Vehicle control method and device, vehicle and computer readable storage medium

Abstract

The invention relates to a vehicle control method and device, a vehicle and a computer readable storage medium, and relates to the technical field of control. The control method comprises the following steps: determining a target mass interval to which a first mass belongs from a plurality of mass intervals of the vehicle according to the first mass of the vehicle in a driving process; determining control parameters of the vehicle according to the current state information of the vehicle and the target quality interval; and the vehicle is controlled according to the control parameters. According to the technical scheme, the stability of vehicle control can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of control technology, and in particular to a vehicle control method, a vehicle control device, a vehicle, and a computer-readable storage medium. Background Art

[0002] With the continuous development of autonomous driving technology, vehicles have gradually shifted from manual control to unmanned control. The control effect of vehicles plays a vital role in transportation efficiency, transportation safety, and transportation stability, directly affecting the driving efficiency, safety, and stability of vehicles.

[0003] In related technologies, vehicle control technology generally adopts control methods such as PID (proportional-integral-derivative) control, adaptive control, robust control, event-triggered control, model predictive control or neural network control. Summary of the Invention

[0004] The inventors of the present disclosure have discovered that the above-mentioned related technologies have the following problems: the vehicle control method has poor stability when used on complex roads.

[0005] In view of this, the present disclosure proposes a vehicle control technology solution that can improve the stability of vehicle control.

[0006] According to some embodiments of the present disclosure, a vehicle control method is provided, comprising: determining, based on a first mass of the vehicle during driving, a target mass interval to which the first mass belongs from multiple mass intervals of the vehicle; determining control parameters of the vehicle based on current state information of the vehicle and the target mass interval; and controlling the vehicle based on the control parameters.

[0007] In some embodiments, according to the target mass interval, the first mass is mapped to a second mass corresponding to the target mass interval; and according to the second mass, a control parameter of the vehicle is determined.

[0008] In some embodiments, future state information of the vehicle is predicted based on the second mass and the current state information of the vehicle; an objective function is constructed with the control increment as a variable, the objective function characterizes a first difference between the future state information and the target state information, and the control increment is the second difference between the current control parameter and the target control parameter; with the goal of minimizing the objective function, the objective function is solved to determine the target control parameters, and the vehicle is controlled according to the target control parameters.

[0009] In some embodiments, based on the second mass, a state space equation of the vehicle is constructed with current state information as a state variable, where the current state information includes at least one of lateral displacement, slip angle, heading angle, and heading angular velocity; based on the state space equation, future state information is predicted.

[0010] In some embodiments, a first weight corresponding to the first difference and a second weight corresponding to the control increment are determined, and the first weight and the second weight are determined according to the second mass; and an objective function is constructed according to a weighted sum of the control increment and the first difference.

[0011] In some embodiments, the objective function is solved to determine at least one linear relationship between the control parameter and the current state information; and the target control parameter is determined based on the at least one linear relationship.

[0012] In some embodiments, at least one linear relationship includes multiple linear relationships, and the objective function is solved to determine multiple state intervals and multiple linear relationships. Different state intervals correspond to different linear relationships. The multiple state intervals and multiple linear relationships are determined based on the second quality. According to the state interval to which the current state information belongs, the target control parameters are determined using the linear relationship corresponding to the state interval.

[0013] In some embodiments, the objective function is solved according to constraints, where the constraints include at least one of a control parameter being in a first interval, a control increment being in a second interval, and a vehicle heading angle being in a third interval.

[0014] In some embodiments, the control parameter includes a front wheel steering angle of the vehicle, and the vehicle is laterally controlled based on the front wheel steering angle of the vehicle.

[0015] According to other embodiments of the present disclosure, a vehicle control device is provided, including: a first determination unit for determining, based on a first mass of the vehicle during driving, a target mass interval to which the first mass belongs from multiple mass intervals of the vehicle; a second determination unit for determining control parameters of the vehicle based on current state information of the vehicle and the target mass interval; and a control unit for controlling the vehicle based on the control parameters.

[0016] In some embodiments, the second determining unit maps the first mass to a second mass corresponding to the target mass interval according to the target mass interval; and determines the control parameter of the vehicle according to the second mass.

[0017] In some embodiments, the second determination unit predicts the future state information of the vehicle based on the second mass and the current state information of the vehicle; constructs an objective function with the control increment as a variable, the objective function characterizes the first difference between the future state information and the target state information, and the control increment is the second difference between the current control parameter and the target control parameter; solves the objective function with the goal of minimizing the objective function to determine the target control parameters, and the control unit controls the vehicle according to the target control parameters.

[0018] In some embodiments, the second determination unit constructs a state space equation of the vehicle based on the second mass and the current state information as a state variable, where the current state information includes at least one of lateral displacement, slip angle, heading angle, and heading angular velocity; and predicts future state information based on the state space equation.

[0019] In some embodiments, the second determination unit determines a first weight corresponding to the first difference and a second weight corresponding to the control increment, the first weight and the second weight are determined according to the second mass; and constructs an objective function according to the weighted sum of the control increment and the first difference.

[0020] In some embodiments, the second determination unit solves the objective function to determine at least one linear relationship between the control parameter and the current state information; and determines the target control parameter based on the at least one linear relationship.

[0021] In some embodiments, at least one linear relationship includes multiple linear relationships, and the second determination unit solves the objective function to determine multiple state intervals and multiple linear relationships. Different state intervals correspond to different linear relationships. Multiple state intervals and multiple linear relationships are determined based on the second quality. According to the state interval to which the current state information belongs, the target control parameters are determined using the linear relationship corresponding to the state interval.

[0022] In some embodiments, the second determination unit solves the objective function according to the constraint conditions, where the constraint conditions include at least one of a control parameter being in a first interval, a control increment being in a second interval, and a heading angle of the vehicle being in a third interval.

[0023] In some embodiments, the control parameter includes a front wheel steering angle of the vehicle, and the control unit performs lateral control of the vehicle according to the front wheel steering angle of the vehicle.

[0024] According to some further embodiments of the present disclosure, a control device for a vehicle is provided, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute the control method in any one of the above embodiments based on instructions stored in the memory device.

[0025] According to some further embodiments of the present disclosure, a vehicle is provided, comprising the vehicle control device of any one of the above embodiments.

[0026] According to some further embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the control method in any of the above embodiments is implemented.

[0027] According to some further embodiments of the present disclosure, a computer program product is provided, comprising instructions, which, when executed by a processor, enable the processor to perform the control method according to any one of the above embodiments.

[0028] In the above embodiment, changes in vehicle mass are incorporated into the control parameter calculation process, and the vehicle control parameters are determined based on the mass interval within which the first mass falls. This allows the determined control parameters to more accurately match the vehicle's current mass information, thereby improving the accuracy of control parameter calculation and, in turn, the stability of vehicle control. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0030] The present disclosure can be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0031] Figure 1 A flowchart illustrating some embodiments of a vehicle control method disclosed herein;

[0032] Figure 2 Schematic diagrams showing some embodiments of the vehicle control method disclosed herein;

[0033] Figure 3 Flowcharts showing other embodiments of the vehicle control method disclosed herein;

[0034] Figure 4 A block diagram showing some embodiments of a control device for a vehicle of the present disclosure;

[0035] Figure 5 A block diagram showing some other embodiments of the vehicle control device of the present disclosure;

[0036] Figure 6 A block diagram showing some further embodiments of the vehicle control device of the present disclosure;

[0037] Figure 7 A block diagram showing some further embodiments of the vehicle control device of the present disclosure;

[0038] Figure 8 A block diagram illustrating some embodiments of the vehicle of the present disclosure is shown. DETAILED DESCRIPTION

[0039] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, 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 disclosure.

[0040] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0041] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, 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 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 like 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] As mentioned earlier, with the continued development of autonomous driving technology in the open-pit mining industry, vehicles have gradually transitioned from manual to unmanned operation. For example, the lateral control of unmanned mining trucks plays a crucial role in transport efficiency, safety, and stability. However, compared to urban roads, open-pit mines are characterized by rugged, complex, and harsh environments. Therefore, determining control strategies for unmanned mining trucks is a key issue in this field.

[0046] In the related art, control technologies for unmanned mining trucks primarily focus on two types of control strategies. One type includes strategies such as PID, adaptive control, and robust control, while the other includes strategies such as event-triggered control, model predictive control, and neural network control. Furthermore, these control methods typically simplify the mining truck's dynamic model for lateral control, thereby reducing the computational complexity of the control algorithm and the computing resources of the central processing unit.

[0047] However, the above-mentioned control method for unmanned mining trucks has poor stability when applied on complex roads in mining areas, and has high computational complexity, which occupies the computing resources of the central processing unit.

[0048] To address at least one of the aforementioned issues, the present disclosure provides a vehicle control method and system that incorporates changes in vehicle mass into the calculation of control parameters and determines the vehicle control parameters based on the mass interval within which a first mass falls. This allows the determined control parameters to more accurately match the vehicle's current mass information, thereby improving the accuracy of control parameter calculation and, in turn, the stability of vehicle control.

[0049] For example, the technical solutions of the present disclosure can be implemented through the following embodiments.

[0050] Figure 1 A flowchart illustrating some embodiments of the vehicle control method of the present disclosure.

[0051] like Figure 1 As shown, in step 110 , based on a first mass of the vehicle during driving, a target mass interval to which the first mass belongs is determined from a plurality of mass intervals of the vehicle.

[0052] In some embodiments, the first mass of the vehicle can be determined by constructing a vehicle body mass evaluation function using formula (1):

[0053]

[0054] In formula (1), m(t) represents the first mass of the vehicle at time t, h i (t) represents the gravity information obtained by the i-th pressure sensor installed on the vehicle, G(·) represents the filtering process of the sensor information to ensure the stability of the sensor information, and g represents the gravitational acceleration.

[0055] This approach, by evaluating the vehicle's first mass in real time at every moment, can address the issue of large variations in vehicle mass. For example, this can address the large variations in vehicle mass caused by the frequent hauling of ore by unmanned mining trucks. This allows for more accurate first mass calculations, enabling more precise calculation of subsequent control parameters and more stable vehicle control.

[0056] In some embodiments, a plurality of equally spaced mass intervals may be divided according to the maximum mass and the minimum mass of the vehicle body, and then a target mass interval to which the first mass belongs may be determined.

[0057] In step 120, the vehicle control parameters are determined based on the vehicle's current state information and the target mass range. For example, the vehicle's current state information includes the vehicle's lateral coordinates, longitudinal coordinates, vertical coordinates, heading angle, heading angular velocity, lateral displacement of the center of mass, and sideslip angle.

[0058] For example, the aforementioned status information can be obtained by reading the vehicle's sensor information in real time. The sensor information can also be calibrated so that the read status information is unified in the vehicle coordinate system. Furthermore, the status information can be filtered, such as with a Kalman filter or particle filter, to ensure the accuracy of the information read from the sensors.

[0059] In some embodiments, a model predictive control algorithm can be constructed to determine vehicle control parameters. For example, a first mass acquired by a pressure sensor that continuously changes over time within a certain range can be converted into a fixed mass, making the control parameters determined by the model predictive control algorithm based on the mass more accurate.

[0060] For example, the first mass may be mapped to a second mass corresponding to the target mass interval according to the target mass interval, and the control parameters of the vehicle may be determined according to the second mass.

[0061] For example, the mass of a vehicle can be divided into five mass intervals: minimum mass, small mass, medium mass, large mass, and maximum mass. The corresponding formulas for each mass interval are as follows:

[0062]

[0063] In formula (2), m min and m max are the minimum and maximum masses of the vehicle, respectively, and m1 to m5 are the second masses corresponding to the five mass intervals. According to the mass interval where the first mass m(t) is located, the corresponding second mass is determined from m1 to m5.

[0064] For example, for a mining truck, the maximum mass and the minimum mass can be determined based on the actual tonnage of the mining truck and the maximum value of the load.

[0065] In this way, by converting the time-varying mass of the vehicle within a certain range into a fixed mass, it can provide the premise support for the determination of subsequent control parameters, thereby determining more accurate control parameters and achieving more stable vehicle control.

[0066] In step 130, the vehicle is controlled according to the control parameters. For example, the control parameters include the front wheel steering angle of the vehicle, and the vehicle can be controlled laterally according to the front wheel steering angle of the vehicle.

[0067] In the above embodiment, the vehicle's first mass is more accurately determined using a mass assessment function. Variations in the vehicle's mass are then incorporated into the calculation of control parameters. The vehicle's control parameters are then determined based on the target mass range within which the first mass falls. This allows the calculated control parameters to more accurately match the vehicle's current mass information, thereby improving the accuracy of control parameter calculation and, in turn, the stability of vehicle control.

[0068] The following describes the method for determining the control parameters in step 120 by way of some embodiments.

[0069] In some embodiments, future state information of the vehicle is predicted based on the second mass and current state information of the vehicle.

[0070] For example, a state space equation of the vehicle can be constructed based on the second mass and the current state information as a state variable; and future state information can be predicted based on the state space equation.

[0071] For example, a dynamic model of the vehicle can be constructed first, and then the state space equation of the vehicle can be constructed based on the dynamic model.

[0072] In some embodiments, a vehicle dynamics model can be established based on a single-vehicle model. Building a dynamics model based on a single-vehicle model is a classic simplification method in vehicle dynamics analysis. Its core approach is to simplify a four-wheeled vehicle into a two-wheeled, planar model with one wheel on each front and rear axle, focusing on the vehicle's lateral, longitudinal, and yaw dynamics. The basic simplifications and assumptions of the single-vehicle model are that the physical structure is simplified to a single wheel, with the left and right wheels being treated as single wheels on the front and rear axles, ignoring differences between the left and right wheels (such as load transfer and tire characteristics). The vehicle is also assumed to move on a level surface, while vertical vibrations (such as pitch and roll) are ignored. Only longitudinal (x-axis), lateral (y-axis), and yaw (z-axis) motions are considered. Key assumptions of the single-vehicle model are that the tire-ground contact relationship is linear, meaning that at small slip angles, the cornering force is proportional to the slip angle. The road surface is also assumed to be flat, without slopes or bumps. Aerodynamic details are also ignored (retaining only simplified drag terms). Furthermore, the vehicle mass is assumed to be uniformly distributed, meaning the center of mass is fixed.

[0073] The following is based on Figure 2 The coordinate system and state information in the figure are schematically illustrated to exemplify the method for constructing the vehicle dynamics model.

[0074] Figure 2 Schematic diagrams showing some embodiments of the vehicle control method disclosed herein.

[0075] According to the above assumptions, combined with Figure 2 Based on the state information shown in , the vehicle dynamics model can be established as follows:

[0076]

[0077] In formulas (3) and (4), v zx (t) represents the longitudinal speed of the vehicle, F zyf (t) and F zyr (t) represents the lateral force on the front and rear wheels respectively, l f and l r Represent the distances from the front wheel and rear wheel to the center of mass, m(t) and I z denote the first mass and moment of inertia of the vehicle, respectively. and are the sideslip angular velocity and the heading angular velocity of the vehicle respectively.

[0078] F zyf (t) and F zyr (t) can be calculated by the following formula:

[0079]

[0080] In formula (5) and formula (6), C zf and C zr Denote the cornering stiffness of the front and rear wheels of the vehicle, α zf and α zr Denote the deviation angles of the front and rear wheels of the vehicle, δ z (t) represents the front wheel steering angle of the vehicle.

[0081] After obtaining the dynamic model of the vehicle, the state variables can be selected as the lateral displacement of the center of mass of the vehicle, the slip angle, the heading angle and the heading angular velocity, that is, The state space equation is obtained as follows:

[0082]

[0083] Y z (t) = C(t)X z (t) (8)

[0084] In formula (7) and formula (8), A z (t), B z (t) and C(t) are parameter matrices, expressed as follows:

[0085]

[0086] According to formula (9) and formula (10), in the parameter matrix A z (t) and parameter matrix B z (t), m(t) and v zx(t) changes with time, so that the parameter matrix A z (t) and parameter matrix B z (t) also changes with time, which may lead to inaccurate solution of control parameters. zx The variation of m(t) is much smaller than that of m(t), so the first mass m(t) that varies with time within a certain range can be mapped to a fixed second mass m according to the mass interval by the above method. r , so that the parameter matrix A is within a certain range z (t) and parameter matrix B z (t) is a constant matrix.

[0087] In this way, by mapping the first mass of vehicles in the same mass interval to the same second mass, the parameter matrix in the state space equation is a constant matrix in each mass interval, thereby making the control parameters determined according to the subsequent method more accurate.

[0088] In some embodiments, the above state-space equation can be converted into a discrete form as shown below:

[0089]

[0090] X z,i (k+1)=(TA z,i (k)+I)X z,i (k)+TB z,i (k)δ z (k)

[0091] =A dz,i (k)X z,i (k)+B dz,i (k)δ z (k) (13)

[0092] In formula (12) and formula (13), A z,i (k) and B z,i (t) is the parameter matrix, which is expressed as follows:

[0093]

[0094] In formulas (14) and (15), i = 1 to 5 corresponds to different second masses m in formula (2). r .

[0095] In some embodiments, the future state information of the vehicle can be predicted based on the discrete state space equations of the vehicle using a model predictive control algorithm.

[0096] The following describes a method for predicting future state information by way of examples.

[0097] In some embodiments, the future state information of the vehicle may be determined by constructing a discrete model prediction algorithm for the vehicle.

[0098] For example, assume that the prediction dimension and control dimension of the model predictive control algorithm are equal, that is, N h is 10, the current moment is k, and Δu is defined i is the control increment of the vehicle, then the future state equation can be written as [X az,i (k+1), X az,i (k+2),…,X az,i (k+10)], the future control increment can be expressed as [Δu i (k+1), Δu i (k+2),…,Δu i (k+10)].

[0099] For example, the future state equation [X az,i (k+1), X az,i (k+2),…,X az,i (k+10)] can be expressed as follows:

[0100] X az,i (k+1)=A dz,i X z,i (k)+B dz,i Δu i (k)

[0101]

[0102] For example, the above future state equation can be converted into the following matrix form:

[0103]

[0104] In formula (17), the parameter matrix and parameter matrix The expression is as follows:

[0105]

[0106] By obtaining the future state equation of the vehicle through the method of the above embodiment, the future state information of the vehicle can be determined. On this basis, an objective function can be constructed based on the future state information to determine the control parameters of the vehicle.

[0107] The following uses some embodiments to exemplify the technical solution of determining the control parameters of a vehicle by constructing an objective function.

[0108] In some embodiments, an objective function is constructed with a control increment as a variable, the objective function represents a first difference between future state information and target state information, and the control increment is a second difference between a current control parameter and a target control parameter.

[0109] For example, a first weight corresponding to the first difference and a second weight corresponding to the control increment can be determined, and the first weight and the second weight are determined according to the second quality; and an objective function is constructed according to the weighted sum of the control increment and the first difference.

[0110] For example, the first weight is used to penalize the first difference between the future state information and the target state information. A larger first weight indicates a greater penalty for the first difference, meaning a lower tolerance for the first difference. This allows the optimization goal of the objective function to prioritize reducing the first difference between the vehicle's future state information and the target state information, thereby improving the vehicle's lateral control accuracy.

[0111] For example, the second weight is used to penalize the control increment. The larger the second weight, the greater the penalty for the vehicle's control increment, which means that the optimization goal of the objective function focuses on reducing the control increment and improving the stability of the vehicle.

[0112] By constructing the objective function in the above way, accurate optimization of the system is achieved. This not only improves the control accuracy but also balances the stability of the system, thereby more accurately determining the control parameters of the vehicle.

[0113] For example, through the vehicle's future state equation, the objective function can be expressed as follows:

[0114]

[0115] In formula (20), Q z,i Represents the first weight matrix corresponding to the first difference, R z,i Represents the second weight matrix corresponding to the control increment, X z,ref (t) represents the target state information of the vehicle, which can be expressed as

[0116] In some embodiments, the objective function is solved with the goal of minimizing the objective function to determine the target control parameters, and the vehicle is controlled according to the target control parameters.

[0117] In some embodiments, the objective function is solved according to constraints, where the constraints include at least one of a control parameter being in a first interval, a control increment being in a second interval, and a vehicle heading angle being in a third interval.

[0118] For example, the constraint can be expressed as:

[0119] ui , min (k+t)<<u i (k+t)<<u i,max (k+t) (21)

[0120] Δu i,min (k+t)<<Δu i (k+t)<<Δu i,max (k+t) (22)

[0121]

[0122] Formula (21), Formula (22) and Formula (23) represent the first interval, the second interval and the third interval in the constraint conditions. i,min (k+t) and u i,max (k+t) represents the minimum and maximum values ​​of the control parameters, Δu i,min (k+t) and Δu i,max (k+t) represents the minimum and maximum values ​​of the control increment, and Indicates the minimum and maximum values ​​of the heading angle.

[0123] By setting constraints, the vehicle's state information can be more reasonably adjusted based on the vehicle's performance during the objective function solution process, avoiding unreasonable control parameters caused by excessive system adjustments, which could affect vehicle control stability. The following examples illustrate the technical solution for solving control parameters using the objective function.

[0124] In some embodiments, the objective function J can be az,i (x) can be converted into a quadratic programming problem and solved, and the optimal control parameters are finally obtained as is the optimal control increment obtained by solving the objective function, u i,i (t-1) and u z,i (t) is the control parameter at adjacent moments.

[0125] For example, the online quadratic programming solution can be converted to an offline solution to address the CPU overhead associated with converting to a quadratic programming solution when calculating control parameters. This can also alleviate controller delays in certain operating scenarios, thereby improving vehicle control stability.

[0126] In some embodiments, the objective function is solved to determine at least one linear relationship between the control parameter and the current state information; and the target control parameter is determined based on the at least one linear relationship. For example, the at least one linear relationship includes multiple linear relationships.

[0127] For example, solve the objective function to determine multiple state intervals and multiple linear relationships, different state intervals correspond to different linear relationships, multiple state intervals and multiple linear relationships are determined according to the second quality, and according to the state interval to which the current state information belongs, the target control parameters are determined using the linear relationship corresponding to the state interval.

[0128] For example, the linear relationship between the control parameter and the current state information can be expressed by the following formula:

[0129]

[0130] In formula (24), i=1~5 corresponds to different vehicle masses, L z1,i ~L zm,i ,l z1,i ~l zm,i ,S z1,i ~S zm,i ,s z1,i ~s zm,i Represents the stored constant matrix. For different second masses, different L z1,i ~L zm,i ,l z1,i ~l zm,i ,S z1,i ~S zm,i ,s z1,i ~s zm,i The value of .

[0131] In this way, by determining and storing the linear relationship between the control parameters and the state information, the control parameters can be solved offline. Compared with solving the control parameters according to the quadratic programming problem each time the control parameters are calculated, this method of directly solving the control parameters according to the linear relationship between the stored control parameters and the state information can improve the calculation efficiency of the control parameters.

[0132] The following takes unmanned mining trucks as an example. Figure 3 The embodiments in the Figure 1 The technical solution of steps 110-130.

[0133] Figure 3 Flowcharts showing other embodiments of the vehicle control method of the present disclosure.

[0134] like Figure 3 As shown, in step 310, sensor data from the unmanned mining truck is read in real time. The sensor data includes steering angle, throttle, vehicle acceleration, vehicle speed, and lateral displacement. For example, an integrated navigation system installed on the unmanned mining truck can be used to obtain information such as the truck's lateral coordinates, longitudinal coordinates, and vertical coordinates, heading angle, and heading angular velocity.

[0135] For example, the wheel angle sensor installed on the unmanned mining truck can be used to obtain the steering angle information of the mining truck body, and the pressure sensor installed on the unmanned mining truck can be used to obtain the pressure information of the mining truck body.

[0136] In step 320, a dynamic model of the unmanned mining truck is constructed based on the sensor data. For example, as described above, the dynamic model can be expressed in the form of formulas (3) to (6).

[0137] In step 330, the mass is divided into multiple mass intervals, and a parameter time-varying state-space equation for lateral control is constructed and discretized to address the problem of large changes in vehicle body mass during unmanned mining truck operation. For example, the parameter time-varying state-space equation can be in the form of equations (12) to (15).

[0138] In step 340, based on the driving characteristics of the unmanned mining truck (such as high driving speed, large mass variation, and severe coupling of the vehicle dynamics model), the model predictive control algorithm is constructed in combination with the parameter time-varying state space equation obtained in step 330. For example, the future state equation can be in the form of formula (16) or converted into the matrix form of formula (17).

[0139] In step 350, the constructed model predictive control algorithm is optimized to generate an explicit model predictive control algorithm to reduce computational complexity. For example, the model predictive control algorithm can determine and store the relationship between the control parameters and the current state information, thereby determining the control parameters based on the linear relationship.

[0140] For example, the linear relationship between the control parameter and the current state information can be expressed by formula (24). As shown in formula (24), different state intervals correspond to different linear relationships. In this way, the control parameter can be determined based on the state interval to which the current state information belongs and the linear relationship corresponding to the state interval, thereby improving the calculation efficiency of the control parameter.

[0141] In step 360, the steering angle input of the mining truck is calculated in real time based on the display model predictive control method, and closed-loop control is performed. This improves the efficiency of control parameter calculation by directly calculating the control parameters offline based on the linear relationship between the stored control parameters and the state information, rather than solving the control parameters using a quadratic programming problem each time.

[0142] In the above embodiment, by constructing and optimizing a model predictive control algorithm, an explicit model predictive control algorithm is generated, which calculates the optimal sequence of control inputs for the mining truck in real time and completes closed-loop control. This reduces computational complexity and improves the control accuracy and stability of unmanned mining trucks in open-pit mines, thereby enhancing transportation efficiency and safety.

[0143] Below through Figure 4 The embodiment in the embodiment is used to illustrate the implementation Figure 3 A control device for the technical solution in FIG.

[0144] Figure 4 A block diagram showing some embodiments of a control device for a vehicle of the present disclosure.

[0145] like Figure 4 As shown, the vehicle control device 4 includes a data acquisition module 41 , an explicit model predictive control algorithm construction module 42 , and a controller solution module 43 .

[0146] The data acquisition module 41 is used to acquire sensor information from the mining truck, including information on the truck's lateral and longitudinal positions, heading angle, and heading angular velocity. The explicit model predictive control algorithm construction module 42 constructs a mining truck dynamics model, designs quality assessment functions, establishes discrete state-space equations with time-varying parameters, and constructs a discrete explicit model predictive control algorithm. The controller solution module 43 solves the explicit model predictive controller to obtain optimal control parameters.

[0147] Figure 5 A block diagram showing some other embodiments of the vehicle control device of the present disclosure.

[0148] According to other embodiments of the present disclosure, a vehicle control device 5 is provided, including: a first determination unit 51, for determining, based on the first mass of the vehicle during driving, a target mass interval to which the first mass belongs from multiple mass intervals of the vehicle; a second determination unit 52, for determining control parameters of the vehicle based on current state information of the vehicle and the target mass interval; and a control unit 53, for controlling the vehicle based on the control parameters.

[0149] In some embodiments, the second determining unit 52 maps the first mass to a second mass corresponding to the target mass interval according to the target mass interval; and determines a control parameter of the vehicle according to the second mass.

[0150] In some embodiments, the second determination unit 52 predicts the future state information of the vehicle based on the second mass and the current state information of the vehicle; constructs an objective function with the control increment as a variable, the objective function characterizes the first difference between the future state information and the target state information, and the control increment is the second difference between the current control parameter and the target control parameter; solves the objective function with the goal of minimizing the objective function to determine the target control parameters, and the control unit controls the vehicle according to the target control parameters.

[0151] In some embodiments, the second determination unit 52 constructs a state space equation of the vehicle based on the second mass and the current state information as a state variable, where the current state information includes at least one of lateral displacement, slip angle, heading angle, and heading angular velocity; and predicts future state information based on the state space equation.

[0152] In some embodiments, the second determination unit 52 determines a first weight corresponding to the first difference and a second weight corresponding to the control increment, the first weight and the second weight being determined according to the second mass; and constructs an objective function according to the weighted sum of the control increment and the first difference.

[0153] In some embodiments, the second determining unit 52 solves the objective function to determine at least one linear relationship between the control parameter and the current state information; and determines the target control parameter according to the at least one linear relationship.

[0154] In some embodiments, at least one linear relationship includes multiple linear relationships, and the second determination unit 52 solves the objective function to determine multiple state intervals and multiple linear relationships. Different state intervals correspond to different linear relationships. Multiple state intervals and multiple linear relationships are determined based on the second quality. According to the state interval to which the current state information belongs, the target control parameters are determined using the linear relationship corresponding to the state interval.

[0155] In some embodiments, the second determination unit 52 solves the objective function according to the constraint conditions, where the constraint conditions include at least one of the control parameter being in the first interval, the control increment being in the second interval, and the vehicle heading angle being in the third interval.

[0156] In some embodiments, the control parameter includes a front wheel steering angle of the vehicle, and the control unit 53 performs lateral control on the vehicle according to the front wheel steering angle of the vehicle.

[0157] Figure 6 A block diagram showing still further embodiments of the vehicle control device of the present disclosure.

[0158] like Figure 6As shown, the vehicle control device 6 of this embodiment includes: a memory 61 and a processor 62 coupled to the memory 61 , and the processor 62 is configured to execute the vehicle control method in any one embodiment of the present disclosure based on instructions stored in the memory 61 .

[0159] The memory 61 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory may store, for example, an operating system, an application program, a boot loader, a database, and other programs.

[0160] Figure 7 A block diagram showing still further embodiments of the vehicle control device of the present disclosure.

[0161] like Figure 7 As shown, the vehicle control device 7 of this embodiment includes: a memory 710 and a processor 720 coupled to the memory 710, and the processor 720 is configured to execute the vehicle control method in any of the aforementioned embodiments based on instructions stored in the memory 710.

[0162] The memory 710 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory may store, for example, an operating system, application programs, a boot loader, and other programs.

[0163] The vehicle control device 7 may also include an input / output interface 730, a network interface 740, a storage interface 750, and the like. These interfaces 730, 740, 750, as well as the memory 710 and the processor 720, may be connected, for example, via a bus 760. The input / output interface 730 provides a connection interface for input / output devices such as a display, mouse, keyboard, touch screen, microphone, and speakers. The network interface 740 provides a connection interface for various networked devices. The storage interface 750 provides a connection interface for external storage devices such as SD cards and USB flash drives.

[0164] Figure 8 A block diagram illustrating some embodiments of the vehicle of the present disclosure is shown.

[0165] like Figure 8 As shown, the vehicle 8 of this embodiment includes the vehicle control device 81 of any one of the above embodiments.

[0166] Those skilled in the art will appreciate that embodiments of the present disclosure may be provided as methods, systems, computer program products, or vehicles. Thus, the present disclosure may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0167] The vehicle control method, vehicle control device, vehicle, and computer-readable storage medium according to the present disclosure have been described in detail. To avoid obscuring the concepts of the present disclosure, some details known in the art have been omitted. Based on the above description, those skilled in the art will fully understand how to implement the technical solutions disclosed herein.

[0168] The methods and systems of the present disclosure may be implemented in many ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers recording media that store programs for executing the methods according to the present disclosure.

[0169] Although some specific embodiments of the present disclosure have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure.

Claims

1. A vehicle control method, comprising: determining, according to a first mass of the vehicle during driving, a target mass interval to which the first mass belongs from a plurality of mass intervals of the vehicle; determining a control parameter of the vehicle according to the current state information of the vehicle and the target mass range; The vehicle is controlled according to the control parameters.

2. The control method according to claim 1, wherein: Determining the control parameters of the vehicle according to the current state information of the vehicle and the target mass range includes: According to the target mass interval, mapping the first mass to a second mass corresponding to the target mass interval; A control parameter of the vehicle is determined based on the second mass.

3. The control method according to claim 2, wherein: Determining the control parameter of the vehicle according to the second mass includes: predicting future state information of the vehicle based on the second mass and current state information of the vehicle; Constructing an objective function with a control increment as a variable, wherein the objective function represents a first difference between the future state information and the target state information, and the control increment is a second difference between the current control parameter and the target control parameter; The objective function is minimized to determine the target control parameters. The controlling the vehicle according to the control parameter includes: The vehicle is controlled according to the target control parameter.

4. The control method according to claim 3, wherein: Predicting the future state information of the vehicle based on the second mass and the current state information of the vehicle includes: constructing a state space equation of the vehicle based on the second mass and taking the current state information as a state variable, wherein the current state information includes at least one of a lateral displacement, a slip angle, a heading angle, and a heading angular velocity; The future state information is predicted according to the state-space equation.

5. The control method according to claim 3, wherein: The objective function constructed by taking the control increment as a variable includes: determining a first weight corresponding to the first difference and a second weight corresponding to the control increment, wherein the first weight and the second weight are determined according to the second mass; The objective function is constructed according to a weighted sum of the control increment and the first difference.

6. The control method according to claim 3, wherein: Solving the objective function to determine the target control parameter includes: solving the objective function to determine at least one linear relationship between the control parameter and the current state information; The target control parameter is determined according to the at least one linear relationship.

7. The control method according to claim 6, wherein: The at least one linear relationship includes a plurality of linear relationships, Solving the objective function to determine at least one linear relationship between the target control parameter and the current state information includes: Solving the objective function to determine a plurality of state intervals and the plurality of linear relationships, wherein different state intervals correspond to different linear relationships, and the plurality of state intervals and the plurality of linear relationships are determined according to the second quality; The determining the target control parameter according to the at least one linear relationship includes: The target control parameter is determined according to the state interval to which the current state information belongs and using the linear relationship corresponding to the state interval.

8. The control method according to claim 3, wherein: The solving the objective function comprises: The objective function is solved according to constraint conditions, where the constraint conditions include at least one of the control parameter being in a first interval, the control increment being in a second interval, and the heading angle of the vehicle being in a third interval.

9. The control method according to any one of claims 1 to 8, wherein: The control parameters include the front wheel steering angle of the vehicle, The controlling the vehicle according to the control parameter includes: The vehicle is laterally controlled according to the front wheel steering angle of the vehicle.

10. A vehicle control device comprising: a first determining unit, configured to determine, based on a first mass of the vehicle during driving, a target mass interval to which the first mass belongs from a plurality of mass intervals of the vehicle; a second determining unit, configured to determine a control parameter of the vehicle according to the current state information of the vehicle and the target mass range; A control unit is used to control the vehicle according to the control parameters.

11. A vehicle control device, comprising: Memory; and A processor coupled to the memory, wherein the processor is configured to execute the control method according to any one of claims 1 to 9 based on instructions stored in the memory.

12. A vehicle comprising: The control device according to claim 10 or 11.

13. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the control method according to any one of claims 1 to 9 is implemented.

14. A computer program product comprising instructions, which, when executed by a processor, cause the processor to perform the control method according to any one of claims 1 to 9.

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