CN117195698A - Method and device for detecting faults of steering system of vehicle and computer equipment - Google Patents
Method and device for detecting faults of steering system of vehicle and computer equipment Download PDFInfo
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Abstract
The application relates to a vehicle steering system fault detection method, a vehicle steering system fault detection device and computer equipment. The method comprises the following steps: measuring the system state according to the current system information data and the current system input to obtain a current measurement output result and a next system state; determining a current estimated output result and a next observer state according to the current observer information data and the current measurement output result; performing estimation error judgment according to the next system state, the next observer state and the current system information data to obtain an estimation error judgment result; and when the estimation error judgment result is within the estimation error threshold, performing fault evaluation according to the current measurement output result and the current estimation output result to obtain a fault evaluation result. By adopting the method, the estimation error can be controlled within the estimation error threshold value so as to reduce the influence on the state estimation, and the estimation error of the state observer constructed based on the neural network is compensated, so that the accurate estimation on the state of the vehicle is improved.
Description
Technical Field
The present application relates to the field of vehicle safety technologies, and in particular, to a method, an apparatus, and a computer device for detecting a failure of a steering system of a vehicle.
Background
The steering system is an important actuator of the vehicle, and the accurate response of the system is of great importance to the safety of the vehicle and the driver. In order to facilitate the implementation of various driving assistance techniques, the steering system of the vehicle almost all completes the drive-by-wire modification. However, compared to conventional mechanical steering systems, steer-by-wire systems are more prone to various faults, which can create potential safety hazards to vehicles and passengers. The fault detection technology is to design a proper fault detection algorithm to detect whether the system has faults or not.
The commonly used fault detection algorithm can be divided into a data-based method and a model-based method, wherein the data-based method is to collect related data of a system and then judge whether the system has faults by using a data analysis method; the model-based method is that firstly, a system model is established, then a state observer is designed based on the model, and finally, whether the system fails or not is judged through errors between the observed state and the measured state. Compared with a fault detection method based on data, the method based on the model is free from complicated data acquisition and is easier to apply to other objects.
However, the conventional model-based fault detection algorithm does not consider errors of a model and an actual system, so that an estimation result is inaccurate, false alarm and missing report of faults possibly occur, and more problems are brought to actual use.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a vehicle steering system fault detection method, apparatus, computer device, computer readable storage medium, and computer program product that can reduce the influence of model estimation errors on a state observer.
In a first aspect, the present application provides a method for detecting a failure of a steering system of a vehicle. The method comprises the following steps:
measuring the system state according to the current system information data and the current system input to obtain a current measurement output result and a next system state;
determining a current estimated output result and a next observer state according to the current observer information data and the current measurement output result;
performing estimation error judgment according to the next system state, the next observer state and the current system information data to obtain an estimation error judgment result;
and when the estimation error judgment result is within the estimation error threshold, performing fault evaluation according to the current measurement output result and the current estimation output result to obtain a fault evaluation result.
In one embodiment, measuring a system state according to current system information data and current system input to obtain a current measurement output result and a next system state, including:
determining current steering information data according to vehicle information data in the current information system data and current system input;
and performing state calculation according to the current information system data and the current steering information data to obtain the next system state.
In one embodiment, the system state measurement is performed according to the current system information data and the current system input to obtain a current measurement output result and a next system state, and the method further includes:
determining current system data according to the vehicle information data and the current vehicle running data in the current information system data;
and determining a measurement output result according to the current system data and the current system state.
In one embodiment, determining the current estimated output and the next observer state from the current observer information data and the current measured output comprises:
determining a current model estimation error according to the preset neural network model and vehicle running data in current system information data;
and calculating the state of the observer according to the current model estimation error, the current observer information data and the current measurement output result to obtain the next observer state.
In one embodiment, determining the current estimated output and the next observer state based on the current observer state and the current measured output further comprises:
and determining a current estimated output result according to the current observer state and the current system data.
In one embodiment, performing estimation error judgment according to the next system state, the next observer state and the current system information data to obtain a current estimation error judgment result includes:
determining a next observation error according to the next system state, the next observer state and the observer information data;
determining a system state difference value of a preset neural network model according to the next observation error and current observer gain data in current observer information data;
determining a current estimation error according to the current observation difference error, the current system state and current observer gain data in the current observer information data;
and carrying out estimation error judgment according to the current estimation error and the system state difference value to obtain a current estimation error judgment result.
In one embodiment, performing fault evaluation according to the current measurement output result and the current estimation output result to obtain a fault evaluation result, including:
determining an evaluation index according to the estimation error;
determining an output difference value according to the current measurement output result and the current estimation output result;
and comparing and judging the output difference value with the evaluation index to obtain a fault evaluation result.
In a second aspect, the application further provides a device for detecting the fault of the steering system of the vehicle. The device comprises:
the system state measuring module is used for measuring the system state according to the current system information data and the current system input to obtain a current measurement output result and a next system state;
the system state estimation module is used for determining a current estimation output result and a next observer state according to the current observer information data and the current measurement output result;
the estimation error judging module is used for carrying out estimation error judgment according to the next system state, the next observer state and the current system information data to obtain an estimation error judging result;
and the system fault evaluation module is used for performing fault evaluation according to the current measurement output result and the current estimation output result when the estimation error judgment result is within the estimation error threshold value, so as to obtain a fault evaluation result.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the following steps when executing the computer program:
measuring the system state according to the current system information data and the current system input to obtain a current measurement output result and a next system state;
determining a current estimated output result and a next observer state according to the current observer information data and the current measurement output result;
performing estimation error judgment according to the next system state, the next observer state and the current system information data to obtain an estimation error judgment result;
and when the estimation error judgment result is within the estimation error threshold, performing fault evaluation according to the current measurement output result and the current estimation output result to obtain a fault evaluation result.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
measuring the system state according to the current system information data and the current system input to obtain a current measurement output result and a next system state;
determining a current estimated output result and a next observer state according to the current observer information data and the current measurement output result;
performing estimation error judgment according to the next system state, the next observer state and the current system information data to obtain an estimation error judgment result;
and when the estimation error judgment result is within the estimation error threshold, performing fault evaluation according to the current measurement output result and the current estimation output result to obtain a fault evaluation result.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, performs the steps of:
measuring the system state according to the current system information data and the current system input to obtain a current measurement output result and a next system state;
determining a current estimated output result and a next observer state according to the current observer information data and the current measurement output result;
performing estimation error judgment according to the next system state, the next observer state and the current system information data to obtain an estimation error judgment result;
and when the estimation error judgment result is within the estimation error threshold, performing fault evaluation according to the current measurement output result and the current estimation output result to obtain a fault evaluation result.
According to the vehicle steering system fault detection method, device, computer equipment, storage medium and computer program product, at the current sampling moment, a model is built according to vehicle transverse dynamics, the current system information data such as the current system state, the current system input and vehicle information and the like at the current moment of the vehicle are utilized to calculate the current measurement output result and the next system state at the next sampling moment, the state observer and the current observer information data such as the current observer state and the like designed based on the built model are utilized to estimate the current estimation output result and the next observer state in the current state, the estimation error judgment is carried out by utilizing the calculated next system state and the next observer state, the influence on state estimation is reduced by controlling the estimation error within the estimation error threshold value, and the estimation error of the state observer constructed based on a neural network is compensated, so that the accurate estimation of the vehicle state is improved.
Drawings
FIG. 1 is a flow chart of a method of detecting a failure of a steering system of a vehicle in one embodiment;
FIG. 2 is a flow diagram of computing system output in one embodiment;
FIG. 3 is a flow diagram of estimating system output in one embodiment;
FIG. 4 is a schematic diagram of a simplified model of a vehicle in one embodiment;
FIG. 5 is a block diagram of a vehicle steering system fault detection device in one embodiment;
fig. 6 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, as shown in fig. 1, a method for detecting a fault of a steering system of a vehicle is provided, and the embodiment is applied to a terminal for illustration by using the method, it is understood that the method can also be applied to a server, and can also be applied to a system including the terminal and the server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the steps of:
and 102, measuring the system state according to the current system information data and the current system input to obtain a current measurement output result and the next system state.
Wherein the current system information data includes vehicle information data, current vehicle travel data, and current system data.
Specifically, a transverse kilometer model of the vehicle is constructed based on a kinetic analysis of the vehicle. Since the two front wheels of the vehicle advance in the same direction and the two rear wheels of the vehicle advance in the same direction, the two front wheels and the two rear wheels are combined into one tire, and thus a bicycle model for representing the transverse dynamics of the vehicle can be constructed, as shown in fig. 4. At the current sampling time, vehicle running data of the current vehicle is collected, and vehicle system data can be calculated and obtained based on the vehicle running data and the vehicle information data.
The current system state of the vehicle, the vehicle information data, and the current system input calculation are acquired to obtain a next system state, and a current measurement output result is determined based on the current system state.
Step 104, determining the current estimated output result and the next observer state according to the current observer information data and the current measured output result.
Wherein the current observer information data includes current observer state and current observer gain data.
Specifically, the current observer gain data at the current sampling time is determined first, a current estimated output result is obtained by calculation using the current observer gain data, the current observer state and the current measured output result, and a next observer state is obtained by calculation using the current observer gain data, the current observer state, the current measured output result, the current model estimation error and the current system data.
And step 106, performing estimation error judgment according to the next system state, the next observer state and the current system information data to obtain an estimation error judgment result.
The estimation error judgment result comprises that the estimation error judgment result is within an estimation error threshold value and the estimation error judgment result exceeds the estimation error threshold value.
Specifically, the next system state and the next observer state are used for calculating to obtain the observation error and the estimation error of the system, the current model estimation error and the current model actual error are obtained through the observation error calculation, and the difference value of the current model estimation error and the current model actual error is compared with the current estimation error to obtain the estimation error judgment result.
And step 108, when the estimation error judgment result is within the estimation error threshold, performing fault evaluation according to the current measurement output result and the current estimation output result to obtain a fault evaluation result.
Wherein the fault evaluation result comprises the existence of faults and no faults.
Specifically, an evaluation index threshold value of the current moment is obtained through calculation according to the estimation error of the current sampling moment, a difference value is calculated by using the current measurement output result and the current estimation output result, and a fault evaluation result is obtained through comparison of the difference value and the evaluation index threshold value.
In the vehicle steering system fault detection method, when the vehicle is in the current sampling moment, a model is constructed according to the transverse dynamics of the vehicle, the current system information data of the current moment of the vehicle such as the current system state, the current system input and the vehicle information are utilized to calculate the current measurement output result and the next system state of the next sampling moment, the state observer designed based on the constructed model and the current observer information data such as the current observer state are utilized to estimate the current estimation output result and the next observer state in the current state, the calculated next system state and the next observer state are utilized to carry out estimation error judgment, and the estimation error is controlled within an estimation error threshold value so as to reduce the influence on state estimation, and the estimation error of the state observer constructed based on the neural network is compensated, so that the accurate estimation of the vehicle state is improved.
In one embodiment, as shown in fig. 2, measuring a system state according to current system information data and current system input, obtaining a current measurement output result and a next system state includes:
step 202, determining current steering information data according to the vehicle information data in the current information system data and the current system input.
Specifically, the current steering information data is calculated according to the vehicle information data in the current information system data of the vehicle and the current system input. The vehicle information data and the steering information data are specifically m is the total weight (kg) of the vehicle, I z Is the moment of inertia (kg.m) 2 )，l f And l r Distances (m) from the vehicle front and rear axles to the vehicle center of mass, C f And C r Rigidity (N/rad), v of front and rear tires of vehicle, respectively x 、v y And γ are the longitudinal speed (m/s), the lateral speed (m/s), and the yaw rate (rad/s) of the vehicle, respectively.
And 204, performing state calculation according to the current information system data and the current steering information data to obtain the next system state.
Specifically, at the current sampling time, vehicle travel data of the current vehicle is acquired, and vehicle system data can be calculated based on the vehicle travel data and the vehicle information data. The method comprises the steps of obtaining the current system state of the vehicle, vehicle information data and current system input calculation to obtain the next system state, wherein the specific calculation mode is as follows:
wherein x (k+1) is the system state at time k+1, θ i In response to vertex v i The corresponding weight, x (k) is the system state at time k, u (k) is the system input at time k, d (k) is the estimation error at time k, f (k) is the system failure at time k,the current system data at the moment i.
And A, B, E is system data.
The specific calculation of the system data A, B, E is:
determining a travel speed range as [ v ] from vehicle travel data xmin ，v xmax ]Wherein v is xmax ]And v xmin An upper limit for longitudinal speed and a lower limit for speed, respectively. Selecting a time-varying parameter pair [ v ] in a system x ，1/v x ]The variation of its parameter pairs will be in a convex polyhedron comprising the following four vertices: v 1 ＝[v xmin ，1/v xmin ]、v 1 ＝[v xmin ，1/v xmax ]、v 1 ＝[v xmax ，1/v xmin ]And v 1 ＝[v xmax ，1/v xmax ]=, θ i In response to vertex v i The corresponding weight calculation method comprises the following steps:
determining current system data according to the vehicle information data and the current vehicle running data in the current information system data; and determining a measurement output result according to the current system data and the current system state.
The current system state x (k) is obtained based on the process of calculating the next system state, and then the current measurement output result can be calculated and obtained, specifically calculated as:
y(k)＝Cx(k)
wherein C is system data, specifically
In this embodiment, by collecting vehicle information data of the vehicle at the time k, the measurement output result, that is, the actual system output, is obtained by rapid calculation through a simplified vehicle model.
In one embodiment, as shown in FIG. 3, determining the current estimated output and the next observer state from the current observer information data and the current measured output comprises:
and step 302, determining a current model estimation error according to the vehicle running data in the preset neural network model and the current system information data.
Wherein the current system information data includes vehicle travel data and current system data, and the current observer information data includes current observer state and current observer gain data.
Specifically, the construction process of the preset neural network model is as follows:
collecting vehicle data to establish a neural network model, collecting sufficient Real vehicle longitudinal speed, transverse speed and yaw rate data through an RTK (Real-time kinetic) sensor and an IMU (Inertial Measurement Unit ) sensor on the vehicle, and then calculating model errors, wherein the specific calculation mode is as follows:
d(k)＝(x m (k)-x v (k))/T
wherein x is m For measuring data, x v Is the actual measurement data of the vehicle.
And calculating the vehicle running data in the preset neural network model and the current system information data to obtain the current model estimation error.
And step 304, calculating the state of the observer according to the current model estimation error, the current observer information data and the current measurement output result, and obtaining the next observer state.
Specifically, after the current observer state, the current model estimation error and the current observer gain data are obtained, the next observer state is obtained by calculation, and the calculation mode is as follows:
where z is the state of the observer,is the estimation error of the current model, J i 、L i 、G i 、M、E i 、J xi And L xi Is the current observer gain data.
And determining a current estimated output result according to the current observer state and the current system data.
Specifically, the current observer state, the current measurement output result, the current system data and the current observer gain data are utilized to estimate the current system estimation state, and the specific calculation mode is as follows:
estimating a current estimation output result based on a current system estimation state, wherein the specific mode is as follows:
in this embodiment, the influence of the model estimation error on the state observation error can be reduced by obtaining the model estimation error using the neural network model, the system state at the previous time, the model input at the current time, and the vehicle other data estimation.
In one embodiment, performing estimation error judgment according to the next system state, the next observer state and the current system information data to obtain a current estimation error judgment result, including:
determining a next observation error according to the next system state, the next observer state and the observer information data; determining a system state difference value of a preset neural network model according to the next observation error and current observer gain data in current observer information data; determining a current estimation error according to the current observation difference error, the current system state and current observer gain data in the current observer information data; and carrying out estimation error judgment according to the current estimation error and the system state difference value to obtain a current estimation error judgment result.
The estimation error judging result includes meeting the estimation error requirement and not meeting the estimation error requirement.
Specifically, the specific way to calculate the observed error ε and the estimated error e is:
wherein,is a system state difference value, and the system state difference value is calculated by +.>
After the observation error epsilon and the estimation error e are calculated, the following requirements are met by utilizing the H-infinity performance index:
where v is a bounded scalar.
In this embodiment, the influence of the model uncertainty on the state estimation is reduced by compensating the model uncertainty and the H-infinity index of the neural network estimation.
In one embodiment, performing fault assessment according to the current measurement output result and the current estimation output result to obtain a fault assessment result, including:
determining an evaluation index according to the estimation error; determining an output difference value according to the current measurement output result and the current estimation output result; and comparing and judging the output difference value with the evaluation index to obtain a fault evaluation result.
Specifically, the evaluation index is calculated according to the model estimation error, and the specific calculation mode is as follows:
wherein T is steering time, k is the kth sampling time, and j is the value of the sampling time.
The calculated evaluation index and the evaluation index threshold V th For comparison, when V (k) > V th The steering system of the vehicle fails and the vehicle gives a responsive warning, otherwise no failure exists.
The setting mode of the threshold value is to inject different faults into the system, and then the method calculates the evaluation index to obtain the minimum value of the evaluation index when the faults exist, namely the proper threshold value.
In the embodiment, the evaluation index threshold is set through engineering experience, so that the accuracy of judging the fault is improved, and false alarm and missing alarm of the fault are avoided.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a vehicle steering system fault detection device for realizing the vehicle steering system fault detection method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the fault detection device for a steering system of a vehicle provided below may be referred to the limitation of the fault detection method for a steering system of a vehicle hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 5, there is provided a vehicle steering system failure detection apparatus including: a system state measurement module 502, a system state estimation module 504, an estimation error determination module 506, and a system fault assessment module 508, wherein:
the system state measurement module 502 is configured to measure a system state according to current system information data and current system input, and obtain a current measurement output result and a next system state.
The system state estimation module 504 is configured to determine a current estimated output result and a next observer state according to the current observer information data and the current measurement output result.
The estimation error judging module 506 is configured to perform estimation error judgment according to the next system state, the next observer state, and the current system information data, and obtain an estimation error judgment result.
And the system fault evaluation module 508 is configured to perform fault evaluation according to the current measurement output result and the current estimation output result when the estimation error judgment result is within the estimation error threshold value, so as to obtain a fault evaluation result.
In one embodiment, the system status measurement module 502 is further configured to determine current steering information data based on the vehicle information data and the current system input in the current information system data; and performing state calculation according to the current information system data and the current steering information data to obtain the next system state.
In one embodiment, the system status measurement module 502 is further configured to determine current system data according to the vehicle information data and the current vehicle driving data in the current information system data; and determining a measurement output result according to the current system data and the current system state.
In one embodiment, the system state estimation module 504 is further configured to determine a current model estimation error according to the preset neural network model and the vehicle driving data in the current system information data; and calculating the state of the observer according to the current model estimation error, the current observer information data and the current measurement output result to obtain the next observer state.
In one embodiment, the system state estimation module 504 is further configured to determine a current estimated output result based on the current observer state and the current system data.
In one embodiment, the estimation error determination module 506 is further configured to determine a next observation error according to the next system state, the next observer state, and the observer information data; determining a system state difference value of a preset neural network model according to the next observation error and current observer gain data in current observer information data; determining a current estimation error according to the current observation difference error, the current system state and current observer gain data in the current observer information data; and carrying out estimation error judgment according to the current estimation error and the system state difference value to obtain a current estimation error judgment result.
In one embodiment, the system fault evaluation module 508 is further configured to determine an evaluation index according to the estimation error; determining an output difference value according to the current measurement output result and the current estimation output result; and comparing and judging the output difference value with the evaluation index to obtain a fault evaluation result.
The respective modules in the above-described vehicle steering system failure detection apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing measurement output results and estimation output result data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of fault detection for a vehicle steering system.
It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A method for detecting a failure of a steering system of a vehicle, the method comprising:
measuring the system state according to the current system information data and the current system input to obtain a current measurement output result and a next system state;
determining a current estimated output result and a next observer state according to the current observer information data and the current measurement output result;
performing estimation error judgment according to the next system state, the next observer state and the current system information data to obtain an estimation error judgment result;
and when the estimation error judgment result is within an estimation error threshold, performing fault evaluation according to the current measurement output result and the current estimation output result to obtain a fault evaluation result.
2. The method of claim 1, wherein the current system information data comprises vehicle information data and current system data; the step of measuring the system state according to the current system information data and the current system input to obtain a current measurement output result and a next system state comprises the following steps:
determining current steering information data according to vehicle information data in the current information system data and current system input;
and performing state calculation according to the current information system data and the current steering information data to obtain a next system state.
3. The method of claim 2, wherein the current system information data further comprises current vehicle travel data; the method for measuring the system state according to the current system information data and the current system input to obtain a current measurement output result and a next system state further comprises the following steps:
determining the current system data according to the vehicle information data and the current vehicle running data in the current information system data;
and determining the measurement output result according to the current system data and the current system state.
4. The method of claim 1, wherein the current system information data comprises vehicle travel data and current system data, the current observer information data comprising current observer state and current observer gain data; the determining a current estimated output result and a next observer state according to the current observer information data and the current measurement output result comprises:
determining a current model estimation error according to a preset neural network model and vehicle running data in the current system information data;
and calculating the state of the observer according to the current model estimation error, the current observer information data and the current measurement output result to obtain the state of the next observer.
5. The method of claim 4, wherein said determining a current estimated output and a next observer state from a current observer state and said current measured output, further comprises:
and determining the current estimated output result according to the current observer state and the current system data.
6. The method according to claim 1, wherein said performing estimation error determination based on the next system state, the next observer state, and the current system information data to obtain a current estimation error determination result comprises:
determining a next observation error according to the next system state, the next observer state and observer information data;
determining a system state difference value of a preset neural network model according to the next observation error and current observer gain data in the current observer information data;
determining a current estimation error according to the current observation difference error, the current system state and current observer gain data in the current observer information data;
and carrying out estimation error judgment according to the current estimation error and the system state difference value to obtain a current estimation error judgment result.
7. The method according to claim 1, wherein performing fault assessment according to the current measurement output result and the current estimation output result to obtain a fault assessment result comprises:
determining an evaluation index according to the estimation error;
determining an output difference value according to the current measurement output result and the current estimation output result;
and comparing and judging the output difference value with the evaluation index to obtain a fault evaluation result.
8. A vehicle steering system failure detection apparatus, characterized by comprising:
the system state measuring module is used for measuring the system state according to the current system information data and the current system input to obtain a current measurement output result and a next system state;
the system state estimation module is used for determining a current estimation output result and a next observer state according to the current observer information data and the current measurement output result;
the estimation error judging module is used for carrying out estimation error judgment according to the next system state, the next observer state and the current system information data to obtain an estimation error judging result;
and the system fault evaluation module is used for performing fault evaluation according to the current measurement output result and the current estimation output result when the estimation error judgment result is within an estimation error threshold value, and obtaining a fault evaluation result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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| CN202311054317.9A CN117195698A (en) | 2023-08-21 | 2023-08-21 | Method and device for detecting faults of steering system of vehicle and computer equipment |
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