mature and superior homogeneous queue control methods can be used for heterogeneous queue control, and the difficulty of control system design is reduced.
The performance indexes of the robust cooperative control algorithm are defined as follows:
Figure BDA00034582325000000811
wherein γ ≧ 0 is a constant.
Further, the method adopts the Lyapunov-Clarsofsky functional method and combines H∞Feedback control gain matrix K obtained by robust control theory1And K2Comprises the following steps:
Figure BDA00034582325000000812
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00034582325000000813
and P is a positive definite matrix, Cv -1And Cl -1Are respectively CvAnd ClThe pseudo-inverse matrix of (2).
From this, a new control law u is obtainedi' can be realized under the control law to control the target:
(1)limt→∞||ξi(k)||＝0，limt→∞||ζi(k)||＝0；
(2)J∞(W(k))≤0；
(3)
Figure BDA0003458232500000091
and gamma is more than or equal to 0 and less than 1, wherein EiAnd the inter-vehicle distance error of the adjacent vehicle under the energy optimal inter-vehicle distance strategy.
The corresponding meanings of the three control targets are respectively as follows:
(1) proving that the established discrete homogeneous queue system is gradually stable;
(2) the robust cooperative control algorithm is proved to meet performance indexes under certain interference;
(3) proving that the transient error of the system is non-increasing along the queue direction;
the algorithm module b controls the execution module b according to the control law. And the execution module b controls the queue system to stably run longitudinally.
Because the conservation of the system can be greatly weakened by the Lyapunov-Classofsky stability theory, the queue stability robust cooperative control algorithm has weak conservation, namely the obtained control gain range is more accurate, and excessive redundancy can not occur.
Finally, it should be pointed out that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Those of ordinary skill in the art will understand that: modifications can be made to the technical solutions described in the foregoing embodiments, or some technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A stable cooperative control method of a multi-vehicle queue system by taking energy conservation as guidance is characterized by comprising the following steps:
the method comprises a queue system and a queue cooperative control method, wherein the queue system is composed of intelligent networked automobiles under an intelligent traffic system and has a certain geometric configuration; the queue system comprises a pilot vehicle and a following vehicle;
the queue cooperative control method comprises the following steps:
step one, in the queue system, all vehicles acquire the real-time state information of the current vehicle and acquire the real-time state information of a pilot vehicle and a vehicle in front of the current vehicle;
step two, establishing an energy optimal speed planning problem by the current vehicle according to the collected energy efficiency of the hub motor and road gradient information, and solving the problem by a dynamic planning method to obtain an energy optimal speed;
step three, after collecting the optimal speed of each following vehicle energy in the queue, establishing an optimal energy inter-vehicle distance strategy which can effectively avoid vehicle collision, and controlling the queue to run under the inter-vehicle distance strategy;
and step four, giving a control law according to an energy optimal inter-vehicle distance strategy, establishing a discrete homogeneous queue model, and obtaining a new control law by applying a queue stability robust cooperative control algorithm, so that the queue system realizes the stability of the queue system under the new control law.
2. The energy-saving-oriented multi-vehicle queue system stable cooperative control method according to claim 1, characterized in that: in the second step, the energy-optimal speed planning problem is as follows:
Figure FDA0003458232490000011
wherein, FXA resultant longitudinal force to maintain vehicle motion; numbering the vehicles in the queue, assuming that the pilot vehicle number is 0, the following vehicles along the queue increase in sequence according to the numbers 1, 2 and …, Jv，iPlanning a problem for the speed of the ith following vehicle in the queue; let the total distance that the queue needs to travel be D, assuming it can be equally divided into nDParts, and each part is marked as delta D;
Figure FDA0003458232490000012
Pio，f/rand Pii，f/rF represents the front wheel and r represents the rear wheel for the power of the hub motor in driving and braking modes respectively;
Λi＝diag{sign(1+Id，f)，sign(1+Id，r)，sign(1-Id，f)，sign(1-Id，r) Sign (■) is a sign function, Id，f/rIndicating the operating state of the motor, Id，f/r1 is in a driving state, Id，f/r-1 is the braking state;
Figure FDA0003458232490000013
Vmin≤Vi(j)≤Vmax，Vminand VmaxRespectively the maximum and minimum vehicle speed allowed for the vehicle, aminAnd amaxThe minimum and maximum acceleration allowed for the vehicle.
3. The energy-saving-oriented multi-vehicle queue system stable cooperative control method according to claim 1, characterized in that: in the third step, the energy-optimal inter-vehicle distance policy is:
Figure FDA0003458232490000014
in the formula
Figure FDA0003458232490000021
Is greater than zero and is greater than zero,
Figure FDA0003458232490000022
Figure FDA0003458232490000023
respectively the ideal displacement of the ith-1 th following vehicle and the ith following vehicle at the moment k under the fixed inter-vehicle distance strategy,
Figure FDA0003458232490000024
the displacement is the ideal displacement under the energy-optimal vehicle speed; didThe ideal inter-vehicle distance between adjacent vehicles under the fixed inter-vehicle distance strategy;
Figure FDA0003458232490000025
4. the energy-saving oriented multi-vehicle queue system stable cooperative control method according to claim 1, characterized in that: in the fourth step, the control rate expression equation is:
ui(k)＝λ1(ξi(k)-ξi-1(k))+λ2(ζi(k)-ζi-1(k))+λ3ξi(k-εt(k))+λ4ζi(k-εt(k))
wherein ξi(k) Is the distance error xi between the following vehicle and the pilot vehicle at the moment ki(k)＝S0(k)-Si(k)-κi(k)+iDid，S0/i(k) Actual displacement of the pilot vehicle and the ith follow-up vehicle at the moment k respectively; zetai(k) To follow the speed error between the car and the lead car at time k,
Figure FDA0003458232490000026
V0/i(k) respectively the longitudinal speed of the pilot vehicle and the ith follow-up vehicle at the moment k,
Figure FDA0003458232490000027
respectively obtaining ideal speeds of a pilot vehicle and an ith following vehicle under an energy optimal inter-vehicle distance strategy; h is1≤εt(k)＝τ(k)+η(k)δt≤h2τ (k) and η (k) are the information time lag and the packet loss number in the vehicle-to-vehicle communication, h1And h2Respectively is the minimum value and the maximum value of the equivalent time lag, and satisfies that h is more than or equal to 01＜h2。
5. The energy-saving-oriented multi-vehicle queue system stable cooperative control method according to claim 4, characterized in that: in the fourth step, the discrete homogeneous queue model is:
Figure FDA0003458232490000028
wherein χ (k) [ ×1(k)，…，χn(k)]T，χi(k)＝[ξi(k)，ζi(k)]TIs the state quantity of the system; w (k) ═ w1(k)，…，wn(k)]T，
Figure FDA00034582324900000215
For the amount of interference, V, of the queuing systemAIs the speed of the wind from the outside world,
Figure FDA00034582324900000216
acceleration of the vehicle under an optimal energy inter-vehicle distance strategy is usually unknown;
Figure FDA00034582324900000217
is the output of the system, and is,
Figure FDA0003458232490000029
Figure FDA00034582324900000210
CAfor equivalent air resistance coefficient, MiThe mass of the whole vehicle is; xi (k) is the system measurement output, phi (k) is the system state equation;
Figure FDA00034582324900000211
Cc、Coand
Figure FDA00034582324900000212
is a coefficient matrix, and K is a control gain matrix to be determined, expressed as:
Figure FDA00034582324900000213
Figure FDA00034582324900000214
in the formula I2Is a second order unit matrix;
Figure FDA0003458232490000031
6. the energy-saving-oriented multi-vehicle queue system stable cooperative control method according to claim 5, characterized in that: in the robust cooperative control algorithm, the performance indexes of robust control are as follows:
Figure FDA0003458232490000032
where γ is a constant and is greater than zero.
7. The energy-saving-oriented multi-vehicle queue system stable cooperative control method according to claim 6, characterized in that: adopts a Lyapunov-Clarsofsky functional method and combines H∞The control gain matrix K to be determined is obtained by robust control theory1And K2Comprises the following steps:
Figure FDA0003458232490000033
wherein the content of the first and second substances,
Figure FDA0003458232490000034
and P is a positive definite matrix, Cv -1And Cl -1Are respectively CvAnd ClThe pseudo-inverse matrix of (2).
8. The utility model provides an use many cars of energy-conserving as direction system to stabilize cooperative control device which characterized in that includes:
the information acquisition device is used for acquiring the real-time state information of the vehicle and acquiring the real-time state information of the pilot vehicle and the vehicles adjacent to the vehicle;
an energy optimal speed planning means for obtaining an energy optimal speed of each following vehicle;
and the robust control device is used for realizing the stable control of the queue system through a queue robust cooperative control algorithm.
9. The apparatus of claim 8, wherein: the energy optimal speed planning device is connected with the information acquisition device;
the optimal speed planning device comprises an algorithm module a and an execution module a; after the algorithm module a receives the relevant data information transmitted by the information acquisition device, establishing a speed planning problem, solving the problem to calculate to obtain the energy optimal speed, and establishing a variable vehicle spacing strategy according to the energy optimal speed;
the execution module a controls the whole queue to run at the distance between the vehicles; the executing module a controls according to the distance between the vehicle and the front vehicle measured by the external environment sensing module and a control distance program stored in the energy optimal speed planning device.
10. The apparatus of claim 8, wherein: the robust control device comprises an algorithm module b and an execution module b; after receiving the real-time state information of a pilot vehicle and an adjacent vehicle in the queue system, the algorithm module b calculates a required control law according to a queue stable robust cooperative control algorithm and controls the execution module b according to the control law; and the execution module b controls the queue system to stably run longitudinally.
CN202210009076.5A 2022-01-06 2022-01-06 Stable cooperative control method and device for multi-vehicle queue system by taking energy conservation as guidance Pending CN114509937A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114510032A (en) * 2022-01-06 2022-05-17 东南大学 Multi-vehicle cooperative control method and device for guaranteeing rapid formation stability of vehicles

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* Cited by examiner, † Cited by third party
Title
徐利伟: "智能网联汽车队列成形控制及队列稳定性研究", 《中国博士学位论文全文数据库工程科技Ⅱ辑》 *

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114510032A (en) * 2022-01-06 2022-05-17 东南大学 Multi-vehicle cooperative control method and device for guaranteeing rapid formation stability of vehicles

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