CN113495518A - Networked vehicle control method under influence of bidirectional network time delay and communication quantization - Google Patents

Networked vehicle control method under influence of bidirectional network time delay and communication quantization Download PDF

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CN113495518A
CN113495518A CN202110880104.6A CN202110880104A CN113495518A CN 113495518 A CN113495518 A CN 113495518A CN 202110880104 A CN202110880104 A CN 202110880104A CN 113495518 A CN113495518 A CN 113495518A
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王建强
许庆
潘济安
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Tsinghua University
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Abstract

The invention discloses a method for controlling networked vehicles under the influence of bidirectional network time delay and communication quantization, which comprises the following steps: step 1, estimating a current network delay range and a transfer matrix thereof, and determining quantization density; step 2, determining a vehicle control system equation; step 3, constructing an augmentation system equation; step 4, constructing a matrix inequality equation set; step 5, calculating control gains of different time delay states; step 6, arranging a cache device of the observed vehicle state information at the sensor end, and packaging and sending the cache device; step 7, the cloud end controller receives the data packet, calculates possible control quantity according to the uplink time delay and the possible downlink time delay, and sends the data packet to a controlled vehicle after quantization coding; and 8, the controlled vehicle decodes the received data packet and selects the corresponding control quantity to execute according to the time delay. The invention can ensure the stability and the safety of the networked vehicle remote control system under the condition that communication time delay and quantification exist among the sensor, the controller and the controlled vehicle.

Description

Networked vehicle control method under influence of bidirectional network time delay and communication quantization
Technical Field
The invention relates to the technical field of intelligent networked automobiles, in particular to a networked vehicle control method and device under the influence of bidirectional network time delay and communication quantization.
Background
Intelligence is a major trend in vehicle development. The existing automatic driving and auxiliary driving technology mainly depends on a sensor and a decision control system at the vehicle end to realize the intelligence of a single vehicle, and the existing automatic driving and auxiliary driving technology has inherent problems in various aspects. For example, reliability of vehicle-mounted sensing is difficult to guarantee, sensing distance is limited, a blind area exists, a single vehicle is controlled by means of local information, and global optimization is difficult to achieve. These problems increase the cost of landing a high-grade autonomous driving technique to some extent, and prevent wide-scale popularization and application of autonomous vehicles.
Meanwhile, due to the development of the internet of vehicles communication technology, the auxiliary automatic driving at different levels outside the vehicle becomes possible, and the collaborative automatic driving becomes a development trend. For example, remote driving is now a typical landing application scenario of 5G communication technology in the field of intelligent transportation. In the future, on the basis of the inter-vehicle-road high-speed reliable communication technology, an internet-connected vehicle control system based on only network information without depending on any vehicle sensor and controller will appear. In the system, the vehicle only needs to be equipped with internet communication equipment, the cloud end controller can obtain the state information (position, speed, course and the like) of the controlled vehicle through a sensor arranged on the road side or through a network, and sends a control instruction through wireless communication, so that the controlled vehicle can run according to a certain track at a certain running speed. Such a solution would greatly reduce the cost of large-scale application of high-level autopilot technology. However, the transmission delay problem associated with some existing communication technologies may cause a certain deviation between the control quantity actually received by the controlled vehicle at the current time and the reasonable control quantity required by the stabilizing system at the current time, which affects the remote control performance of the networked vehicle and even leads to unsafe driving conditions. Meanwhile, due to the limitation of communication bandwidth, the actual communication feedback channel must have quantization processing (logarithmic quantization or uniform quantization) on the transmission information, which affects the accuracy of the feedback information. Although some prior arts focus on the internet-connected vehicle control method under the influence of communication, most of the prior arts only consider the influence of fixed time delay or the influence of one-way random time delay, and have high computational complexity, so that the method is difficult to be applied to actual vehicle control. Therefore, the networked vehicle control method under the influence of the bidirectional network delay and the communication quantification needs to be further researched.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, an object of the present invention is to provide a method for controlling networked vehicles under the influence of bidirectional network delay and communication quantization, which can ensure that a vehicle running at a constant speed stably tracks an assigned route under the influence of certain random bidirectional network delay and logarithmic quantization communication.
The invention also aims to provide a networked vehicle control device under the influence of bidirectional network time delay and communication quantification.
In order to achieve the above object, an embodiment of the present invention provides a method for controlling an internet vehicle under the influence of bidirectional network delay and communication quantization, including the following steps:
s1, estimating the current network delay range and the state transition matrix thereof, and determining the quantization density;
s2, establishing a control equation of the vehicle control system;
s3, constructing an augmentation system equation according to the transfer matrix and the vehicle control system control equation;
s4, constructing a non-linear matrix inequality equation set for solving according to the augmented system equation;
s5, solving the numerical solution of the nonlinear matrix inequality equation set through linearization iteration to calculate the control gains of different time delay states;
s6, recording the state quantities of a plurality of discrete step times before the current time according to the vehicle state information, and sending the state quantities of the plurality of discrete step times to the cloud-end controller;
s7, obtaining actual time delay through the sending time stamp and the receiving time stamp, calculating a stabilizing control quantity according to the actual time delay and the follow-up possible time delay, and sending the stabilizing control quantity to a controlled vehicle after quantization coding;
s8, decoding the stabilized control quantity after quantization coding through the controlled vehicle, and selecting the corresponding control quantity to execute according to the actual time delay;
s9, when the communication environment or the communication quantization density changes, the stabilizing control amount is recalculated S1.
In order to achieve the above object, an embodiment of the present invention provides an internet vehicle control apparatus under the influence of bidirectional network delay and communication quantization, including:
the estimation module is used for estimating the current network delay range and the state transition matrix thereof and determining the quantization density;
the first construction module is used for establishing a vehicle control system control equation;
the second construction module is used for constructing an augmentation system equation according to the transfer matrix and the vehicle control system control equation;
the third construction module is used for constructing a nonlinear matrix inequality equation set for solving according to the augmented system equation;
the calculation module is used for solving the numerical solution of the nonlinear matrix inequality equation set through linearization iteration so as to calculate the control gains of different time delay states;
the sending module is used for recording the state quantities of a plurality of discrete step times before the current time according to the vehicle state information and sending the state quantities of the discrete step times to the cloud-end controller;
the control module is used for obtaining actual time delay through the sending time stamp and the receiving time stamp, calculating a stabilizing control quantity according to the actual time delay and the follow-up possible time delay, and sending the stabilizing control quantity to a controlled vehicle after quantization coding;
the execution module is used for decoding the stabilized control quantity after the quantization coding through the controlled vehicle and selecting the corresponding control quantity to execute according to the actual time delay;
and the adjusting module is used for calling other modules to recalculate the stabilizing control quantity when the communication environment or the communication quantization density changes.
According to the networked vehicle control method and device under the influence of bidirectional network time delay and communication quantization, control gains corresponding to different sensor-cloud end controller/cloud end control-controlled vehicle time delay and quantization states are solved based on a matrix inequality solving method. The control system collects vehicle running state information through a roadside sensor, caches historical state information, packages and sends the vehicle running state information to the cloud end controller, the cloud end controller calculates all possible alternative control quantities of the stabilizing system according to actual sensor-cloud end controller time delay and possible cloud end controller-controlled vehicle time delay, packages and sends the vehicle to the controlled vehicle after logarithmic quantization coding, the controlled vehicle receives data packets and then carries out logarithmic quantization decoding, and selects corresponding control quantities according to actual cloud end controller-controlled vehicle time delay, so that the stability and the safety of the networked vehicle remote control system in an actual communication environment are guaranteed.
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/or 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 schematic diagram of a networked vehicle control system under the influence of bidirectional network delay and communication quantization according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for controlling networked vehicles under the influence of two-way network delay and communication quantization according to an embodiment of the present invention;
FIG. 3 is a logic diagram of a control method for networked vehicles under the influence of bidirectional network delay and communication quantization according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an internet vehicle control device under the influence of bidirectional network delay and communication quantization 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 and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The invention provides a method for remotely controlling a networked vehicle under the influence of random two-way network time delay and communication quantization, which aims to remotely control a vehicle running at a constant speed (the vehicle is hereinafter referred to as a controlled vehicle) to track a specified track through a network and ensure the stability of a system under the influence of the two-way network time delay and the communication quantization.
Fig. 1 shows the information transfer relationship of the networked vehicle remote control system under the conditions of random two-way network delay and communication quantification. As shown in fig. 1, information such as speed, position, heading and the like of the controlled vehicle is obtained by the road side sensor in real time and is sent to the cloud end controller through wireless communication. The communication process comprises random bounded network time delay, namely the process from the moment when the sensor sends out the controlled vehicle state information data packet to the moment when the cloud-end controller receives the state information data packet. The cloud control device receives information sent by the sensor, calculates control parameters (expected steering wheel rotation angle) under the network delay of the controller and the controlled vehicle according to the actual sensor-controller network delay and the received vehicle state parameter data packet, codes the control parameters through the logarithmic encoder, and sends the control parameters to the controlled vehicle through wireless communication. The communication process comprises random bounded network time delay, namely the process from the time when the cloud-end controller sends out the control information data packet to the time when the controlled vehicle receives the control information data packet. And the controlled vehicle carries out quantization decoding after receiving the information sent by the upper layer controller, and determines a final control quantity according to the specific cloud end controller-controlled vehicle network time delay, wherein the control quantity is the control quantity for controlling the controlled vehicle to track a specified track at a constant speed.
The following describes a method and a device for controlling networked vehicles under the influence of bidirectional network delay and communication quantization according to an embodiment of the present invention with reference to the accompanying drawings.
First, a method for controlling a networked vehicle under the influence of bidirectional network delay and communication quantization according to an embodiment of the present invention will be described with reference to the accompanying drawings.
Fig. 2 is a flowchart of a method for controlling an internet vehicle under the influence of bidirectional network delay and communication quantization according to an embodiment of the present invention. Fig. 3 is a logic diagram of a control method of an internet vehicle under the influence of bidirectional network delay and communication quantization according to an embodiment of the invention.
As shown in fig. 2 and 3, the method for controlling the networked vehicle under the influence of the bidirectional network delay and the communication quantization includes the following steps:
in step S1, the current network delay range and its state transition matrix are estimated, and the quantization density is determined.
Optionally, in an embodiment of the present invention, estimating the current network latency range includes: and estimating the network delay range from the sensor to the cloud end controller and the network delay range from the cloud end controller to the controlled vehicle, and respectively discretizing the delay ranges to obtain a possible discrete delay group.
Specifically, according to the wireless communication mode and the communication environment condition from the sensor to the cloud end controller and from the cloud end controller to the controlled vehicle, the network delay range [ T _ S ] from the sensor to the cloud end controller is preliminarily estimatedmin,T_Smax]And the time delay range from the cloud end controller to the controlled vehicle network [ T _ C [ ]min,T_Cmax]And discretizing the continuous time delay possibly obtained according to the control precision requirement and the calculation capacity limit, wherein the obtained forms are [ Delta T,2 Delta T, … and tau Delta T]And [ Delta T,2 Delta T, …, d Delta T]Possibly discrete groups of time delays. Where Δ T is the discrete delay accuracy. It should be added that the higher the control accuracy is, the smaller the discrete interval of the communication delay should be, but the longer the calculation time required by the corresponding controller parameter is, otherwise, if the calculation resource is limited, the larger discrete interval of the delay may be considered. The time delays in the "possibly discrete time delay group" can be understood as all the considered possible time delays in the augmented system equation constructed in step S3.
According to the current communication environment condition and the experience value, the distribution situation of random network time delay from the sensor to the cloud end controller and from the cloud end controller to the controlled vehicle is respectively estimated, namely a state transition matrix P between different discrete time delays is estimatedsAnd PCRespectively is as follows:
Figure BDA0003191867620000051
transition matrix PSEach element in the table represents the probability of the network delay from the sensor to the cloud-end controller to be transferred from i delta T to i delta T, and the probability is lambdaijTransition matrix PCEach element in the index table represents that the probability of the network delay from the cloud end controller to the cloud end controller transferring from i delta T to i delta T is piij。
Further, determining the quantization density comprises: and determining the communication quantization density rho from the cloud-end controller to the controlled vehicle according to the bandwidth limiting condition.
The communication mode from the sensor to the cloud end controller and from the cloud end controller to the controlled vehicle can adopt DSRC, LTE-V, WiFi or 5G and the like. The "current communication environment condition" mainly refers to whether a signal is good or whether communication is congested or the like.
In step S2, a vehicle control system control equation is established.
Optionally, in one embodiment of the invention, establishing a vehicle control system control equation comprises:
determining a control equation of a vehicle control system according to the lateral system dynamics of the controlled vehicle, and determining equation parameters according to the vehicle size parameters and the control requirement:
x(k+1)＝Ax(k)+Bδ(k)， (1)
wherein k represents the current discrete time, x (k) represents the state variable of the transverse control system at the time k, δ (k) represents the expected front wheel steering angle at the time k, and A and B are coefficient matrixes.
The expression of each state quantity and coefficient matrix in the system equation is as follows:
x(k)＝(β(k),r(k),ψL(k),yl(k))T， (2)
δ(k)＝-Kx(k)， (3)
in the formula: beta (k) represents the vehicle centroid slip angle, r (k) represents the vehicle yaw rate, psiL(k) Indicating a vehicle heading error, yl(k) Watch (A)And showing the lateral error of the vehicle from the expected track at the pre-aiming distance.
The coefficient matrixes A and B in the discrete equation are calculated by a continuous equation as follows:
Figure BDA0003191867620000052
Figure BDA0003191867620000053
in the formula:
Figure BDA0003191867620000061
Figure BDA0003191867620000062
Figure BDA0003191867620000063
Figure BDA0003191867620000064
in the formula: c. Cf、crRespectively controlled front and rear tire cornering stiffness, |f、lrThe distances from the mass center of the controlled vehicle to the front axle and the rear axle respectively, m is the total mass of the controlled vehicle, IzIs the moment of inertia of the controlled vehicle around the vertical axis, v is the longitudinal running speed of the controlled vehicle, lsFor the pre-aiming distance, T is a model discrete time interval which is determined by computational accuracy requirements, and if the computational accuracy requirements on the dynamic model are high, a small discrete interval should be selected as much as possible. In the present embodiment, T and Δ T are set to the same value in order to simplify the matrix form for calculation, and Δ T may be an integer multiple of T in actual implementation.
In step S3, an augmented system equation is constructed from the transition matrix and the vehicle control system control equation.
According to the state transition matrix P established in step S1SAnd PCAnd the system equation constructed in the step 2 is used for constructing an augmentation system equation for computing and including time delay and quantitative influence from the sensor to the cloud end controller/the cloud end controller to the controlled vehicle network:
Figure BDA0003191867620000065
Figure BDA0003191867620000066
Xaug(k)＝[x(k)T,x(k-ΔT)T,…,x(k-(τ+d)ΔT)T]T， (12)
Figure BDA0003191867620000067
Figure BDA0003191867620000068
in the formula: k represents the current discrete time, x (k) represents the state variable of the system at the time k, u (k) represents the control input of the system at the time k, [ x (k)T,x(k-ΔT)T,…,x(k-(τ+d)ΔT)T]TRepresents the previous T + d discrete time system state quantities, Xaug(k) Represents an augmented system state variable that is,
Figure BDA0003191867620000069
a coefficient matrix representing the state variables of the augmented system,
Figure BDA00031918676200000610
representing the coefficient matrix of the control variables of the augmented system, N representing the dimension of the system (1) (for the lateral control dynamics of the vehicle)The value is 4) in theory), 0N·NA zero matrix of dimension N is represented,
Figure BDA00031918676200000611
the i + j th element in the matrix (14) is an N-dimensional unit matrix, and Q (x) is the logarithmic quantization of x.
In step S4, a nonlinear matrix inequality equation set for solution is constructed from the augmented system equation.
Constructing a nonlinear matrix inequality equation set for solution according to the augmented system equations (10) and (11) of step S3, wherein τ · d nonlinear matrix inequalities are included, and each matrix inequality can be expressed as:
Figure BDA0003191867620000071
Figure BDA0003191867620000072
Figure BDA0003191867620000073
Figure BDA0003191867620000074
wherein i is 1, …, τ, r is 1, …, d, λijFor transferring the matrix PτRepresents the probability of the network delay of the sensor-controller shifting from i delta T to j delta T, pii，jFor transferring the matrix PCThe element in (b) represents the probability of the controller-controlled vehicle network time delay transferring from i delta T to j delta T, represents the transpose of the symmetric element of the matrix,
Figure BDA0003191867620000075
in order to augment the system state variable coefficient matrix,
Figure BDA0003191867620000076
to augment the system control variable coefficient matrix,
Figure BDA0003191867620000077
is an observation matrix of the corresponding augmentation system when the sensor-controller time delay is i delta T and the controller-controlled vehicle time delay is j delta T, K(i，j)P represents the quantization density of the logarithmic quantizer Q for the corresponding augmented system control gain when the sensor-controller delay is i Δ T and the controller-controlled vehicle delay is j Δ T. It is to be noted that, in the nonlinear matrix inequalities described in (15) to (18), P(i，j)、K(i，j)Is a variable matrix to be solved.
In step S5, the numerical solutions of the non-linear matrix inequality equation sets are iteratively solved by linearization to calculate the control gains for different delay states.
The numerical solution of the above-mentioned non-linear matrix inequality can be solved by some existing Linearization iterative methods, for example, by a Cone Complementary Linearization (con Complementary Linearization) method. Solved K(i，j)Namely, the corresponding control parameter is the corresponding control parameter when the network delay from the sensor to the cloud end controller is i delta T and the network delay from the network controller to the controlled vehicle is j delta T.
In step S6, the state quantities at a plurality of discrete step times before the current time are recorded based on the vehicle state information, and the state quantities at the plurality of discrete step times are transmitted to the cloud-side controller.
Setting a state caching device of observed vehicle state information at a sensor sending end, and recording state quantity of tau + d discrete step time before the current time k, namely [ X (k), …, X (k- (tau + d) delta T)]Packing it as a whole at TstThe time k is sent to the controller via a communication channel that includes a random time delay.
In step S7, the actual time delay is obtained from the transmission time stamp and the reception time stamp, the controlled variable is calculated from the actual time delay and the possible subsequent time delay, and the controlled variable is transmitted to the controlled vehicle after being quantized and encoded.
Cloud end controller at TcrThe data packet sent in step S6 is received, and the actual time delay T of the frame data sent from the sensor end to the controller end is obtained through the time stamp technologycr-TstAnd the discrete processing shown by equation (19) approximates the discrete delay state determined in step S1.
Figure BDA0003191867620000081
In the formula, the discrete interval Δ T is determined in step 1, and round (×) represents an integer function.
At this time, the received vehicle state data packet is analyzed according to the discrete time delay size as follows:
[x(k-iΔT)T,x(k-(i+1)ΔT)T，…，x(k-(i+(τ+d)ΔT)T]T (20)
according to the calculation requirement, deleting the items which are not used for calculation in the data packet, namely:
[x(k-iΔT)T,x(k-(i+1)ΔT)T,…,x(k-(τ+d)ΔT)T]T (21)
according to the actual network time delay T from the sensor to the cloud end controllersCalculating all possible calm control quantities, i Δ T and all possible cloud-end controller-to-sensor delays:
[u(i,1),…,u(i,d)]＝[K(i,1)·x(k-(i+1)ΔT),…,K(i,d)·x(k-(i+d)ΔT)](22)
all the control quantities obtained by the above calculation are subjected to logarithmic quantization coding according to the quantization density specified in step S1, as shown in formula (23):
Figure BDA0003191867620000082
quantized control quantity l1,…,ld]Is packaged at T through a wireless communication networkctAnd sending the data to the controlled vehicle at any moment.
In step S8, the controlled vehicle decodes the quantized and encoded controlled variable, and selects and executes the corresponding controlled variable according to the actual time delay.
At a certain time TarThe controlled vehicle receives the frame information, and according to the timestamp of the received data packet, the actual time delay T from the cloud end sensor corresponding to the currently received data packet to the controlled vehicle can be accurately calculatedct-TarBy a step similar to step S6, approximation to a certain discrete time delay can be discretized:
Figure BDA0003191867620000083
in the formula, the discrete interval Δ T is determined in step S1, and round (×) represents an integer function.
According to the actual time delay obtained through dispersion, selecting corresponding control quantity in the received data packet, and carrying out logarithmic quantization decoding, namely:
Figure BDA0003191867620000084
the final control quantity u calculated by the equation (25) is the expected front wheel steering angle of the controlled vehicle, and the control quantity is sent to the steering motor controller to be executed. Using the control input, the system can be stabilized under the influence of the random bi-directional network delay profile estimated in step S1 and the determined communication quantization density. It should be noted that in the next discrete cycle, the sensor, the cloud-end controller, and the controlled vehicle will calculate the control input at this time again according to the actual time delay according to steps S6-S8.
In step S9, when the communication environment or the communication quantization density changes, S1 recalculates the amount of stabilization control is performed.
If the communication environment between the vehicle and the road is changed or the quantization density is changed, the steps S1 are repeated, the communication delay distribution is re-estimated, and a new control gain parameter is obtained by performing loop calculation according to steps S2-S5.
Taking a set of specific parameters as an example, the solving process will be described below, and it should be noted that, in order to simplify the calculation, only one simplified communication delay case is considered here, that is, only 2 possible network delays from the sensor to the cloud-end controller and from the cloud-end controller to the controlled vehicle are considered separately.
Step 1, considering a networked vehicle cruise control scene, in the scene, a road side system obtains all information of a controlled vehicle through a road side sensor, and the communication from the road side sensor to a cloud end controller and the communication from the cloud end controller to the controlled vehicle are carried out through LTE-V. According to the technical parameters of the communication equipment, the ranges of the network delay are as follows: and [ 0.04-0.11 ] (seconds), discretizing the possible communication time delay into [0.05,0.1] (seconds) according to the control precision requirement and the computing capacity limit, and respectively estimating the state transition probability of the network time delay from the sensor to the cloud end controller and the network time delay from the cloud end controller to the controlled vehicle according to empirical values as follows:
Figure BDA0003191867620000091
Figure BDA0003191867620000092
wherein: lambda [ alpha ]ijRepresents the probability of the network delay from the sensor to the cloud-end controller to be transferred from i multiplied by 0.05 to j multiplied by 0.05, piijThe probability that the network delay from the cloud end controller to the cloud end controller is transferred from i × 0.05 to j × 0.05 is represented. According to the communication bandwidth requirement, the communication quantization density is set to be p 0.6.
Step 2, the model of the patrol transverse control system is as follows:
x(k+1)＝Ax(k)+Bδ(k)
wherein the system state variables are:
x(k)＝(β(k),r(k),ψL(k),yl(k))T
the coefficient matrixes A and B in the discrete equation are calculated by a continuous equation as follows:
Figure BDA0003191867620000093
Figure BDA0003191867620000094
according to the dynamics characteristics of the controlled vehicle, parameters in the equation are determined as follows:
lf＝1.14m,lr＝1.4m,m＝1500kg,Iz＝2420kg·m2,v＝7m/s
cf＝44000N/rad,cr＝47000N/rad
in summary, after the discretization with the interval of 0.05s, the final discretization system equation is in the form of:
Figure BDA0003191867620000101
step 3, constructing a jump system equation for calculation and containing communication delay according to the system equation constructed in the step 2 and the time-varying communication delay model established in the step 1:
Figure BDA0003191867620000102
Figure BDA0003191867620000103
wherein: i and r respectively correspond to different network delay states from the sensor to the cloud end sensor and from the cloud end controller to the controlled vehicle, according to the estimation in the step 1, two types of i and r which are possibly different (i, r belongs to [1,2]) respectively correspond to different discrete sensor-cloud end controller network delays determined in the step 1, and different r correspond to different discrete cloud end controller-controlled vehicle network delays determined in the step 1, namely:
i＝1→Ts＝0.05r＝1→Tc＝0.05
i＝2→Ts＝0.1r＝2→Tc＝0.1
the state variable of the augmentation system and the corresponding coefficient matrix form in the above equation are:
Xaug(k)＝[x(k)T,x(k-0.05)T,x(k-0.1)T，x(k-0.15)T，x(k-0.2)T]T
Figure BDA0003191867620000104
Figure BDA0003191867620000105
Figure BDA0003191867620000106
Figure BDA0003191867620000107
Figure BDA0003191867620000108
wherein: 04×4Representing a 4-dimensional zero matrix, I4×4Representing a 4-dimensional identity matrix.
Step 4, according to the augmented system equation in the step 3, a linear matrix inequality equation set for solving is constructed, and the linear matrix inequality equation set comprises the following 4 matrix inequalities:
Figure BDA0003191867620000111
Figure BDA0003191867620000112
wherein the content of the first and second substances,
Figure BDA0003191867620000113
Figure BDA0003191867620000114
Figure BDA0003191867620000115
Figure BDA0003191867620000116
Figure BDA0003191867620000117
Figure BDA0003191867620000118
Figure BDA0003191867620000119
Figure BDA00031918676200001110
Y(1，1)＝(λ11P(1，1)+λ12P(1，1))-1，Y(1，2)＝(λ11P(1，2)+λ12P(1，2))-1
Y(2，1)＝(λ21P(2，1)+λ22P(2，1))-1，Y(2，2)＝(λ21P(2，2)+λ22P(2，2))-1
wherein λ isijAnd piijThe specific value is added to the definition in step 1,
Figure BDA0003191867620000121
see definition in step 2. It should be noted that, in the above-mentioned matrix inequality, [ P ](1,1),P(1,2),P(2,1),P(2,2)]And [ K ](1,1),K(1,2)，K(2，1)，K(2，2)]Are the variables to be solved.
And 5, obtaining a numerical solution of the matrix inequality constructed in the step 4 by using a Cone compensation Linearization (Cone Complementary Linearization) method under the condition that the set calculation precision is 0.05, so as to obtain the controller parameters corresponding to different time delay states, wherein the result is as follows:
K(1，1)＝[-0.0089,-0.0196,-0.1616，-0.1007]
K(1，2)＝[-0.0049，-0.0019,-0.1894-0.0839]
K(2，1)＝[-0.0047，-0.0025，-0.2095-0.0800]
K(2,2)＝[0.0056,0.0159,-0.1149-0.0564]
and 6, setting a state caching device of the observed vehicle state information at a sending end of the sensor, recording state quantities of 4 discrete step time moments before the current time k, namely [ x (k), x (k-0.05), x (k-0.1), x (k-0.15) and x (k-0.2), packing the whole state quantities, and sending the packed state quantities to the controller through a communication channel containing random time delay at the moment Tst (k).
Step 7, the cloud end controller is in TcrThe data packet sent in the step 6 is received, and the actual time delay T of the frame data sent from the sensor end to the controller end is obtained through a time stamp technologycr-TstAssume that the communication delay size at a certain time is 0.04s, and the discrete processing shown by equation (19) approximates the discrete delay state determined in step 1, i.e. the delay of the current data packet is considered to be 0.05 s.
At this time, the received vehicle state data packet is analyzed according to the discrete time delay size as follows:
[x(k-0.05)T,x(k-0.1)T,x(k-0.15)T,x(k-0.2)T,x(k-0.25)T]T
according to the calculation requirement, deleting the items which are not used for calculation in the data packet, namely:
[x(k-0.05)T,x(k-0.1)T,x(k-0.15)T,x(k-0.2)T]T
according to the actual network time delay T from the sensor to the cloud end controllersCalculating all possible calm control quantities, 0.05 and all possible cloud-end controller-to-sensor time delays:
[u(1,1),u(1,2)]＝[K(1,1)·x(k-0.1ΔT),K(1,2)·x(k-0.15ΔT)]
all the control quantities obtained by the above calculation are subjected to logarithmic quantization coding according to the quantization density specified in step 1:
Figure BDA0003191867620000122
quantized control quantity l1,l2]Is packaged at T through a wireless communication networkctAnd sending the data to the controlled vehicle at any moment.
Step 8, at a certain time TarThe controlled vehicle receives the frame information, and according to the timestamp of the received data packet, the actual time delay T from the cloud end sensor corresponding to the currently received data packet to the controlled vehicle can be accurately calculatedct-TarAssuming that the time delay at this time is 0.11s, the discretization can be approximated to a discretization time delay of 0.1s by a procedure similar to step 6.
According to the actual time delay obtained through dispersion, selecting corresponding control quantity in the received data packet, and carrying out logarithmic quantization decoding, namely:
Figure BDA0003191867620000131
the final control quantity u calculated by the above formula is the expected front wheel steering angle of the controlled vehicle, and the control quantity is sent to the steering motor controller to be executed. The control input can be used to achieve a calm system under the influence of the random two-way network delay distribution estimated in step 1 and the determined communication quantization density. In the next discrete period, the sensor, the cloud-end controller and the controlled vehicle calculate the calm control input at the moment according to the actual time delay according to the steps 6-8.
And 9, if the communication environment between the vehicle roads is changed or the quantization density is changed, repeating the step 1, re-estimating the communication delay distribution situation, and circularly calculating according to the steps 2-5 to obtain a new control gain parameter.
According to the networked vehicle control method under the influence of the bidirectional network time delay and the communication quantization, control gains corresponding to different sensor-cloud end controller/cloud end control-controlled vehicle time delays and quantization states are solved based on a matrix inequality solving method. The control system collects vehicle running state information through a roadside sensor, caches historical state information, packages and sends the vehicle running state information to the cloud end controller, the cloud end controller calculates all possible alternative control quantities of the stabilizing system according to actual sensor-cloud end controller time delay and possible cloud end controller-controlled vehicle time delay, packages and sends the vehicle to the controlled vehicle after logarithmic quantization coding, the controlled vehicle receives data packets and then carries out logarithmic quantization decoding, and selects corresponding control quantities according to actual cloud end controller-controlled vehicle time delay, so that the stability and the safety of the networked vehicle remote control system in an actual communication environment are guaranteed.
Next, a networked vehicle control device under the influence of bidirectional network delay and communication quantization according to an embodiment of the present invention will be described with reference to the drawings.
Fig. 4 is a schematic structural diagram of an internet vehicle control device under the influence of bidirectional network delay and communication quantization according to an embodiment of the present invention.
As shown in fig. 4, the networked vehicle control apparatus 10 under the influence of the bidirectional network delay and the communication quantization includes: an estimation module 100, a first construction module 200, a second construction module 300, a third construction module 400, a calculation module 500, a sending module 600, a control module 700, an execution module 800 and an adjustment module 900.
The estimation module 100 is configured to estimate a current network delay range and a state transition matrix thereof, and determine quantization density. A first building block 200 is used to build a vehicle control system control equation. A second construction module 300 for constructing an augmented system equation based on the transfer matrix and the vehicle control system control equation. And a third constructing module 400, configured to construct a non-linear matrix inequality equation set for solving according to the augmented system equation. And the calculating module 500 is used for solving the numerical solution of the nonlinear matrix inequality equation set through linearization iteration to calculate the control gains of different time delay states. The sending module 600 is configured to record state quantities of multiple discrete step times before the current time according to the vehicle state information, and send the state quantities of the multiple discrete step times to the cloud-end controller. And the control module 700 is configured to obtain an actual time delay through the sending time stamp and the receiving time stamp, calculate a stabilizing control quantity according to the actual time delay and a subsequent possible time delay, and send the stabilizing control quantity to the controlled vehicle after quantization coding. And the execution module 800 is configured to decode the quantized and encoded stabilized control amount through the controlled vehicle, and select a corresponding control amount according to an actual time delay to execute the control amount. And an adjusting module 900, configured to invoke other modules to recalculate the calm control amount when the communication environment or the communication quantization density changes.
Optionally, in an embodiment of the present invention, estimating the current network latency range includes:
and estimating the network delay range from the sensor to the cloud end controller and the network delay range from the cloud end controller to the controlled vehicle, and respectively discretizing the delay ranges to obtain a possible discrete delay group.
Optionally, in one embodiment of the present invention, determining the quantization density comprises: and determining the communication quantization density from the cloud-end controller to the controlled vehicle according to the bandwidth limiting condition.
Optionally, in an embodiment of the present invention, according to the current communication environment condition and the empirical value, the distribution of random network delays from the sensor to the cloud end controller and from the cloud end controller to the controlled vehicle are respectively estimated, and a state transition matrix between different discrete delays is estimated, where the state transition matrix is:
Figure BDA0003191867620000141
wherein the transfer matrix PSEach element in the table represents the probability of the network delay from the sensor to the cloud-end controller to be transferred from i delta T to i delta T, and the probability is lambdaijTransition matrix PCEach element in the cloud end data represents that the probability of the network time delay from the cloud end controller to the controlled vehicle transferring from i delta T to i delta T is piij。
Optionally, in one embodiment of the invention, establishing a vehicle control system control equation comprises:
determining a control equation of a vehicle control system according to the lateral system dynamics of the controlled vehicle, and determining equation parameters according to the vehicle size parameters and the control requirement:
x(k+1)＝Ax(k)+Bδ(k)
wherein k represents the current discrete time, x (k) represents the state variable of the transverse control system at the time k, δ (k) represents the expected front wheel steering angle at the time k, and A and B are coefficient matrixes.
Optionally, in an embodiment of the present invention, the augmented system equation is:
Figure BDA0003191867620000142
Figure BDA0003191867620000143
Xaug(k)＝[x(k)T,x(k-ΔT)T,…,x(k-(τ+d)ΔT)T]T
Figure BDA0003191867620000151
Figure BDA0003191867620000152
wherein k represents the current discrete time, x (k) represents the state variable of the horizontal control system at the time k, u (k) represents the control input of the system at the time k, and [ x (k)T,x(k-ΔT)T,…,x(k-(τ+d)ΔT)T]TRepresenting the system state quantity, X, at τ + d discrete moments beforeaug(k) Represents an augmented system state variable that is,
Figure BDA0003191867620000153
a coefficient matrix representing the state variables of the augmented system,
Figure BDA0003191867620000154
expressing the coefficient matrix of the control variable of the augmentation system, N expressing the dimension of the control equation of the system vehicle control system,
Figure BDA0003191867620000155
is the corresponding augmented system observation matrix when the sensor-controller time delay is i delta T and the controller-controlled vehicle time delay is j delta T, 0N·NA zero matrix of dimension N is represented,
Figure BDA0003191867620000156
representation matrix
Figure BDA0003191867620000157
The (i + j) th element is an N-dimensional identity matrix, and q (x) represents the logarithmic quantization of x.
Optionally, in an embodiment of the invention, the set of nonlinear matrix inequalities includes a plurality of nonlinear matrix inequalities, each of the nonlinear matrix inequalities being:
Figure BDA0003191867620000158
Figure BDA0003191867620000159
Figure BDA00031918676200001510
Figure BDA00031918676200001511
wherein i is 1, …, τ, r is 1, …, d, λijIs a state transition matrix PSElement (ii) ofi,jFor transferring the matrix PCThe elements in (2) represent transposes of the symmetric elements of the matrix, K(,)For the corresponding control gain of the augmentation system to be solved when the sensor-controller time delay is i delta T and the controller-controlled vehicle time delay is j delta T, P represents the quantization density, P(i,j)、Y(i,d)Is the matrix variable to be solved.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of this embodiment, and is not repeated herein.
According to the networked vehicle control device under the influence of the bidirectional network time delay and the communication quantization, control gains corresponding to different sensor-cloud end controller/cloud end control-controlled vehicle time delays and quantization states are solved based on a matrix solving inequality method. The control system collects vehicle running state information through a roadside sensor, caches historical state information, packages and sends the vehicle running state information to the cloud end controller, the cloud end controller calculates all possible alternative control quantities of the stabilizing system according to actual sensor-cloud end controller time delay and possible cloud end controller-controlled vehicle time delay, packages and sends the vehicle to the controlled vehicle after logarithmic quantization coding, the controlled vehicle receives data packets and then carries out logarithmic quantization decoding, and selects corresponding control quantities according to actual cloud end controller-controlled vehicle time delay, so that the stability and the safety of the networked vehicle remote control system in an actual communication environment are guaranteed.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (4)

1. A control method of networked vehicles under the influence of bidirectional network time delay and communication quantification is characterized by comprising the following steps:
s1, estimating the current network delay range and the state transition matrix thereof, and determining the quantization density;
s2, establishing a control equation of the vehicle control system;
s3, constructing an augmentation system equation according to the transfer matrix and the vehicle control system control equation;
s4, constructing a non-linear matrix inequality equation set for solving according to the augmented system equation;
s5, solving the numerical solution of the nonlinear matrix inequality equation set through linearization iteration to calculate the control gains of different time delay states;
s6, recording the state quantities of a plurality of discrete step times before the current time according to the vehicle state information, and sending the state quantities of the plurality of discrete step times to the cloud-end controller;
s7, obtaining actual time delay through the sending time stamp and the receiving time stamp, calculating a stabilizing control quantity according to the actual time delay and the follow-up possible time delay, and sending the stabilizing control quantity to a controlled vehicle after quantization coding;
s8, decoding the stabilized control quantity after quantization coding through the controlled vehicle, and selecting the corresponding control quantity to execute according to the actual time delay;
s9, when the communication environment or the communication quantization density changes, the stabilizing control amount is recalculated S1.
2. The method of claim 1, wherein the set of non-linear matrix inequalities comprises a plurality of non-linear matrix inequalities, each non-linear matrix inequality being:
Figure FDA0003191867610000011
Figure FDA0003191867610000012
Figure FDA0003191867610000013
Figure FDA0003191867610000014
wherein i is 1, …, τ, r is 1, …, d, λijIs a state transition matrix PSElement (ii) ofi,jFor transferring the matrix PCThe elements in (1) represent matrix pairsTransposition of scale elements, K(i,j)For the corresponding control gain of the augmentation system to be solved when the sensor-controller time delay is i delta T and the controller-controlled vehicle time delay is j delta T, P represents the quantization density, P(i,j)、Y(i,d)Is the matrix variable to be solved.
3. A networking vehicle control device under the influence of bidirectional network time delay and communication quantization is characterized by comprising:
the estimation module is used for estimating the current network delay range and the state transition matrix thereof and determining the quantization density;
the first construction module is used for establishing a vehicle control system control equation;
the second construction module is used for constructing an augmentation system equation according to the transfer matrix and the vehicle control system control equation;
the third construction module is used for constructing a nonlinear matrix inequality equation set for solving according to the augmented system equation;
the calculation module is used for solving the numerical solution of the nonlinear matrix inequality equation set through linearization iteration so as to calculate the control gains of different time delay states;
the sending module is used for recording the state quantities of a plurality of discrete step times before the current time according to the vehicle state information and sending the state quantities of the discrete step times to the cloud-end controller;
the control module is used for obtaining actual time delay through the sending time stamp and the receiving time stamp, calculating a stabilizing control quantity according to the actual time delay and the follow-up possible time delay, and sending the stabilizing control quantity to a controlled vehicle after quantization coding;
the execution module is used for decoding the stabilized control quantity after the quantization coding through the controlled vehicle and selecting the corresponding control quantity to execute according to the actual time delay;
and the adjusting module is used for calling other modules to recalculate the stabilizing control quantity when the communication environment or the communication quantization density changes.
4. The apparatus of claim 3, wherein the set of non-linear matrix inequalities comprises a plurality of non-linear matrix inequalities, each non-linear matrix inequality being:
Figure FDA0003191867610000021
Figure FDA0003191867610000022
Figure FDA0003191867610000023
Figure FDA0003191867610000031
wherein i is 1, …, τ, r is 1, …, d, λijIs a state transition matrix PSElement (ii) ofi,jFor transferring the matrix PCThe elements in (2) represent transposes of the symmetric elements of the matrix, K(i,j)For the corresponding control gain of the augmentation system to be solved when the sensor-controller time delay is i delta T and the controller-controlled vehicle time delay is j delta T, P represents the quantization density, P(i,j)、Y(i,d)Is the matrix variable to be solved.
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