CN111196275A - Multi-target self-adaptive cruise control method, device and equipment - Google Patents

Multi-target self-adaptive cruise control method, device and equipment Download PDF

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CN111196275A
CN111196275A CN201811366959.1A CN201811366959A CN111196275A CN 111196275 A CN111196275 A CN 111196275A CN 201811366959 A CN201811366959 A CN 201811366959A CN 111196275 A CN111196275 A CN 111196275A
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vehicle
expected acceleration
self
increment
obtaining
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章军辉
王福艳
李庆
陈大鹏
章长庆
梁艳菊
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Kunshan Branch Institute of Microelectronics of CAS
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Kunshan Branch Institute of Microelectronics of CAS
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/14—Adaptive cruise control
    • B60W30/143—Speed control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions

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  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

The invention discloses a multi-target self-adaptive cruise control method, which comprises the following steps: under the process state constraint condition, the system I/O constraint condition and the fleet constraint condition, establishing a quadratic performance functional according to the control input, the control input increment and the regularization item; acquiring state parameters of a self vehicle and a front vehicle in real time; according to the state parameters, obtaining the expected acceleration increment of the self-vehicle through quadratic performance functional rolling online optimization prediction, and obtaining the expected acceleration according to the expected acceleration increment; and controlling the self-vehicle according to the expected acceleration increment so that the actual acceleration of the self-vehicle converges to the expected acceleration. The method comprehensively coordinates control targets such as expected response of a driver, following safety, motorcade stability and the like, and adopts an incremental control strategy, so that the working condition adaptability of the motorcade can be effectively improved, and the overall quality of the motorcade is guaranteed. The invention also discloses a multi-target self-adaptive cruise control device, equipment and a computer readable storage medium, which have the technical effects.

Description

Multi-target self-adaptive cruise control method, device and equipment
Technical Field
The invention relates to the technical field of self-adaptive cruise control, in particular to a multi-target self-adaptive cruise control method; also relates to a multi-target self-adaptive cruise control device, equipment and a computer readable storage medium.
Background
Early ACC (Adaptive Cruise Control) System design mainly meets the requirements of driving comfort and vehicle safety, and with the rapid development of ITS (Intelligent Transportation System) System, it has attracted the high attention of researchers and automobile manufacturers to use AHS (Automated Highway System) to enhance the safety construction of highways, alleviate traffic congestion and improve road traffic capacity.
Currently, a CTH (constant time headway) strategy is generally adopted for controlling a single vehicle ACC. The strategy focuses on the time interval between the workshops, and has certain challenge on the selection of the time interval between the workshops. When the time interval between workshops is preset to be too small, psychological tension and discomfort of drivers and passengers can be caused, and the potential possibility of rear-end collision is increased; when the time interval between vehicles is too large, the road volume rate and the throughput are reduced, and the non-civilized events such as the merging of vehicles on adjacent roads and the forced insertion of vehicles on adjacent roads can be induced. For multi-vehicle ACC control, besides the above-mentioned inter-vehicle time intervals, other factors may affect the stability of the fleet, the overall quality of the fleet, such as the response time of the fleet, the fluctuation range of the vehicle distance error beams, and the adaptive capacity of the working conditions, so it is especially necessary to research the multi-vehicle ACC control.
Therefore, how to provide a multi-vehicle ACC control scheme to improve the working condition adaptability of a fleet and ensure the overall quality of the fleet is a technical problem to be solved urgently by those skilled in the art.
Disclosure of Invention
The invention aims to provide a multi-target self-adaptive cruise control method, which can effectively improve the working condition adaptability of a motorcade and ensure the overall quality of the motorcade; another object of the present invention is to provide a multi-target adaptive cruise control apparatus, device and computer readable storage medium, all of which have the above technical effects.
In order to solve the technical problem, the invention provides a multi-target adaptive cruise control method, which comprises the following steps:
under the process state constraint condition, the system I/O constraint condition and the fleet constraint condition, establishing a quadratic performance functional according to the control input, the control input increment and the regularization item;
acquiring state parameters of a self vehicle and a front vehicle in real time;
according to the state parameters, obtaining an expected acceleration increment of the self-vehicle through online optimization prediction of the quadratic performance functional rolling, and obtaining an expected acceleration according to the expected acceleration increment;
and controlling the self-vehicle according to the expected acceleration increment to make the actual acceleration of the self-vehicle converge to the expected acceleration.
Optionally, the obtaining the state parameters of the self-vehicle and the front vehicle in real time includes:
and acquiring the state parameters of the self vehicle in real time through a CAN bus, and acquiring the state parameters of the front vehicle in real time through V2V communication.
Optionally, the obtaining, according to the state parameter, an expected acceleration increment of the host vehicle through the quadratic form performance functional rolling online optimization prediction, and obtaining an expected acceleration according to the expected acceleration increment, includes:
according to the state parameters, rolling optimization solution
Figure BDA0001868830300000021
Obtaining a predicted sequence
Figure BDA0001868830300000022
Selecting a first component Deltau in the prediction sequence*(k) For desired acceleration increments, in combination with a saturation function
Figure BDA0001868830300000023
Obtaining the expected acceleration;
wherein, the
Figure BDA0001868830300000024
For an augmented control sequence, H is a positive definite Hessian matrix, f is a primary term coefficient vector, Ω is a constraint coefficient matrix, and T is a feasible domain upper bound, the method includes the steps of
Figure BDA0001868830300000025
To control the upper limit of the input, the
Figure BDA0001868830300000026
Inputting a lower limit for the control; the epsilon1、ε2And epsilon3Are all relaxation factors.
Optionally, the method further includes:
detecting whether the expected acceleration increment, the expected acceleration and the state vector exceed the constraint boundary of the controller working domain in real time;
and if the expected acceleration increment or the expected acceleration or the state vector exceeds the working domain constraint boundary, the corresponding relaxation factor is positively increased.
In order to solve the above technical problem, the present invention further provides a multi-target adaptive cruise control apparatus, including:
the building module is used for building a quadratic performance functional according to the control input, the control input increment and the regularization item under the process state constraint condition, the system I/O constraint condition and the fleet constraint condition;
the acquisition module is used for acquiring the state parameters of the self vehicle and the front vehicle in real time;
the calculation module is used for obtaining the expected acceleration increment of the self-vehicle through the quadratic performance functional rolling online optimization prediction according to the state parameters and obtaining the expected acceleration according to the expected acceleration increment;
and the control module is used for controlling the self-vehicle according to the expected acceleration increment so that the actual acceleration of the self-vehicle is converged to the expected acceleration.
Optionally, the obtaining module includes:
the first acquisition unit is used for acquiring the state parameters of the self-vehicle in real time through a CAN bus;
and the second acquisition unit is used for acquiring the state parameters of the front vehicle in real time through V2V communication.
Optionally, the calculation module includes:
a solving unit for rolling optimization solving according to the state parameters
Figure BDA0001868830300000031
Obtaining a predicted sequence
Figure BDA0001868830300000032
A selection unit for selecting a first component Δ u in the prediction sequence*(k) For desired acceleration increments, in combination with a saturation function
Figure BDA0001868830300000033
Obtaining the expected acceleration;
wherein, the
Figure BDA0001868830300000034
For an augmented control sequence, H is a positive definite Hessian matrix, f is a primary term coefficient vector, Ω is a constraint coefficient matrix, and T is a feasible domain upper bound, the method includes the steps of
Figure BDA0001868830300000035
To control the upper limit of the input, the
Figure BDA0001868830300000036
Inputting a lower limit for the control; the epsilon1、ε2And epsilon3Are all relaxation factors.
Optionally, the method further includes:
the detection module is used for detecting whether the expected acceleration increment, the expected acceleration and the state parameter exceed the constraint boundary of the working domain of the controller in real time;
and the adjusting module is used for positively increasing the corresponding relaxation factor if the expected acceleration increment or the expected acceleration or the state vector exceeds the constraint boundary of the work domain of the controller.
In order to solve the above technical problem, the present invention also provides a multi-target adaptive cruise control apparatus, including:
a memory for storing a computer program;
a processor for implementing the steps of the method of multi-objective adaptive cruise control as claimed in any one of the above when said computer program is executed.
To solve the above technical problem, the present invention further provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the steps of the method for multi-target adaptive cruise control according to any one of the above.
The invention provides a multi-target self-adaptive cruise control method, which comprises the following steps: under the process state constraint condition, the system I/O constraint condition and the fleet constraint condition, establishing a quadratic performance functional according to the control input, the control input increment and the regularization item; acquiring state parameters of a self vehicle and a front vehicle in real time; according to the state parameters, obtaining an expected acceleration increment of the self-vehicle through online optimization prediction of the quadratic performance functional rolling, and obtaining an expected acceleration according to the expected acceleration increment; and controlling the self-vehicle according to the expected acceleration increment to make the actual acceleration of the self-vehicle converge to the expected acceleration.
Therefore, according to the multi-target self-adaptive cruise control method provided by the invention, under the process state constraint condition, the system I/O constraint condition and the motorcade constraint condition, a quadratic performance functional is established according to the control input, the control input increment and the regularization item, the multi-target self-adaptive cruise control problem is converted into a quadratic programming problem with a linear matrix inequality constraint condition, so that on the basis of acquiring the state parameters of the self-vehicle and the front vehicle in real time, the control targets of expected response, following safety, motorcade stability and the like of a driver are comprehensively coordinated, an incremental control strategy is adopted, the expected acceleration increment of the self-vehicle is obtained through the quadratic performance functional, and the actual acceleration of the self-vehicle is controlled to be converged to the expected acceleration according to the expected acceleration increment. The multi-target self-adaptive cruise control method can effectively improve the working condition adaptability of the motorcade and guarantee the overall quality of the motorcade.
The multi-target self-adaptive cruise control device, equipment and the computer readable storage medium provided by the invention have the technical effects.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed in the prior art and the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart of a multi-target adaptive cruise control method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a multi-target adaptive cruise control apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a multi-target adaptive cruise control apparatus according to an embodiment of the present invention.
Detailed Description
The core of the invention is to provide a multi-target self-adaptive cruise control method, which can effectively improve the working condition adaptability of a motorcade and ensure the overall quality of the motorcade; another core of the present invention is to provide a multi-target adaptive cruise control apparatus, a device and a computer readable storage medium, all having the above technical effects.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
Referring to fig. 1, fig. 1 is a schematic flow chart of a multi-target adaptive cruise control method according to an embodiment of the present invention; referring to fig. 1, the multi-target adaptive cruise control method includes:
s10: under the process state constraint condition, the system I/O constraint condition and the fleet constraint condition, establishing a quadratic performance functional according to the control input, the control input increment and the regularization item;
specifically, the step aims to establish a quadratic performance functional to convert the adaptive cruise Control problem of comprehensively coordinating multiple Control targets such as fuel economy, driving comfort and fleet stability into a quadratic programming problem with linear matrix inequality constraint under an MPC (Model Predictive Control) Control framework. Specifically, under the constraint conditions of process states, system I/O (input/output) constraint conditions and fleet constraint conditions, a quadratic performance functional is established according to control inputs, control input increments and regularization terms; the process state constraint conditions comprise vehicle distance error constraint conditions, relative speed constraint conditions and actual acceleration constraint conditions of the self vehicle; the system I/O constraint condition comprises a control input constraint condition, a control input increment constraint condition and a system output constraint condition; the constraint conditions of the motorcade comprise a constraint condition of motorcade stability and a constraint condition of motorcade overall quality, the constraint condition of motorcade stability further comprises constraint conditions of workshop time distance, motorcade scale, multi-target weight distribution and the like, and the constraint condition of motorcade overall quality comprises constraint conditions of motorcade response time, vehicle distance error beam fluctuation amplitude and the like. Wherein, the constraint condition of the time distance between the workshops is tauh≥τcr，τhFor a fixed time interval, τcrThe safe time interval is the safe time interval when the motorcade is asymptotically stable; the constraint condition of fleet scale is N is less than or equal to NcrN is the actual fleet size, NcrGauge for vehicle fleetAnd the fleet size constraint does not contain external disturbance introduced by the merging of adjacent vehicles.
It can be understood that the operation of establishing the quadratic performance functional under each constraint condition can be performed only once, that is, after the quadratic performance functional is constructed, in the process of performing the multi-target adaptive cruise control, the quadratic performance functional is directly used in the subsequent steps without repeatedly performing the establishing steps. Of course, if the quadratic performance functional needs to be changed in the meantime, the construction can be carried out again.
S20: acquiring state parameters of a self vehicle and a front vehicle in real time;
specifically, the state parameters of the self-vehicle and the front-vehicle are the basis for carrying out multi-target adaptive cruise control, so that the step aims to carry out the acquisition of the state parameters in real time, including the speed of the self-vehicle, the actual acceleration of the self-vehicle and the like, the actual distance between the front-vehicle and the self-vehicle, the speed of the front-vehicle and the like. In addition, environmental parameters such as road adhesion coefficients and the like are obtained in real time, and then follow-up operation is executed according to the parameters.
In order to reduce the measurement error introduced by the state estimation of the leading vehicle and reduce the complexity of the subsequent analysis and calculation, optionally, the obtaining of the state parameters of the leading vehicle in real time may include obtaining the state parameters of the leading vehicle in real time through a CAN bus and obtaining the state parameters of the leading vehicle in real time through V2V communication.
Specifically, unlike the conventional method of acquiring the state parameters of the leading vehicle by the millimeter wave radar, the fleet in the present embodiment is equipped with a V2V communication module, and the leading vehicle can acquire the state parameters of the leading vehicle by V2V communication. The V2V communication is data communication among vehicles based on wireless, which can realize reliable transmission and real-time sharing of information such as speed, position, brake and the like among vehicles in a certain range, and provides accurate data reference for multi-target adaptive cruise control of each vehicle in a fleet. In addition, the state parameters of the self-vehicle CAN be obtained in real time through the vehicle-mounted CAN bus.
S30: according to the state parameters, obtaining the expected acceleration increment of the self-vehicle through quadratic performance functional rolling online optimization prediction, and obtaining the expected acceleration according to the expected acceleration increment;
s40: and controlling the self-vehicle according to the expected acceleration increment so that the actual acceleration of the self-vehicle converges to the expected acceleration.
Specifically, to achieve good control quality, the present invention employs an incremental control strategy. Specifically, according to the state parameters, the expected acceleration increment of the vehicle is obtained through quadratic performance functional rolling online optimization prediction, and further the expected acceleration is obtained according to the expected acceleration increment, so that the accelerator and the brake are controlled according to the expected acceleration increment in the following process, and the actual acceleration of the vehicle is converged to the expected acceleration.
Optionally, the obtaining, according to the state parameter, an expected acceleration increment of the vehicle through quadratic form performance functional rolling online optimization prediction, and obtaining an expected acceleration according to the expected acceleration increment may include:
rolling optimization solution according to state parameters
Figure BDA0001868830300000071
Obtaining a predicted sequence
Figure BDA0001868830300000072
Selecting a first component Deltau in a prediction sequence*(k) For desired acceleration increments, in combination with a saturation function
Figure BDA0001868830300000073
Obtaining a desired acceleration;
specifically, in the present embodiment
Figure BDA0001868830300000074
That is, the quadratic form performance functional constructed in step S10 is obtained, and further, on the basis of completing the construction of the quadratic form performance functional and obtaining the state parameters of the vehicle and the preceding vehicle in real time, according to the obtained state parameters, the rolling optimization solution is performed
Figure BDA0001868830300000075
Obtaining the prediction sequence, and further selecting a first component delta u in the prediction sequence*(k) For desired acceleration increments, in combination with a saturation handling function
Figure BDA0001868830300000076
The desired acceleration is obtained.
Wherein the content of the first and second substances,
Figure BDA0001868830300000077
for an augmented control sequence, H is a positive hessian matrix, f is a primary term coefficient vector, omega is a constraint coefficient matrix, and Gamma is an upper bound of a feasible region,
Figure BDA0001868830300000078
in order to control the upper limit of the input,
Figure BDA0001868830300000079
inputting a lower limit for the control; epsilon1、ε2And epsilon3Are all relaxation factors.
In summary, according to the multi-target adaptive cruise control method provided by the invention, under the process state constraint condition, the system I/O constraint condition and the fleet constraint condition, a quadratic performance functional is established according to the control input, the control input increment and the regularization term, and the multi-target adaptive cruise control problem is converted into a quadratic programming problem with the linear matrix inequality constraint condition, so that on the basis of obtaining the state parameters of the self-vehicle and the front vehicle in real time, the control targets such as the expected response of a driver, the following safety, the fleet stability and the like are comprehensively coordinated, an incremental control strategy is adopted, the expected acceleration increment of the self-vehicle is obtained through the quadratic performance functional, and the actual acceleration of the self-vehicle is controlled to be converged to the expected acceleration according to the expected acceleration increment. The multi-target self-adaptive cruise control method can effectively improve the working condition adaptability of the motorcade and guarantee the overall quality of the motorcade.
On the basis of the above embodiment, optionally, the method further includes: detecting whether the expected acceleration increment, the expected acceleration and the state parameter exceed the constraint boundary of the working domain of the controller in real time; if the desired acceleration increment or the desired acceleration or state vector exceeds the controller's operating domain constraint boundary, the corresponding relaxation factor is increased in the forward direction.
Specifically, in the p-step finite prediction time domain rolling optimization solving process, whether the expected acceleration increment, the expected acceleration and the state vector exceed the boundary of the working domain of the controller or not is detected in real time, and if the expected acceleration increment, the expected acceleration and the state vector do not exceed the constraint boundary of the working domain of the controller, the relaxation factor is taken as 0; if one or more of the expected acceleration, the expected acceleration increment and the state vector exceeds the constraint boundary of the controller working domain, the corresponding relaxation factor is increased in the positive direction to expand the controller working domain, so that the quadratic programming with the constraint has a feasible solution, the expected acceleration increment can be obtained, and the multi-target self-adaptive cruise control is normally implemented.
The following are to the above
Figure BDA0001868830300000081
The construction process specifically develops: in order to meet the expected response of a driver in the following process, multi-target coordination control is carried out on the basis of an MPC control framework, and a prediction time domain [ k, k + p-1 ] is established]Is equal to sigma (J)T+JF+JC)＝XTWX+UTRU+C；
Wherein, JT＝wΔdΔd2+wΔvΔv2；wΔdWeight coefficient, w, of the plant error Δ dΔvA weighting factor for the relative vehicle speed Δ v;
Figure BDA0001868830300000082
wafor desired acceleration a of the vehiclef,desWeight coefficient of (d), wjIs the weight coefficient of the impact jerk;
JC＝wc(af,ref-af)2，af,ref＝kvΔv+kdΔd；wcis a weight coefficient, af,refFor driver reference acceleration, afIs the actual acceleration, kd，kvAre weight coefficients.
C is a constant term; x ═ X (k +1| k), X (k +2| k), …, X (k + p | k)]T(ii) a Wherein x (k +1| k) is the prediction of the state at the time k +1, x (k +2| k) is the prediction of the state at the time k +2, and sequentially from the beginning, x (k + p | k) is the prediction of the state at the time k + p, namely p-step prediction.
W＝diag(Wt,Wt,…,Wt)+diag(Wc,Wc,…,Wc)；Wt＝diag(wΔd,wΔv,0)；
Figure BDA0001868830300000091
U＝[u(k)，u(k+1)，…，u(k+p-1)]T(ii) a Wherein u (k) is the control input at the time of k, u (k +1) is the control input at the time of k +1, and in sequence, u (k + p-1) is the control input at the time of k + p-1;
Figure BDA0001868830300000092
wherein, TsIs the sampling period.
Further, boundary constraint is carried out on the controller working domain by combining factors such as the physical limit of the vehicle, and system I/O constraint conditions are established as follows:
Figure BDA0001868830300000093
wherein u ismin、ΔuminIs the lower bound of the allowable control set umax、ΔumaxTo allow the upper bound of the control set, ymin＝[Δdmin，Δvmin，af,min]TOutput lower bound, y, for the systemmax＝[Δdmax，Δvmax，af,max]TAnd outputting an upper bound for the system.
Furthermore, under transient working conditions, hard constraint conditions are easy to cause the problem of no feasible solution in the rolling optimization process, so that relaxation vectors are introduced to relax the hard constraint conditions so as to expand and solve feasible domains. The rigid constraint of the following safety and the motorcade stability is combined to relax the system I/O constraint condition to obtain
Figure BDA0001868830300000094
Wherein i is ∈ [0, p-1 ]]The relaxation factor satisfies epsilon1≥0、ε2≥0、ε3Not less than 0, the relaxation coefficient satisfies
Figure BDA0001868830300000095
Figure BDA0001868830300000096
Further, to obtain good control quality, an incremental control strategy is employed. Defining the difference between the control inputs at time k and time k-1 as the control increment, i.e., Δ u (k) -u (k-1), then at [ k, k + p-1 [ ]]In the prediction time domain, satisfy U-K1u(k-1)+K2ΔU；ΔU＝[Δu(k)，Δu(k+1)，…，Δu(k+p-1)]TFor predicting the control increment sequence of the time domain, each coefficient matrix satisfies:
Figure BDA0001868830300000101
in addition, if the relaxation factor is automatically adjusted too much in the solving process, the controller working domain may deviate from the allowable range, and the boundary constraint effect fails. Therefore, in order to inhibit the infinite relaxation capacity of the relaxation factors on the constraint boundaries, a regularization method is adopted, and an L2 regularization term is introduced into the cost function, so that the closed-loop system seeks balance between the optimization feasibility of the constraint optimization problem and the relaxation degree of the constraint boundaries, and J (y, u, delta u, epsilon) is obtained as J + epsilonTρε；
Wherein, epsilon is [ epsilon [ ]1,ε2,ε3]TAs vector relaxation factor, ρ ═ diag (ρ)1,ρ2,ρ3) Is a penalty coefficient matrix used for penalizing the relaxation capability of the relaxation factor expansion constraint boundary.
Will be provided with
Figure BDA0001868830300000102
And U is K1u(k-1)+K2Substituting Δ U into J (y, U, Δ U, ε) ═ J + εTIn rho epsilon, constant terms are sorted and ignored to obtain
Figure BDA0001868830300000103
Wherein x (k) is a state vector, e (k) is an error correction term,
Figure BDA0001868830300000104
in order to perturb the sequence of events,
Figure BDA0001868830300000105
the acceleration of the vehicle ahead at time k,
Figure BDA0001868830300000106
the acceleration of the front vehicle at the moment k +1 is sequentially backward,
Figure BDA0001868830300000107
the acceleration of the front vehicle at the moment k + p-1; each coefficient matrix satisfies:
Ap＝[A，A2，…，Ap]T
Figure BDA0001868830300000108
Mp＝[M，AM，…，Ap-1M]T
Cp＝diag(C，C，…，C)
wherein the content of the first and second substances,
Figure BDA00018688303000001010
τhfor a fixed time interval, TLFor execution of system time lag, KLIs the gain.
Thus, obtaining
Figure BDA0001868830300000111
On the basis of (A), further obtain
Figure BDA0001868830300000112
Wherein the content of the first and second substances,
Figure BDA0001868830300000113
Figure BDA0001868830300000114
Figure BDA0001868830300000115
which is distinguished from the matrix transposition symbol T.
Wherein, E ═ diag (1, 1, …, 1)
T1＝Ymax-CpApx(k)-CpGpΦ-CpMpe(k)
T2＝-Ymin+CpApx(k)+CpGpΦ+CpMpe(k)
T3＝-Dsafe+Ddes+LpCp[Apx(k)+GpΦ+Mpe(k)]
Dsafe＝[dsafe(k)，dsafe(k)，…，dsafe(k)]T
Ddes＝[ddes(k)，ddes(k)，…，ddes(k)]T
Lp＝diag(l，l，…，l)，l＝[1，0，0]；
Figure BDA0001868830300000116
Figure BDA0001868830300000117
Figure BDA0001868830300000118
Figure BDA0001868830300000119
The invention also provides a multi-target self-adaptive cruise control device, which can be correspondingly referred to with the method described above; referring to fig. 2, fig. 2 is a schematic diagram of a multi-target adaptive cruise control apparatus according to an embodiment of the present invention, as shown in fig. 2, the apparatus includes:
the building module 10 is used for building a quadratic performance functional according to control input, control input increment and regularization items under process state constraint conditions, system I/O constraint conditions and fleet constraint conditions;
the acquiring module 20 is used for acquiring the state parameters of the self vehicle and the front vehicle in real time;
the calculation module 30 is used for obtaining an expected acceleration increment of the self-vehicle through quadratic performance functional rolling online optimization prediction according to the state parameters and obtaining an expected acceleration according to the expected acceleration increment;
and the control module 40 is used for controlling the self-vehicle according to the expected acceleration increment so that the actual acceleration of the self-vehicle converges to the expected acceleration.
On the basis of the foregoing embodiment, optionally, the obtaining module 20 includes:
the first acquisition unit is used for acquiring the state parameters of the self-vehicle in real time through a CAN bus;
and the second acquisition unit is used for acquiring the state parameters of the front vehicle in real time through V2V communication.
On the basis of the above embodiment, optionally, the calculation module 30 includes:
a solving unit for rolling optimization solution according to the state parameters
Figure BDA0001868830300000121
Obtaining a predicted sequence
Figure BDA0001868830300000122
A selection unit for selecting a first component Deltau in the prediction sequence*(k) For desired acceleration increments, in combination with a saturation function
Figure BDA0001868830300000123
Obtaining a desired acceleration;
wherein the content of the first and second substances,
Figure BDA0001868830300000124
for an augmented control sequence, H is a positive hessian matrix, f is a primary term coefficient vector, omega is a constraint coefficient matrix, and Gamma is an upper bound of a feasible region,
Figure BDA0001868830300000125
in order to control the upper limit of the input,
Figure BDA0001868830300000126
inputting a lower limit for the control; epsilon1、ε2And epsilon3Are all relaxation factors.
On the basis of the above embodiment, optionally, the method further includes:
the detection module is used for detecting whether the expected acceleration increment, the expected acceleration and the state parameter exceed the constraint boundary of the working domain of the controller in real time;
and the adjusting module is used for increasing the corresponding relaxation factor in the positive direction if the expected acceleration increment or the expected acceleration or the state vector exceeds the constraint boundary of the controller working domain.
Referring to fig. 3, fig. 3 is a schematic diagram of a multi-target adaptive cruise control apparatus according to an embodiment of the present invention, and as can be seen from fig. 3, the multi-target adaptive cruise control apparatus includes: a memory 1 and a processor 2;
a memory 1 for storing a computer program;
a processor 2 for implementing the following steps when executing the computer program:
under the process state constraint condition, the system I/O constraint condition and the fleet constraint condition, establishing a quadratic performance functional according to the control input, the control input increment and the regularization item; acquiring state parameters of a self vehicle and a front vehicle in real time; according to the state parameters, obtaining the expected acceleration increment of the self-vehicle through quadratic performance functional rolling online optimization prediction, and obtaining the expected acceleration according to the expected acceleration increment; and controlling the self-vehicle according to the expected acceleration increment so that the actual acceleration of the self-vehicle converges to the expected acceleration.
The present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
under the process state constraint condition, the system I/O constraint condition and the fleet constraint condition, establishing a quadratic performance functional according to the control input, the control input increment and the regularization item; acquiring state parameters of a self vehicle and a front vehicle in real time; according to the state parameters, obtaining the expected acceleration increment of the self-vehicle through quadratic performance functional rolling online optimization prediction, and obtaining the expected acceleration according to the expected acceleration increment; and controlling the self-vehicle according to the expected acceleration increment so that the actual acceleration of the self-vehicle converges to the expected acceleration.
The computer-readable storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Because the situation is complicated and cannot be illustrated by one list, those skilled in the art can realize that a plurality of examples can exist in combination with the actual situation under the basic principle of the embodiment provided by the invention, and the invention is within the scope of the invention without enough inventive work.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The multi-target adaptive cruise control method, apparatus, device and computer readable storage medium provided by the present invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims (10)

1. A multi-target adaptive cruise control method is characterized by comprising the following steps:
under the process state constraint condition, the system I/O constraint condition and the fleet constraint condition, establishing a quadratic performance functional according to the control input, the control input increment and the regularization item;
acquiring state parameters of a self vehicle and a front vehicle in real time;
according to the state parameters, obtaining an expected acceleration increment of the self-vehicle through online optimization prediction of the quadratic performance functional rolling, and obtaining an expected acceleration according to the expected acceleration increment;
and controlling the self-vehicle according to the expected acceleration increment to make the actual acceleration of the self-vehicle converge to the expected acceleration.
2. The multi-target adaptive cruise control method according to claim 1, wherein the obtaining of the state parameters of the own vehicle and the preceding vehicle in real time comprises:
and acquiring the state parameters of the self vehicle in real time through a CAN bus, and acquiring the state parameters of the front vehicle in real time through V2V communication.
3. The multi-objective adaptive cruise control method according to claim 2, wherein the obtaining of the expected acceleration increment of the self-vehicle through the quadratic performance functional rolling online optimization prediction according to the state parameters and the obtaining of the expected acceleration according to the expected acceleration increment comprises the following steps:
according to the state parameters, rolling optimization solution
Figure FDA0001868830290000011
Obtaining a predicted sequence
Figure FDA0001868830290000012
Selecting a first component Deltau in the prediction sequence*(k) For desired acceleration increments, in combination with a saturation function
Figure FDA0001868830290000013
Obtaining the expected acceleration;
wherein, the
Figure FDA0001868830290000014
For an augmented control sequence, H is a positive definite Hessian matrix, f is a primary term coefficient vector, Ω is a constraint coefficient matrix, and T is a feasible domain upper bound, the method includes the steps of
Figure FDA0001868830290000015
To control the upper limit of the input, the
Figure FDA0001868830290000016
Inputting a lower limit for the control; the epsilon1、ε2And epsilon3Are all relaxation factors.
4. The multi-target adaptive cruise control method according to claim 3, further comprising:
detecting whether the expected acceleration increment, the expected acceleration and the state vector exceed the constraint boundary of the controller working domain in real time;
and if the expected acceleration increment or the expected acceleration or the state vector exceeds the working domain constraint boundary, the corresponding relaxation factor is positively increased.
5. A multi-target adaptive cruise control apparatus, comprising:
the building module is used for building a quadratic performance functional according to the control input, the control input increment and the regularization item under the process state constraint condition, the system I/O constraint condition and the fleet constraint condition;
the acquisition module is used for acquiring the state parameters of the self vehicle and the front vehicle in real time;
the calculation module is used for obtaining the expected acceleration increment of the self-vehicle through the quadratic performance functional rolling online optimization prediction according to the state parameters and obtaining the expected acceleration according to the expected acceleration increment;
and the control module is used for controlling the self-vehicle according to the expected acceleration increment so that the actual acceleration of the self-vehicle is converged to the expected acceleration.
6. The multi-target adaptive cruise control of claim 5, wherein said obtaining module comprises:
the first acquisition unit is used for acquiring the state parameters of the self-vehicle in real time through a CAN bus;
and the second acquisition unit is used for acquiring the state parameters of the front vehicle in real time through V2V communication.
7. The multi-objective adaptive cruise control according to claim 6, characterized in that said calculation module comprises:
a solving unit for rolling optimization solving according to the state parameters
Figure FDA0001868830290000021
Obtaining a predicted sequence
Figure FDA0001868830290000022
A selection unit for selecting a first component Δ u in the prediction sequence*(k) For desired acceleration increments, in combination with a saturation function
Figure FDA0001868830290000023
Obtaining the expected acceleration;
wherein, the
Figure FDA0001868830290000024
For an augmented control sequence, H is a positive definite Hessian matrix, f is a primary term coefficient vector, Ω is a constraint coefficient matrix, and T is a feasible domain upper bound, the method includes the steps of
Figure FDA0001868830290000025
To control the upper limit of the input, the
Figure FDA0001868830290000026
Inputting a lower limit for the control; the epsilon1、ε2And epsilon3Are all relaxation factors.
8. The multi-target adaptive cruise control according to claim 7, further comprising:
the detection module is used for detecting whether the expected acceleration increment, the expected acceleration and the state parameter exceed the constraint boundary of the working domain of the controller in real time;
and the adjusting module is used for positively increasing the corresponding relaxation factor if the expected acceleration increment or the expected acceleration or the state vector exceeds the constraint boundary of the work domain of the controller.
9. A multi-target adaptive cruise control apparatus, characterized by comprising:
a memory for storing a computer program;
processor for implementing the steps of the method of multi-objective adaptive cruise control according to any one of claims 1 to 4 when executing said computer program.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when being executed by a processor, carries out the steps of the method of multi-objective adaptive cruise control according to any one of claims 1 to 4.
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