Intermittent control method for communication network of intelligent traffic unmanned vehicle

Abstract

The invention relates to the technical field of vehicle communication, in particular to an intermittent control method for an intelligent traffic unmanned vehicle communication network, and the method comprises the steps: carrying out the precise modeling of the unmanned vehicle communication network of an intelligent traffic system through employing a positive complex network model, so as to obtain a state space model, and then assuming an isolated node, and communicating with other network clusters to realize information interaction. And secondly, by designing an observer and a periodic intermittent control protocol thereof, the unmanned vehicle communication network of the intelligent traffic system can normally achieve synchronization without real-time communication, so that the stability of the unmanned vehicle communication network under various actual conditions is ensured, and the control efficiency of each unmanned vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle communication, and particularly to an intermittent control method for an intelligent transportation driverless vehicle communication network. Background Art

[0002] With the growth of urbanization and traffic demand, traditional traffic management methods are no longer sufficient to cope with complex traffic conditions. The application of modern information technology and data communication technology makes it possible to collect, transmit and process traffic information in real time, which is crucial for improving traffic conditions. Autonomous vehicles are a key part of intelligent transportation systems. They use on-vehicle sensors and advanced algorithms to achieve autonomous navigation, which not only changes driving habits but also provides new opportunities for the intelligence of traffic systems.

[0003] Communication networks are crucial for information exchange between autonomous vehicles and for collaborative control with traffic infrastructure. They allow vehicles to receive road condition and traffic signal information in real time, enabling them to make decisions. In addition, communication networks also promote information sharing among vehicles, enhancing traffic flow and safety.

[0004] Although significant progress has been made in driverless technology in recent years, it is still in the development stage as a whole, and the technical maturity needs to be improved. On the one hand, driverless vehicles need to cope with complex traffic environments, including different weather conditions, road types, traffic flows, etc. Technical deficiencies may cause vehicles to be unable to monitor their own and surrounding vehicle states in real time, leading to traffic accidents. On the other hand, driverless vehicles do not always require real-time data transmission during driving, such as during a red light, and the intelligent transportation system is a dynamic system. The peak and off-peak periods of traffic flow, the flow of various different means of transportation, and changes in traffic routes will all have an impact on it.

[0005] Therefore, how to detect the states of driverless vehicles and surrounding vehicles, how to accurately model driverless vehicles, how to reduce unnecessary data transmission, etc., so as to improve the travel of driverless vehicles, has become one of the important issues in studying the normal driving of driverless vehicles.

[0006] A network cluster refers to a network with a large number of nodes and connection relationships, which together constitute a complex and interdependent intelligent transportation system. In the intelligent transportation system, driverless vehicles, traffic infrastructure, sensors, communication devices, etc. can all be regarded as nodes in the complex network cluster, and the information exchange and communication between them constitute the connection relationships of the network. At the same time, the communication network cluster in the intelligent transportation system includes various topological structures, such as star-shaped, ring-shaped, mesh-shaped, etc. Complex network theory can describe and analyze these different topological structures and their impacts on the performance of the intelligent transportation system. In addition, the network cluster in the intelligent transportation system is dynamically changing and the number of communication networks is always non-negative. The movement of vehicles and the change of traffic conditions will cause changes in the network topology.

[0007] Chinese Patent Publication No.: CN118590952A discloses a communication method, system and driverless vehicle based on multi-base station handover. Among them, the communication method based on multi-base station handover includes: when the driverless vehicle moves from the stable connection range of the current base station to the base station handover range, based on the position information of the driverless vehicle, it is determined whether the driverless vehicle is in the current base station handover range; if so, the first communication state of at least one CPE device connected to the current base station is obtained, and at least one CPE device not communicating with the current base station is connected to the next base station and the corresponding second communication state is obtained; if the second communication state is better than the first communication state, the CPE device connected to the current base station is communicated with the next base station, otherwise, at least one CPE device is maintained to communicate with the current base station; thus, the following problems exist in the above technical solution: The traffic architecture continuously exchanges real-time data with each driverless vehicle, resulting in redundant communication data volume and affecting the control efficiency of each driverless vehicle. Summary of the Invention

[0008] Therefore, the present invention provides an intermittent control method for the communication network of intelligent transportation driverless vehicles to overcome the problem in the prior art that the traffic architecture continuously exchanges real-time data with each driverless vehicle, resulting in redundant communication data volume and affecting the control efficiency of each driverless vehicle.

[0009] To achieve the above object, the present invention provides an intermittent control method for the communication network of intelligent transportation driverless vehicles, including:

[0010] Step 1, establish a state space model of the communication network of driverless vehicles in the traffic architecture;

[0011] Step 2, construct isolated nodes of the communication network of driverless vehicles in the traffic architecture, determine the isolated nodes as synchronization targets, and the state feedback controller realizes the positivity of the isolated nodes;

[0012] Step 3: Construct an observer for each network cluster to build a complex communication network, and based on the output of the observer, the traffic architecture is used to analyze, mine, and predict the data output by the observer to formulate traffic management strategies;

[0013] Step 4: Establish a periodic intermittent control protocol based on the observer for each driverless vehicle in the traffic architecture, and the intermittent synchronization controller outputs a periodic intermittent control protocol for the corresponding driverless vehicle;

[0014] Step 5: Establish a closed-loop architecture for each driverless vehicle in the traffic architecture;

[0015] Step 6: Determine the conditions for the stable operation of the communication network architecture of each driverless vehicle in the traffic architecture.

[0016] Furthermore, in the said Step 1, the process of establishing the state space model of the communication network of the driverless vehicle in the traffic architecture includes:

[0017]

[0018] y i (k)=C σ x i (k),

[0019] wherein, x i (k)=(x i1 (k), x i2 (k), …, x in (k))∈R n , x i (k) represents the state vector of the complex communication network of the i-th driverless vehicle, and x i (k) contains the state information of the communication network at time k, and the state information includes vehicle position, speed, and fuel consumption; u i (k)=(u i1 (k), u i2 (k), …, u is (k))∈R s , u i (k) represents the control input vector of the communication network of the i-th driverless vehicle, and u i (k) contains the external instructions and signals received by the communication network at time k, and the external instructions and signals include start, acceleration, and steering; y i (k)=(y i1 (k), y i2 (k), …, y im (k))∈R m , y i(k) represents the control output vector of the communication network of the i-th driverless vehicle, y i (k) includes the signals and information sent by the communication network according to the state information x i (k); Π = (π ij ) ∈ R n×n is a symmetric and irreducible matrix used to represent the topological structure of the communication network, and this matrix satisfies and

[0020] When π ij (≠π ji ) > 0, the nodes of the i-th communication network can obtain the communication sent by the nodes of the j-th communication network;

[0021] When π ij = 0, the nodes of the i-th communication network cannot obtain the communication sent by the nodes of the j-th communication network;

[0022] is a positive semi-definite internal coupling matrix for t i ≥ 0, A σ ∈ R n×n , B σ ∈ R n×s , C σ ∈ R m×n are matrices with dimensions; n, m, s, N, σ are positive integers, and it is set that A σ ≥ 0, B σ ≥ 0, C σ ≥ 0.

[0023] Furthermore, the process of constructing the isolated nodes of the communication network of the driverless vehicle in step 2 includes:

[0024] s i (k + 1) = A 0σ s i (k) + B 0σ u 0i (k),

[0025] y 0i = C 0i s i (k),

[0026] where s i (k) ∈ R n is the state vector of the isolated node, u 0i (k) ∈ R s is the input vector, y 0i (k) ∈ R m is the output vector;

[0027] A0σ ∈R n×n , B 0σ ∈R n×s and C 0σ ∈R m×n Be a system matrix with dimension, set A 0σ ≥0, B 0σ ≥0, C 0σ ≥0;

[0028] Set u 0i (k) = F σ s i (k), F σ is the gain matrix of the state feedback controller.

[0029] Furthermore, in step 3, the process of determining the output of the observer of the positive communication complex network of unmanned vehicles in the traffic framework includes:

[0030]

[0031] Among them, G σ is the observer matrix, is the observer state, is the output of the observer; L σ (k) represents the observer gain matrix,

[0032] At the time k∈[mT,mT+τ), the observer gain matrix is ​​expressed as

[0033] At the time k∈[mT+τ,(m+1)T), the observer gain matrix is ​​expressed as

[0034] Furthermore, in step 4, the process of establishing an observer-based periodic intermittent control protocol for unmanned vehicles in a traffic framework includes:

[0035]

[0036] K σ (k) and M σ (k) are the gain matrices of the network cluster controller;

[0037]

[0038] When k∈[mT,mT+τ), K σ (k) = K σ And M σ (k) = M σ ; When k∈[mT+τ,(m+1)T), Kσ (k) = M σ (k) = 0;

[0039] Let T be the control period, T > 0, and τ be the control step, τ > 0 and τ < T.

[0040] Furthermore, in step 5, establish the closed-loop architecture of each driverless vehicle in the traffic architecture:

[0041]

[0042] Wherein,

[0043]

[0044] represents the observation error, e i (k) = x i (k) - s i (k) represents the local synchronization error,

[0045] Furthermore, in step 6, the process of determining the conditions for the stable operation of the communication network architecture of the driverless vehicles in the traffic architecture includes:

[0046] Set constants 0 < μ 1 < 1, μ 2 > 1, λ > 1, R n vector v 1 > 0, v 2 > 0, v 3 > 0, v′ 1 > 0, v′ 2 > 0, v′ 3 > 0, and R s vector such that

[0047]

[0048]

[0049] Determine the parameter range of the traffic architecture.

[0050] Furthermore, the gain matrix for the communication network architecture of the driverless vehicles in the traffic architecture described in step 5 to achieve positivity and synchronization is:

[0051]

[0052] Meanwhile, each isolated node in the communication network architecture satisfies 2lnλ + μ 1 τ + μ 2 (T - τ) < 0

[0053] Wherein, is the Kronecker product operator, represents an n-dimensional column vector with the nth element being 1 and the rest being 0, where n is the number of nodes in the complex network.

[0054] Furthermore, the observer is used to continuously monitor the operating state of the driverless vehicle, and the operating state includes traffic congestion, traffic accidents, and driverless vehicle failures.

[0055] Furthermore, the communication network includes isolated nodes and several network clusters, and a single network cluster includes several network nodes.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows: by using a positive complex network model to accurately model the communication network of driverless vehicles in an intelligent transportation system to obtain a state space model, and then assuming an isolated node to communicate with the remaining network clusters to achieve information interaction. Secondly, by designing an observer and its periodic intermittent control protocol, the communication network of driverless vehicles in the intelligent transportation system can achieve synchronization normally without real-time communication, ensuring the stability of the communication network of driverless vehicles in various actual situations and improving the control efficiency of each driverless vehicle.

[0057] Furthermore, the isolated nodes of the communication network can plan the optimal traffic flow path according to real-time traffic data.

[0058] The network nodes then adjust their own operating states according to the plan of the isolated nodes of the communication network to achieve the optimal scheduling of the entire traffic network.

[0059] Through the guidance of the isolated nodes of the communication network and the cooperation of the remaining network nodes, traffic congestion can be effectively reduced.

[0060] Furthermore, the periodic intermittent control protocol allows the traffic architecture to turn off or reduce the operating power of the corresponding devices in the driverless vehicle during a specific time period, thereby optimizing resource allocation. In the communication network of the traffic architecture, this means that the operating state of the communication devices can be dynamically adjusted according to real-time traffic flow and communication requirements, avoiding unnecessary resource waste. During periods of low traffic flow, some communication devices can be turned off to save energy and reduce operating costs.

[0061] Furthermore, in the traffic architecture, the control accuracy of each driverless vehicle is crucial for the performance and safety of the entire traffic architecture. By formulating traffic management strategies based on observers and observing the state information and operating status of driverless vehicles in real time, the control inputs of driverless vehicles can be calculated more accurately. The control inputs include steering wheel angle and acceleration, thereby improving the control accuracy of driverless vehicles. At the same time, the consistency and stability of the traffic architecture are ensured, and the traffic flow is optimized and the traffic efficiency is improved.

[0062] Furthermore, a closed-loop architecture for each driverless vehicle in the traffic architecture is established to monitor the output state of the closed-loop architecture through a feedback mechanism and compare it with the desired state, thereby adjusting the input to eliminate the deviation. In the communication network of the traffic architecture, this means that traffic signals, vehicle scheduling, and communication resource allocation can be precisely controlled to improve the overall performance and stability of the traffic architecture and ultimately achieve synchronization. By real-time monitoring of traffic flow and vehicle speed, the closed-loop architecture of driverless vehicles in the traffic architecture can dynamically adjust the speed of driverless vehicles, thereby effectively alleviating traffic congestion and improving road capacity.

[0063] Furthermore, intermittent control, also known as periodic intermittent control or discontinuous control, is different from the conventional control method that continuously applies control. Its characteristic is that the control action is discontinuous in time, that is, the intermittent synchronization controller is only activated within specific time intervals and does not operate at other times. During actual driving, the unmanned vehicle and the surrounding vehicles are not always measurable by sensors. Therefore, an observer is added to indirectly obtain the state information of the unmanned vehicle. The intermittent control based on the observer in the communication network of unmanned vehicles in the intelligent transportation system has the following advantages: (1) Reducing communication overhead: The intermittent control strategy is more economical in data transmission because it only communicates when necessary. Compared with the continuous communication method, it can significantly reduce the communication frequency and data volume, thereby reducing communication costs. In the intelligent transportation system, vehicles need to frequently exchange real-time data, and intermittent control helps reduce unnecessary communication consumption and improve the cost-effectiveness of the system. (2) Optimizing network resource utilization: Through intermittent control, network resources can be allocated and utilized more effectively. In the communication network of intelligent transportation vehicles, although information such as the position, speed, and direction of vehicles is constantly changing, not all information needs to be updated continuously. Intermittent control allows data transmission only when there are important changes in the information or according to specific strategies, thereby improving the transmission efficiency and resource utilization rate of the network. (3) Supporting real-time decision-making: Although intermittent control reduces the communication frequency, it can still support real-time decision-making and control in the intelligent transportation system. For example, in case of an emergency or when a path change is required, communication can be immediately activated to send critical data to ensure that the system can respond quickly and execute necessary control measures. (4) Indirectly observing the state: The observer can monitor the state of the unmanned vehicle and its communication network in real time, including key information such as the vehicle's position, speed, driving direction, and communication quality. These data are the basis for the traffic architecture to analyze, make decisions, and control. Through the observer, the traffic architecture can obtain the operating state of the vehicle communication network in real time, providing strong support for subsequent traffic management and optimization. Determining the periodic intermittent control protocol can effectively improve the data interaction efficiency of unmanned vehicles. Description of the Drawings

[0064] Figure 1 It is a flowchart of the steps of the intermittent control method for the communication network of intelligent transportation unmanned vehicles according to an embodiment of the present invention;

[0065] Figure 2 It is a schematic diagram of the communication network of unmanned vehicles in the intelligent transportation system according to an embodiment of the present invention;

[0066] Figure 3 It is a block diagram of the modules of the communication network according to an embodiment of the present invention;

[0067] Figure 4This is a logic decision diagram for determining the observer gain matrix in the embodiments of the present invention. Detailed implementation manners

[0068] In order to make the objectives and advantages of the present invention more clear and understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0069] The preferred implementation manners of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0070] It should be noted that in the description of the present invention, the terms indicating the direction or positional relationship such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0071] In addition, it should also be noted that in the description of the present invention, unless otherwise clearly defined and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0072] Please refer to Figure 1 , Figure 2 , Figure 3 and Figure 4 as shown, which are respectively the step flow chart of the intermittent control method for the communication network of driverless vehicles in the intelligent transportation in the embodiments of the present invention, the schematic diagram of the communication network of driverless vehicles in the intelligent transportation system, the module block diagram of the communication network, and the logic decision diagram for determining the observer gain matrix; an intermittent control method for the communication network of driverless vehicles in the intelligent transportation in the embodiments of the present invention includes:

[0073] Step 1: Establish a state space model of the communication network of driverless vehicles in the traffic architecture;

[0074] Step 2: Construct isolated nodes of the communication network of driverless vehicles in the traffic architecture;

[0075] Step 3: Design a positive communication complex network observer for driverless vehicles in the traffic architecture;

[0076] Step 4: Establish an observer-based periodic intermittent control protocol for driverless vehicles in the traffic architecture;

[0077] Step 5: Establish a closed-loop architecture for driverless vehicles in the traffic architecture;

[0078] Step 6: Design the conditions for the stable operation of the communication network architecture of driverless vehicles in the traffic architecture;

[0079] Step 7: Verify the positivity of the communication network of driverless vehicles in the traffic architecture;

[0080] Step 8: Verify the synchronization of the communication network of driverless vehicles in the traffic architecture.

[0081] Specifically, the specific structure of the traffic architecture is not limited and can be a logic component that can arbitrarily implement data control processing. The traffic architecture is the intelligent transportation system.

[0082] Specifically, Step 1: Establish a state space model of the communication network of driverless vehicles in the intelligent transportation system, in the following form:

[0083]

[0084] y i (k) = C σ x i (k),

[0085] where x i (k) = (x i1 (k), x i2 (k), …, x in (k)) ∈ R n , represents the state vector of the complex communication network of the i-th driverless vehicle in the intelligent transportation system, which contains all the state information of the complex communication network at time k, such as vehicle position, speed, fuel consumption, etc.; u i (k) = (u i1 (k), u i2 (k), …, u is (k)) ∈ R s , represents the control input vector of the complex communication network of the i-th driverless vehicle in the intelligent transportation system, which contains all the external instructions or signals received by the complex communication network at time k, such as start, acceleration, steering, etc.; y i (k) = (y i1 (k), y i2 (k), …, y im (k)) ∈ R m , represents the control output vector of the complex communication network of the i-th driverless vehicle in the intelligent transportation system, which contains all the information based on the state x within time k of the complex communication networki All signals or information sent by (k). Π = (π ij ) ∈ R n×n is a symmetric and irreducible matrix representing the topology of a complex communication network, which needs to satisfy and If the i-th node of the complex communication network can receive communication sent by the j-th network node, then π ij (≠ π ji ) > 0, otherwise, π ij = 0. T = diag{t 1 , t 2 , …, t n} ∈ R n×n is a positive semi-definite internal coupling matrix with t i ≥ 0. A σ ∈ R n×n , B σ ∈ R n×s , C σ ∈ R m×n are matrices with dimensions. n, m, s, N, σ are positive integers. Assume A σ ≥ 0, B σ ≥ 0, C σ ≥ 0.

[0086] Step 2: Construct isolated nodes of the communication network of driverless vehicles in the intelligent transportation system:

[0087] s i (k + 1) = A 0σ s i (k) + B 0σ u 0i (k),

[0088] y 0i = C 0i s i (k),

[0089] Isolated nodes of the communication network can plan the optimal traffic flow path according to real-time traffic data. The remaining network nodes then adjust their own operating states according to the plan of the isolated nodes of the communication network to achieve the optimal scheduling of the entire traffic network. Through the guidance of the isolated nodes of the communication network and the cooperation of the remaining network nodes, traffic congestion can be effectively reduced. Among them, the state vector s i (k) ∈ R n , the input vector u 0i (k) ∈ R s and the output vector y 0i (k) ∈ R m represent the state, output, and output of the network isolated nodes respectively. A 0σ ∈ Rn×n , B 0σ ∈R n×s , C 0σ ∈R m×n is a matrix with dimensions. Set A 0σ ≥0, B 0σ ≥0, C 0σ ≥0; Define u 0i (k) = F σ s i (k), F σ is the controller gain matrix to be designed.

[0090] Step 3, Design the positive communication complex network observer for driverless vehicles in the intelligent transportation system:

[0091]

[0092] In the intelligent transportation system, the observer can continuously monitor the operating state of the transportation system, including abnormal conditions such as traffic congestion, accidents, and faults. Based on the data collected by the observer, the intelligent transportation system can perform data analysis, mining, and prediction to formulate more reasonable traffic management strategies. Among them, is the observer state, is the observer output. represents the gain matrix of the observer. At the moment k ∈ [mT, mT + τ), the gain matrix of the observer is expressed as When k ∈ [mT + τ, (m + 1)T), the gain matrix of the observer is expressed as

[0093] Step 4, Establish the observer-based periodic intermittent control protocol for driverless vehicles in the intelligent transportation system, and its form is as follows:

[0094]

[0095] In the intelligent transportation system, the control accuracy of vehicles is crucial for the performance and safety of the entire system. The observer-based control method can calculate the required control inputs of vehicles, such as steering wheel angle, acceleration, etc., more accurately by real-time observing the vehicle state and external environment information, thereby improving the control accuracy of road vehicles. At the same time, it plays an important role in ensuring the consistency and stability of the intelligent transportation system, optimizing traffic flow and improving traffic efficiency, and realizing emerging applications such as intelligent driving and vehicle networking.

[0096] and represent the gain matrices of the network cluster controller. When k ∈ [mT, mT + τ), K σ (k) = K σ and Mσ K(k) = M σ ; when k ∈ [mT + τ, (m + 1)T), K σ K(k) = M σ K(k) = 0. Intermittent control allows the system to turn off or reduce the operating power of certain devices within a specific time period, thereby optimizing resource allocation. In an intelligent transportation communication network system, this means that the operating state of communication devices can be dynamically adjusted according to real-time traffic flow and communication requirements, avoiding unnecessary resource waste. For example, during periods of low traffic flow, some communication devices can be turned off to save energy and reduce operating costs. T > 0 represents the control period, and τ > 0 is the control step size that satisfies τ < T.

[0097] Specifically, the communication network architecture is the communication network system. The specific structure of the communication network architecture is not limited. It can be any logical component that can implement program control. It can be understood that as long as the communication network architecture can be made to realize dynamically adjusting the operating state of the communication devices of driverless vehicles according to real-time traffic flow and communication requirements, this will not be elaborated here.

[0098] Step 5: Establish a closed-loop architecture for driverless vehicles in an intelligent transportation system:

[0099]

[0100] Establishing a closed-loop architecture for driverless vehicles in an intelligent transportation system can be achieved through a feedback mechanism, which can monitor the output state of the system in real time and compare it with the desired state, thereby adjusting the input to eliminate the deviation. In an intelligent transportation communication network system, this means that traffic signals, vehicle scheduling, communication resource allocation, etc. can be precisely controlled to improve the overall performance and stability of the system and ultimately achieve synchronization. For example, by monitoring traffic flow and vehicle speed in real time, the closed-loop architecture of driverless vehicles in an intelligent transportation system can dynamically adjust the vehicle speed, thereby effectively alleviating traffic congestion and improving road capacity. Among them, represents the observation error, e i K(k) = x i (k) - s i K(k) represents the local synchronization error.

[0101]

[0102] Step 6: Design the conditions for the stable operation of the communication network system of driverless vehicles in an intelligent transportation system as follows:

[0103] Design a constant 0 < μ 1 < 1, μ 2 > 1, λ > 1, R n vector v 1> 0, v 2 > 0, v 3 > 0, v 1 ′ > 0, v 2 ′ > 0, v 3 ′ > 0, and R s vector such that

[0104]

[0105]

[0106] Determine the parameter range of the traffic architecture.

[0107] Then, the communication network of the driverless vehicle in the intelligent transportation system described in step 5 achieves positivity and synchronization, and the gain matrix is:

[0108]

[0109] Meanwhile, the isolated nodes of the communication network satisfy

[0110] 2lnλ + μ 1 τ + μ 2 (T - τ) < 0.

[0111] Wherein, is a Kronecker product operator, represents an n-dimensional column vector with the nth element being 1 and the remaining elements being 0.

[0112] Step 7. The process of verifying the positivity of the communication network of the driverless vehicle in the intelligent transportation system is as follows:

[0113] 7.1. According to condition (3) in step 6, it can be obtained that

[0114]

[0115] Wherein,

[0116]

[0117] By it can be obtained that Thus,

[0118]

[0119] 7.2. According to the conditions in step 6, it can be obtained that

[0120]

[0121] 7.3. According to the conditions in step 6, it can be obtained that

[0122]

[0123] Among them,

[0124]

[0125] 7.4. According to the conditions in step 6, it can be obtained that

[0126]

[0127] Among them,

[0128]

[0129] 7.5. According to the conditions in step 6, it can be obtained that

[0130] Through the condition Obtain Furthermore, it is obtained that

[0131]

[0132] 7.6. According to the conditions in step 6, it can be obtained that

[0133]

[0134] and

[0135]

[0136] Among them,

[0137]

[0138]

[0139] Therefore, in the intelligent transportation system, the positive complex network composed of driverless vehicles exhibits positive characteristics. During the operation of driverless vehicles, the positivity of the driving mileage and energy consumption of driverless vehicles can be verified.

[0140] The verification process of the synchronization of the communication network of driverless vehicles in the intelligent transportation system in step 8 is as follows:

[0141] 8.1. Select a piecewise CLF as follows:

[0142]

[0143] Among them, and

[0144] 8.2. When the cycle time \(k\in[mT, mT + \tau)\), the complex network cluster in step 5 is affected by the control protocol in step 4. Specifically, it is manifested as follows:

[0145]

[0146] 8.3. Obtain

[0147]

[0148]

[0149] 8.4. Combining 8.2 and 8.3, further

[0150] \(\Delta V(k)\leq(\mu 1 - 1)V 1 (k).

[0151] Through the conditions and obtain \(V 1 (k + 1)\leq\mu 1 V 1 (k). Thus, when the cycle time \(k\in[mT, mT + \tau)\), through the observer-based intermittent control protocol designed in step 4, the complex network cluster in step 5 can achieve synchronization while maintaining the positivity condition.

[0152] 8.5. When the cycle time \(k\in[mT+\tau, (m + 1)T)\), similar to 8.2, 8.3, and 8.4, it can be obtained that

[0153]

[0154] \(\Delta V(k)\leq(\mu 2 - 1)V 2 (k).

[0155] Through the formula in 8.5, further obtain \(V 2 (k + 1)\leq\mu 2 V 2 (k).

[0156] 8.6. Through the analysis from 8.1 to 8.5, when the time \(k\in[mT, mT + \tau)\), it is obtained that

[0157]

[0158] When the time \(k\in[mT+\tau, (m + 1)T)\), it is obtained that

[0159]

[0160] Therefore, the communication network of driverless vehicles in the intelligent transportation system achieves synchronization. Through efforts in adopting advanced communication technologies, designing observers, and their intermittent synchronization control algorithms, etc., the development and improvement of the intelligent transportation system can be gradually promoted, providing a safer, more efficient, and more convenient solution for the normal travel of driverless vehicles.

[0161] Specifically, a positive complex network model is used to accurately model the communication network of driverless vehicles in the intelligent transportation system to obtain a state-space model. Then, an isolated node is assumed to communicate with the rest of the network clusters to achieve information interaction. Secondly, by designing an observer and its periodic intermittent control protocol, the communication network of driverless vehicles in the intelligent transportation system can achieve synchronization normally without real-time communication, ensuring the stability of the communication network of driverless vehicles under various actual conditions and improving the control efficiency of each driverless vehicle.

[0162] As Figure 2 shown, in the schematic diagram of the communication network of driverless vehicles in the intelligent transportation system, each aggregated vehicle group forms a single network cluster, and each network cluster is boxed. The single vehicle with a certain degree of dispersion is determined as an isolated node, and the isolated node is selected with a circle.

[0163] Specifically, the positive complex network cluster can model this dynamic communication network, so that the network clusters in the intelligent transportation system can be optimized and controlled. The positive complex network cluster provides a powerful theoretical framework and analysis tool for the modeling of the communication network of driverless vehicles in the intelligent transportation system, which helps to understand and optimize the performance of the intelligent transportation system.

[0164] Specifically, intermittent control, also known as periodic intermittent control or discontinuous control, is different from the conventional control method that continuously applies control. Its characteristic is that the control action is discontinuous in time, that is, the intermittent synchronization controller is only activated within specific time intervals and does not operate at other times. During actual driving, the unmanned vehicle and the surrounding vehicles are not always measurable by sensors. Therefore, an observer is added to indirectly obtain the state information of the unmanned vehicle. The intermittent control based on the observer adopted in the communication network of unmanned vehicles in the intelligent transportation system has the following advantages: (1) Reducing communication overhead: The intermittent control strategy is more economical in data transmission because it only communicates when necessary. Compared with the continuous communication method, it can significantly reduce the communication frequency and data volume, thus reducing the communication cost. In the intelligent transportation system, vehicles need to frequently exchange real-time data, and intermittent control helps reduce unnecessary communication consumption and improve the cost-effectiveness of the system. (2) Optimizing network resource utilization: Through intermittent control, network resources can be allocated and utilized more effectively. In the communication network of intelligent transportation vehicles, although information such as the position, speed, and direction of vehicles is constantly changing, not all information needs to be updated continuously. Intermittent control allows data transmission only when there are important changes in the information or according to specific strategies, thereby improving the transmission efficiency and resource utilization rate of the network. (3) Supporting real-time decision-making: Although intermittent control reduces the communication frequency, it can still support real-time decision-making and control in the intelligent transportation system. For example, in case of an emergency or when a path change is needed, communication can be immediately activated to send critical data to ensure that the system can respond quickly and execute necessary control measures. (4) Indirectly observing the state: The observer can monitor the state of the unmanned vehicle and its communication network in real time, including key information such as the vehicle's position, speed, driving direction, and communication quality. These data are the basis for the traffic architecture to conduct analysis, decision-making, and control. Through the observer, the traffic architecture can obtain the operating state of the vehicle communication network in real time, providing strong support for subsequent traffic management and optimization. Determining the periodic intermittent control protocol can effectively improve the data interaction efficiency of unmanned vehicles.

[0165] Specifically, the positivity and synchronization of the intelligent transportation system with an intermittent synchronization controller are proven through matrix decomposition, piecewise copositive Lyapunov function theory, and linear programming methods, ensuring the stability of the communication network of unmanned vehicles under various actual conditions. This solution provides a resource-saving, stable, and efficient intermittent control technology for the communication network of unmanned vehicles in the intelligent transportation system based on an observer.

[0166] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0167] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for intermittent control of communication network of intelligent traffic unmanned vehicle, characterized in that: include: Step 1, establish a state space model of the communication network of unmanned vehicles in the traffic architecture; Step 2, constructing isolated nodes of the communication network of the unmanned vehicle in the traffic architecture, determining the isolated nodes as synchronization targets, and realizing the positivity of the isolated nodes by the state feedback controller; Step 3: construct an observer for each network cluster to construct a complex communication network, and according to the output of the observer, the traffic architecture is used to analyze, mine and predict the data output by the observer to formulate a traffic management strategy; Step 4, establishing an observer-based periodic intermittent control protocol for each unmanned vehicle in the traffic architecture, and the intermittent synchronization controller outputs the periodic intermittent control protocol for the corresponding unmanned vehicle; Step 5, establishing a closed-loop architecture for each unmanned vehicle in the traffic architecture; Step 6, determine the conditions for the smooth operation of the communication network architecture of each unmanned vehicle in the traffic architecture.

2. The intermittent control method for communication network of intelligent transportation unmanned vehicle according to claim 1 is characterized in that: In step 1, the process of establishing a state space model of the communication network of the unmanned vehicle in the traffic architecture includes: y i (k)=C σ x i (k), Among them, x i (k)=(x i1 (k),x i2 (k),…,x in (k))∈R n , x i (k) represents the state vector of the complex communication network of the i-th unmanned vehicle, x i (k) contains the state information of the communication network at time k, including vehicle position, speed, and fuel consumption; u i (k)=(u i1 (k),u i2 (k),…,u is (k))∈R s ,u i (k) represents the control input vector of the communication network of the i-th unmanned vehicle, u i (k) contains the external commands and signals received by the communication network at time k, including start, acceleration, and steering; y i (k)=(y i1 (k),y i2 (k),…,y im (k))∈R m ,y i (k) represents the control output vector of the communication network of the i-th unmanned vehicle, y i (k) contains the communication network in time k according to the state information x i (k) Signals and information sent; Π=(π ij )∈R n×n is a symmetric and irreducible matrix used to represent the topology of the communication network, which satisfies and In π ij (≠π ji )>0, the nodes of the i-th communication network can obtain the communication sent by the nodes of the j-th communication network; In π ij = 0, the nodes of the i-th communication network cannot receive the communication sent by the nodes of the j-th communication network; t i ≥0 positive semidefinite internal coupling matrix, A σ ∈R n×n , B σ ∈R n×s , C σ ∈R m ×n is a matrix with dimension; n, m, s, N, σ are positive integers, set A σ ≥0, B σ ≥0, C σ ≥0.

3. The intermittent control method for communication network of intelligent transportation unmanned vehicle according to claim 2 is characterized in that: The process of constructing an isolated node of the communication network of the unmanned vehicle in step 2 includes: s i (k+1)=A 0σ s i (k)+B 0σ u 0i (k), y 0i =C 0i s i (k), Among them, s i (k)∈R n is the state vector of the isolated node, u 0i (k)∈R s is the input vector, y 0i (k)∈R m is the output vector; A 0σ ∈R n×n , B 0σ ∈R n×s and C 0σ ∈R m×n Be a system matrix with dimension, set A 0σ ≥0, B 0σ ≥0, C 0σ ≥0; Set u 0i (k) = F σ s i (k), F σ is the gain matrix of the state feedback controller.

4. The intermittent control method for the communication network of unmanned vehicles in a traffic structure according to claim 3, characterized in that: In step 3, the process of determining the output of the observer of the positive communication complex network of unmanned vehicles in the traffic framework includes: Among them, G σ is the observer matrix, is the observer state, is the output of the observer; L σ (k) represents the observer gain matrix, At the time k∈[mT,mT+τ), the observer gain matrix is ​​expressed as At the time k∈[mT+τ,(m+1)T), the observer gain matrix is ​​expressed as 5. The intermittent control method for communication network of intelligent transportation unmanned vehicle according to claim 4 is characterized in that: In step 4, the process of establishing an observer-based periodic intermittent control protocol for an unmanned vehicle in a traffic framework includes: K σ (k) and M σ (k) are the gain matrices of the network cluster controller; When k∈[mT,mT+τ), K σ (k) = K σ And M σ (k) = M σ ; When k∈[mT+τ,(m+1)T), K σ (k) = M σ (k) = 0; T is the control period, T>0, τ is the control step, τ>0 and τ<T.

6. The intermittent control method for communication network of intelligent transportation unmanned vehicle according to claim 5, characterized in that: In step 5, a closed-loop architecture of the unmanned vehicles in the traffic architecture is established: in, represents the observation error, e i (k) = x i (k)-s i (k) represents the local synchronization error, 7. The intermittent control method for communication network of intelligent transportation unmanned vehicle according to claim 6 is characterized in that: In step 6, the process of determining the conditions for the smooth operation of the communication network architecture of the unmanned vehicle in the traffic architecture includes: Setting constants 0<μ1<1, μ2>1, λ>1, R n Vector v1>0, v2>0, v3>0, v′1>0, v′2>0, v′3>0, and R s vector π σσ <0 makes Determine the parameters of the transportation architecture.

8. The intermittent control method for communication network of intelligent transportation unmanned vehicle according to claim 7, characterized in that: The gain matrix for achieving positivity and synchronization of the communication network architecture of the unmanned vehicle in the traffic architecture described in step 5 is: At the same time, each isolated node in the communication network architecture satisfies 2lnλ+μ1τ+μ2(T-τ)<0 in, is the Kronecker product operator, Represents an n-dimensional column vector whose nth element is 1 and the rest of the elements are 0, where n is the number of nodes in the complex network.

9. The intermittent control method for communication network of intelligent transportation unmanned vehicle according to claim 8, characterized in that: The observer is used to continuously monitor the operating status of the unmanned vehicle, and the operating status includes traffic congestion, traffic accidents, and unmanned vehicle failures.

10. The intermittent control method for communication network of intelligent transportation unmanned vehicle according to claim 9, characterized in that: The communication network includes isolated nodes and a plurality of network clusters, and a single network cluster includes a plurality of network nodes.

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