CN116740922B - Control method of intelligent traffic system based on fuzzy observation protocol - Google Patents
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Abstract
The invention discloses a control method of an intelligent traffic system based on a fuzzy observation protocol, which comprises the following steps: step 1, combining an urban road traffic system to establish a state space model; step 2, establishing a communication network between vehicles in an intelligent traffic system; step 3, constructing a state observer of the intelligent traffic control system; step 4, constructing a control protocol of the intelligent traffic control system; step 5, constructing a closed-loop control system based on an observer control law; and 6, designing a controller protocol of a state observer for the urban road traffic system. The invention improves the control precision of the road vehicle and the overall calculation capability of the system, and ensures that the system achieves consistency and the tasks are smoothly developed and completed.
Description
Technical Field
The invention relates to the technical field of automation, in particular to a control method of an intelligent traffic system based on a fuzzy observation protocol.
Background
Urban road traffic is an essential component of urban society, economy and material structure, and is the main body of urban traffic systems. With the rapid development of social economy and the continuous improvement of urban modernization level, resources and labor force are continuously concentrated towards cities, so that the development of urban economy is quickened, various vehicles provided by industrial development into urban traffic are more and more, the years of the fastest growth of motor vehicles in larger cities in recent years are more and more, and the growth trend is still rising. According to analysis of the maintenance quantity increase rate of motor vehicles in China, whenever the annual increase rate of the maintenance quantity of motor vehicles exceeds 20%, urban traffic deterioration in the current year and the following years is caused, and the problem of urban traffic jam is more and more serious. The rapid increase of the urban road traffic flow often causes road jam, traffic accidents frequently occur, and normal travel and personal safety of people are affected. The original traffic system can not meet the social development requirement, and the intelligent traffic city system is generated.
The scheme of more convenient action for travel of intelligent transportation (2017-2020) issued by the transportation department is the first policy of intelligent transportation special in China. The smart city traffic system, as shown in fig. 2, is an important component of a smart city, and accelerates information exchange and information interaction between vehicles through a 5G technology, and it is reasonable to model the vehicles as intelligent agents by using a multi-intelligent agent system. The intelligent traffic has high requirements on network stability, the interconnection technology of an intelligent traffic system is greatly improved by the introduction of the 5G technology, vehicles can be monitored in real time, and a large number of monitoring videos can be transmitted to hands of traffic police at a very high speed. Meanwhile, the intelligent traffic system can also collect data such as road conditions and weather, and provides references for running of vehicles. The photographed video is analyzed by using an artificial intelligence technology to achieve reasonable scheduling, so that long-term traffic jam is prevented, and traffic accidents are reduced. Future cities will be developed towards a high degree of intelligence, emphasizing the promotion of urban governance service transformation by future technologies, and achieving efficient, inclusive and sustainable urban development. The core of future cities will be to build four cities (4C) depending on intelligent traffic and future technologies, namely holographic perception cities (PercentCity), online deduction cities (DeducionCity), closed-loop management cities (ManagingCity) and full-service cities (Service City).
Traffic flow in a traffic system is always non-negative. Traffic congestion control is mainly to reduce traffic operating pressure and congestion by managing traffic flow entering a certain road. Most of the existing road traffic flow management methods are based on real-time data of roads, and the purpose of congestion relief is achieved through manual management and control. During peak traffic, this artificial management has little effect. It is particularly important how to estimate road traffic flow by means of automation technology and to take corresponding flow control measures.
Disclosure of Invention
In order to solve the technical problems, the road traffic flow dynamic system is constructed by adopting the positive system, and the model is more accurate by utilizing the system modeling with the non-negative characteristics. In a smart city traffic system, the traffic flows of the same road are different in different time periods, the traffic flows of different road sections are mutually influenced, the congestion of one road section can cause the congestion of the road of other road sections, and the traffic flows of all road sections are mutually coupled and mutually influenced, so that obvious nonlinear characteristics are presented in modeling. In principle, a nonlinear process of the vehicle flow change can be described by a nonlinear positive system. However, the nonlinear system is not easy to handle, and even if a control method of the nonlinear road traffic system is designed, the nonlinear road traffic system is difficult to realize. Therefore, the nonlinear system is approximately modeled as a system with linear characteristics by means of a TS fuzzy model, the system is more convenient to process, and the related observation and control method of the design is easier to realize. A smart city traffic system is composed of a large number of vehicles having communication and autonomous capabilities, as shown in fig. 3. Therefore, the multi-driving vehicles in the system are regarded as multi-agents, and the establishment of the multi-agent system model is more proper. In view of the fact that traffic flow is not always measurable, the present invention proposes a state observer protocol method to estimate road traffic flow. And finally, constructing a fuzzy intelligent control protocol based on the estimated traffic flow information, so that all vehicles can reach an expected driving route, keep consistent operation and avoid congestion.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a control method of an intelligent traffic system based on a fuzzy observation protocol comprises the following steps:
step 1, combining an urban road traffic system to establish a state space model;
step 2, establishing a communication network between vehicles in an intelligent traffic system;
step 3, constructing a state observer of the intelligent traffic control system;
step 4, constructing a control protocol of the intelligent traffic control system;
step 5, constructing a closed-loop control system based on an observer control law;
and 6, designing a controller protocol of a state observer for the urban road traffic system.
Preferably, the structural form of the state observer is as follows:
wherein,status of status observation, ++>Is the output of the state observer, L r 、K r 、F r Is the gain matrix of the state observer to be designed, y i (t)∈R s Representing the pose of the ith vehicle acquired by a sensor at the moment t, i epsilon 1,2, N, N epsilon N + Indicating the number of vehicles in the intelligent traffic system, u i (t) represents a control input to the next running state of the ith vehicle at time t, h r (θ (t)) represents the use of the vehicle in the intelligent transportation system, A ε R n×n ，B∈R n×p ，R n 、N + 、R n×n 、R n×p Respectively representing n-dimensional vector, positive integer, n×n-dimension, n×p-dimension.
Preferably, the structural form of the control protocol is as follows:
wherein [ A ]]Is a matrix related to communication topology between agents, the dimension of which is related to the number of agents in a multi-agent system, [ A ]] ij Elements representing row i and column j of matrix a, i, j e 1,2, M, M represents the number of agents in the multi-agent system, if the ith agent and the jth agent can communicate [ A ]] ij =1, otherwise, [ a ]] ij ＝0。
Preferably, the construction of the closed-loop control system based on the observer control law specifically comprises the following steps:
combining the step 1, the step 3 and the step 4 to obtain a closed-loop control system of the intelligent traffic system:
order theThen it is further expressed as:
wherein,
M i representing a collection of agents that can communicate with agent i,
definition matrixThe diagonal block elements of (a) are:
the off-diagonal block elements are:
preferably, the construction of the controller protocol of the state observer for the urban road traffic system enables the vehicles in the intelligent traffic system to realize consistency control under the controller protocol, and the construction method comprises the following steps:
step 6.1, the state observer and the control protocol gain matrix are as follows:
wherein 1 is n An n-dimensional vector representing all 1's of elements,an n-dimensional vector representing the iota element as 1, the remaining elements as 0,
step 6.2, set constant λ > 0, presence vector z c ＞0，z f ＞0，q r1 ＞0，q r2 > 0, such that the following inequality:
if any I e n is satisfied, the intelligent traffic closed-loop control system obtained in step 5 is positive and stable under the condition that the control protocol designed in step 4 satisfies the state observer and controller gain matrix designed in step 6.1, where I represents the identity matrix,
step 6.3, the positive verification process of the intelligent traffic closed-loop control system is as follows:
according to the condition of ensuring the system positive in the step 6.2, the following steps are obtained:
i.e. matrix A r +l max B r K r -B r F r For Metzler matrix, A r -L r C s Also the matrix of the Metzler is described,
according to step 6.2, step 6.3, we get:
-[A] ij ≤0
so that the ratio of the first to the second phase is- [ A ]] ij B r K r Not less than 0, i.eIs a Metzler matrix, so the urban road traffic closed-loop control system is positive,
step 6.4, the stability verification process of the urban road traffic closed-loop control system is as follows:
constructing a positive Lyapunov function:
wherein,
step 6.5, further deriving V (t) according to the conditions in step 6.2, to obtain:
wherein,
definition of the definition
According to step 6.1, step 6.2, we get:
wherein the method comprises the steps ofAnd define l min Is l i Minimum value of l max Is l i Maximum value ρ of max For ρ i Is set at the maximum value of (c),
further, combining the conditions in step 6.1 and step 6.2, yields:
i.e.By further deriving +.>
According to step 6.5, the state of the vehicle finally reaches consistency under the consistency control protocol designed in step 6.
Based on the technical scheme, the invention has the beneficial effects that: the invention provides a consistency control method based on a state observer based on a positive TS fuzzy multi-agent model and a state observer design method, which aims at data acquisition of state information in the process of intelligent urban traffic vehicles, so that the control precision of road vehicles and the overall computing capacity of the system are improved, and the consistency of the system and smooth development and completion of tasks are ensured. The model built by the invention fully considers the characteristics of the positive property, the nonlinearity and the like of the actual system, and has higher application value in the actual road vehicle control.
Drawings
FIG. 1 is a diagram of a mathematical model of a system in a control method of an intelligent traffic system based on a fuzzy observation protocol in one embodiment;
FIG. 2 is a schematic diagram of a smart traffic system in a control method of the smart traffic system based on a fuzzy observation protocol according to an embodiment;
FIG. 3 is a topology diagram of a communication network between vehicles in a control method of an intelligent transportation system based on a fuzzy observation protocol in one embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The embodiment provides a control method of an intelligent traffic system based on a fuzzy observation protocol. The method considers a road model of a smart city traffic system, as shown in fig. 2, and the schematic diagram shows the situation of vehicles at the urban intersection. The traffic flow is greatly increased during rush hours of commutes or holidays, and particularly, traffic jam is easy to occur at some crossroads. Therefore, constructing a proper mathematical model according to traffic flow data of a certain intersection so as to predict the traffic flow of the next time period has important significance for the proposal of a control strategy. Considering that traffic flow in a smart city traffic system is always non-negative, modeling with a positive system is more accurate. In the intelligent urban traffic system, the traffic flow of different roads in different time periods is different, and the traffic flow is nonlinear, so that the process of changing the traffic flow can be effectively described by using the nonlinear positive system. But nonlinear systems are not easily handled, so the nonlinear system is approximated with many line segments using a TS blur model. A smart city traffic system consists of a large number of vehicles with communication capabilities. Thus, multiple driving vehicles in the system are treated as multiple agents, thereby establishing a positive multiple-agent system model consistent with reality, see fig. 3, providing a communication topology between vehicles. Considering that the state of the vehicle on the road is not always directly measurable, the invention proposes a state observer design method to obtain an estimated value of the actual system state, and reduce the problem of urban traffic jam, see fig. 1. The method specifically comprises the following steps:
and step 1, combining an urban road traffic system to establish a state space model.
The method comprises the steps of collecting traffic flow data of each intersection of the smart city traffic, and establishing a state space model of a smart city traffic system, wherein the state space model is as follows:
wherein,the running state of the ith vehicle in the t moment intelligent traffic system is y i (t)∈R s Representing the pose of the ith vehicle acquired by a sensor at the moment t, i epsilon 1,2, N, N epsilon N + Indicating the number of vehicles in the intelligent traffic system, u i (t) represents a control input to the next running state of the ith vehicle at time t, h r (θ (t)) represents the use of the vehicle in the intelligent transportation system, A ε R n×n ，B∈R n×p ，C∈R q×n Is a system matrix, R n 、N + 、R n×n 、R n×p 、R q×n Respectively representing an n-dimensional vector, a positive integer, an n x n-dimensional, an n x p-dimensional, and a q x n-dimensional matrix.
And 2, establishing a communication network of m workshops, wherein the communication network topology is a connected directed graph.
The directed graph is represented as:
Ω＝(M,Θ,A),
wherein, m= {1,2, M, M e N + Representing a node set into which a vehicle is abstracted;a set of edges representing communications between vehicles; />Representing an adjacency matrix when node i can receive information from node j, [ A ]] ij =1, otherwise [ a] ij ＝0。
Further, laplacian matrix is introducedDescribing communications between Smart City automobilesThe topology is such that,the definition is as follows:
where Σ represents the summation symbol.
And 3, constructing a state observer of the intelligent traffic control system.
The structure form of the state observer of the intelligent traffic control system is as follows:
wherein,status of status observation, ++>Is the output of the state observer, L r Is the gain matrix of the state observer to be designed.
And 4, constructing a control protocol of the intelligent traffic control system.
The control protocol of the intelligent traffic control system has the following structural form:
wherein,status signal indicating controller, u i (t) representsInput signal of controller, K r Is the gain matrix of the control protocol that needs to be designed. [ A ]]Is a matrix related to the communication topology between agents, and its dimension is related to the number of agents in the multi-agent system. [ A ]] ij Elements representing row i and column j of matrix a, i, j e 1,2, M, M represents the number of agents in the multi-agent system, if the ith agent and the jth agent can communicate [ A ]] ij =1, otherwise, [ a ]] ij ＝0。
And 5, constructing a closed-loop control system based on an observer control law, which specifically comprises the following steps:
combining the step 1, the step 3 and the step 4 to obtain a closed-loop control system of the intelligent traffic system:
order theThen it is further expressed as:
wherein,
M i representing a collection of agents that can communicate with agent i.
Definition matrixThe diagonal block elements of (a) are:
the off-diagonal block elements are:
and 6, designing a controller protocol of a state observer for the urban road traffic system.
6.1 State observer and control protocol gain matrix designed as follows:
wherein 1 is n An n-dimensional vector representing all 1's of elements,an n-dimensional vector representing the iota element as 1 and the remaining elements as 0.
6.2 setting a constant λ > 0, presence vectorz c ＞0，z f ＞0，/>q r1 ＞0，q r2 > 0, such that the following inequality:
if any I e n is satisfied, the intelligent traffic closed-loop control system obtained in step 5 is positive and stable under the condition that the control protocol designed in step 4 satisfies the state observer and the controller gain matrix designed in step 6.1, where I represents the identity matrix.
6.3 Positive verification procedure of intelligent traffic closed-loop control system is as follows:
according to the condition of ensuring the system positive in the step 6.2, the following steps are obtained:
i.e. matrix A r +l max B r k r -B r F r For Metzler matrix, A r -L r C s Also the Metzler matrix.
According to step 6.2, step 6.3, we get:
-[A] ij ≤0
so that the ratio of the first to the second phase is- [ A ]] ij B r K r Not less than 0, i.eIs a Metzler matrix. The urban road traffic closed loop control system is therefore positive.
6.4 the stability verification process of the urban road traffic closed-loop control system is as follows:
constructing a positive Lyapunov function:wherein,
6.5 further deriving V (t) according to the conditions in 6.2, yielding:wherein,
definition of the definition
According to step 6.1, step 6.2, we get:
wherein,and define l min Is l i Minimum value of l max Is l i Maximum value ρ of max For ρ i Is a maximum value of (a).
Further, combining the conditions in step 6.1 and step 6.2, yields:
i.e.By further deriving +.>
According to step 6.5, the state of the vehicle finally reaches consistency under the consistency control protocol designed in step 6.
The foregoing is merely a preferred implementation of the control method of the intelligent traffic system based on the fuzzy observation protocol disclosed in the present invention, and is not intended to limit the protection scope of the embodiments of the present specification. Any modification, equivalent replacement, improvement, or the like made within the spirit and principles of the embodiments of the present specification should be included in the protection scope of the embodiments of the present specification.
Claims (1)
1. A control method of an intelligent traffic system based on a fuzzy observation protocol is characterized by comprising the following steps:
step 1, combining an urban road traffic system to establish a state space model;
step 2, establishing a communication network between vehicles in an intelligent traffic system;
step 3, constructing a state observer of the intelligent traffic control system, wherein the structural form of the state observer is as follows:
wherein,status of status observation, ++>Is the output of the state observer, L r 、K r 、F r Is the gain matrix of the state observer to be designed, y i (t)∈R s Representing the pose of the ith vehicle acquired by a sensor at the moment t, i epsilon 1,2, N, N epsilon N + Indicating the number of vehicles in the intelligent traffic system, u i (t) represents a control input to the next running state of the ith vehicle at time t, h r (θ (t)) represents the use of the vehicle in the intelligent transportation system, A ε R n×n ，B∈R n×p ，R n 、N + 、R n×n 、R n×p Respectively representing n-dimensional vectors, positive integers, n×n dimensions and n×p dimensions;
and 4, constructing a control protocol of the intelligent traffic control system, wherein the structural form of the control protocol is as follows:
wherein [ A ]]Is a matrix related to communication topology between intelligent agents, its dimension and multi-intelligent agent systemThe number of the intelligent agents is related [ A ]] ij Elements representing row i and column j of matrix a, i, j e 1,2, M, M represents the number of agents in the multi-agent system, if the ith agent and the jth agent can communicate [ A ]] ij =1, otherwise, [ a ]] ij ＝0；
And 5, constructing a closed-loop control system based on an observer control law, which specifically comprises the following steps:
combining the step 1, the step 3 and the step 4 to obtain a closed-loop control system of the intelligent traffic system:
order theThen it is further expressed as:
wherein,
M i representing a collection of agents that can communicate with agent i,
definition matrixThe diagonal block elements of (a) are:
the off-diagonal block elements are:
step 6, constructing a controller protocol of a state observer for the urban road traffic system, so that the vehicles in the intelligent traffic system realize consistency control under the controller protocol, and specifically comprising the following steps:
step 6.1, the state observer and the control protocol gain matrix are as follows:
wherein 1 is n An n-dimensional vector representing all 1's of elements,an n-dimensional vector representing the iota element as 1, the remaining elements as 0,
step 6.2, set constant λ > 0, presence vector d z＜0，z c ＞0，z f ＞0，/>q r1 ＞0，q r2 > 0, such that the following inequality:
if any I e n is satisfied, the intelligent traffic closed-loop control system obtained in step 5 is positive and stable under the condition that the control protocol designed in step 4 satisfies the state observer and controller gain matrix designed in step 6.1, where I represents the identity matrix,
step 6.3, the positive verification process of the intelligent traffic closed-loop control system is as follows:
according to the condition of ensuring the system positive in the step 6.2, the following steps are obtained:
i.e. matrix A r +l max B r K r -B r F r For Metzler matrix, A r -L r C s Also the matrix of the Metzler is described,
according to step 6.2, step 6.3, we get:
z d ＜0，-[A] ij ≤0
so that the ratio of the first to the second phase is- [ A ]] ij B r K r Not less than 0, i.eIs a Metzler matrix, so the urban road traffic closed-loop control system is positive,
step 6.4, the stability verification process of the urban road traffic closed-loop control system is as follows:
constructing a positive Lyapunov function:
wherein,
step 6.5, further deriving V (t) according to the conditions in step 6.2, to obtain:
wherein,
definition of the definition
According to step 6.1, step 6.2, we get:
wherein the method comprises the steps ofAnd define l min Is l i Minimum value of l max Is l i Maximum value ρ of max For ρ i Is set at the maximum value of (c),
further, combining the conditions in step 6.1 and step 6.2, yields:
i.e. omega i1 ＜0，Ω i2 < 0, by further derivation, yields
According to step 6.5, the state of the vehicle finally reaches consistency under the consistency control protocol designed in step 6.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013178899A2 (en) * | 2012-06-01 | 2013-12-05 | Renault S.A.S. | Device for controlling the course of a vehicle |
| CN108427273A (en) * | 2018-03-13 | 2018-08-21 | 杭州电子科技大学 | A kind of Feedback Control Design method reducing traffic congestion phenomenon |
| WO2018215910A1 (en) * | 2017-05-22 | 2018-11-29 | Oara Cristian | Method for automated vehicle platooning |
| CN111176327A (en) * | 2020-01-16 | 2020-05-19 | 北京航空航天大学 | Multi-agent system enclosure control method and system |
| CN114664089A (en) * | 2022-04-06 | 2022-06-24 | 杭州电子科技大学 | PI control method for traffic flow of urban road traffic system |
| CN114935931A (en) * | 2022-05-07 | 2022-08-23 | 苏州科技大学 | Time-varying heterogeneous multi-agent consistency control method and system |
| WO2022205526A1 (en) * | 2021-04-01 | 2022-10-06 | 江苏科技大学 | Dynamic positioning information fusion method for unmanned underwater vehicle cluster |
| WO2022259581A1 (en) * | 2021-06-11 | 2022-12-15 | Mitsubishi Electric Corporation | Method, device, and system for controlling state parameter by communicating device |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112084636B (en) * | 2020-08-24 | 2024-03-26 | 北京交通大学 | Multi-train cooperative control method and device |
| CN113206842B (en) * | 2021-04-27 | 2022-06-28 | 东南大学 | Distributed safety state reconstruction method based on double-layer dynamic switching observer |
-
2023
- 2023-05-08 CN CN202310507587.4A patent/CN116740922B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013178899A2 (en) * | 2012-06-01 | 2013-12-05 | Renault S.A.S. | Device for controlling the course of a vehicle |
| WO2018215910A1 (en) * | 2017-05-22 | 2018-11-29 | Oara Cristian | Method for automated vehicle platooning |
| CN108427273A (en) * | 2018-03-13 | 2018-08-21 | 杭州电子科技大学 | A kind of Feedback Control Design method reducing traffic congestion phenomenon |
| CN111176327A (en) * | 2020-01-16 | 2020-05-19 | 北京航空航天大学 | Multi-agent system enclosure control method and system |
| WO2022205526A1 (en) * | 2021-04-01 | 2022-10-06 | 江苏科技大学 | Dynamic positioning information fusion method for unmanned underwater vehicle cluster |
| WO2022259581A1 (en) * | 2021-06-11 | 2022-12-15 | Mitsubishi Electric Corporation | Method, device, and system for controlling state parameter by communicating device |
| CN114664089A (en) * | 2022-04-06 | 2022-06-24 | 杭州电子科技大学 | PI control method for traffic flow of urban road traffic system |
| CN114935931A (en) * | 2022-05-07 | 2022-08-23 | 苏州科技大学 | Time-varying heterogeneous multi-agent consistency control method and system |
Non-Patent Citations (9)
| Title |
|---|
| Coordination control method of regional traffic flow;LIANG C等;《Journal of Traffic and Transportation Engineering》;全文 * |
| 城市路网的鲁棒预测控制;刘安东;李佳;俞立;;大连理工大学学报;20160315(第02期);全文 * |
| 基于ε-支持向量回归理论的区域交通信号智能控制;游子毅;陈世国;王义;;计算机应用(第05期);全文 * |
| 基于牵制控制带有协议失效多智能体的一致性;王国良;孙媛媛;;辽宁石油化工大学学报(第05期);全文 * |
| 崔玉龙 ; 高利新 ; 陈文海 ; 郑海鹰 ; .离散时间下广义多智能体系统基于观测器的分布式一致协议.系统科学与数学.2016,(第03期),全文. * |
| 模型不确定多智能体系统的鲁棒一致性控制;黄小龙;陈阳舟;詹璟原;;华中科技大学学报(自然科学版)(第09期);全文 * |
| 王国良 ; 孙媛媛 ; .基于牵制控制带有协议失效多智能体的一致性.辽宁石油化工大学学报.2020,(第05期),全文. * |
| 离散时间下广义多智能体系统基于观测器的分布式一致协议;崔玉龙;高利新;陈文海;郑海鹰;;系统科学与数学(第03期);全文 * |
| 黄小龙 ; 陈阳舟 ; 詹璟原 ; .模型不确定多智能体系统的鲁棒一致性控制.华中科技大学学报(自然科学版).2020,(第09期),全文. * |
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