Networked vehicle data driving switching gain state estimation method

Abstract

The invention relates to a networked vehicle data-driven switching gain state estimation method. The method comprises the following steps: collecting system data of subsystems in each networked vehicle to construct a nonlinear network system as a state space model; collecting process data, and establishing a data-driven switching gain state observer; defining error data, and constructing an error system; setting stable operation conditions of the nonlinear network system according to the constant quantity and the gain matrix; and the nonlinear network system operates under a stable operation condition, so that the state estimation of the networked vehicle data-driven switching gain is realized. A large amount of system data of subsystems in each networked vehicle is collected to extract, insight, predict and emphasize the data volume and the data quality, so that the networked vehicle has the characteristics of self-adaption and flexibility; due to the fact that the data-driven switching gain state observer is established, real-time monitoring and switching gain control can be carried out on a network system, the efficiency and the quality of network data transmission are improved, and the running stability of the networked vehicle is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of networked vehicle automation control, and in particular to a method for estimating a data-driven switching gain state of a networked vehicle. Background Art

[0002] Connected vehicles use modern communication technology, automatic control technology, vehicle-mounted self-sensing technology, and cloud computing platforms to achieve information exchange and sharing between vehicles, vehicles and roads, vehicles and people, and vehicles and cloud platforms. Through sensors, cameras, GPS, vehicle-mounted terminals and other equipment installed on the vehicle, the vehicle's own status information and external environment information are collected, and this information is transmitted to other vehicles, traffic management systems or cloud platforms through the network system. At the current stage, connected vehicles have been widely used in daily life and have made great contributions to the development of the network information society. The development of connected vehicles is very rapid because the demand for road transportation tools among users is increasing, and people's requirements for the convenience of vehicle driving are getting higher and higher. Traditional vehicles can no longer meet people's needs. Connected vehicles mainly exchange information such as vehicle location, speed, driving direction, roadside infrastructure, etc. directly through the network to improve driving safety and reduce the occurrence of traffic accidents. It is worth noting that in the process of network information transmission, the control of system stability must be strengthened, which requires the design of a complete data communication system control method to ensure the effectiveness of data transmission between connected vehicles.

[0003] With the development of intelligent connected vehicle technology, the transmission of data through the network not only faces the challenge of privacy leakage of personal information, but also faces new safety issues such as the increasing number of intelligent connected vehicles losing control during driving in recent years. In addition, due to the incompleteness and cost factors of environmental perception and road planning decisions, various problems that affect driving safety cannot be effectively handled.

[0004] Therefore, traditional connected vehicles often experience unstable network transmission when faced with emergencies on the road, thus affecting driving safety. Summary of the invention

[0005] Based on this, in order to solve the above technical problems, a method for estimating the state of switching gain driven by a networked vehicle is provided, which can improve the transmission speed and quality of network data when the network transmission is unstable.

[0006] A method for estimating a data-driven switching gain state of a connected vehicle, the method comprising:

[0007] respectively collecting system data of the subsystems in each connected vehicle, and constructing a nonlinear network system as a state space model according to the system data;

[0008] Collecting process data generated during the operation of each of the subsystems in the state space model;

[0009] Determine an observability constant, and establish a data-driven switching gain state observer based on the observability constant, the state space model, and the process data;

[0010] defining error data, and constructing an error system for the data-driven switching gain state observer based on the error data;

[0011] Determine the constant quantity of convergence of the nonlinear network system, determine the gain matrix under data drive based on the data driven switching gain state observer, and set the stable operation condition of the nonlinear network system according to the constant quantity and the gain matrix;

[0012] The nonlinear network system operates under the stable operating conditions to achieve data-driven switching gain state estimation for the networked vehicle.

[0013] In one embodiment, system data of subsystems in each networked vehicle are collected respectively, and a nonlinear network system is constructed as a state space model according to the system data, including:

[0014] Determine the system state and input data parameters of each subsystem in each connected vehicle, and determine the forward component corresponding to the system state;

[0015] Get vector-valued continuous functions, unknown Euclidean matrix space;

[0016] Based on the vector-valued continuous function and the unknown Euclidean matrix space, the system state, input data parameters and forward components are used as system data to construct a nonlinear network system composed of the subsystems with unknown parameters as a state space model.

[0017] In one embodiment, determining an observability constant, and establishing a data-driven switching gain state observer based on the observability constant, the state space model, and the process data, includes:

[0018] Obtaining a gain matrix, and establishing a state observer under a conventional model according to the gain matrix and the state space model;

[0019] determining an observability constant, and establishing a data-driven switching gain state observer based on the state observer using the process data;

[0020] Wherein, the data driven switching gain state observer satisfies the observability constant.

[0021] In one embodiment, error data is defined, and an error system is constructed for the data-driven switching gain state observer according to the error data, including:

[0022] defining error data, and determining an error representation of the nonlinear network system under the data driven switching gain state observer based on the error data;

[0023] The error representation of the linear network system is adjusted and equivalently represented as an error system, thereby completing the construction of the data-driven switching gain state observer error system.

[0024] In one embodiment, determining a constant number for convergence of the nonlinear network system, determining a gain matrix under data drive based on the data driven switching gain state observer, and setting a stable operation condition of the nonlinear network system according to the constant number and the gain matrix, comprises:

[0025] Under the condition that the data-driven switching gain state observer satisfies the observability constant, designing initial constants and initial matrices;

[0026] Determining the constant quantity of the convergence of the nonlinear network system according to the initial constant, and determining the gain matrix under data driving according to the initial matrix;

[0027] The stable operation condition of the nonlinear network system is set according to the constant quantity and the gain matrix.

[0028] In one embodiment, the method further comprises:

[0029] Performing stability analysis on the nonlinear network system using Lyapunov function to obtain analysis results;

[0030] The nonlinear network system is adjusted according to the analysis result.

[0031] In one embodiment, using Lyapunov function to perform stability analysis on the nonlinear network system includes:

[0032] The initial matrix is ​​calculated using Schur's complement lemma, and the calculated matrix is ​​substituted into the gain matrix to obtain a target matrix;

[0033] The switching time and the switching period are set, and the stability analysis of the nonlinear network system is performed using the Lyapunov function based on the switching time and the switching period.

[0034] In one embodiment, the method further comprises:

[0035] determining a switching sequence, for which a stability analysis is performed using the Lyapunov function based on the initial matrix;

[0036] During the stability analysis, the Lyapunov function decreases at a minimum exponential convergence rate, indicating that the nonlinear network system is stable.

[0037] A data-driven switching gain state estimation system for a connected vehicle, the system comprising:

[0038] A model building module, used to respectively collect system data of subsystems in each connected vehicle, and build a nonlinear network system as a state space model according to the system data;

[0039] A process data collection module, used to collect process data generated during the operation of each of the subsystems in the state space model;

[0040] An observer establishment module, used for determining an observability constant, and establishing a data-driven switching gain state observer based on the observability constant, the state space model, and the process data;

[0041] an error construction module, used for defining error data and constructing an error system for the data driven switching gain state observer according to the error data;

[0042] An operating condition setting module, used to determine the constant quantity for convergence of the nonlinear network system, determine the gain matrix under data drive based on the data driven switching gain state observer, and set the stable operating condition of the nonlinear network system according to the constant quantity and the gain matrix;

[0043] The gain state estimation module is used for the nonlinear network system to operate under the stable operating conditions and realize the gain state estimation of the networked vehicle data-driven switching.

[0044] The above-mentioned data-driven switching gain state estimation method for connected vehicles extracts, understands and predicts by collecting a large amount of system data from subsystems in each connected vehicle, emphasizing data quantity and data quality, so that the connected vehicles have the characteristics of adaptability and flexibility; due to the establishment of a data-driven switching gain state observer, the network system can be monitored in real time for switching gain control, so as to improve the efficiency and quality of network data transmission and ensure the stability of connected vehicle operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A diagram of an application environment of a method for estimating a switching gain state driven by data of a networked vehicle in one embodiment;

[0046] Figure 2A schematic diagram of a flow chart of a method for estimating a data-driven switching gain state of a networked vehicle in one embodiment;

[0047] Figure 3 A data driven switching gain switching control structure diagram of a nonlinear network system in one embodiment;

[0048] Figure 4 A structural block diagram of a data-driven switching gain state estimation system for a networked vehicle in one embodiment;

[0049] Figure 5 The figure is a diagram of the internal structure of the background control center in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] Data-driven control is a business decision-making and action method based on lean analysis and data closed-loop concepts. Its core lies in the use of large amounts of data to extract, gain insights and predict. It emphasizes data quantity and data quality, is adaptive and flexible, and uses machine learning algorithms for pattern recognition and prediction. Compared with traditional drive methods, data-driven methods do not rely on pre-defined models. When the switching system is disturbed by external environment or internal factors, it can switch to a prepared controller to improve the stability of the control system and obtain good dynamic performance.

[0052] The networked vehicle data-driven switching gain state estimation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Figure 1As shown, the application environment includes a background control center 110 and each networked vehicle 120, and the background control center 110 is connected and communicated with each networked vehicle 120 through a network. The background control center 110 can collect system data of subsystems in each networked vehicle respectively, and construct a nonlinear network system as a state space model according to the system data; the background control center 110 can collect process data generated during the operation of each subsystem in the state space model; the background control center 110 can determine the observability constant, and establish a data-driven switching gain state observer based on the observability constant, the state space model, and the process data; the background control center 110 can define error data, and construct an error system for the data-driven switching gain state observer according to the error data; the background control center 110 can determine the constant number of convergence of the nonlinear network system, determine the gain matrix under data drive based on the data-driven switching gain state observer, and set the stable operation conditions of the nonlinear network system according to the constant number and the gain matrix; the background control center 110 can operate the nonlinear network system under stable operation conditions to realize the data-driven switching gain state estimation of the networked vehicle. The background control center 110 may be, but is not limited to, various personal computers, notebook computers and other devices.

[0053] In one embodiment, Figure 2 As shown, a method for estimating a data-driven switching gain state of a connected vehicle is provided, comprising the following steps:

[0054] Step 202 , respectively collect system data of the subsystems in each networked vehicle, and construct a nonlinear network system as a state space model based on the system data.

[0055] The state space model of the nonlinear network system of the connected vehicle can be established at the background control center. Specifically, in one embodiment, a method for estimating the state of the data-driven switching gain of a connected vehicle can also include a process of constructing a state space model, and the specific process includes: determining the system state and input data parameters of each subsystem in each connected vehicle, and determining the forward component corresponding to the system state; obtaining a vector value continuous function and an unknown Euclidean matrix space; based on the vector value continuous function and the unknown Euclidean matrix space, taking the system state, input data parameters, and forward components as system data, constructing a nonlinear network system composed of various subsystems with unknown parameters as a state space model.

[0056] The backend control center first establishes a nonlinear network system consisting of M interrelated subsystems in the connected vehicle, in the following form:

[0057]

[0058] in, Represents x z The forward component of is the state of the zth subsystem of the connected vehicle at time k, n z is the state dimension of the z-th subsystem; is the input amount of data packets in the zth subsystem of the connected vehicle at time k, m z The amount of data input for the zth subsystem; and is the number of data measured by sensors of the zth subsystem of the connected vehicle, s z The dimension of the data obtained for the measurement of the zth subsystem. and is a vector-valued continuous function; and Respectively n z ×m z dimensional unknown Euclidean matrix space, for formula (1), if the zth subsystem can receive information from the oth subsystem, then otherwise

[0059] Then, the backend control center can design a nonlinear network system consisting of M interconnected subsystems with unknown exact parameters, and there exists a continuous function Make and For all and Established, and assuming ||d z (x z )||≤ε||x z ||, then:

[0060]

[0061] Among them, ε is a constant that satisfies the nonlinear network model, and B z , is unknown.

[0062] Step 204: collect process data generated during the operation of each subsystem in the state space model.

[0063] Among them, the collected process data can be expressed as:

[0064]

[0065] And assume Available

[0066]

[0067] Step 206, determining an observability constant, and establishing a data-driven switching gain state observer based on the observability constant, the state space model, and the process data.

[0068] Specifically, when establishing a data-driven switching gain state observer, two processes may be included: establishing a state observer under a conventional model, and establishing a data-driven state observer through collected process data.

[0069] In one embodiment, a method for estimating a data-driven switching gain state of a connected vehicle provided may also include a process of establishing a data-driven switching gain state observer, the specific process including: obtaining a gain matrix, and establishing a state observer under a conventional model based on the gain matrix and a state space model; determining an observability constant, and establishing a data-driven switching gain state observer based on the state observer through process data; wherein the data-driven switching gain state observer satisfies the observability constant.

[0070] Among them, the state observer under the conventional model can be expressed as:

[0071]

[0072] Among them, σ∈{1,2}, yes The estimated value of is the gain matrix.

[0073] Next, the background control center uses the collected process data to establish a data-driven state observer, which can be specifically expressed as:

[0074]

[0075] in, and It is of full row rank. It satisfies the following formula:

[0076]

[0077] Next, we need to ensure the observability of formula (4):

[0078]

[0079] in, As a constant that satisfies observability of the connected vehicle system.

[0080] Step 208, defining error data, and constructing an error system for a data-driven switching gain state observer according to the error data.

[0081] In one embodiment, a method for estimating a data-driven switching gain state of a connected vehicle may also include a process of constructing an error system, the specific process including: defining error data, and determining the error representation of a nonlinear network system under a data-driven switching gain state observer based on the error data; adjusting the error representation of the linear network system, equivalently expressing it as an error system, and completing the construction of the error system of the data-driven switching gain state observer.

[0082] Defining Error Then, under the data-driven switching gain state observer, the error system of the connected vehicle system can be expressed as:

[0083]

[0084] in,

[0085] Specific, definition and The error system formula (5) can be expressed equivalently as:

[0086]

[0087] in,

[0088] Step 210, determine the constant number of convergence of the nonlinear network system, determine the gain matrix under data drive based on the data driven switching gain state observer, and set the stable operation condition of the nonlinear network system according to the constant number and the gain matrix.

[0089] In one embodiment, a method for estimating a data-driven switching gain state of a connected vehicle is provided, which may also include a process for setting stable operating conditions for a nonlinear network system. The specific process includes: designing initial constants and initial matrices when a data-driven switching gain state observer satisfies observability constants; determining the number of constants for convergence of the nonlinear network system based on the initial constants, and determining the gain matrix under data drive based on the initial matrix; and completing the setting of stable operating conditions for the nonlinear network system based on the number of constants and the gain matrix.

[0090] In this embodiment, the conditions for the smooth operation of the connected vehicle system are designed as follows:

[0091] When the data-driven switching gain state observer is observable, the design constant ε>0, τ>0,0<μ σ <1 and matrix σ∈{1,2} such that

[0092]

[0093] P1≥(1-μ2) τ P2, P2 ≥ (1-μ1) τ P1, (8)

[0094] in, μ σ is a constant that ensures the convergence of the connected vehicle system.

[0095]

[0096] The gain matrix under data driving is:

[0097]

[0098] Step 212 , the nonlinear network system operates under stable operating conditions to achieve networked vehicle data-driven switching gain state estimation.

[0099] In one embodiment, a provided method for estimating a data-driven switching gain state of a connected vehicle may also include a process of performing a stability analysis on the network, and the specific process includes: using a Lyapunov function to perform a stability analysis on a nonlinear network system to obtain an analysis result; and adjusting the nonlinear network system according to the analysis result.

[0100] Specifically, in this embodiment, when performing stability analysis, the initial matrix can be calculated using the Schur complement lemma, and the calculated matrix can be substituted into the gain matrix to obtain the target matrix; the switching time and switching period are set, and the Lyapunov function is used based on the switching time and switching period to perform stability analysis on the nonlinear network system.

[0101] When analyzing the stability of the nonlinear network system of connected vehicles, the Schur complement lemma can be used to calculate formula (7), and we can get

[0102] Substituting formula (9) into formula (10) we can obtain:

[0103]

[0104] in,

[0105] Next, we can design the switching time k n and switching period k n +τ, select the Lyapunov function So that: According to the above assumptions ‖d z (x z )‖≤ε‖x z ‖ can be given We can get:

[0106] in,

[0107] According to the designed system stable operation conditions:

[0108] ΔV σ (k n )≤-μ σ V σ (k n ).(13)

[0109] Therefore, it can be concluded that:

[0110] V1(k n +τ)≤(1-μ1)V1(k n +τ-1)≤(1-μ1) 2 V1(k n +τ-2)≤…≤(1-μ1) τ V1(k n ). (14)

[0111] Using the same method, we can get:

[0112] V2(k n +τ)≤(1-μ2) τ V2(k n ),(15)

[0113]

[0114] V2(k n +τ)≤V1(k n ). (17)

[0115] The above analysis shows that in the system, the convergence rate of the Lyapunov function decreases based on the minimum exponent μ2.

[0116] In another embodiment, a method for estimating a connected vehicle data-driven switching gain state may further include a stability analysis process, the specific process including: determining a switching sequence, and for the switching sequence, performing a stability analysis using a Lyapunov function based on an initial matrix; during the stability analysis process, the Lyapunov function decreases at a minimum exponential convergence rate, indicating that the nonlinear network system is stable.

[0117] Among them, for the switching sequence 0≤k n <k n +τ<k n +2τ<…<k n +mτ. Combining formula (8), formula (13), formula (14) and formula (17), we can get:

[0118] V2(k n +mτ)≤V1(k n +(m-1)τ)≤(1-μ1) τ V1(k n +(m-2)τ))≤V2(k n +(m-3)τ))≤(1-μ2) τ V2(k n +(m-4)τ))≤V1(k n +(m-5)τ))≤…≤V σ (k n ),σ={1,2}, (18)

[0119] It shows that whether switching occurs or not, the Lyapunov function decreases at the minimum exponential convergence rate. Therefore, the estimation error converges to 0, that is, the system is globally asymptotically stable.

[0120] In one embodiment, a data driven switching gain switching control structure diagram of a nonlinear network system is provided as follows: Figure 3 As shown: Under the action of the driver, each connected vehicle starts to work, and collects system data from each unknown subsystem in each connected vehicle through sensors; extracts the process data generated by each unknown subsystem, and then establishes a data-driven switching gain state observer, thereby realizing the data-driven switching gain state estimation of the connected vehicle through the network.

[0121] It should be understood that, although the various steps in the above-mentioned flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above-mentioned flow chart may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0122] In one embodiment, Figure 4 As shown, a networked vehicle data-driven switching gain state estimation system is provided, including: a model building module 410, a process data collection module 420, an observer establishment module 430, an error construction module 440, an operating condition setting module 450 and a gain state estimation module 460, wherein:

[0123] A model building module 410 is used to collect system data of subsystems in each connected vehicle, and build a nonlinear network system as a state space model according to the system data;

[0124] The process data collection module 420 is used to collect process data generated during the operation of each subsystem in the state space model;

[0125] An observer establishment module 430 is used to determine an observability constant and establish a data-driven switching gain state observer based on the observability constant, the state space model, and the process data;

[0126] An error construction module 440, for defining error data and constructing an error system for a data driven switching gain state observer according to the error data;

[0127] An operating condition setting module 450 is used to determine the constant quantity for convergence of the nonlinear network system, determine the gain matrix under data drive based on the data driven switching gain state observer, and set the stable operating condition of the nonlinear network system according to the constant quantity and the gain matrix;

[0128] The gain state estimation module 460 is used for the nonlinear network system to operate under stable operating conditions and realize the gain state estimation of the networked vehicle data-driven switching.

[0129] In one embodiment, the model building module 410 is also used to determine the system state and input data parameters of each subsystem in each connected vehicle, and determine the forward component corresponding to the system state; obtain a vector-valued continuous function and an unknown Euclidean matrix space; based on the vector-valued continuous function and the unknown Euclidean matrix space, the system state, input data parameters, and forward components are used as system data to construct a nonlinear network system composed of subsystems with unknown parameters as a state space model.

[0130] In one embodiment, the observer establishment module 430 is also used to obtain the gain matrix, establish a state observer under a conventional model according to the gain matrix and the state space model; determine the observability constant, and establish a data-driven switching gain state observer based on the state observer through process data; wherein the data-driven switching gain state observer satisfies the observability constant.

[0131] In one embodiment, the error construction module 440 is also used to define error data, and determine the error representation of the nonlinear network system under the data-driven switching gain state observer based on the error data; adjust the error representation of the linear network system, and represent it equivalently as an error system, thereby completing the construction of the data-driven switching gain state observer error system.

[0132] In one embodiment, the operating condition setting module 450 is also used to design initial constants and initial matrices when the data-driven switching gain state observer satisfies the observability constant; determine the number of constants for convergence of the nonlinear network system according to the initial constants, and determine the gain matrix under data-driven according to the initial matrix; and complete the setting of stable operating conditions of the nonlinear network system according to the number of constants and the gain matrix.

[0133] In one embodiment, a provided connected vehicle data-driven switching gain state estimation system also includes a stability analysis module for performing stability analysis on a nonlinear network system using a Lyapunov function to obtain analysis results; and adjusting the nonlinear network system according to the analysis results.

[0134] In one embodiment, the stability analysis module is also used to calculate the initial matrix using Schur's complement lemma, and substitute the calculated matrix into the gain matrix to obtain the target matrix; set the switching time and switching period, and use the Lyapunov function to perform stability analysis on the nonlinear network system based on the switching time and switching period.

[0135] In one embodiment, the stability analysis module is also used to determine the switching sequence. For the switching sequence, a stability analysis is performed using a Lyapunov function based on an initial matrix. During the stability analysis, the Lyapunov function decreases at a minimum exponential convergence rate, indicating that the nonlinear network system is stable.

[0136] In one embodiment, a background control center is provided. The background control center may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown. The background control center includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the background control center is used to provide computing and control capabilities. The memory of the background control center includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the background control center is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for estimating the switching gain state driven by a networked vehicle data is implemented. The display screen of the background control center can be a liquid crystal display screen or an electronic ink display screen, and the input device of the background control center can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the shell of the background control center, or an external keyboard, touchpad or mouse, etc.

[0137] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the background control center to which the solution of the present application is applied. The specific background control center may include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0138] In one embodiment, a background control center is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the method for estimating the data-driven switching gain state of a networked vehicle are implemented.

[0139] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for estimating the data-driven switching gain state of a connected vehicle are implemented.

[0140] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0141] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0142] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for estimating a data-driven switching gain state of a connected vehicle, characterized in that: The method comprises: respectively collecting system data of the subsystems in each connected vehicle, and constructing a nonlinear network system as a state space model according to the system data; Collecting process data generated during the operation of each of the subsystems in the state space model; Determine an observability constant, and establish a data-driven switching gain state observer based on the observability constant, the state space model, and the process data; defining error data, and constructing an error system for the data-driven switching gain state observer based on the error data; Determine the constant quantity of convergence of the nonlinear network system, determine the gain matrix under data drive based on the data driven switching gain state observer, and set the stable operation condition of the nonlinear network system according to the constant quantity and the gain matrix; The nonlinear network system operates under the stable operating conditions to achieve data-driven switching gain state estimation for the networked vehicle.

2. The method for estimating the state of a connected vehicle data-driven switching gain according to claim 1, characterized in that: The system data of the subsystems in each connected vehicle are collected respectively, and a nonlinear network system is constructed as a state space model according to the system data, including: Determine the system state and input data parameters of each subsystem in each connected vehicle, and determine the forward component corresponding to the system state; Get vector-valued continuous functions, unknown Euclidean matrix space; Based on the vector-valued continuous function and the unknown Euclidean matrix space, the system state, input data parameters and forward components are used as system data to construct a nonlinear network system composed of the subsystems with unknown parameters as a state space model.

3. The method for estimating the state of a connected vehicle data-driven switching gain according to claim 1, characterized in that: Determine an observability constant, and establish a data-driven switching gain state observer based on the observability constant, the state space model, and the process data, including: Obtaining a gain matrix, and establishing a state observer under a conventional model according to the gain matrix and the state space model; Determining an observability constant, and establishing a data-driven switching gain state observer based on the state observer using the process data; Wherein, the data driven switching gain state observer satisfies the observability constant.

4. The method for estimating the state of a connected vehicle data-driven switching gain according to claim 1, characterized in that: Defining error data, and constructing an error system for the data-driven switching gain state observer according to the error data, including: defining error data, and determining an error representation of the nonlinear network system under the data driven switching gain state observer based on the error data; The error representation of the linear network system is adjusted and equivalently represented as an error system, thereby completing the construction of the data-driven switching gain state observer error system.

5. The method for estimating the state of a connected vehicle data-driven switching gain according to claim 1, characterized in that: Determining the constant quantity of convergence of the nonlinear network system, determining the gain matrix under data drive based on the data driven switching gain state observer, and setting the stable operation condition of the nonlinear network system according to the constant quantity and the gain matrix, including: Under the condition that the data-driven switching gain state observer satisfies the observability constant, designing initial constants and initial matrices; Determining the constant quantity of the convergence of the nonlinear network system according to the initial constant, and determining the gain matrix under data driving according to the initial matrix; The stable operation condition of the nonlinear network system is set according to the constant quantity and the gain matrix.

6. The method for estimating the state of a connected vehicle data-driven switching gain according to claim 5, characterized in that: The method further comprises: Perform stability analysis on the nonlinear network system using Lyapunov function to obtain analysis results; The nonlinear network system is adjusted according to the analysis result.

7. The method for estimating the state of a connected vehicle data-driven switching gain according to claim 6, characterized in that: The stability analysis of the nonlinear network system is performed using the Lyapunov function, including: The initial matrix is ​​calculated using Schur's complement lemma, and the calculated matrix is ​​substituted into the gain matrix to obtain a target matrix; The switching time and the switching period are set, and the stability analysis of the nonlinear network system is performed using the Lyapunov function based on the switching time and the switching period.

8. The method for estimating the state of a data-driven switching gain of a networked vehicle according to claim 7, characterized in that: The method further comprises: determining a switching sequence, for which a stability analysis is performed using the Lyapunov function based on the initial matrix; During the stability analysis, the Lyapunov function decreases at a minimum exponential convergence rate, indicating that the nonlinear network system is stable.

9. A data-driven switching gain state estimation system for a connected vehicle, characterized in that: The system comprises: A model building module, used to respectively collect system data of subsystems in each connected vehicle, and build a nonlinear network system as a state space model according to the system data; A process data collection module, used to collect process data generated during the operation of each of the subsystems in the state space model; An observer establishment module, used for determining an observability constant, and establishing a data-driven switching gain state observer based on the observability constant, the state space model, and the process data; an error construction module, used for defining error data and constructing an error system for the data driven switching gain state observer according to the error data; An operating condition setting module, used to determine the constant quantity for convergence of the nonlinear network system, determine the gain matrix under data drive based on the data driven switching gain state observer, and set the stable operating condition of the nonlinear network system according to the constant quantity and the gain matrix; The gain state estimation module is used for the nonlinear network system to operate under the stable operating conditions and realize the gain state estimation of the networked vehicle data-driven switching.

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