Vehicle operation control method and device, vehicle and medium

Abstract

The invention relates to a vehicle operation control method and device, a vehicle and a medium, and the method comprises the steps that the operation parameter value of the vehicle in the current control period is obtained; wherein the operating parameter values comprise a vehicle control level, a vehicle position and a speed; according to the operation parameter value and a preset observation model, determining an estimated value of an operation control parameter of the vehicle in the next control period; and controlling the operation of the vehicle in the next control period according to the estimated value of the operation control parameter.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle control technology, and more specifically, to a vehicle operation control method, device, vehicle and medium. Background Art

[0002] At present, the control algorithm implemented by the automatic train operation system ATO (Automatic Train Operation) needs to use a single train operation parameter (for example, position or speed) as feedback to estimate the train operation control parameters (for example, acceleration) in the next control cycle, thereby adjusting the train operation. However, in the actual operation of the train, since the determination of the train operation parameters is affected by measurement errors such as positioning error and speed pulse error, the determined train operation parameters are greatly different from the actual ones. At this time, if the operation control parameters are estimated based on a single train operation parameter as feedback, the accuracy of the estimated operation control parameters is low, which in turn affects the operation control of the vehicle. Summary of the invention

[0003] One purpose of the embodiments of the present disclosure is to provide a technical solution for estimating operating parameters based on an observation model, which can avoid the technical problem of low accuracy in estimating train operating control parameters based on a single train operating parameter as feedback in the related art, thereby improving the accuracy of the estimation of the operating control parameters.

[0004] According to a first aspect of the present disclosure, there is provided an embodiment of a vehicle control method, the method comprising:

[0005] Obtaining operating parameter values ​​of the vehicle in the current control cycle; wherein the operating parameter values ​​include vehicle control level, vehicle position and speed;

[0006] Determining an estimated value of the operating control parameter of the vehicle in the next control cycle according to the operating parameter value and a preset observation model;

[0007] The operation of the vehicle in the next control cycle is controlled according to the estimated value of the operation control parameter.

[0008] Optionally, the preset observation model is a linear extended state observation model obtained by expanding the disturbance value of the control amount on the basis of the linear observation model.

[0009] Optionally, the preset observation model includes an error correction term, and the error correction term is associated with the observation state value of the preset observation model in the current control cycle and the operating parameter value.

[0010] Optionally, the preset observation model includes at least one of an acceleration prediction model, a speed prediction model and a prediction model of a disturbance value of a control variable.

[0011] Optionally, the preset observation model includes an acceleration prediction model, and determining an estimated value of an operation control parameter of the vehicle in a next control cycle according to the operation parameter value and the preset observation model includes:

[0012] Determine the acceleration error correction value according to the operating parameter value and the observation state value of the preset observation model in the current control cycle; wherein the observation state value includes the observation position value, the observation speed value and the disturbance value of the control amount;

[0013] An estimated acceleration value of the vehicle in a next control cycle is determined according to the acceleration error correction value, the disturbance value of the control variable, and the vehicle control level.

[0014] Optionally, the preset observation model includes a speed prediction model, and determining an estimated value of an operation control parameter of the vehicle in a next control cycle according to the operation parameter value and the preset observation model includes:

[0015] Determine the speed error correction value according to the operating parameter value and the observation state value of the preset observation model in the current control cycle; wherein the observation state value includes the observation position value, the observation speed value and the disturbance value of the control amount;

[0016] An estimated speed value of the vehicle in a next control cycle is determined according to the speed error correction value and the observed speed value.

[0017] Optionally, the preset observation model includes a disturbance value prediction model of the control amount, and determining the estimated value of the operation control parameter of the vehicle in the next control cycle according to the operation parameter value and the preset observation model includes:

[0018] Determine the error correction value of the disturbance value of the control amount according to the operating parameter value and the observation state value of the preset observation model in the current control cycle; wherein the observation state value includes the observation position value, the observation speed value and the disturbance value of the control amount;

[0019] The error correction value of the disturbance value of the control variable is used as the change rate of the disturbance value of the control variable.

[0020] Optionally, determining the observation state value of the preset observation model in the current control cycle includes:

[0021] Determine the change amount of the observed state value corresponding to the current control cycle according to the estimated value of the operation control parameter corresponding to the current control cycle and the control cycle duration;

[0022] The observation state value of the preset observation model in the current control cycle is determined according to the observation state value corresponding to the previous control cycle and the change amount of the observation state value corresponding to the current control cycle.

[0023] Optionally, constructing the preset observation model includes:

[0024] Determining a state space equation of the vehicle; wherein the state space equation is used to characterize the relationship between the vehicle state change rate and the vehicle operating parameters;

[0025] According to the state space equation, a state update equation is constructed; wherein the state update equation is used to characterize the relationship between the vehicle observation state change rate and the observation state value of the preset observation model, the vehicle control level and the error correction term;

[0026] Determining a gain coefficient matrix of the state update equation;

[0027] The state update equation is updated according to the gain coefficient matrix to obtain the preset observation model.

[0028] Optionally, determining a gain coefficient matrix of the preset observation model includes:

[0029] Determining an observation error expression according to a difference between the vehicle state change rate and the vehicle observed state change rate;

[0030] According to the eigenvalue of the observation error expression, a gain coefficient matrix of the preset observation model is determined.

[0031] According to a second aspect of the present disclosure, an embodiment of a vehicle operation control device is provided, the device comprising a memory and a processor, the memory storing executable instructions for controlling the processor to operate to execute the vehicle operation control method as described in the first aspect.

[0032] According to a third aspect of the present disclosure, an embodiment of a vehicle is provided, the vehicle comprising the vehicle operation control device according to the second aspect of the present disclosure.

[0033] One beneficial effect of the embodiments of the present disclosure is that, according to the vehicle operation control method of the embodiments of the present application, by estimating the operation control parameters of the next control cycle based on the vehicle operation parameters, and controlling the operation of the vehicle based on the operation control parameters, it is possible to estimate the vehicle operation control parameters by integrating the vehicle position, speed and vehicle control level, thereby avoiding the technical problem of low accuracy in the related art due to estimating the operation control parameters based on a single train operation parameter as feedback, and improving the accuracy of the estimation of the operation control parameters.

[0034] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0036] Figure 1 is a flow chart of a vehicle operation control method according to an embodiment of the present application;

[0037] Figure 2 is a curve diagram of changes in operating parameters and operating control parameters in each control cycle according to an embodiment of the present application;

[0038] Figure 3 is a structural schematic diagram of a vehicle operation control device according to an embodiment of the present application;

[0039] Figure 4 It is a schematic diagram of the structure of a vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0040] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0041] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0042] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0043] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0044] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0045] Figure 1 A flow chart of a vehicle operation control method according to an embodiment of the present application is shown. The method can be implemented by a vehicle operation control device, such as Figure 1 As shown, the method includes: step S11 to step S13.

[0046] Step S11, obtaining the operating parameter values ​​of the vehicle in the current control cycle.

[0047] In this embodiment, the control cycle can be, for example, 0.1s, etc. The vehicle operation control device can collect the vehicle operation parameters through sensors and vehicle control systems in each control cycle. The operation parameter values ​​include vehicle control level, vehicle position and speed. Among them, the vehicle control level reflects the current power output state of the vehicle, such as acceleration, deceleration, and speed maintenance.

[0048] In one example, the vehicle control level is obtained by reading data from a vehicle control unit.

[0049] In one example, the precise location of the vehicle may be determined via a global positioning system (GPS) and possibly an inertial measurement unit (IMU).

[0050] In one example, the speed may be calculated from a vehicle speed sensor or GPS data.

[0051] Step S12, determining an estimated value of the operating control parameter of the vehicle in the next control cycle according to the operating parameter value and a preset observation model.

[0052] In this embodiment, the preset observation model can be used to estimate the operation control parameters of the next control cycle according to the vehicle operation parameter values ​​of the current control cycle. The estimated value of the operation control parameter includes at least one of the estimated speed value, the estimated acceleration value, and the estimated disturbance value of the control amount.

[0053] In one embodiment, the preset observation model is a linear extended state observation model obtained by expanding the disturbance value of the control amount on the basis of the linear observation model.

[0054] In this embodiment, the preset observation model may be LESO (Linear Extended State Observer; linear extended state observation model), referred to as LESO observation model. LESO observation model is a linear extended state observation model obtained by expanding the disturbance value of the control variable on the basis of the linear observation model.

[0055] The disturbance value of the controlled variable can be the error variable of the controlled variable. It represents the influence of the unmodeled, unknown or difficult to measure external disturbances or internal parameter changes on the controlled variable. The disturbance value of the controlled variable may be caused by environmental factors, sensor noise, actuator errors or other uncertain factors.

[0056] The disturbance value of the control quantity can be regarded as a deviation or correction to the desired control quantity.

[0057] For example, in motor control, the disturbance value of the control variable may represent the deviation of the motor output torque due to factors such as load change, friction or temperature change. By estimating this disturbance value, the control strategy can be adjusted to compensate for these deviations, thereby improving the performance of the power system.

[0058] According to the embodiment of the present application, by expanding the disturbance value of the control quantity in the LESO observation model, accurate estimation of the control parameters can be achieved when the operating parameter value is inaccurate or there is additional crosstalk. In addition, the LESO observation model has a small amount of calculation, which avoids the large amount of calculation caused by exponential calculation.

[0059] In one embodiment, constructing the preset observation model includes: steps S21 to S24.

[0060] Step S21, determining the state space equation of the vehicle.

[0061] In this embodiment, the state space equation of the vehicle is used to characterize the relationship between the vehicle state change rate and the vehicle operating parameters, that is, to characterize the relationship between the vehicle state change rate and the vehicle control level, vehicle position and speed.

[0062] In one example, constructing the state space equation of the vehicle includes the following steps S211 to S213.

[0063] Step S211, determining the state space equation of the vehicle from time n to time n+1 according to the state equation of the linear system.

[0064] In this embodiment, the state space equation of the vehicle from time n to time n+1 is used to characterize the relationship between the state change rate of the vehicle at time n+1 and the state space vector at time n, the vehicle control level at time n, and the crosstalk. The state space equation formula (1) is shown as follows:

[0065]

[0066] Where x is the vehicle state space vector, which includes the vehicle position s and velocity v, is its derivative form, representing the state change rate, A is the state transfer coefficient matrix, B is the input matrix, u is the input control quantity, in the vehicle control system, u is the vehicle control level, and d(n) is the crosstalk error term.

[0067] The vehicle position s and speed v are both obtained based on sensor detection.

[0068] In one example, if Figure 2As shown in the figure, the horizontal axis represents the number of control cycles, for example, "50" represents the 50th control cycle, and the vertical axis represents position, speed and acceleration from top to bottom. The change of vehicle position s corresponding to multiple control cycles can be as follows Figure 2 As shown in the pos curve, the speed v of multiple control cycles can be Figure 2 As shown in the v curve shown.

[0069] Step S212, based on the relationship between the vehicle state space vector and the vehicle position and speed, and the relationship between the vehicle control level and the vehicle acceleration, the expansion of the vehicle state space equation from time n to time n+1 is obtained.

[0070] In this embodiment, the relationship between the vehicle state space vector and the vehicle position and speed can be shown as formula (2) and formula (3).

[0071] x(n)=[s(n) v(n)] T (2)

[0072]

[0073] The relationship between the vehicle control level u and acceleration can be expressed by the following formula:

[0074] a u =k u u+b u +d u (4)

[0075] Where bu is the linear bias and du is the nonlinear component. Since the LESO observation model can fit the nonlinear error and bu and du are summed, the two terms can be combined into (1) d(n), that is, acceleration a u It is determined by the vehicle control level u and the crosstalk error term d(n). The specific values ​​of ku, bu and du can be obtained from the motor design parameters or the actual level output test.

[0076] From the laws of kinematics we know that:

[0077]

[0078] Substituting the above formulas (2), (3), (4) and (5) into formula (1), we can obtain the expanded form of the state space equation, as shown in formula (6):

[0079]

[0080] d2=b u +d u +d a (7)

[0081] Among them, d1 is the environmental velocity disturbance term, and da is the environmental acceleration disturbance term.

[0082] Therefore, the state transfer coefficient matrix A and the input coefficient matrix B are:

[0083]

[0084] B=[0 k u ] T (9)

[0085] Step S22: construct a state update equation based on the state space equation.

[0086] In this embodiment, based on the state space equation, the disturbance about the control quantity is expanded to obtain the state update equation. The state update equation is used to characterize the relationship between the vehicle observation state change rate and the observation state value of the preset observation model, the vehicle control level and the error correction term.

[0087] In one example, the state update equation is shown in formula (10):

[0088]

[0089] in, is the speed estimate, is the estimated acceleration, is the estimated value of the disturbance value of the control quantity, x3 is the disturbance value of the control quantity, is the state space vector of the observed value, y is the state output value, l1 is the velocity error correction coefficient, l2 is the acceleration error correction coefficient, l3 is the error correction coefficient of the error correction coefficient change rate of the disturbance value of the controlled variable, and C is the output coefficient matrix, which can be expressed as [1t n 0], t n is the set value.

[0090] in, The state space vector of the observed value is calculated based on the LESO observer, which is composed of the observed position value Observed speed value The disturbance value x3 of the control quantity is composed of, as shown in formula (11). Speed ​​estimation Acceleration estimate Estimated value of the disturbance value of the controlled quantity The rate of change of vehicle observation state The relationship can be expressed as shown in formula (12).

[0091]

[0092]

[0093] In some examples, the observed position values ​​for multiple control cycles It can be Figure 2 The pos_obs curve shown is the observed speed value of multiple control cycles It can be Figure 2 The v_obs curve shown.

[0094] Furthermore, the state update equation of formula (10) can be written in matrix form, as shown in formula (13).

[0095]

[0096] Will Using the gain coefficient matrix L, equation (10) can be expressed as:

[0097]

[0098]

[0099] B=[0 k u 0] T (16)

[0100] L=[l1 l2 l3] T (17)

[0101] Formula (14) is the preset observation model, where is the error correction term, and then according to is the observed state value corresponding to the n control cycle, and since y(n)=Cx(n), x(n) is the operating parameter of the current control cycle, that is, the error correction term is associated with the observed state value corresponding to the current control cycle and the operating parameter of the current control cycle.

[0102] Step S23, determining the gain coefficient matrix of the state update equation.

[0103] In this embodiment, since the gain coefficient matrix L in the preset observation model can be used to adjust the convergence speed of the estimation error, the size of the gain coefficient matrix directly affects the convergence speed of the observation error. An appropriate gain coefficient matrix L can enable the observer to quickly track the actual state of the vehicle control system, reduce the estimation error, and improve the estimation accuracy.

[0104] In some embodiments, determining the gain coefficient matrix of the preset observation model in step S23 includes: step S231 and step S232.

[0105] Step S231, determining an observation error expression according to the difference between the vehicle state change rate and the vehicle observed state change rate.

[0106] In this embodiment, in order to make the estimated value of the operation control parameter output by the preset observation model converge to the actual control parameter value, that is, the state space vector of the vehicle state vector x and the observed value The error e x Need to gradually converge to 0, where Subtracting (14) from (1) yields:

[0107]

[0108] Among them, since d(n) in formula (1) is the crosstalk error term, it can be merged into the error correction term, so d(n) can be omitted in the derivation, then, formula (22) is the differential equation of the observation error, that is, the observation error expression.

[0109] Step S232: determining a gain coefficient matrix of the preset observation model according to the eigenvalue of the observation error expression.

[0110] In this embodiment, the process of calculating the eigenvalue using formula (22) is as follows:

[0111] det(λI-(A-LC))=0

[0112]

[0113] λ 3 +(l1+12t n ) 2 +(l2+l3t n )λ+l3=0(23)

[0114] To simplify the parameters, the bandwidth coefficient matrix w0 is introduced, and the characteristic equation can be simplified to the following form:

[0115] (λ+w0) 3 =0

[0116]

[0117] Substituting equation (23) into equation (24), we obtain the following equation system:

[0118]

[0119] From the automatic control theory, we know that when the eigenvalue of the differential equation of the observation error is in the left half plane of the s domain plane, the signal tends to decay and eventually converges to a steady state. Therefore, when w0 is greater than 0, the observation error can eventually converge to 0. Therefore, the value of the gain coefficient matrix L can be determined.

[0120] According to the embodiment of the present application, by determining the value of the gain coefficient matrix L, it is possible to adjust the convergence speed of the estimation error, optimize the robustness of the system, and balance the estimation accuracy and computational complexity, so as to achieve accurate estimation of the error correction term, and further, achieve accurate estimation of the estimated value of the operating control parameter.

[0121] Step S24, updating the state update equation according to the gain coefficient matrix to obtain the preset observation model.

[0122] In this embodiment, the value of the gain coefficient matrix L is substituted into the state update equation (14) to obtain the preset observation model.

[0123] In one embodiment, the preset observation model includes at least one of an acceleration prediction model, a speed prediction model and a prediction model of a disturbance value of a control variable.

[0124] In this embodiment, the preset observation model can realize at least one of the estimation of acceleration, the estimation of speed, and the estimation of the disturbance value of the control amount. In other words, the preset observation model can estimate at least one of the acceleration, speed, and the disturbance value of the control amount based on the operating parameter value of the vehicle.

[0125] In one embodiment, the preset observation model includes an acceleration prediction model, and determining the estimated value of the operating control parameter of the vehicle in the next control cycle according to the operating parameter value and the preset observation model in step S12 includes: steps S31 to S32.

[0126] Step S31, determining an acceleration error correction value according to the operating parameter value and the observation state value of the preset observation model in the current control cycle.

[0127] In this embodiment, the preset observation model includes an acceleration prediction model, and the relationship included in the model is:

[0128]

[0129] in, When calculating the acceleration error correction value, the first output state value y(n) can be calculated based on the operating parameters of the n control cycle, and then the second output state value y(n) can be calculated based on the observed state value of the preset observation model in the n control cycle. The acceleration error correction value is obtained according to the first output state value, the second output state value, and the acceleration error correction coefficient l2. The observed state value includes the observed position value, the observed velocity value, and the disturbance value of the control amount.

[0130] The observed position value can be the system position information estimated by the LESO observer. The observed speed value represents the system speed information estimated by the LESO observer. The disturbance value of the control quantity represents the influence of some unmodeled, unknown or difficult to measure external disturbance or internal parameter change on the control quantity.

[0131] In one embodiment, determining the observation state value of the preset observation model in the current control cycle includes: step S41 and step S42.

[0132] Step S41, determining the change amount of the observed state value corresponding to the current control cycle according to the estimated value of the operation control parameter corresponding to the current control cycle and the control cycle duration.

[0133] Step S42, determining the observation state value of the preset observation model in the current control cycle according to the observation state value corresponding to the previous control cycle and the change amount of the observation state value corresponding to the current control cycle.

[0134] Exemplarily, the current control cycle is the (n+1) control cycle, and the observed state value of the (n+1) control cycle can be calculated by formula (27).

[0135]

[0136] Where t is the control cycle duration, is the estimated value of the operating control parameters of the current control cycle, is the observed state value corresponding to the previous control cycle.

[0137] It should be noted that the initial observation state value of the preset observation model can be obtained based on the vehicle position and speed collected by the sensor for the first time. In addition, the initial value of the disturbance value of the control amount is set to 0.

[0138] Step S32, determining an estimated acceleration value of the vehicle in the next control cycle according to the acceleration error correction value, the disturbance value of the control variable, and the vehicle control level.

[0139] In this embodiment, the acceleration error correction value corresponding to the n control cycle, the disturbance value of the control amount, and the control level of the vehicle are substituted into formula (26) to obtain the acceleration estimate of the n+1 control cycle.

[0140] In some examples, the acceleration estimate corresponding to each control cycle can be as follows: Figure 2 The acceleration curve is shown.

[0141] In one embodiment, the preset observation model includes a speed prediction model, and determining the estimated value of the operating control parameter of the vehicle in the next control cycle according to the operating parameter value and the preset observation model in step S12 includes: steps S51 to S52.

[0142] Step S51, determining a speed error correction value according to the operating parameter value and the observed state value of the preset observation model in the current control cycle; wherein the observed state value includes an observed position value, an observed speed value and a disturbance value of the control amount.

[0143] Step S52, determining an estimated speed value of the vehicle in the next control cycle according to the speed error correction value and the observed speed value.

[0144] For example, the speed prediction model can be expressed by formula (28):

[0145]

[0146] in, The calculation method of the velocity error correction value is similar to the calculation method of the acceleration error correction value, the difference is that the acceleration error correction coefficient is different from the velocity error correction coefficient.

[0147] The observation state value of the preset observation model in the current control cycle can be calculated according to the above steps S41 and S42, which will not be repeated here.

[0148] In one embodiment, the preset observation model includes a disturbance value prediction model of the control quantity, and in step S12, determining the estimated value of the operating control parameter of the vehicle in the next control cycle according to the operating parameter value and the preset observation model includes: step S61 and step S62.

[0149] Step S61, determining an error correction value of a disturbance value of a control variable according to the operating parameter value and an observation state value of the preset observation model in a current control cycle.

[0150] Step S62: Using the error correction value of the disturbance value of the control variable as the rate of change of the disturbance value of the control variable.

[0151] In this embodiment, the prediction model of the disturbance value of the control quantity predicts the change rate of the disturbance value of the control quantity, which can be expressed as the following formula (29):

[0152]

[0153] in, The calculation method of the error correction value of the disturbance value of the control quantity is similar to the calculation method of the acceleration error correction value, the difference is that the acceleration error correction coefficient is different from the error correction coefficient of the disturbance value of the control quantity. The observed state value includes the observed position value, the observed velocity value and the disturbance value of the control quantity.

[0154] The observation state value of the preset observation model in the current control cycle can be calculated according to the above steps S41 and S42, which will not be repeated here.

[0155] It should be noted that the derivative of the disturbance value of the controlled variable can represent the rate of change of the disturbance value over time. It reflects the dynamic characteristics of the disturbance value, that is, how the disturbance value changes over time. In the control system, understanding the rate of change of the disturbance value is crucial to predicting the future behavior of the system and designing an effective control strategy.

[0156] For example, in an autonomous vehicle, a sudden obstacle on the road or the sudden appearance of a pedestrian may cause the vehicle to require emergency braking. At this time, the derivative of the disturbance value will reflect the rapid change of the disturbance value in this emergency situation, thereby helping the vehicle control system to respond quickly and ensure the safe driving of the vehicle.

[0157] Step S13: adjusting the operation of the vehicle in the next control cycle according to the estimated value of the operation control parameter.

[0158] For example, the output level of the vehicle in the next control cycle may be adjusted according to the estimated acceleration, that is, the power output of the vehicle may be adjusted.

[0159] According to the embodiments of the present application, by estimating the operating control parameters of the next control cycle based on the operating parameters of the vehicle and controlling the operation of the vehicle based on the operating control parameters, it is possible to estimate the vehicle operating control parameters by integrating the vehicle position, speed and vehicle control level, thereby avoiding the technical problem of low accuracy in the related art due to estimating the operating control parameters of the train in the next control cycle based on a single train operating parameter as feedback, thereby improving the accuracy of the estimated operating control parameters.

[0160] The present disclosure also provides a vehicle operation control device 300, such as Figure 3 As shown, it includes a memory 101 and a processor 102. The memory 101 stores executable instructions, and the instructions are used to control the processor 102 to operate to execute any vehicle operation control method in the above embodiments, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0161] The embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, any one of the vehicle operation control methods in the above embodiments can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0162] The present disclosure also provides a vehicle 400, such as Figure 4 As shown, it includes a vehicle operation control device as in the embodiment of the present disclosure.

[0163] In some embodiments, the vehicle operation control device may be as follows Figure 3 The vehicle operation control device shown.

[0164] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0165] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0166] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0167] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.

[0168] Various aspects of the present invention are described herein with reference to the flow charts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each box of the flow chart and / or block diagram and the combination of each box in the flow chart and / or block diagram can be implemented by computer-readable program instructions.

[0169] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0170] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0171] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or an instruction, and a part of the module, a program segment or an instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0172] Embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A vehicle operation control method, characterized in that: include: Obtaining operating parameter values ​​of the vehicle in the current control cycle; wherein the operating parameter values ​​include vehicle control level, vehicle position and speed; Determining an estimated value of the operating control parameter of the vehicle in the next control cycle according to the operating parameter value and a preset observation model; The operation of the vehicle in the next control cycle is controlled according to the estimated value of the operation control parameter.

2. The method according to claim 1, characterized in that The preset observation model is a linear extended state observation model obtained by expanding the disturbance value of the control quantity on the basis of the linear observation model.

3. The method according to claim 1, characterized in that: The preset observation model includes an error correction term, and the error correction term is associated with the observation state value of the preset observation model in the current control cycle and the operating parameter value.

4. The method according to claim 1, characterized in that The preset observation model includes at least one of an acceleration prediction model, a speed prediction model and a prediction model of a disturbance value of a control variable.

5. The method according to claim 1, characterized in that The preset observation model includes an acceleration prediction model, and determining the estimated value of the operation control parameter of the vehicle in the next control cycle according to the operation parameter value and the preset observation model includes: Determine the acceleration error correction value according to the operating parameter value and the observation state value of the preset observation model in the current control cycle; wherein the observation state value includes the observation position value, the observation speed value and the disturbance value of the control amount; An estimated acceleration value of the vehicle in a next control cycle is determined according to the acceleration error correction value, the disturbance value of the control variable, and the vehicle control level.

6. The method according to claim 1, characterized in that The preset observation model includes a speed prediction model, and determining the estimated value of the operation control parameter of the vehicle in the next control cycle according to the operation parameter value and the preset observation model includes: Determine the speed error correction value according to the operating parameter value and the observation state value of the preset observation model in the current control cycle; wherein the observation state value includes the observation position value, the observation speed value and the disturbance value of the control amount; An estimated speed value of the vehicle in a next control cycle is determined according to the speed error correction value and the observed speed value.

7. The method according to claim 1, characterized in that The preset observation model includes a disturbance value prediction model of the control amount, and the step of determining the estimated value of the operation control parameter of the vehicle in the next control cycle according to the operation parameter value and the preset observation model includes: Determine the error correction value of the disturbance value of the control amount according to the operating parameter value and the observation state value of the preset observation model in the current control cycle; wherein the observation state value includes the observation position value, the observation speed value and the disturbance value of the control amount; The error correction value of the disturbance value of the control variable is used as the change rate of the disturbance value of the control variable.

8. The method according to any one of claims 5 to 7, characterized in that Determining the observation state value of the preset observation model in the current control cycle includes: Determine the change amount of the observed state value corresponding to the current control cycle according to the estimated value of the operation control parameter corresponding to the current control cycle and the control cycle duration; The observation state value of the preset observation model in the current control cycle is determined according to the observation state value corresponding to the previous control cycle and the change amount of the observation state value corresponding to the current control cycle.

9. The method according to claim 1, characterized in that: Constructing the preset observation model includes: Determining a state space equation of the vehicle; wherein the state space equation is used to characterize the relationship between the vehicle state change rate and the vehicle operating parameters; According to the state space equation, a state update equation is constructed; wherein the state update equation is used to characterize the relationship between the vehicle observation state change rate and the observation state value of the preset observation model, the vehicle control level and the error correction term; Determining a gain coefficient matrix of the state update equation; The state update equation is updated according to the gain coefficient matrix to obtain the preset observation model.

10. The method according to claim 9, characterized in that The step of determining the gain coefficient matrix of the preset observation model comprises: Determining an observation error expression according to a difference between the vehicle state change rate and the vehicle observed state change rate; According to the eigenvalue of the observation error expression, a gain coefficient matrix of the preset observation model is determined.

11. A vehicle operation control device, characterized in that: The method comprises a memory and a processor, wherein the memory stores executable instructions, and the instructions are used to control the processor to operate so as to execute the vehicle operation control method according to any one of claims 1 to 10.

12. A computer-readable storage medium storing a computer program, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

13. A vehicle, characterized in that: It comprises the vehicle operation control device as claimed in claim 11.

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