A vehicle control method, system, electronic device, and storage medium

Abstract

The application discloses a vehicle control method and system, electronic equipment and a storage medium. The method comprises the following steps: obtaining reference state data of a target vehicle; obtaining intermediate state data obtained by processing the reference state data by a control module; performing delay estimation on the reference state data and the intermediate state data based on a delay estimation module to obtain a delay estimation value; determining predicted state data of the target vehicle based on current state data of a controlled object, the delay estimation value and the intermediate state data by using a vehicle state prediction module; processing the reference state data and the predicted state data by the control module to determine target state data of the controlled object; and controlling the controlled object of the target vehicle to operate based on the target state data of the controlled object. The scheme determines the delay estimation value to complete the prediction of the predicted state data, determines the target state data based on the predicted state data, solves the problem that the vehicle input end cannot be processed due to a large delay, and improves the accuracy and stability of vehicle control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, and in particular to a vehicle control method and system, an electronic device and a storage medium. BACKGROUND

[0002] With the rapid development of unmanned technology and Internet of Vehicles technology, the development of intelligent chassis not only brings more interactive information for the vehicle network, but also brings a large amount of external information from remote information processing, which may cause inevitable delay in signal transmission and information exchange process. These induced delays may worsen the control performance and make the system unstable, and in serious cases, may cause traffic accidents.

[0003] The Lyapunov-Krasovskii functional method is the most common method for handling systems with delay at the input end of vehicle control. This method needs to set the upper bound of delay in advance, and in the linear matrix inequality (LMI) framework, the delay-dependent condition for the existence of state feedback controller is calculated, which guarantees the asymptotic stability in the sense of Lyapunov. The stability condition obtained based on the Lyapunov-Krasovskii functional design controller usually has small conservatism, and this method often needs to set the upper limit of delay, which leads to the problem of being unable to handle large delay, as well as poor stability and poor flexibility. SUMMARY

[0004] The present application provides a vehicle control method, system, electronic device and storage medium to solve the problem of being unable to handle large delay at the input end of vehicle control, as well as poor stability and poor flexibility in the prior art.

[0005] According to an aspect of the present application, a vehicle control method is provided, comprising:

[0006] Obtaining reference state data of a target vehicle, wherein the reference state data includes steering wheel angle data, yaw rate data, side slip angle data and four-wheel drive torque data;

[0007] Obtaining intermediate state data obtained by processing the reference state data by a control module, and estimating the delay of the reference state data and the intermediate state data based on a delay estimation module to obtain a delay estimation value;

[0008] Obtaining current state data of a controlled object in the target vehicle, and determining predicted state data of the target vehicle based on the current state data of the controlled object, the delay estimation value and the intermediate state data by a vehicle state prediction module;

[0009] Processing the reference state data and the predicted state data by the control module to determine target state data of the controlled object;

[0010] controlling the controlled object of the target vehicle based on the target state data of the controlled object.

[0011] Optionally, the intermediate state data comprises front wheel steering angle state data; the time delay estimation module is used to estimate the reference state data and the intermediate state data to obtain a time delay estimation value, comprising: inputting the steering wheel steering angle state data, the yaw rate state data, the side slip angle state data, the four-wheel driving torque state data and the front wheel steering angle state data into the time delay estimation module to obtain the time delay estimation value.

[0012] Optionally, the reference state data of the target vehicle is obtained by: receiving a reference control instruction issued by a vehicle control module, and determining the reference state data based on the reference control instruction.

[0013] Optionally, before the controlled object of the target vehicle is controlled based on the target state data of the controlled object, the target state data is further processed by a preset fault observation module to obtain a fault observation value corresponding to the target state data; and a fault prediction result corresponding to the target state data is determined based on the fault observation value, wherein the fault prediction result comprises a fault existing state and a fault non-existing state.

[0014] Optionally, the fault prediction result corresponding to the target state data is determined based on the fault observation value, comprising: judging whether the fault observation value meets a preset fault observation threshold value; in the case that the fault observation value meets the preset fault observation threshold value, the fault prediction result corresponding to the target state data is determined as the fault existing state, and the target state data is updated as preset state data; and in the case that the fault observation value does not meet the preset fault observation threshold value, the fault prediction result corresponding to the target state data is determined as the fault non-existing state, and the target state data does not need to be updated.

[0015] Optionally, the control module comprises a proportional-integral-derivative (PID) controller; the reference state data and the prediction state data are processed by the control module to determine the target state data of the controlled object, comprising: determining deviation data based on the reference state data and the prediction state data; calculating a proportional value, an integral value and a derivative value of the deviation data based on a PID operator, and determining the target state data of the controlled object based on the proportional value, the integral value and the derivative value.

[0016] According to an aspect of the present application, a vehicle control system is provided, the control system comprising a control module, a vehicle state prediction module, a time delay estimation module and an information acquisition module, wherein,

[0017] The information collection module is configured to collect reference state data, intermediate state data corresponding to the control module, and current state data of a controlled object in the target vehicle, transmit the reference state data and the intermediate state data to the delay estimation module, and transmit the current state data of the controlled object in the target vehicle and the intermediate state data to the vehicle state prediction module.

[0018] The delay estimation module is configured to receive the reference state data and the intermediate state data, determine a delay estimation value based on the reference state data and the intermediate state data, and transmit the delay estimation value to the vehicle state prediction module.

[0019] The vehicle state prediction module is configured to receive the delay estimation value, the current state data of the controlled object in the target vehicle, and the intermediate state data, determine predicted state data of the target vehicle based on the delay estimation value, the current state data of the controlled object in the target vehicle, and the intermediate state data, and feed back the predicted state data of the target vehicle to the control module.

[0020] The control module is configured to receive the reference state data of the target vehicle and the predicted state data of the target vehicle, and determine target state data of the controlled object based on the reference state data of the target vehicle and the predicted state data.

[0021] Optionally, the control system further comprises a fault observation module. The fault observation module is configured to receive the target state data, determine a corresponding fault observation value based on the target state data, and determine a fault prediction result corresponding to the target state data based on the fault observation value, wherein the fault prediction result includes a fault state and a non-fault state.

[0022] According to another aspect of the present application, an electronic device is provided, the electronic device comprising:

[0023] at least one processor; and

[0024] a memory communicatively connected to the at least one processor; wherein

[0025] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the vehicle control method of any embodiment of the present application.

[0026] According to another aspect of the present application, a computer readable storage medium is provided, the computer readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle control method of any embodiment of the present application when executed by the processor.

[0027] According to another aspect of the present application, a computer program product is provided, the computer program product comprises a computer program, and the computer program implements the vehicle control method of any embodiment of the present application when executed by a processor.

[0028] The technical scheme of the embodiment of the present application, by acquiring reference state data of the target vehicle, wherein the reference state data includes steering wheel angle data, yaw rate data, side slip angle data and four-wheel drive torque data; acquiring intermediate state data obtained by processing the reference state data by the control module, based on the delay estimation module, the reference state data and the intermediate state data are estimated, and the delay estimation value is obtained; acquire the current state data of the controlled object in the target vehicle, based on the vehicle state prediction module, the current state data of the controlled object, the delay estimation value and the intermediate state data are determined to obtain the predicted state data of the target vehicle; the reference state data and the predicted state data are processed by the control module to determine the target state data of the controlled object; based on the target state data of the controlled object, the controlled object of the target vehicle is controlled to run. The scheme realizes accurate estimation of the delay estimation value, determines the predicted state data according to the delay estimation value, and is used to determine the target state data, which can solve the problem that the existing vehicle control method cannot process the large delay of the vehicle input end, and can quickly process the delay problem, to improve the stability and flexibility of vehicle control, and improve the accuracy of vehicle control.

[0029] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0031] Figure 1 is a flow chart of a vehicle control method provided by the first embodiment of the present application;

[0032] Figure 2 is a flow chart of a vehicle control method provided by the second embodiment of the present application;

[0033] Figure 3 is a structural schematic diagram of a vehicle control system provided by the third embodiment of the present application;

[0034] Figure 4 is a structural schematic diagram of an electronic device provided by the fourth embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present application.

[0036] It should be noted that the terms "first feature data", "second feature data" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] The acquisition, storage and / or processing of data in the technical scheme of the present application comply with the relevant provisions of national laws and regulations.

[0038] Embodiment one

[0039] Figure 1 is a flowchart of a vehicle control method provided by the first embodiment of the present application. The present embodiment can be applied to the case of controlling a vehicle. The method can be executed by a vehicle control device, which can be realized in the form of hardware and / or software. The vehicle control device can be configured in electronic devices such as electronic devices and controllers provided by the embodiments of the present application. As shown in the figure, the method comprises: Figure 1

[0040] S110, acquiring reference state data of a target vehicle, wherein the reference state data comprises steering wheel angle data, yaw rate data, side slip angle data and four-wheel drive torque data.

[0041] ​The target vehicle can be specifically understood as a vehicle to be controlled. The reference state data can be specifically understood as state data to be executed by the target vehicle, which is issued by the vehicle controller. It should be noted that there is a response delay problem in the process of the vehicle controller issuing control instructions to the control end of the vehicle execution component. The factors inducing the delay problem include but are not limited to signal transmission delay and execution component execution delay. Therefore, there is a certain delay problem when the reference state data is transmitted to the input end of the execution component. It can be understood that different execution components receive different reference state data, which is specifically determined according to the control instructions received by the execution component. In this embodiment, the execution component can be a front wheel system of the vehicle, and the reference state data includes steering wheel angle data, yaw rate data, side slip angle data and four-wheel drive torque data, which are used to control the front wheel steering control process of the vehicle.

[0042] Specifically, the reference state data issued by the remote control end in real time is received, or the reference state data issued by the vehicle controller is received, wherein the reference state data includes but is not limited to steering wheel angle data, yaw rate data, side slip angle data and four-wheel drive torque data.

[0043] Optionally, the reference state data of the target vehicle is obtained, including: receiving the reference control instruction issued by the vehicle control module, and determining the reference state data based on the reference control instruction.

[0044] It should be noted that the vehicle control model issues a control instruction, that is, a reference control instruction, and the reference control instruction includes a control variable and a corresponding variable value. The received reference control instruction is subjected to a preset matrix conversion to obtain reference state data that can be processed by the control module.

[0045] Specifically, the reference control instruction issued by the vehicle control module in real time is received, and the reference control instruction is subjected to a preset matrix conversion to obtain reference state data that can be processed by the state equation corresponding to the control model in the control module.

[0046] S120, obtaining intermediate state data processed by the control module from the reference state data, and estimating the delay of the reference state data and the intermediate state data based on the delay estimation module to obtain a delay estimation value.

[0047] The intermediate state data can be specifically understood as output data of the control module, which can be obtained in real time by calling a pre-developed program package. The output data is used as the intermediate state data, and the output result of each reference state data is the intermediate state data of the reference state data. The delay estimation module includes a delay estimator, and the dynamics of the delay estimator is modeled. The structure of the delay estimator is as follows, For the delay size that the delay estimator can identify, τ is the delay actually existing in the vehicle system, u(t-τ) is the input of the system affected by the delay, and ρ is the adjustment parameter of the delay estimator. The size of the delay appearing in the system can be monitored in real time by the following model of the delay estimator:

[0048]

[0049] Specifically, the reference state data is input data of the control model, and each reference state data has a corresponding output result after being processed by the control module. The output result of the control module is collected in real time through a pre-developed program package to obtain intermediate state data corresponding to the reference state data. The reference state data and the corresponding intermediate state data are input into the delay estimation module, and the delay estimation module estimates the reference state data and the corresponding intermediate state data to obtain a corresponding delay estimation value.

[0050] In this embodiment, the input data and the output data of the control module are collected in real time as input data of the delay estimation module, and the delay estimation value is determined in real time through the delay estimation module, so that no matter how large the delay in the actual control process is, the corresponding delay estimation value can be determined through the delay estimation module, the delay problem can be quickly processed, and the problem of being unable to process large delays can be solved, thereby improving the stability and flexibility of vehicle control.

[0051] Optionally, the intermediate state data includes front wheel steering angle state data; the delay estimation module estimates the reference state data and the intermediate state data to obtain the delay estimation value, including: inputting the steering wheel angle state data, the yaw rate state data, the side slip angle state data, the four-wheel driving torque state data, and the front wheel steering angle state data into the delay estimation module to obtain the delay estimation value.

[0052] In this embodiment, the intermediate state data can be understood as representing the output result of the control module, and is data used for controlling the lateral control of the vehicle. Therefore, the intermediate state data can be set to include the front wheel steering angle data.

[0053] Specifically, the steering wheel angle state data, the yaw rate state data, the side slip angle state data, the four-wheel driving torque state data, and the front wheel steering angle state data collected in real time are input into the delay estimation module, and the corresponding delay estimation value is obtained after being processed by the delay estimation module.

[0054] S130, acquiring current state data of a controlled object in a target vehicle, and determining predicted state data of the target vehicle based on the vehicle state prediction module, the current state data of the controlled object, the delay estimation value, and the intermediate state data.

[0055] The controlled object can specifically be understood as each execution component on the vehicle, including but not limited to the front wheels and the steering wheel. The current state data of the controlled object can be the front wheel steering angle data or the steering wheel steering angle data.

[0056] Specifically, the current state data of the controlled object is collected in real time through a pre-developed program package, that is, the front wheel steering angle data of the controlled object is collected in real time. The obtained current state data of the controlled object, the delay estimation value and the intermediate state data are taken as input data of the vehicle state prediction module, and the corresponding prediction state data of the target vehicle is obtained after the vehicle state prediction module is processed.

[0057] The delay existing in the vehicle system is processed, a delay compensation strategy is designed, and the delay is compensated through the predictor theory. The vehicle system dynamics model affected by the delay is obtained through the following dynamics model of the predictor, wherein The prediction state data affected by the delay, that is, the prediction state data compensated by the delay, The general prediction state affected by the delay, x(t) is the current state data of the controlled object, e(t) is the error data of the vehicle state, and r(t) is the reference state data of the vehicle, The delay estimation value that can be identified by the delay estimator has been determined through the delay estimation module.

[0058]

[0059] Since the predictor theory here can compensate the delay effect of the control input, the prediction state data of the target vehicle compensated by the delay is obtained.

[0060] S140, the reference state data and the prediction state data are processed through the control module to determine the target state data of the controlled object.

[0061] The control module includes a control model, and the corresponding control model can be set in the control module according to actual needs. The control model includes but is not limited to a proportional integral derivative control model and a robust predictive control model.

[0062] Specifically, the reference state data and the prediction state data are taken as input parameters of the control module, and the target state data of the controlled object is obtained after the reference state data and the prediction state data are processed by the control module. It can be understood that the target state data output here can be used as the intermediate state data required for the next delay estimation processing.

[0063] Optionally, the control module comprises a proportional-integral-derivative controller; the target state data of the controlled object is determined by processing the reference state data and the predicted state data by the control module, comprising: determining deviation data based on the reference state data and the predicted state data; calculating a proportional value, an integral value and a derivative value of the deviation data based on a PID operator, and determining the target state data of the controlled object based on the proportional value, the integral value and the derivative value.

[0064] Specifically, the reference state data and the predicted state data are subjected to difference operation to obtain corresponding deviation data, the deviation data is processed by the PID operator to obtain the proportional value, the integral value and the derivative value of the deviation data, and the target state data of the controlled object is obtained by summing the proportional value, the integral value and the derivative value.

[0065] In the embodiment, the delay in the system is processed by a predictor designed based on a delay estimator and a proportional-integral-derivative controller combined predictor, and the strategy of the PID controller can process the delay at discrete time; the target state data can be quickly and accurately obtained, various sizes of delay can be processed, the problem of being unable to process large delay is solved, and the accuracy and stability of vehicle control are improved.

[0066] S150, controlling the controlled object of the target vehicle to operate based on the target state data of the controlled object.

[0067] The controlled object can be specifically understood as an executable component on the target vehicle, and in the embodiment, the controlled object can be a front wheel system of the target vehicle.

[0068] Specifically, the target state data of the controlled object is used to control the controlled object of the target vehicle to operate according to the target state data, so as to complete the control process of the target vehicle. The corresponding control instruction can be generated according to the target state data, and the control instruction is sent to the controlled object of the target vehicle, so that the controlled object of the target vehicle operates according to the control instruction.

[0069] The technical scheme of the embodiment of the application, by acquiring reference state data of the target vehicle, wherein the reference state data includes steering wheel angle data, yaw rate data, side slip angle data and four-wheel drive torque data; acquiring intermediate state data obtained by processing the reference state data by a control module, performing delay estimation on the reference state data and the intermediate state data based on a delay estimation module to obtain a delay estimation value; acquiring current state data of a controlled object in the target vehicle, determining predicted state data of the target vehicle based on a vehicle state prediction module on the current state data of the controlled object, the delay estimation value and the intermediate state data; processing the reference state data and the predicted state data by the control module to determine target state data of the controlled object; and controlling the controlled object of the target vehicle based on the target state data of the controlled object. The scheme can realize real-time observation of delay, accurately estimate the delay estimation value, determine the predicted state data according to the delay estimation value, and determine the target state data, without needing to pay attention to the size of the delay, but only needing to determine the target state data according to the reference state data and the current state data of the vehicle and the modules constructed in combination, to solve the problem that the existing vehicle control method cannot handle large delay existing in the input end of the vehicle, quickly handle the delay problem, and improve the stability and flexibility of vehicle control and the accuracy of vehicle control.

[0070] Embodiment two

[0071] Figure 2 is a flowchart of a vehicle control method provided by the second embodiment of the application, and the embodiment is a further optimization of the method of the above-mentioned embodiment. Optionally, the target state data is processed by a preset fault observation module to obtain a fault observation value corresponding to the target state data; a fault prediction result corresponding to the target state data is determined based on the fault observation value, wherein the fault prediction result includes an existing fault state and a non-existing fault state; and the target state data is updated to preset state data when it is determined that the fault prediction result corresponding to the target state data is the existing fault state. As shown in Figure 2 , the method comprises:

[0072] S210, acquiring reference state data of the target vehicle, wherein the reference state data includes steering wheel angle data, yaw rate data, side slip angle data and four-wheel drive torque data.

[0073] S220, acquiring intermediate state data obtained by processing the reference state data by a control module, and performing delay estimation on the reference state data and the intermediate state data based on a delay estimation module to obtain a delay estimation value.

[0074] S230, acquire current state data of the controlled object in the target vehicle, and determine predicted state data of the target vehicle based on the current state data of the controlled object, the delay estimation value and the intermediate state data by using the vehicle state prediction module.

[0075] S240, process the reference state data and the predicted state data by using the control module, and determine target state data of the controlled object.

[0076] S250, process the target state data by using the preset fault observation module, and obtain a fault observation value corresponding to the target state data.

[0077] The fault observation value can be understood as an index for predicting a fault caused by the target state data. The greater the fault observation value, the greater the probability of a fault caused by the controlled object of the target vehicle executing an instruction corresponding to the target state data. The fault observation value can be obtained by observation through the preset fault observation module.

[0078] The preset fault observation module includes a fault observer. The fault observer is modeled as follows: a vehicle fault detection strategy based on a state observer is proposed. Through a combination of a predictor theory of the vehicle state prediction module and a control strategy, the control input delay of the vehicle system can be compensated. A serial control strategy is adopted. The dynamic model of the vehicle system with a fault is transformed as follows:

[0079]

[0080] A, B, C and D are preset unit matrices of a preset dimension set by the dynamic model.

[0081] Through the above dynamic model, the fault f(t) is augmented into the state matrix to obtain:

[0082]

[0083] A set of state observers is developed for the execution component. The state observer is dynamically modeled. M, G, L and H are preset matrices of a preset dimension designed in the observer. The matrices can be obtained by trial method or designed Lyapunov function to solve the related matrices by linear matrix inequality method.

[0084]

[0085] Specifically, the target state data is input into the preset fault observation module. The target state data is processed by the fault observation module to obtain a fault observation value corresponding to the target state data.

[0086] In the embodiment, the fault observation module can effectively observe the fault of the system in real time, especially for the complex working conditions in the mine, can simultaneously process the delay and the adverse effect of the fault on the target vehicle, avoid unreasonable target state data causing the fault problem of the target vehicle, and ensure the safety of the target vehicle operation.

[0087] S260, determine the fault prediction result corresponding to the target state data based on the fault observation value, wherein the fault prediction result includes the existence of the fault state and the non-existence of the fault state.

[0088] Specifically, the fault prediction result corresponding to the different fault observation value can be determined by comparing the mapping table of the fault observation value and the fault observation result. The corresponding fault observation threshold can also be set according to the actual demand to determine the fault prediction result corresponding to the target state data, that is, to predict whether the target state data has a fault state.

[0089] Optionally, it is judged whether the fault observation value meets the preset fault observation threshold, and in the case that the fault observation value meets the preset fault observation threshold, it is determined that the fault prediction result corresponding to the target state data is the existence of the fault state, and in the case that the fault observation value does not meet the preset fault observation threshold, it is determined that the fault prediction result corresponding to the target state data is the non-existence of the fault state.

[0090] Specifically, the fault observation value can be compared with the corresponding preset fault observation threshold according to the corresponding preset fault observation threshold matched from the preset storage space by the executed object, in the case that the fault observation value meets the preset fault observation threshold, it is indicated that the target state data will cause a fault problem, and it is determined that the fault prediction result corresponding to the target state data is the existence of the fault state. In the case that the fault observation value does not meet the preset fault observation threshold, it is indicated that the target state data will not cause a fault problem, and it is determined that the fault prediction result corresponding to the target state data is the non-existence of the fault state.

[0091] S270, update the target state data to the preset state data in the case that the fault prediction result corresponding to the target state data is determined to be the existence of the fault state.

[0092] The preset state data can be understood as the preset state data set in advance according to the fault state, which is used to control the operation of the controlled object of the target vehicle. For example, the preset state data can be set to 0, which is set according to the actual demand.

[0093] Specifically, if it is determined that the fault prediction result corresponding to the target state data is a fault state, the target state data needs to be adjusted. The target state data can be set to the preset state data according to actual needs. Preferably, for the vehicle scene underground, if it is predicted that the target state data will cause a fault problem, the preset state needs to be set to 0, that is, the vehicle is directly controlled to execute a shutdown process, so as to avoid accidents caused by improper control in the complex environment underground and improve the safety of vehicle control.

[0094] Optionally, if the fault prediction result corresponding to the target state data is a non-fault state, the target state data does not need to be updated.

[0095] Specifically, if it is predicted that the fault prediction result corresponding to the target state data is a non-fault state, it indicates that the target state data will not cause a fault problem, and the target state data can be directly executed without updating the target state data.

[0096] S280, controlling the controlled object of the target vehicle to operate based on the target state data of the controlled object.

[0097] The technical solution of this invention involves: acquiring reference state data of the target vehicle, including steering wheel angle data, yaw rate data, sideslip angle data, and four-wheel drive torque data; acquiring intermediate state data obtained by the control module after processing the reference state data; performing delay estimation on the reference state data and intermediate state data based on the delay estimation module to obtain a delay estimate value; acquiring the current state data of the controlled object in the target vehicle; determining the predicted state data of the target vehicle based on the current state data, delay estimate value, and intermediate state data of the controlled object using the vehicle state prediction module; determining the target state data of the controlled object by processing the reference state data and predicted state data through the control module; and processing the target state data through a preset fault observation module to obtain a fault observation value corresponding to the target state data. Based on the fault observation value, determining the fault prediction result corresponding to the target state data, whereby the fault prediction result includes a fault-existing state and a fault-free state. If the fault prediction result corresponding to the target state data is determined to be a fault-existing state, the target state data is updated to the preset state data. The controlled object of the target vehicle is controlled based on the target state data of the controlled object. This solution monitors the vehicle's status information in real time. A delay estimation module enables real-time observation of delays, accurately estimating the delay value. Based on this estimate, predicted state data is determined to identify the target state data. This eliminates the need to focus on the magnitude of the delay; the target state data can be determined simply by combining the vehicle's reference and current state data with the various modules constructed from these data. The designed control module can handle the negative impacts of delays and faults, addressing the problem of large delays at vehicle inputs that existing vehicle control methods cannot handle. It allows for rapid processing of delay issues, improving the stability, flexibility, and accuracy of vehicle control.

[0098] Example 3

[0099] Figure 3 This is a schematic diagram of a vehicle control system provided in Embodiment 3 of the present invention. Figure 3 As shown, the control system includes: a control module 340, a vehicle state prediction module 330, a delay estimation module 320, and an information acquisition module 310, wherein...

[0100] The information acquisition module 310 is used to acquire reference state data, intermediate state data and current state data of the controlled object in the target vehicle corresponding to the control module, transmit the reference state data and intermediate state data to the delay estimation module, and transmit the current state data and intermediate state data of the controlled object in the target vehicle to the vehicle state prediction module.

[0101] The time delay estimation module 320 is configured to receive the reference state data and the intermediate state data, determine a time delay estimation value based on the reference state data and the intermediate state data, and transmit the time delay estimation value to the vehicle state prediction module;

[0102] The vehicle state prediction module 330 is configured to receive the time delay estimation value, the current state data and the intermediate state data of the controlled object in the target vehicle, determine the predicted state data of the target vehicle based on the time delay estimation value, the current state data and the intermediate state data of the controlled object in the target vehicle, and feed back the predicted state data of the target vehicle to the control module.

[0103] The control module 340 is configured to receive the reference state data of the target vehicle and the predicted state data of the target vehicle, and determine the target state data of the controlled object based on the reference state data and the predicted state data of the target vehicle.

[0104] The technical scheme of the embodiment comprises the following steps: collecting, by the information collection module, the reference state data and the intermediate state data corresponding to the control module, the current state data of the controlled object in the target vehicle, transmitting the reference state data and the intermediate state data to the time delay estimation module, and transmitting the current state data and the intermediate state data of the controlled object in the target vehicle to the vehicle state prediction module; receiving, by the time delay estimation module, the reference state data and the intermediate state data, determining a time delay estimation value based on the reference state data and the intermediate state data, and transmitting the time delay estimation value to the vehicle state prediction module; receiving, by the vehicle state prediction module, the time delay estimation value, the current state data and the intermediate state data of the controlled object in the target vehicle, determining the predicted state data of the target vehicle based on the time delay estimation value, the current state data and the intermediate state data of the controlled object in the target vehicle, and feeding back the predicted state data of the target vehicle to the control module; and receiving, by the control module, the reference state data of the target vehicle and the predicted state data of the target vehicle, and determining the target state data of the controlled object based on the reference state data and the predicted state data of the target vehicle. The present scheme can realize real-time observation of the time delay through the time delay estimation module, accurately estimate the time delay estimation value, determine the predicted state data based on the time delay estimation value, and determine the target state data, so that the problem of large time delay existing in the input end of the vehicle can be solved without the need to focus on the size of the time delay, and the stability and flexibility of the vehicle control can be improved, and the accuracy of the vehicle control can be improved.

[0105] On the basis of the above-mentioned embodiments, optionally, the control system further comprises a fault observation module; the fault observation module is configured to receive target state data, determine corresponding fault observation values based on the target state data, and determine a fault prediction result corresponding to the target state data based on the fault observation values, wherein the fault prediction result comprises a fault state and a non-fault state.

[0106] Optionally, the intermediate state data comprises front wheel steering angle state data; the delay estimation module 320 is further configured to receive and process the steering wheel steering angle state data, the yaw rate state data, the side slip angle state data, the four-wheel driving torque state data and the front wheel steering angle state data to obtain a delay estimation value.

[0107] Optionally, the information collection module 310 receives a reference control instruction issued by a vehicle control module, and determines reference state data based on the reference control instruction.

[0108] Optionally, the control system further comprises a fault prediction module configured to, before controlling the controlled object of the target vehicle based on target state data of the controlled object, process the target state data by using a preset fault observation module to obtain fault observation values corresponding to the target state data, and determine a fault prediction result corresponding to the target state data based on the fault observation values, wherein the fault prediction result comprises a fault state and a non-fault state. The fault prediction module is further configured to determine whether the fault observation values meet a preset fault observation threshold, and in a case where the fault observation values meet the preset fault observation threshold, determine that the fault prediction result corresponding to the target state data is the fault state, and update the target state data to preset state data; in a case where the fault observation values do not meet the preset fault observation threshold, determine that the fault prediction result corresponding to the target state data is the non-fault state, and there is no need to update the target state data.

[0109] Optionally, the control module 340 comprises a proportional-integral-derivative controller; the control module 340 is specifically configured to determine deviation data based on the reference state data and the predicted state data, calculate a proportional value, an integral value and a derivative value of the deviation data based on a PID operator, and determine the target state data of the controlled object based on the proportional value, the integral value and the derivative value.

[0110] The vehicle control device provided in the embodiments of the present application can execute the vehicle control method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0111] Embodiment Four

[0112] Figure 4is a structural schematic diagram of an electronic device provided by Embodiment Four of the present application. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0113] As shown in Figure 4 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0114] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, a speaker, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0115] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning module algorithms, a digital signal processor (DSP), and any appropriate processor, control module, micro-control module, etc. The processor 11 performs various methods and processes described above, such as the vehicle control method.

[0116] In some embodiments, the vehicle control method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, part or all of the computer program can be loaded onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of the vehicle control method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the vehicle control method by any other suitable means, e.g., by means of firmware.

[0117] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0118] Computer programs implementing methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0119] Embodiment Five

[0120] Embodiment five of the present application also provides a computer readable storage medium, the computer readable storage medium storing computer instructions, the computer instructions being used for causing a processor to execute a vehicle control method, the method comprising:

[0121] obtaining reference state data of the target vehicle, wherein the reference state data comprises steering wheel angle data, yaw rate data, side slip angle data and four-wheel drive torque data;

[0122] Obtain intermediate state data processed by the control module from the reference state data, and perform time delay estimation on the reference state data and the intermediate state data based on the time delay estimation module to obtain a time delay estimation value;

[0123] Obtain current state data of the controlled object in the target vehicle, and determine predicted state data of the target vehicle based on the vehicle state prediction module from the current state data of the controlled object, the time delay estimation value and the intermediate state data;

[0124] Process the reference state data and the predicted state data by the control module to determine target state data of the controlled object;

[0125] Control the operation of the controlled object in the target vehicle based on the target state data of the controlled object.

[0126] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0127] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0128] The systems and techniques described here can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here, or a combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0129] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0130] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in series, or executed in different orders, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

Claims

1. A vehicle control method, characterized in that, include: Acquire reference state data of the target vehicle, wherein the reference state is the state data that the target vehicle needs to execute, and the reference state data includes steering wheel angle state data, yaw rate state data, sideslip angle state data and four-wheel drive torque state data; The intermediate state data obtained by the control module through processing the reference state data is acquired, and the delay estimation module performs delay estimation on the reference state data and the intermediate state data to obtain a delay estimation value; wherein, the intermediate state data includes the state data output by the control module after processing each data item in the reference state data respectively; The current state data of the controlled object in the target vehicle is obtained, and the predicted state data of the target vehicle is determined based on the current state data of the controlled object, the reference state data, the delay estimate, and the intermediate state data by the vehicle state prediction module; wherein, the predicted state data includes the corresponding predicted data obtained after delay compensation of each state data in the intermediate state data based on the delay compensation strategy in the vehicle state prediction module. The control module determines the deviation data based on the reference state data and the predicted state data, and processes the deviation data to determine the target state data of the controlled object. The controlled object, namely the target vehicle, is controlled to operate based on the target state data of the controlled object.

2. The method according to claim 1, characterized in that, The intermediate state data includes front wheel steering angle state data; The delay estimation module performs delay estimation on the reference state data and the intermediate state data to obtain delay estimation values, including: The steering wheel angle state data, yaw rate state data, sideslip angle state data, four-wheel drive torque state data, and front wheel angle state data are input into the delay estimation module to obtain the delay estimation value.

3. The method according to claim 1, characterized in that, The acquisition of reference state data for the target vehicle includes: Receive reference control commands issued by the vehicle control module, and determine the reference state data based on the reference control commands.

4. The method according to claim 1, characterized in that, Before controlling the operation of the controlled object based on the target state data of the controlled object, the method further includes: The target state data is processed by a preset fault observation module to obtain the fault observation value corresponding to the target state data; Based on the fault observations, a fault prediction result corresponding to the target state data is determined, wherein the fault prediction result includes a fault state and a fault state that does not exist.

5. The method according to claim 4, characterized in that, The step of determining the fault prediction result corresponding to the target state data based on the fault observations includes: Determine whether the fault observation value meets the preset fault observation threshold. If the fault observation value meets the preset fault observation threshold, then determine that the fault prediction result corresponding to the target state data is a fault state, and update the target state data to the preset state data. If the fault observation value does not meet the preset fault observation threshold, the fault prediction result corresponding to the target state data is determined to be a non-fault state, and there is no need to update the target state data.

6. The method according to claim 1, characterized in that, The control module includes a proportional-integral-derivative controller; The process of processing the deviation data to determine the target state data of the controlled object includes: The PID operator calculates the proportional, integral, and derivative values ​​of the deviation data, and determines the target state data of the controlled object based on the proportional, integral, and derivative values.

7. A vehicle control system, characterized in that, The control system includes a control module, a vehicle state prediction module, a delay estimation module, and an information acquisition module, wherein... The information acquisition module is used to acquire reference state data, intermediate state data, and current state data of the controlled object in the target vehicle corresponding to the control module. It transmits the reference state data and the intermediate state data to the delay estimation module, and transmits the current state data of the controlled object in the target vehicle, the reference state data, and the intermediate state data to the vehicle state prediction module. The reference state data refers to the state data that the target vehicle needs to execute, including steering wheel angle state data, yaw rate state data, sideslip angle state data, and four-wheel drive torque state data. The intermediate state data includes the state data output by the control module after processing each item in the reference state data. The delay estimation module is used to receive the reference state data and the intermediate state data, determine the delay estimation value based on the reference state data and the intermediate state data, and transmit the delay estimation value to the vehicle state prediction module. The vehicle state prediction module is used to receive the delay estimate, the current state data of the controlled object in the target vehicle, the reference state data, and the intermediate state data; determine the predicted state data of the target vehicle based on the delay estimate, the current state data of the controlled object in the target vehicle, the reference state data, and the intermediate state data; and feed back the predicted state data of the target vehicle to the control module. The predicted state data includes corresponding predicted data obtained after applying delay compensation to each state data item in the intermediate state data based on the delay compensation strategy in the vehicle state prediction module. The control module is configured to receive reference state data and predicted state data of the target vehicle, determine deviation data based on the reference state data and predicted state data of the target vehicle, process the deviation data, and determine the target state data of the controlled object.

8. The system according to claim 7, characterized in that, The control system also includes a fault observation module; The fault observation module is used to receive the target state data, determine the corresponding fault observation value based on the target state data, and determine the fault prediction result corresponding to the target state data based on the fault observation value, wherein the fault prediction result includes a fault state and a fault state that does not exist.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle control method according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the vehicle control method of any one of claims 1-6.

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