CN108388247B - Unmanned vehicle formation driving method based on directed communication network - Google Patents

Unmanned vehicle formation driving method based on directed communication network Download PDF

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CN108388247B
CN108388247B CN201810090570.2A CN201810090570A CN108388247B CN 108388247 B CN108388247 B CN 108388247B CN 201810090570 A CN201810090570 A CN 201810090570A CN 108388247 B CN108388247 B CN 108388247B
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叶菲
张卫东
樊玮璐
李茂峰
乔磊
胡智焕
陆宇
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Shanghai Jiaotong University
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    • G05—CONTROLLING; REGULATING
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    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
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    • G05D1/0295—Fleet control by at least one leading vehicle of the fleet

Abstract

The invention relates to an unmanned vehicle formation driving method based on a directed communication network, which comprises the following steps: 1) identifying a transfer function model of an individual unmanned vehicle in the unmanned vehicle formation; 2) setting the communication network topology of the unmanned vehicle formation and acquiring the input degree of each vehicle; 3) construction of individual unmanned vehicle H according to performance index2An optimal formation controller; 4) at H2Optimal formation controller post-series filter fi(s), acquiring a stable domain of the performance degree, and quantitatively adjusting in the stable domain to realize compromise between nominal performance and robust performance; 5) to make the individual unmanned vehicle H2Converting the optimal formation controller into an optimal PID controller; 6) discretizing the optimal PID formation controller to obtain a control signal sequence and outputting a formation control command. Compared with the prior art, the method has the advantages of distributed independent design, convenience for practical application, balanced nominal performance and robust performance and the like.

Description

Unmanned vehicle formation driving method based on directed communication network
Technical Field
The invention relates to the technical field of automobile engineering, in particular to an unmanned vehicle formation driving method based on a directional communication network.
Background
Thanks to a lot of government funds and vision leading new energy technology, China increasingly becomes the largest global electric vehicle market, and meanwhile, the automatic driving technology and the active safety system of new energy vehicles become hot issues concerned by current vehicle research and development engineers. According to statistics, in recent traffic accidents, the traffic accidents caused by fatigue driving or misoperation of a driver account for more than 60% of all traffic accidents, and the life and property safety of people is seriously influenced. The implementation data shows that when the vehicle runs close to the constant speed, the fuel consumption and the exhaust emission are reduced, and the running safety coefficient is improved. A distant view target of the current automatic driving technology is to enable a plurality of automobiles to realize automatic driving of formation of automobiles under the application of the electronic communication technology.
In the formation driving system of the multiple unmanned vehicles, each unmanned vehicle has the functions of information acquisition, calculation and communication, the information of the unmanned vehicle and the information of the neighboring vehicles are utilized to adjust the behavior of the unmanned vehicle, and the individuals cooperate with each other to complete complex tasks which cannot be completed by a single vehicle, such as safety patrol, logistics transportation, multi-vehicle guard and the like. The formation technology of the automatic driving vehicles can enhance the active safety of the vehicles and improve the road utilization rate and the economic benefits of the vehicles. Apparently, the formation driving idea of multiple unmanned vehicles is very intuitive, but actually, it is very difficult to really realize the formation control of the automatic driving vehicles, and a series of problems of environment perception, target intelligent identification, dynamic self-organizing network, real-time task planning, distributed cooperative control and the like of the unmanned vehicles are involved. The main goal of multi-unmanned vehicle formation driving is to design the formation controller of a single vehicle such that the multi-unmanned vehicle system implements one common global behavior only in case of local information interaction. At present, the domestic research on the design aspect of unmanned vehicle formation driving controllers has the following two limitations:
one is embodied in the aspect of engineering environment practicability. At present, most of research achievements of unmanned vehicle formation driving control stay in academic circles, a specific research idea is to convert a control problem of multiple unmanned vehicle formations into a stability analysis problem, and whether a system gradually converges or not is judged by constructing a Lyapunov function so as to judge whether the formations can be formed or not. Such numerical design methods only give a stable domain of the controller parameters, but the way of parameter adjustment needs to be based on experience and repeated trial and error, and different engineers will obtain different control results. In comparison, the analytic design method is more rigorous in theory and more accurate in result, and is a relatively high-level controller design method.
And secondly, the control performance optimization is embodied. As much as the research on control theory focuses too much on stability, many unmanned formation driving controls also focus on the convergence of the analysis system. In the case of formation control design, convergence is only the premise of ensuring system stability, but the design is often stopped at convergence, and few people study the optimization problem of formation driving performance indexes, especially the improvement of transient performance. In actual multi-vehicle formation application, only little improvement on the system performance can obtain great improvement on energy consumption reduction or control efficiency improvement, so that the practical research significance is provided for the optimal controller design of the multi-unmanned vehicle formation system.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an unmanned vehicle formation driving method based on a directional communication network.
The purpose of the invention can be realized by the following technical scheme:
an unmanned vehicle formation driving method based on a directed communication network comprises the following steps:
1) identifying a transfer function model of an individual unmanned vehicle in the unmanned vehicle formation;
2) setting the communication network topology of the unmanned vehicle formation and acquiring the input degree of each vehicle;
3) construction of individual unmanned vehicle H according to performance index2An optimal formation controller;
4) at H2Optimal formation controller post-series filter fi(s), acquiring a stable domain of the performance degree, and quantitatively adjusting in the stable domain to realize compromise between nominal performance and robust performance;
5) to make the individual unmanned vehicle H2Converting the optimal formation controller into an optimal PID controller;
6) discretizing the optimal PID formation controller to obtain a control signal sequence and outputting a formation control command.
The driving system of the unmanned vehicle formation consists of N (N is more than 2) heterogeneous unmanned vehicles, and all the unmanned vehicles are divided into two parts: the front M (M ≠ 0) leader vehicles acquire the same external reference signal as the state of a reference target in the leader-follower topological graph, the leader vehicles take the system deviation as the input of the self controller, and the remaining N-M follower vehicles take the relative output deviation with the neighboring unmanned vehicle as the input of the self controller.
In the step 1), a transfer function model G of the unmanned vehiclei(s) is:
Figure BDA0001563572220000021
wherein N is+(s) and M+(s) is a polynomial of root in the right half-plane, N-(s) and M-(s) is a polynomial rooted at the left half-plane, and N+(0)＝N-(0)＝M+(0)＝M_(0) The real positive number K is the static gain and the real positive number θ is the pure input time lag, i.e., the reaction time of the unmanned vehicle to the control effect.
In the step 1), the transfer function model is identified by a step identification method or a relay feedback identification method.
In the step 3), the performance index includes:
J1(s) is the transfer matrix 2 norm minimum from the individual reference signal to the system bias, or the transfer matrix 2 norm minimum from the individual output load disturbance to the system output;
J2(s) the 2-norm minimum of the transfer matrix for individual system input load disturbances to the system output.
In the step 3), H of the ith unmanned vehicle2Optimal formation controller CiThe expression of(s) is:
Figure BDA0001563572220000031
when considering the performance index J1(s) then:
Figure BDA0001563572220000032
when considering the performance index J2(s) then:
Figure BDA0001563572220000033
wherein deg (i) is the input degree of the i-th unmanned vehicle, which represents the number of unmanned vehicles capable of communicating with the i-th unmanned vehicle, A(s) is a rational polynomial, N+(-s) is a polynomial rooted at the left half-plane, and N+Root and N of (-s)+The roots of(s) are mutually opposite numbers.
In the step 4), the filter fiThe expression of(s) is:
Figure BDA0001563572220000034
wherein σiTaking positive real number, beta, as a degree of performancem、…、β1、β0The coefficients of the molecular polynomial of the filter transfer function are required to satisfy the stability requirement in the system, niThe order of the denominator polynomial of the filter transfer function needs to be large enough to make Qi(s) semi-canonical.
The performance degree sigmaiThe stable domain of (a) is:
Figure BDA0001563572220000035
Figure BDA0001563572220000036
Figure BDA0001563572220000037
wherein the content of the first and second substances,
Figure BDA0001563572220000041
in order to be a matrix of the transfer function,
Figure BDA0001563572220000042
is composed of
Figure BDA0001563572220000043
The diagonal elements of the ith row are,
Figure BDA0001563572220000044
is composed of
Figure BDA0001563572220000045
The element of the jth row and ith column,
Figure BDA0001563572220000046
is a transfer function matrix of all controlled unmanned vehicles, L is a topological matrix of a multi-unmanned vehicle communication structure,
Figure BDA00015635722200000411
it is meant that the sum of the dimensions is,
Figure BDA0001563572220000047
IMis an identity matrix whose dimension is M, 0N-MIs a zero matrix of dimension N-M.
In the step 5), H is expanded by adopting a Meglannin expansion mode2The optimal formation controller is converted into an optimal PID controller C(s), and the expression of the optimal PID controller C(s) is as follows:
Figure BDA0001563572220000048
wherein, Kc，TI，TDAnd TFAre all control parameters of the optimal PID formation controller.
In step 6), the control signal sequence is:
Figure BDA0001563572220000049
Figure BDA00015635722200000410
where e is the system deviation, T is the control period, n is the sampling number, n is 0,1,2, …, e (n-1) and e (n) are deviation signals obtained by the n-1 th and nth samples, u (n-1) is the pre-filtering control signal at the n-1 th time, and u (n-1) and u (n) are the control quantities at the n-1 th and nth times.
Compared with the prior art, the invention has the following advantages:
firstly, distributed individual design: the formation controller corresponding to each unmanned vehicle can be designed independently, does not need to acquire global information, and is completely distributed, namely only the relative output information of each unmanned vehicle and the neighboring unmanned vehicles is utilized. Compared with a centralized control method, the same requirements can be met by mutual cooperation of a plurality of small distributed systems, and the fully distributed cooperative control has the advantages of low operation cost, less system requirements, strong robustness, adaptability and expandability and the like.
Secondly, the practical application is convenient: the H2 optimal formation controller can be converted into a common PID controller, the PID controller is the controller which is the most widely used controller in engineering, and the equivalent conversion thereof into practical application provides convenience.
And thirdly, balancing the nominal performance and the robust performance: the adjustable parameters of the controller can not only ensure the stability of the whole formation system, but also quantitatively balance the nominal performance and the robust performance of a single system through a simple adjusting mode.
Drawings
Fig. 1 is a flowchart of an unmanned vehicle formation driving method based on a directional communication network according to the present invention.
Fig. 2 is a communication network topology diagram of the unmanned formation driving system in the embodiment of the invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
In order to solve the problem of multi-vehicle formation control in the automatic driving technology and reduce the probability of traffic accidents caused by fatigue driving or misoperation of a driver, the invention provides an unmanned vehicle formation driving method based on a directed communication network, obtains the analytic form of an optimal formation controller through strict theoretical derivation, and provides a concrete engineering implementation mode of the method.
The driving system of the multi-unmanned vehicle formation researched by the invention consists of N (N is more than 2) heterogeneous unmanned vehicles, and all the unmanned vehicles are divided into two parts: the front M (M ≠ 0) vehicles of the leader can obtain the same external reference signal, the input signal is regarded as the state of a reference target in the leader-follower topological graph, and the leader takes the system deviation as the input of the self controller; the remaining N-M follower vehicles take as input to their own controller the relative output deviation from the neighboring unmanned vehicle.
Each unmanned vehicle is provided with a positioning system, a communication device and an automatic driving control system. The positioning system is used for positioning the vehicle to obtain position information of the vehicle; the communication equipment is connected with the positioning system and the CAN bus thereof, is in communication connection with the adjacent vehicle and is used for sharing position and driving state parameters with the adjacent vehicle; the automatic driving control system is respectively connected with the positioning system and the communication equipment thereof and is used for outputting a formation control command. The detection device is a laser radar, is arranged above and on the left side and the right side of a front windshield of the automobile, is connected with the automatic driving control system and is used for detecting the distance between the automobile and an adjacent automobile and the speed of the adjacent automobile; the information such as the speed and the acceleration of the vehicle CAN be obtained from the CAN bus.
The formation control method provided by the invention comprises the following specific steps:
1) firstly, a controlled system transfer function model of the ith vehicle is identified. The input signal of the controlled system is ui(t), which may be a master cylinder pressure in a vehicle braking system, or an accelerator opening in a vehicle drive system; the output signal being the speed v of the vehiclei(t) of (d). Taking the laplace transform yields:
Figure BDA0001563572220000051
wherein N is+(s) and M+(s) denotes a polynomial rooted at the right half-plane, N-(s) and M-(s) denotes a polynomial rooted at the left half-plane, N+(0)＝N-(0)＝M+(0)＝M-(0)＝1。
Figure BDA0001563572220000052
I.e. the controlled object has rpAn unstable pole pj，ljIs pjThe number of times. The positive real number K is called static gain, and the positive real number θ represents a pure input time lag, i.e., a reaction time of the unmanned vehicle to the control effect. The specific method for identifying the model can adopt the most common step identification method or relay feedback identification method.
2) The dynamics of the ith unmanned vehicle may be described as:
vi(s)＝Gi(s)[ui(s)+di-in(s)]+di-out(s)
ui(s)＝Ci(s)ei(s)
wherein, Ci(s),di-in(s),di-out(s) and ei(s) are the controller, input disturbance, output disturbance and system bias, respectively, for the ith unmanned vehicle. The N unmanned vehicles are coupled to each other by a communication topology as follows:
Figure BDA0001563572220000061
wherein v is*(s) is the required fleet reference vehicle speed. The communication network topology is represented as a laplacian matrix L ═ (L)ij)N×NThe characteristic value is lambdai,i＝1,2,…,N。
Figure BDA0001563572220000062
deg (i) represents the input degree of the i-th unmanned vehicle. In addition, the first and second substrates are,
Figure BDA0001563572220000063
order to
Figure BDA0001563572220000064
3) The comprehensive performance index needed by the actual engineering is J(s), two items are selected, and J is respectively1(s) -optimal reference signal tracking/optimal output disturbance rejection "and" J2(s) -optimal input disturbance rejection ". Wherein J1(s) means that the 2-norm of the transfer matrix from the individual reference signal to the system bias is the minimum, or means that the 2-norm of the transfer matrix from the individual output load disturbance to the system output is the minimum; j. the design is a square2(s) means that the 2-norm minimum of the transfer matrix from individual system input load disturbances to system outputs.
4) According to the selected performance index, independently designing H for each unmanned vehicle2Optimal formation controller Ci(s)，
Figure BDA0001563572220000065
Tracking/outputting disturbance suppression performance index J according to optimal reference signal1(s)：
Figure BDA0001563572220000066
Suppression of performance according to optimal input disturbance2(s)：
Figure BDA0001563572220000067
Wherein, A(s) is a rational polynomial which needs to be calculated off line and meets the following requirements:
Figure BDA0001563572220000071
5) the filter connected in series behind the optimal formation controller in the step 4) is as follows:
Figure BDA0001563572220000072
adjustable parameter sigmaiReferred to as the degree of performance. Suppose that
Figure BDA0001563572220000073
And
Figure BDA0001563572220000074
there are the same number of right half-plane poles, and
1+[1+deg(i)]Ci(s)Gi(s),i＝1,2,…,M
1+deg(i)Ci(s)Gi(s),i＝M+1,M+2,…,N
are all stable, the performance degree sigma of each filteriMust be within the following stability domains:
Figure BDA0001563572220000075
this inequality is referred to as the column diagonal dominance criterion. The controller parameters may be monotonically incrementally adjusted within the stability domain to trade-off nominal and robust performance, σ, of the individual unmanned vehicle control systemiAlways taking positive real number, σiThe smaller, the better the nominal performance of the single system; sigmaiAnd the single system performance is sub-optimal when the single system performance is increased, and the robust performance can be gradually improved.
6) Converting the designed optimal formation controller into an actual PID controller by using a Meglalin expansion, namely:
Figure BDA0001563572220000076
let g(s) sc(s), three parameters can be calculated as follows:
Figure BDA0001563572220000077
discretizing the obtained optimal PID formation controller to obtain a control signal sequence as follows:
Figure BDA0001563572220000078
Figure BDA0001563572220000079
wherein e is the system deviation, T is the control period, n is the sampling number, n is 0,1,2, …, e (n-1) and e (n) are deviation signals obtained by the n-1 th and nth sampling, u (n-1) is the control signal before filtering at the n-1 th time, and u (n-1) and u (n) are the control quantity at the n-1 th and nth times.
Examples
As shown in fig. 1, a method for unmanned vehicle formation driving based on a directional communication network, in which the calculation is implemented by software in an automatic driving control system of an unmanned vehicle; the detection devices are laser radars and are arranged above and at the left side and the right side of the front windshield of the automobile for detecting the speed of the adjacent automobile; the speed of the vehicle is obtained from the CAN bus. The sampled data is filtered after being transmitted through an analog input channel, and is converted into a digital input signal after being A/D converted, and a system deviation signal e is calculated according to the digital input signal, and the digital input signal is used as an input signal of the automatic driving control system. Taking four heterogeneous electrically-driven automatic driving vehicles as an example, the transfer function of each unmanned vehicle linear controlled system is as follows:
Figure BDA0001563572220000081
Figure BDA0001563572220000082
setting an initial value to [ v ]1(0),v2(0),v3(0),v4(0)](15,10,8, 5). Each vehicle is provided with a communication device for outputting the speed of the vehicle and receiving the speed of the neighboring vehicle, the directed communication network topology structure is shown as figure 2, each node in the figure represents each unmanned vehicle in the formation driving system, and connecting lines between the nodes represent the communication modes of a plurality of unmanned vehicles. The laplacian matrix for this figure is:
Figure BDA0001563572220000083
only the No. 1 unmanned vehicle can obtain the reference speed v*The input degree per unmanned vehicle is deci (1,2,3,4) ═ 5/s, which is obvious.
According to the formation driving method shown in fig. 1, the formation driving controller of each unmanned vehicle can be obtained by independent analysis and calculation before being put into system application, and each optimal output disturbance suppression controller connected with a filter in series is as follows:
Figure BDA0001563572220000084
Figure BDA0001563572220000085
wherein the vehicle 3 has a parameter beta to be determined. The vehicle 3 has an unstable pole s ═ 1, and β ═ is calculated as (σ ═ 1)3+1)2e0.5-1. After the controller structure of each unmanned vehicle is independently designed, the controller parameter sigma can be adjusted according to the robust performance of a single systemi. Debugging each unmanned vehicle controlled system independently, and taking sigma1＝0.3,σ2＝0.5,σ3＝0.4,σ4＝1,β＝2.2315。
Converting the optimal formation controller obtained by the No. 3 vehicle into an actual PID controller by using a Meglangen expansion, wherein each control parameter can be obtained by calculation:
Figure BDA0001563572220000086
discretizing the obtained optimal PID formation controller to obtain a control signal sequence as follows:
Figure BDA0001563572220000091
Figure BDA0001563572220000092
wherein e is the system error, T is the control period, n is the sampling number, n is 0,1,2, …, e (n-1) and e (n) are the deviation signals obtained by the n-1 th and n-th sampling, u is the error signal obtained by the n-1 th and n-th sampling1(n-1) is the pre-filter control signal at time n-1, and u (n-1) and u (n) are the control quantities at time n-1 and n. And compiling a control algorithm program according to the control signal sequence calculation formula.
And outputting a formation control command, executing a programmed control algorithm program by an automatic driving control system, driving a motor through PWM waves with different duty ratios, giving corresponding driving force or braking force, and adjusting the running speed of the unmanned vehicle. The whole system can realize formation driving with consistent speed of multiple unmanned vehicles and effectively restrain the influence of output disturbance.

Claims (6)

1. An unmanned vehicle formation driving method based on a directed communication network is characterized by comprising the following steps:
1) the method comprises the following steps of identifying a transfer function model of an individual unmanned vehicle in an unmanned vehicle formation, wherein a driving system of the unmanned vehicle formation consists of N heterogeneous unmanned vehicles, N is more than 2, and all unmanned vehicles are divided into two parts: the front M leader vehicles obtain the same external reference signal, M is not equal to 0 and is used as the state of a reference target in a leader-follower topological graph, the leader vehicle takes the system deviation as the input of a self controller, the rest N-M follower vehicles take the relative output deviation with the neighbor unmanned vehicle as the input of the self controller, and a transfer function model G of the unmanned vehiclei(s) is:
Figure FDA0002631515440000011
wherein N is+(s) and M+(s) is a polynomial of root in the right half-plane, N-(s) and M-(s) is a polynomial rooted at the left half-plane, and N+(0)＝N-(0)＝M+(0)＝M-(0) When the real number is 1, the positive real number K is static gain, and the positive real number theta is pure input time lag, namely the reaction time of the unmanned vehicle to the control effect;
2) setting the communication network topology of the unmanned vehicle formation and acquiring the input degree of each vehicle;
3) construction of individual unmanned vehicle H according to performance index2The performance indexes of the optimal formation controller comprise: j. the design is a square1(s) is the transfer matrix 2 norm minimum from the individual reference signal to the system bias, or the transfer matrix 2 norm minimum from the individual output load disturbance to the system output;
J2(s) a transfer matrix 2 norm minimum for individual system input load disturbances to system output;
h of ith unmanned vehicle2Optimal formation controller CiThe expression of(s) is:
Figure FDA0002631515440000012
when considering the performance index J1(s) then:
Figure FDA0002631515440000013
when considering the performance index J2(s) then:
Figure FDA0002631515440000014
wherein deg (i) is the input degree of the i-th unmanned vehicle, which represents the number of unmanned vehicles capable of communicating with the i-th unmanned vehicle, A(s) is a rational polynomial, N+(-s) is a polynomial rooted at the left half-plane, and N+Root and N of (-s)+The roots of(s) are mutually opposite numbers;
4) at H2Optimal formation controller post-series filter fi(s), acquiring a stable domain of the performance degree, and quantitatively adjusting in the stable domain to realize compromise between nominal performance and robust performance;
5) to make the individual unmanned vehicle H2Converting the optimal formation controller into an optimal PID controller;
6) discretizing the optimal PID formation controller to obtain a control signal sequence and outputting a formation control command.
2. The unmanned formation driving method based on directional communication network of claim 1, wherein in the step 1), the transfer function model is identified by a step identification method or a relay feedback identification method.
3. The unmanned formation driving method based on directional communication network of claim 1, wherein in step 4), the filter f isiThe expression of(s) is:
Figure FDA0002631515440000021
wherein σiIs a degree of performance, betam、…、β1、β0Being the coefficient of a molecular polynomial of the filter transfer function, niIs the order of the denominator polynomial of the filter transfer function.
4. The method of claim 3, wherein the performance level σ is determined by a method of unmanned formation driving over a directional communication networkiThe stable domain of (a) is:
Figure FDA0002631515440000022
Figure FDA0002631515440000023
Figure FDA0002631515440000024
wherein the content of the first and second substances,
Figure FDA0002631515440000025
in order to be a matrix of the transfer function,
Figure FDA0002631515440000026
is composed of
Figure FDA0002631515440000027
The diagonal elements of the ith row are,
Figure FDA0002631515440000028
is composed of
Figure FDA0002631515440000029
The element of the jth row and ith column,
Figure FDA00026315154400000210
is a transfer function matrix of all controlled unmanned vehicles, L is a topological matrix of a multi-unmanned vehicle communication structure,
Figure FDA00026315154400000211
it is meant that the sum of the dimensions is,
Figure FDA00026315154400000212
IMis an identity matrix whose dimension is M, 0N-MIs a zero matrix of dimension N-M.
5. The unmanned formation driving method based on directional communication network as claimed in claim 1, wherein in step 5), H is formed by using a Mecany expansion method2The optimal formation controller is converted into an optimal PID controller C(s), and the expression of the optimal PID controller C(s) is as follows:
Figure FDA00026315154400000213
wherein, Kc，TI，TDAnd TFAre all control parameters of the optimal PID formation controller.
6. The unmanned formation driving method based on directional communication network of claim 5, wherein in step 6), the control signal sequence is:
Figure FDA0002631515440000031
Figure FDA0002631515440000032
where e is the system deviation, T is the control period, n is the sampling number, n is 0,1,2, …, e (n-1) and e (n) are deviation signals obtained by the n-1 th and nth samples, u (n-1) is the pre-filtering control signal at the n-1 th time, and u (n-1) and u (n) are the control quantities at the n-1 th and nth times.
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