A super highway control method and system based on state feedback

Abstract

The embodiment of the present invention discloses a super highway control method and system based on state feedback, which relates to the field of intelligent control of highways. The method includes: using traffic sensing equipment to measure the current real traffic state of the super highway network, wherein the traffic state includes traffic flow density; inputting the measured real traffic state into multiple traffic state observers, and each traffic state observer estimates the future traffic state of the road network respectively; fusing the estimation results of each traffic state observer and inputting them into a feedback controller, and the feedback controller performs feedback control on the input of the road network, wherein the input includes the incoming vehicle flow. This embodiment performs feedback control on the highway network based on state estimation to improve the accuracy of intelligent control.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of highway intelligent control, and in particular to a super highway control method and system based on state feedback. Background Art

[0002] With the continuous advancement of automobile technology, the design speed of ordinary cars is mostly above 180km / h, and even some racing cars have a design speed of 400km / h, which far exceeds the maximum design speed of traditional highways in my country. Therefore, super highways with a design speed of 100-180km / h have become a trend. Super highways not only have higher design speeds, but also use big data through intelligent cloud control systems to increase the speed of high-speed vehicles. However, in the face of such operating speeds, manual drivers have large differences in driving behavior, and they need a certain amount of reaction time to take measures such as braking in an emergency, which further increases the probability of accidents and creates a greater safety hazard.

[0003] Therefore, for ultra-high-speed sections, on the one hand, more intelligent autonomous driving vehicles are needed, which can quickly take corresponding control measures in case of emergency situations; on the other hand, for a long time, there will still be a mixed scene of autonomous driving and manual driving, and it is urgent to adopt more intelligent means to obtain more accurate traffic information, and then adopt more effective control strategies to ensure the safe operation of vehicles. Patent CN110400471B discloses a control method for the safe operation of urban transportation systems, and patent CN116913093A discloses a method for coordinated management of intelligent highways based on feedback control, but these methods cannot be directly applied to ultra-high-speed sections, nor can they achieve accurate and effective control. Summary of the invention

[0004] The embodiments of the present invention provide a super highway control method and system based on state feedback to solve the above technical problems.

[0005] In a first aspect, an embodiment of the present invention provides a super highway control method based on state feedback, comprising:

[0006] Using traffic sensing equipment, measuring the current real traffic status of the ultra-highway network, wherein the traffic status includes traffic flow density;

[0007] The measured real traffic state is input into multiple traffic state observers, and each traffic state observer estimates the future traffic state of the road network respectively;

[0008] The estimation results of each traffic state observer are integrated and input into a feedback controller, and the feedback controller performs feedback control on the input of the road network, wherein the input includes the incoming vehicle flow.

[0009] In a second aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0010] one or more processors;

[0011] a memory for storing one or more programs,

[0012] When the one or more programs are executed by the one or more processors, the one or more processors implement the super highway control method based on state feedback described in any embodiment.

[0013] In a third aspect, an embodiment of the present invention further provides a super highway control system based on state feedback, comprising:

[0014] Traffic sensing equipment, used to measure the current real traffic status of the ultra-highway network, wherein the traffic status includes traffic flow density;

[0015] Multiple traffic state observers are used to estimate the future traffic state of the road network based on the measured actual traffic state;

[0016] The control center is used to fuse the estimation results of each traffic state observer and input them into the feedback controller, and the feedback controller performs feedback control on the input of the road network, wherein the input includes the incoming vehicle flow.

[0017] The embodiment of the present invention provides a super highway control method based on state feedback, which adopts a variety of sensing devices (including intelligent connected vehicles) to collect road-end information under the super high-speed operation state, and uses a state observer with multi-source information fusion to accurately estimate the traffic state of the highway network; at the same time, a feedback controller matching the state observer is constructed, and feedback control is performed on the highway network based on the comprehensive estimation result of multi-source information, so as to realize refined control based on traffic state prediction and ensure the stability and efficiency of traffic operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is a flow chart of a super highway control method based on state feedback provided by an embodiment of the present invention;

[0020] Figure 2is a structural diagram of a feedback controller based on a traffic state observer provided in an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0023] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0024] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" 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 a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] Figure 1 This is a flow chart of a super highway control method based on state feedback provided by an embodiment of the present invention. This method is applicable to intelligent control scenarios of highways, especially to sections of super high-speed roads with a designed vehicle speed of 160-180 km / h, and is executed by electronic equipment. Figure 1 As shown, the method specifically includes:

[0026] S110. Using traffic sensing equipment, measure the current real traffic status of the superhighway network, wherein the traffic status includes traffic flow density.

[0027] With the increase in the types of sensing devices, there are multiple data collection devices on the highway, such as radar, video, floating car, ETC gantry, microwave and other types of traffic sensors. This embodiment converts the data collected by different devices into traffic state parameters through corresponding conversion relationships as the measurement results of the real traffic state, providing a data source for the entire method.

[0028] In a specific implementation, the expressway can be divided into several sections according to the locations of entrance and exit ramps, curvature radius changes, ETC gantry locations, lane number changes, etc. Each section is called a cell, and the divided cells are numbered in sequence to facilitate marking of traffic sensor deployment locations.

[0029] Optionally, when traffic flow density is used to represent the traffic state, for cells with ETC gantries at both the entrance and exit, the vehicle density of the cell can be calculated by the traffic flow collected by the ETC at the entrance and exit within a sampling period, that is:

[0030]

[0031] Among them, ρ k (t) and ρ k (t+1) represents the traffic flow density of cell k at time t and time (t+1), T represents the sampling period (unit: second), L k represents the length of cell k (unit: meter), q k-in The traffic volume entering cell k in a sampling period, q k-out The vehicle flow out of cell k within a sampling period.

[0032] In particular, for ultra-high-speed operation (especially ultra-high-speed operation of 160-180km / h), in order to ensure the high speed and safety of vehicle operation, the proportion of intelligent networked vehicles on the road section is relatively high. Intelligent networked vehicles can also be used as a type of traffic sensor (i.e., perception device). Such sensors directly obtain information such as vehicle latitude and longitude, which can be converted into traffic flow density through the following steps:

[0033] First, the number of intelligent connected vehicles m in each cell is calculated based on the longitude and latitude of each vehicle obtained at the same time. 车 Optionally, the coordinates of the four vertices of cell k are recorded as (x kg ,y kg ), g = 1, 2, 3, 4, if the longitude of the vehicle x 车h , latitude y 车h The following conditions must be met at the same time:

[0034]

[0035] Then the vehicle is judged to be in cell k, and the number of all intelligent connected vehicles in cell k at the same time is calculated. kg and maxx kg Respectively represent the maximum and minimum values ​​of the x coordinates of the four vertices, miny kg and max y kg Represents the maximum and minimum values ​​of the y coordinates of the four vertices respectively.

[0036] Then, according to the speed of each intelligent connected vehicle in the same cell, the average speed of the cell is calculated, that is:

[0037]

[0038] Among them, V p is the speed of the pth intelligent connected vehicle.

[0039] Finally, based on the relationship between speed and traffic flow density, the traffic flow density of the cell is calculated.

[0040] After the vehicle density of each cell is obtained, it is arranged into a vector to reflect the actual traffic status of the expressway network. This embodiment uses the result as a measurement value of the traffic status.

[0041] S120, inputting the measured real traffic state into a plurality of traffic state observers, and each traffic state observer estimates the future traffic state of the road network respectively.

[0042] Each traffic state observer is constructed based on the dynamic traffic flow model of the highway network, so the dynamic traffic flow model of the highway network is explained first. Taking the proportional integral state observer as an example, the road network dynamic traffic flow model corresponding to the observer is as follows:

[0043]

[0044] Where x∈R n represents the traffic flow density vector, x(t) and x(t+1) represent the traffic flow density vectors at time t and time (t+1) respectively, and n represents the number of divided cells; u represents the control input (or input) of the system, such as the traffic flow entering the main road through the entrance ramp, and u(t) represents the input at time t; y represents the measurement value of the traffic state by the road network perception device, and y(t) represents the traffic state measured at time t; A σ(t) It is a matrix related to the state of the road network system, called the system matrix; B σ(t) is the input matrix of the road network system; C is the matrix related to the sensing devices deployed in the road network, called the output matrix; F σ(t) is a constant matrix; σ(t) represents the mixing rate of different autonomous driving vehicles.σ(t) , B σ(t) , C and F σ(t) Together, they reflect the inherent properties of the road network and its sensing devices. When the road network is determined, and The value or expression of each element in can be predetermined according to the road network structure combined with the general traffic flow transmission law. The specific process is the existing technology and will not be repeated here.

[0045] Next, we determine C for different sensing devices. For a road section with multiple types of traffic sensing devices, the corresponding output equation for each type of sensing device is:

[0046] y r (t) = C r x(t), r=1,2,...,l (5)

[0047] Among them, l represents the type of traffic sensing equipment, y r (t) represents the measured output of the rth type of sensing device at time t, C r is the corresponding output matrix. Since different types of sensing devices collect different traffic information, but can be uniformly converted into traffic flow density through corresponding conversion relationships, the output matrices corresponding to different types of sensing devices can be expressed in a unified way, and the specific form is as follows:

[0048]

[0049] Among them, C ri C r The element corresponding to the i-th cell in , that is, as long as the corresponding sensing device is deployed in the i-th cell, the element at the corresponding position of its output matrix is ​​1, otherwise it is 0.

[0050] After completing the highway modeling and output matrix design, the traffic state observer of multi-source information fusion can be constructed. In a specific implementation, first, according to the observability judgment criterion, the system (A σ(t) ,C r )(r=1,2,...,l) is observable or detectable. If there are m systems that are observable or detectable, then m corresponding state observers are constructed. Optionally, a traffic state sensor with the following structure can be constructed for each of the m types of sensing devices:

[0051]

[0052] Where a=1,2,...,m, represents the traffic flow density estimated by the a-th traffic state observer, and They represent the traffic flow density at time t and time (t+1) estimated by the traffic state observer, L σ(t) Represents the gain matrix of the traffic state observer. Observers corresponding to different types of sensing devices use different C.

[0053] The traffic flow density measured in S110 is used as y a (t), and then substitute it into the corresponding state observer (7), and we can get the estimation result of the state observer The output matrices of the remaining 1 m unobservable / undetectable systems are recombined to obtain several new systems. The system matrices and output matrices are used to determine whether the system is observable / detectable. If several newly combined systems meet the requirements of observability / detectability, the system corresponding to the traffic sensors with the least number of sensors and the most uniform distribution is selected to construct the observer and estimate / predict the corresponding traffic flow density of the highway section. So far, a total of m+1 estimated / predicted values ​​of traffic flow density have been obtained.

[0054] S130, integrating the estimation results of each traffic state observer and inputting them into a feedback controller, and the feedback controller performs feedback control on the input of the road network, wherein the input includes the incoming vehicle flow.

[0055] This embodiment uses a feedback controller based on a state observer to perform feedback control on the input of the highway network. In order to improve the control accuracy, the m+1 density estimation values ​​obtained in S120 are first weighted and summed to obtain a more accurate estimation. The details are as follows:

[0056] 1) Assume that each observer's estimate / prediction value has a weight ω a ,a=1,…,m+1, so m+1 weighted combination pairs {ω1,ω2,…,ω m+1}, the weighted sum of traffic flow density is:

[0057]

[0058] 2) Calculate the estimation / prediction results of m+1 observers respectively and The difference between the two a :

[0059]

[0060] 3) Perform cyclic calculations in each sampling period to find the weighted combination pair {ω1,ω2,…,ω m+1} as the final weight:

[0061]

[0062] 4) The corresponding as the final estimated result.

[0063] Based on the fused estimation results, this embodiment adopts Figure 2 The feedback controller shown controls the road network input u, and the control term can be expressed as:

[0064]

[0065] in, represents the gain matrix of the feedback controller, The key to constructing the above feedback controller is to determine the gain matrix The following is a detailed description of the matrix solution method. In a specific implementation, after adding the control term, the controlled object (dynamic traffic flow model) is written as follows:

[0066]

[0067] The feedback state (i.e. the estimated result after the state observer fusion) is:

[0068]

[0069] in, Represents the estimation error of the state observer, then the estimation error system of the state observer can be expressed as:

[0070]

[0071] in, and are the estimation errors at time t and (t+1) respectively.

[0072] According to equations (12)-(14), the following augmented error system can be constructed:

[0073]

[0074] in, represents the augmented error, and They represent the augmented errors at time t and time (t+1) respectively.

[0075] make Substituting into formula (15) we get:

[0076]

[0077] To ensure that the augmented error system (15) is stable, we can construct the Lyapunov function: The design principle of asymptotic stability of the augmented error system is converted into the constraint condition of the energy decay of the Lyapunov function. That is, if there exists a symmetric positive definite matrix P such that the energy of the Lyapunov function decays over time Then equation (15) is convergent:

[0078]

[0079] Let P = -Q, according to inequality (17) and Schur's complement theorem, we can obtain:

[0080]

[0081] Right now

[0082]

[0083] Formula (19) is a typical linear matrix inequality. Using the linear matrix inequality solver, we can solve the matrix Q and The matrix A σ(t) , L σ(t) , B σ(t) and C are known, substitute The gain matrix of the feedback controller can be obtained More specifically, since m+1 observers are constructed in this embodiment, each observer corresponds to a different C, a Each The average is taken as the final gain matrix; you can also select the observer with the least number of sensors and the most uniform distribution among the m+1 observers, and use the C of this observer to calculate

[0084] The fusion estimation results Substitution By determining formula (11), the corresponding control input u can be obtained. According to this quantity, the corresponding control strategy can be formulated to accurately control the road network system.

[0085] Optionally, in another specific implementation, the traffic state observer may also adopt the following structure:

[0086]

[0087] In this state observer, the highway network is divided into multiple subsystems according to regions. Each subsystem includes at least one cell. A state observer is set in each subsystem, which together constitutes a distributed traffic state observer of the entire road network. and They represent the traffic status of each subsystem of the road network estimated by the observer at time t and time t+1 respectively; K σ(t) represents the observer gain matrix; u(t) represents the input of each subsystem at time t; y(t) represents the measured output of each subsystem at time t; The decoupling block matrix of each subsystem is used to characterize the transmission law of the internal traffic state of each subsystem; Represents the interconnection items between subsystems, is a known matrix constructed based on the interconnection relationship between the traffic states of each subsystem; B σ(t) It is used to characterize the impact of each subsystem's input on the traffic status; C is used to characterize the relationship between each subsystem's measured output and the traffic status; F σ(t) represents the affine term.

[0088] It should be noted that the above-mentioned distributed state observer and proportional integral state observer both follow the basic principle of state observer, so multiple variable symbols appear in both state observers, such as x, y, A σ(t) , B σ(t) , However, the meanings and values ​​of these symbols in the two observers are not necessarily the same, nor are they necessarily related, and should be treated differently according to their specific interpretations in the two state observers.

[0089] The construction process of the above-mentioned state observer is described below. In a specific implementation, the construction process may include the following steps:

[0090] Step 1: Construct the traffic status dynamic model of subsystem i:

[0091]

[0092] Where N is the number of subsystems, x i represents the traffic state vector of subsystem i, x i (t) and x i (t+1) represents the traffic state vector of subsystem i at time t and time t+1, respectively, x j (t) represents the traffic state vector of all adjacent subsystems j of subsystem i at time t. For example, when the traffic state is traffic flow density, x i (t) is the vector composed of the traffic flow density of each road section in subsystem i at time t, x i (t+1) and x j (t) is similar, and x i The time and subsystem number in (t) can be replaced by t+1 and j. i represents the input of subsystem i, u i(t) represents the input of subsystem i at time t. For example, when the input includes the incoming traffic flow of each road segment, u i (t) is the vector composed of the traffic flow density changes caused by the incoming vehicles on each road section in subsystem i at time t. i represents the measured output vector of subsystem i, y i (t) represents the measured output vector of subsystem i at time t, which can be expressed by the matrix C i Converted to the traffic state x of subsystem i at time t i (t). Matrix C i and are the system matrix, input matrix, output matrix and affine term of subsystem i respectively, is the matrix associated with the adjacent subsystem j, represents the upstream adjacent subsystem of subsystem i, Represents the downstream adjacent subsystem of subsystem i. and Together they reflect the inherent properties of subsystem i. When the subsystem is determined, and The value or expression of each element in can be predetermined according to the road network structure combined with the general traffic flow transmission law. The specific process is the existing technology and will not be repeated here.

[0093] Furthermore, the dynamic equations of each subsystem are combined to obtain the dynamic model of the traffic status of the entire highway network:

[0094]

[0095] Among them, x represents the traffic status of the highway network, x(t) and x(t+1) represent the traffic status of the highway network at time t and time t+1 respectively; u represents the input of the highway network, u=[u1 T ,…u N T ] T , u(t) represents the input of the highway network at time t; y represents the measured output of the highway network, y = [y1 T ,…y N T ] T , y(t) represents the measured output of the highway network at time t. σ(t) , B σ(t) , C and F σ(t)The system matrix, input matrix, output matrix and affine terms of the highway network are respectively:

[0096]

[0097] C = blkdiag(C i ), Likewise, B σ(t) and F σ(t) Together, they reflect the inherent properties of the highway network system. When the highway network system is determined, B σ(t) and F σ(t) It is also known in advance.

[0098] Step 2: Based on the dynamic model shown in formula (21), the traffic state observer structure of subsystem i is constructed as follows:

[0099]

[0100] in, represents the traffic state vector of subsystem i estimated by the observer of subsystem i, and They represent the traffic state vectors of subsystem i at time t and time t+1 estimated by the observer of subsystem i, respectively. represents the traffic state vector of subsystem j at time t estimated by the observer of the neighboring subsystem j of subsystem i; represents the measurement output vector of the observer of subsystem i, Represents the measured output vector of the observer at time t. represents the observer gain matrix of subsystem i, It is an unknown quantity to be determined, and its value needs to be determined in subsequent operations to ensure that the estimation error of the observer is closed-loop convergent.

[0101] It can be seen that the distributed state observer of this embodiment not only uses all the information of the subsystem itself, but also the interconnection information between the subsystems. Also taken into account, all valid information of the road network system is included as comprehensively as possible. Similarly, the traffic state observers of each subsystem can be merged to obtain the traffic state observer of the entire highway network:

[0102]

[0103] in, represents the traffic status of the highway network estimated by each observer, It means that each observer is measuring the output. K σ(t) At this time, the observer gain matrix to be determined is: The meanings of the remaining variables are the same as those in formula (20).

[0104] Step 3: Using the observer gain matrix and the known interconnected terms, the estimated error system of the distributed state observer is represented. Specifically, the observer's estimated error e is defined as the observer's estimated value of the traffic state of each subsystem The difference between the true value x, that is, Substituting formulas (22) and (25) into this definition, we can express it as follows:

[0105]

[0106] Wherein, e(t) and e(t+1) represent the estimated error of the observer at time t and time t+1 respectively. This formula represents the estimated error system of the distributed state observer as the observer gain matrix K σ(t) and interconnected items The form of K σ(t) Provides a basis for the solution.

[0107] Step 4: According to the estimation error system and the closed-loop observer design principle of estimation error convergence, solve the observer gain. Specifically, according to the closed-loop observer design principle, the observation error system shown in formula (26) is required to be asymptotically stable; at the same time, according to the necessary and sufficient conditions of observer design, if the system (A σ(t) ,C) is observable or detectable, then we can definitely design a set of gain matrices K σ(t) So that the matrix is Shure stable. However, due to the interconnected terms in the error system The existence of It is Schur stable, and it is difficult to ensure the stability of the error system (26). Therefore, this embodiment introduces the S-procedure and the Lyapunov function to transform the stability problem of the error system (26) into a linear matrix inequality solution problem.

[0108] In a specific implementation, firstly introduce a symmetric positive definite matrix P (P>0) and construct a Lyapunov function V(e)=e T Pe, and convert the closed-loop observer design principle of estimation error convergence into the constraint condition that the energy of estimation error decays over time. That is, if the observation error system (26) is to be asymptotically stable, then inequality (27) must hold:

[0109]

[0110] Here, e is the abbreviation of e(t), which is equivalent to the estimation error at any moment; v is the abbreviation of v(t).

[0111] At the same time, v(t) satisfies the following lemma:

[0112] v T v≤λ max e T e (28)

[0113] Among them, λ max For the matrix The maximum eigenvalue of The solution is obtained.

[0114] Combining inequalities (27) and (28) using the S-Procedure theorem, we can obtain that there must be a positive constant μ such that the following inequality holds:

[0115]

[0116] Then we get the quadratic inequality of formula (30):

[0117]

[0118] Right now

[0119]

[0120] in,

[0121] Due to the existence of bilinear terms, the matrix inequality (31) is not a linear matrix inequality and cannot be solved directly. Therefore, by replacing the variable Q σ(t) =PK σ(t) , Convert equation (31) into a linear matrix inequality:

[0122]

[0123] in,

[0124] The linear matrix inequality solver is used to solve inequality (32) and obtain P and Q σ(t) By K σ(t) =P - 1 Q σ(t) , and obtain the observer gain K σ(t) .

[0125] Step 4: Set the observer gain Kσ(t) Substituting into formula (24) or (25), we can obtain the complete distributed state observer.

[0126] When using the above distributed state observer for state estimation, the output matrix shown in formula (6) can also be constructed for multiple types of traffic sensing devices, and multiple distributed state observers can be designed according to the observability judgment criterion to obtain multiple state estimation results respectively; multiple state estimation results are weighted and fused to obtain a more accurate traffic state estimation value. The specific process is similar to that of the proportional integral state observer and will not be repeated here.

[0127] Based on the fused estimation results, the following Figure 2 The feedback controller shown controls the network input u, and the control term is shown in equation (11). Different from the feedback controller based on proportional-integral state observer, under the distributed state observer, the gain matrix of the feedback controller is solved When , the controlled object (dynamic traffic flow model) can be written as follows:

[0128]

[0129] The feedback state (i.e. the estimated result after the state observer fusion) is:

[0130]

[0131] in, Represents the estimation error of the state observer, then the estimation error system of the state observer can be expressed as:

[0132]

[0133] in, and are the estimation errors at time t and (t+1) respectively.

[0134] According to equations (33)-(35), the augmented error system of the traffic state observer can be constructed:

[0135]

[0136] in, represents the augmented error, and They represent the augmented errors at time t and time (t+1) respectively.

[0137] make Substituting into formula (36), we get:

[0138]

[0139] To ensure that the augmented error system (36) is stable, we can construct the Lyapunov function: The design principle of asymptotic stability of the augmented error system is converted into the constraint condition of the energy decay of the Lyapunov function. That is, if there exists a symmetric positive definite matrix P such that the energy of the Lyapunov function decays over time Then equation (36) is convergent:

[0140]

[0141] Let P = -Q, according to inequality (38) and Schur's complement theorem, we can obtain:

[0142]

[0143] Right now

[0144]

[0145] Formula (40) is a typical linear matrix inequality. Using the linear matrix inequality solver, we can solve the matrix Q and The matrix K σ(t) , B σ(t) and C are known, substitute The gain matrix of the feedback controller can be obtained More specifically, since m+1 observers are constructed in this embodiment, each observer corresponds to a different C, a Each The average is taken as the final gain matrix; you can also select the observer with the least number of sensors and the most uniform distribution among the m+1 observers, and use this observer to calculate

[0146] The fusion estimation results Substitution By determining formula (11), the corresponding control input u can be obtained. According to this quantity, the corresponding control strategy can be formulated to accurately control the road network system.

[0147] In summary, this embodiment provides a super highway control method based on state feedback, which uses a variety of sensing devices (including intelligent networked vehicles) to collect road-end information under super high-speed operation, and uses a state observer with multi-source information fusion to accurately estimate the traffic state of the highway network; at the same time, a feedback controller matching the state observer is constructed, and feedback control of the highway network is performed based on the comprehensive estimation results of multi-source information, so as to achieve refined control based on traffic state prediction and ensure the stability and efficiency of traffic operation. In particular, in the construction of the feedback controller, this embodiment constructs an augmented error system, and determines the gain matrix of the feedback controller through the asymptotic stability of the augmented error system, while ensuring the matching of the feedback controller and the state observer.

[0148] In addition, this embodiment also proposes a distributed state observer, which incorporates the interconnected items between the subsystems of the road network into the construction process of the state observer, fully considering the mutual influence of the traffic state between the subsystems, and further improving the accuracy of traffic state prediction. In order to ensure the solvability of the observer gain matrix, this embodiment uses the interconnected item matrix The maximum eigenvalue λ max The inequality (28) that the estimation error and the interconnected term should satisfy is obtained, and is applied to the S-Procedure theorem together with the inequality (27) for the asymptotic stability of the observation error system, and the inequality that the interconnected term and the observer gain matrix jointly satisfy is obtained, thereby solving the distributed state observer that satisfies the interconnected term. Based on the distributed state observer, this embodiment further constructs a matching feedback controller, and performs feedback control on the highway network through more accurate multi-source information estimation results to ensure the stability and efficiency of traffic operation.

[0149] This embodiment also provides a super highway control system based on state feedback. The system includes:

[0150] Traffic sensing equipment, used to measure the current real traffic status of the ultra-highway network, wherein the traffic status includes traffic flow density;

[0151] Multiple traffic state observers are used to estimate the future traffic state of the road network based on the measured actual traffic state;

[0152] The control center is used to fuse the estimation results of each traffic state observer and input them into the feedback controller, and the feedback controller performs feedback control on the input of the road network, wherein the input includes the incoming vehicle flow.

[0153] The system of this embodiment is based on the same inventive concept as any of the above method embodiments, and may include the specific features of any of the above embodiments to achieve the same technical effects as any of the above embodiments.

[0154] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the device includes a processor 60, a memory 61, an input device 62 and an output device 63; the number of processors 60 in the device can be one or more. Figure 3 A processor 60 is taken as an example; the processor 60, the memory 61, the input device 62 and the output device 63 in the device can be connected by a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0155] The memory 61 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the super highway control method based on state feedback in the embodiment of the present invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 61, that is, realizing the above-mentioned super highway control method based on state feedback.

[0156] The memory 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 61 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include a memory remotely arranged relative to the processor 60, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0157] The input device 62 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 63 may include a display device such as a display screen.

[0158] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the super highway control method based on state feedback of any embodiment is implemented.

[0159] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0160] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0161] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0162] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A super highway control method based on state feedback, characterized in that: include: Using traffic sensing equipment, measuring the current real traffic status of the ultra-highway network, wherein the traffic status includes traffic flow density; The measured real traffic state is input into multiple traffic state observers, and each traffic state observer estimates the future traffic state of the road network respectively; The estimation results of each traffic state observer are integrated and input into a feedback controller, and the feedback controller performs feedback control on the input of the road network, wherein the input includes the incoming vehicle flow; The multiple traffic state observers are divided according to the types of sensing devices, and the structure of each observer is: in, and They represent the road network traffic status at time t and time (t+1) estimated by the traffic status observer, u(t) represents the road network input at time t, y(t) represents the road network traffic status measured at time t, and A σ(t) represents the system matrix of the road network, B σ(t) represents the input matrix of the road network, C represents the output matrix related to the sensing device, and F σ(t) is a constant matrix, L σ(t) represents the gain matrix of the traffic state observer; The feedback controller is: in, represents the gain matrix of the feedback controller, represents the estimation result after fusion; Before the estimation results of each traffic state observer are integrated and input into the feedback controller, and the feedback controller performs feedback control on the input of the road network, the method further includes: Construct the augmented error system: Among them, x represents the actual traffic status of the road network, represents the estimation error of the traffic state observer, represents the augmented error, and Represent the augmented errors at time t and (t+1) respectively; make Substituting into formula (15) we get: Constructing Lyapunov functions The design principle of asymptotic stability of the augmented error system is converted into the constraint condition of energy decay of the Lyapunov function: in, and denote the Lyapunov function values ​​at time t and (t+1) respectively, and P is a symmetric positive definite matrix; Will Substituting the constraints into equation (16), we get the following inequality: Solving inequality (17), we obtain Thus, the gain matrix of the feedback controller is obtained 2. The method according to claim 1, characterized in that The sensing device includes an Internet vehicle; The method of using traffic sensing equipment to measure the current real traffic status of the ultra-highway network includes: According to the location information of each Internet vehicle obtained at the same time, the number of intelligent connected vehicles m on each road section is calculated 车 Specifically, the coordinates of the four vertices of section k are recorded as (x kg ,y kg ), g = 1, 2, 3, 4, if the longitude of the vehicle x 车h , latitude y 车h The following conditions must be met at the same time: Then it is judged that the vehicle is in section k; Calculate the average speed of the road section based on the speed of each intelligent connected vehicle in the same road section Among them, V p is the speed of the pth intelligent connected vehicle; The actual traffic status of each road section is calculated according to the average speed.

3. The method according to claim 1, characterized in that Solving inequality (17), we obtain Thus, the gain matrix of the feedback controller is obtained include: Let P = -Q, according to inequality (17) and Schur's complement theorem, we can obtain: Right now Using the linear matrix inequality solver to solve equation (19), we get the matrices Q and Will A σ(t) , L σ(t) , B σ(t) and C into Get the gain matrix of the feedback controller 4. The method according to claim 1, characterized in that: The multiple traffic state observers are divided according to the types of sensing devices, and the structure of each observer is replaced by: Among them, K σ(t) represents the observer gain matrix; The decoupling block matrix of each subsystem is used to characterize the transmission law of the internal traffic state of each subsystem; Represents the interconnection items between subsystems, It is a known matrix constructed according to the interconnection relationship between the traffic states of each subsystem; In each step, σ(t) Replace with The L in each step σ(t) Replace with K σ(t) .

5. The method according to claim 1, characterized in that The plurality of traffic state observers are divided according to the types of sensing devices; The estimation results of each traffic state observer are integrated, including: weighted integration of the estimation results of the traffic state observer corresponding to each type of sensing device to obtain a final estimation result.

6. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the super highway control method based on state feedback as described in any one of claims 1-5.

7. A super highway control system based on state feedback, characterized in that: include: Traffic sensing equipment, used to measure the current real traffic status of the ultra-highway network, wherein the traffic status includes traffic flow density; Multiple traffic state observers are used to estimate the future traffic state of the road network based on the measured actual traffic state; A control center is used to fuse the estimation results of each traffic state observer and input them into a feedback controller, and the feedback controller performs feedback control on the input of the road network, wherein the input includes the incoming vehicle flow; The multiple traffic state observers are divided according to the types of sensing devices, and the structure of each observer is: in, and They represent the road network traffic status at time t and time (t+1) estimated by the traffic status observer, u(t) represents the road network input at time t, y(t) represents the road network traffic status measured at time t, and A σ(t) represents the system matrix of the road network, B σ(t) represents the input matrix of the road network, C represents the output matrix related to the sensing device, and F σ(t) is a constant matrix, L σ(t) represents the gain matrix of the traffic state observer; The feedback controller is: in, represents the gain matrix of the feedback controller, represents the estimation result after fusion; Before the estimation results of each traffic state observer are integrated and input into the feedback controller, and the feedback controller performs feedback control on the input of the road network, the method further includes: Construct the augmented error system: Among them, x represents the actual traffic status of the road network, represents the estimation error of the traffic state observer, represents the augmented error, and Represent the augmented errors at time t and (t+1) respectively; make Substituting into formula (15) we get: Constructing Lyapunov functions The design principle of asymptotic stability of the augmented error system is converted into the constraint condition of energy decay of the Lyapunov function: in, and denote the Lyapunov function values ​​at time t and (t+1) respectively, and P is a symmetric positive definite matrix; Will Substituting the constraints into equation (16), we get the following inequality: Solving inequality (17), we obtain Thereby, the gain of the feedback controller is obtained.

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