Intelligent driving bus control method, device and equipment and storage medium

Abstract

The embodiment of the invention relates to the field of vehicles, in particular to an intelligent driving bus control method, device and equipment and a storage medium, and the method comprises the steps: building a linear discrete time bus dynamics model which is used for describing the longitudinal motion information of a bus; based on the bus dynamics model, a self-adaptive iterative learning controller for high-order pseudo partial derivative estimation is generated, the self-adaptive iterative learning controller is used for controlling the longitudinal motion information, and the error between the longitudinal motion information and target longitudinal motion information input by a user is smaller than an error threshold value; and controlling the longitudinal operation of the bus based on a self-adaptive iterative learning control controller. The technical problem of low longitudinal control precision of the automatic driving bus in the prior art is solved, and the technical effect of improving the longitudinal control precision of the automatic driving bus is achieved.

Description

A method, device, equipment and storage medium for controlling an intelligent driving bus Technical Field

[0001] This application relates to the field of vehicles, and more particularly to a method, apparatus, device, and storage medium for controlling an intelligent driving bus. Background Technology

[0002] Autonomous vehicle control is a core technology of intelligent transportation systems. It utilizes sensors and information systems to enable vehicles to operate autonomously, achieving goals such as reducing human-caused accidents and improving traffic efficiency. Autonomous vehicle operation control systems can generally be divided into longitudinal and lateral motion control systems. The longitudinal motion control system tracks the vehicle's speed, while the lateral motion control system controls the vehicle's position and attitude.

[0003] However, for the lateral and longitudinal control of autonomous vehicles, most current model-based control methods suffer from errors in longitudinal control due to the inability to obtain accurate mathematical models. Furthermore, current autonomous driving control schemes are mostly designed for high-speed trains and small vehicles; there is a lack of research on control schemes for autonomous buses. For systems like buses, where models are more difficult to establish, using approximate model estimations will worsen the control performance and increase the difficulty of modeling and control.

[0004] Therefore, existing technologies suffer from the technical problem of low longitudinal control accuracy in autonomous buses.

[0005] The purpose of this application is to provide a control method, device, equipment, and storage medium for intelligent driving buses, so as to improve the longitudinal control accuracy of autonomous driving buses.

[0006] In a first aspect, this application provides an intelligent driving bus control method, comprising: establishing a linear discrete-time bus dynamics model, wherein the bus dynamics model is used to describe the longitudinal motion information of the bus; generating an adaptive iterative learning controller based on the bus dynamics model, wherein the adaptive iterative learning controller is used to control the longitudinal motion information, and the error between the controller and the target longitudinal motion information input by the user is less than an error threshold; and controlling the longitudinal operation of the bus based on the adaptive iterative learning controller.

[0007] Furthermore, a linear discrete-time bus dynamics model is established, including: obtaining the linear form of the incremental product of the higher-order pseudo-partial derivatives and the input of the bus dynamics model; using the linear form to represent the change of the output of the bus dynamics model between adjacent iterations, so as to realize the dynamic linearization of the nonlinear discrete-time bus dynamics model, wherein the higher-order pseudo-partial derivatives are time-varying parameters, and the higher-order pseudo-partial derivatives satisfy the preset boundedness conditions.

[0008] Furthermore, the linear discrete-time bus dynamics model satisfies: , For vehicle speed, Sampling time, For vehicle quality, As the driving force, For braking force, For air resistance, For road resistance.

[0009] Furthermore, the control law of the controller satisfies: = + , This is the total control input at time t during the k-th iteration. For the feedforward part, For the feedback part, feedforward part The update is based on the pseudo-partial derivative estimate from the previous iteration and a higher-order learning algorithm, with the feedback part... Adjustments are made based on the tracking error of the current iteration.

[0010] Furthermore, the adaptive iterative learning controller for generating higher-order pseudo-partial derivative estimates includes: estimating pseudo-partial derivatives using a higher-order estimation algorithm with a parameter reset mechanism to obtain pseudo-partial derivative estimates; and resetting the pseudo-partial derivative estimates to their initial values ​​when the pseudo-partial derivative estimates meet the reset condition, wherein the update formula for the pseudo-partial derivatives satisfies: , This is the estimated value of the pseudo-partial derivative at time t in the k-th iteration. As the first weighting factor, As a regulating factor, For input increment, This is the output increment.

[0011] Furthermore, the feedforward section of the controller The update formula satisfies: , For learning gain, As the second weighting factor, This represents the tracking error from the previous iteration. Further, the feedback section... The update formula satisfies: , For feedback gain, For controller parameters, This represents the tracking error for the current iteration.

[0012] Secondly, this application also provides an intelligent driving bus control device, comprising: a modeling module for establishing a linear discrete-time bus dynamics model, wherein the bus dynamics model is used to describe the longitudinal motion information of the bus; a generation module for generating an adaptive iterative learning controller based on the bus dynamics model, wherein the adaptive iterative learning controller is used to control the longitudinal motion information, and the error between the controller and the target longitudinal motion information input by the user is less than an error threshold; and a control module for controlling the longitudinal operation of the bus based on the adaptive iterative learning controller.

[0013] Thirdly, this application also provides an electronic device, including: a processor; a memory for storing processor-executable instructions; and a processor for executing the above-described intelligent driving bus control method by running the instructions in the memory.

[0014] Fourthly, this application also provides a computer storage medium storing instructions that, when executed, implement the aforementioned intelligent driving bus control method.

[0015] This application embodiment establishes a linear discrete-time bus dynamics model, which describes the longitudinal motion information of the bus. Based on the bus dynamics model, an adaptive iterative learning controller with high-order pseudo-partial derivative estimation is generated. This adaptive iterative learning controller controls the longitudinal motion information, and the error between it and the target longitudinal motion information input by the user is less than an error threshold. Based on the adaptive iterative learning controller, the longitudinal operation of the bus is controlled. This solves the technical problem of low longitudinal control accuracy in existing autonomous driving buses and achieves the technical effect of improving the longitudinal control accuracy of autonomous driving buses.

[0016] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and constitute a part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 is a flowchart of an intelligent driving bus control method provided in an embodiment of this application; Figure 2 is a flowchart of another intelligent driving bus control method provided in an embodiment of this application; Figure 3 is a schematic diagram of force analysis of a bus longitudinal control system provided in an embodiment of this application; Figure 4 is a structural diagram of an intelligent driving bus control device provided in an embodiment of this application; Figure 5 is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] To facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. For example, the first threshold and the second threshold are only used to distinguish different thresholds and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0019] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0020] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, a combination of a and b, a combination of a and c, a combination of b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0021] In existing technologies, research on longitudinal control for autonomous driving mainly relies on model predictive control (MPC). To improve the vehicle's tracking accuracy of longitudinal speed, a hierarchical longitudinal controller based on MPC is designed. The upper-level MPC controller calculates the vehicle's desired acceleration, while the lower-level controller employs fuzzy PID control structures for throttle and braking. However, this model-based control method has limitations; it cannot obtain an accurate mathematical model, leading to errors in longitudinal control.

[0022] Due to the massive amount of data in control systems, efficiently utilizing the data information within the system to improve its control performance is of significant research value, making data-driven methods a prominent approach. To address the technical problem of low longitudinal control accuracy in existing autonomous buses, this application provides an intelligent driving bus control method, as shown in Figure 1. Figure 1 is a flowchart of an intelligent driving bus control method provided in this application, including: S101: Establishing a linear discrete-time bus dynamics model, where the bus dynamics model describes the longitudinal motion information of the bus; S102: Based on the bus dynamics model, generating an adaptive iterative learning controller with high-order pseudo-partial derivative estimation, where the adaptive iterative learning controller controls the longitudinal motion information, and the error between the controller and the target longitudinal motion information input by the user is less than an error threshold; S103: Controlling the longitudinal movement of the bus based on the adaptive iterative learning controller.

[0023] It should be noted that this application proposes a data-driven control method for autonomous buses, specifically for such systems. For discrete-time nonlinear systems with high repetitiveness and difficult-to-establish system models, iterative learning control is an effective method that can improve the system's tracking convergence and control accuracy. However, it suffers from limitations such as poor transient performance of the system output along the iteration axis. Furthermore, the design and analysis of traditional iterative learning control laws rely on certain knowledge of the system, and the selection of learning gains is often arbitrary. Therefore, based on traditional ILC and incorporating the characteristics of model-free adaptive control, a model-free adaptive iterative learning control method based on high-order pseudo-partial derivative estimation of optimal performance indices is designed. This method relies solely on the I / O data of the controlled system, ensuring that the tracking error converges monotonically and has good tracking accuracy while providing relaxed initial conditions, thus improving system robustness. The use of a high-order learning algorithm further enhances the system's convergence performance and control quality. Based on the theoretical research of the above control method, it is applied to a transportation system to achieve speed tracking control.

[0024] Figure 2 shows a flowchart of another intelligent driving bus control method provided in this embodiment. A linear discrete-time bus dynamics model is established, which describes the longitudinal motion information of the bus. Based on the bus dynamics model, an adaptive iterative learning controller with high-order pseudo-partial derivative estimation is generated. This adaptive iterative learning controller controls the longitudinal motion information, and the error between it and the target longitudinal motion information input by the user is less than an error threshold. Based on the adaptive iterative learning controller, the longitudinal operation of the bus is controlled. This solves the technical problem of low longitudinal control accuracy in existing autonomous driving buses and achieves the technical effect of improving the longitudinal control accuracy of autonomous driving buses.

[0025] In an optional embodiment, S101: Establishing a linear discrete-time bus dynamics model includes: S1011: Obtaining the linear form of the incremental product of the higher-order pseudo-partial derivatives and the input of the bus dynamics model; S1012: Using the linear form, representing the change of the output of the bus dynamics model between adjacent iterations, so as to realize the dynamic linearization of the nonlinear discrete-time bus dynamics model, wherein the higher-order pseudo-partial derivatives are time-varying parameters, and the higher-order pseudo-partial derivatives satisfy the preset boundedness condition.

[0026] Optionally, the linear discrete-time bus dynamics model satisfies: , For vehicle speed, Sampling time, For vehicle quality, As the driving force, For braking force, For air resistance, For road resistance.

[0027] Optionally, the controller's control rate satisfies: = + , This is the total control input at time t during the k-th iteration. For the feedforward part, For the feedback part, feedforward part The update is based on the pseudo-partial derivative estimate from the previous iteration and a higher-order learning algorithm, with the feedback part... Adjustments are made based on the tracking error of the current iteration.

[0028] In an optional embodiment, S102: the adaptive iterative learning controller for generating higher-order pseudo-partial derivative estimates includes: S1021: estimating pseudo-partial derivatives using a higher-order estimation algorithm with a parameter reset mechanism to obtain pseudo-partial derivative estimates; S1022: when the pseudo-partial derivative estimates meet the reset condition, resetting the pseudo-partial derivative estimates to their initial values, wherein the update formula for the pseudo-partial derivatives satisfies: , This is the estimated value of the pseudo-partial derivative at time t in the k-th iteration. As the first weighting factor, As a regulating factor, For input increment, This is the output increment.

[0029] Optionally, the feedforward section of the controller The update formula satisfies: , For learning gain, As the second weighting factor, This represents the tracking error from the previous iteration. Optional, feedback section. The update formula satisfies: , For feedback gain, For controller parameters, This represents the tracking error for the current iteration.

[0030] In an exemplary embodiment, this application provides a control method for an intelligent driving bus. Targeting discrete-time nonlinear systems with high repetitiveness and difficulty in establishing system models, this method combines the similarities between model-free adaptive control and iterative learning control with the advantages of high-order learning control algorithms. Based on existing methods, a model-free adaptive iterative learning control method with high-order pseudo-partial derivative estimation is designed. This method ensures that the system has relaxed initial conditions while maintaining monotonically convergent tracking error and good tracking accuracy, thus improving system robustness. This completes the key fundamental theoretical research for the project. Finally, simulation studies are conducted to demonstrate its effectiveness. The current system design process is as follows: first, dynamic linearization is performed, then controller design is carried out. For the controller design model, convergence analysis is performed, and its effectiveness can be verified through numerical simulation. The specific steps are as follows: Step 1: System modeling, considering the following repetitive nonlinear discrete-time single-input single-output system:

[0031] in, Indicates time, Indicates the number of iterations. It is the corresponding vector-valued function. The first Second iteration The system output and control input at any given time. For repeated bounded external disturbances, the system must satisfy the following assumptions: Assumption 1: the function Satisfying the consistent global Lipschitz condition, i.e.

[0032] in, and This is the Lipschitz constant.

[0033] Assumption 2: Given the desired trajectory , There exists a unique bounded control , , making

[0034] The transformation can also be described as:

[0035] in, and These are two unknown positive integers representing the order of the system.

[0036] Assumption 3: Except for finite points in time, Regarding the ( The partial derivatives of the ) variables exist and are continuous.

[0037] Assumption 4: The system satisfies the Lipschitz condition along the iteration axis, i.e. and ,like Then the following formula holds true:

[0038] in, , , It is a constant.

[0039] For a nonlinear discrete system that satisfies assumptions 3 and 4, when , At that time, there must exist a time-varying parameter called the pseudo-partial derivative. This will be converted into a dynamic model.

[0040] in, This is called satisfaction. The pseudo-partial derivatives, It is a constant.

[0041] Step 2: Controller design, algorithm as follows:

[0042]

[0043] ,like or or .

[0044] in, Indicates the first The tracking error of the next iteration. Indicates the feedforward portion. This indicates the feedback section. (Among them...) It is a weighting factor. It is a regulatory factor. For feedback gain, It is a sufficiently small positive number. This represents a higher-order estimate, referring to the parameters of the controlled object model. yes The initial value of .

[0045] Step 3: Verification and Analysis. As shown in Figure 3, Figure 3 is a schematic diagram of the force analysis of the longitudinal control system of the bus provided in this embodiment. According to Newton's laws of motion and the dynamic model, the longitudinal dynamic model of the bus can be described as follows:

[0046] The longitudinal dynamic equation can be obtained:

[0047] The longitudinal dynamics model can be further transformed into a discrete-time longitudinal dynamics model for buses: .

[0048] The relevant parameters for bus operation during actual testing are shown in the table below:

[0049] Therefore, the simulation duration for this application test is 170 seconds. According to the relevant provisions of the "Road Traffic Safety Law of the People's Republic of China," the maximum speed limit for this bus on urban roads is 54.5 km / h. The input-output relationship of the dynamic model used in the simulation is described as follows:

[0050] This control method has been verified to effectively control the longitudinal speed of driverless buses. Currently, the feasibility of this method is only being theoretically verified, and a data-driven autonomous driving bus control system can be designed.

[0051] Based on the same concept, this application also provides an intelligent driving bus control device. Please refer to Figure 4, which is a structural diagram of an intelligent driving bus control device provided in this application embodiment. It includes: a modeling module 201, used to model a linear discrete-time bus dynamics model, wherein the bus dynamics model is used to describe the longitudinal motion information of the bus; a generation module 202, used to generate an adaptive iterative learning controller with high-order pseudo-partial derivative estimation based on the bus dynamics model, wherein the adaptive iterative learning controller is used to control the longitudinal motion information, and the error between the controller and the target longitudinal motion information input by the user is less than an error threshold; and a control module 203, used to control the longitudinal operation of the bus based on the adaptive iterative learning controller.

[0052] By establishing a linear discrete-time bus dynamics model, which describes the longitudinal motion information of the bus, and generating an adaptive iterative learning controller based on the bus dynamics model and high-order pseudo-partial derivative estimation, the longitudinal motion information is controlled, with the error between the controller and the user-inputted target longitudinal motion information being less than an error threshold. The longitudinal operation of the bus is then controlled based on this adaptive iterative learning controller. This solves the technical problem of low longitudinal control accuracy in existing autonomous driving buses, achieving a significant improvement in the longitudinal control accuracy of autonomous driving buses.

[0053] Furthermore, the establishment module 201 includes an acquisition unit and a representation unit.

[0054] The acquisition unit is used to acquire the linear form of the incremental product of the higher-order pseudo-partial derivatives and the input of the bus dynamics model; the representation unit is used to represent the change of the output of the bus dynamics model between adjacent iterations in a linear form, so as to realize the dynamic linearization of the nonlinear discrete-time bus dynamics model. The higher-order pseudo-partial derivatives are time-varying parameters and satisfy the preset boundedness conditions.

[0055] Furthermore, the linear discrete-time bus dynamics model satisfies: , For vehicle speed, Sampling time, For vehicle quality, As the driving force, For braking force, For air resistance, For road resistance.

[0056] Furthermore, the control law of the controller satisfies: = + , This is the total control input at time t during the k-th iteration. For the feedforward part, For the feedback part, feedforward part The update is based on the pseudo-partial derivative estimate from the previous iteration and a higher-order learning algorithm, with the feedback part... Adjustments are made based on the tracking error of the current iteration.

[0057] Furthermore, the generation module 202 includes a calculation unit and a reset unit. The calculation unit is used to estimate the pseudo-partial derivatives using a higher-order estimation algorithm with a parameterized reset mechanism, obtaining pseudo-partial derivative estimates. The reset unit is used to reset the pseudo-partial derivative estimates to their initial values ​​when the pseudo-partial derivative estimates meet the reset conditions, wherein the update formula for the pseudo-partial derivatives satisfies: , This is the estimated value of the pseudo-partial derivative at time t in the k-th iteration. As the first weighting factor, As a regulating factor, For input increment, This is the output increment.

[0058] Furthermore, the feedforward section of the controller The update formula satisfies: , For learning gain, As the second weighting factor, This represents the tracking error from the previous iteration. Further, the feedback section... The update formula satisfies: , For feedback gain, For controller parameters, This represents the tracking error for the current iteration.

[0059] This application also provides an electronic device. Please refer to Figure 5, which is a structural diagram of the electronic device provided in an embodiment of this application.

[0060] As shown in Figure 5, the electronic device 400 includes a processor 410.

[0061] As shown in Figure 5, the processor 410 can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program in this application.

[0062] As shown in Figure 5, the electronic device 400 may further include a communication line 440. The communication line 440 may include a path for transmitting information between the components.

[0063] Optionally, as shown in Figure 5, the above-mentioned electronic device may further include a communication interface 420. There may be one or more communication interfaces 420. The communication interface 420 can use any transceiver-like device for communicating with other devices or communication networks.

[0064] Optionally, as shown in FIG5, the electronic device may further include a memory 430. The memory 430 is used to store computer execution instructions for implementing the present application's solution, and its execution is controlled by a processor. The processor is used to execute the computer execution instructions stored in the memory, thereby implementing the intelligent driving bus control method provided in the embodiments of this application.

[0065] As shown in Figure 5, the memory 430 can be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or it can be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory 430 can exist independently and be connected to the processor 410 via communication line 440. The memory 430 can also be integrated with the processor 410.

[0066] Optionally, the computer execution instructions in the embodiments of this application may also be referred to as application code, and the embodiments of this application do not specifically limit this.

[0067] In a specific implementation, as shown in FIG5, the processor 410 may include one or more CPUs, such as CPU0 and CPU1 in FIG5.

[0068] In a specific implementation, as shown in FIG5, the terminal device may include multiple processors, such as the first processor 4101 and the second processor 4102 in FIG5. Each of these processors may be a single-core processor or a multi-core processor.

[0069] The methods disclosed in the embodiments of this application can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above-mentioned intelligent driving bus control method.

[0070] This application also provides a computer-readable storage medium storing instructions that, when executed, implement the functions performed by the terminal device in the above embodiments.

[0071] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid-state drive (SSD).

[0072] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0073] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A method for controlling an intelligent driving bus, characterized in that, include: A linear discrete-time bus dynamics model is established, wherein the bus dynamics model is used to describe the longitudinal motion information of the bus; based on the bus dynamics model, an adaptive iterative learning controller for estimating higher-order pseudo-partial derivatives is generated, wherein the adaptive iterative learning controller is used to control the longitudinal motion information, and the error between the controller and the target longitudinal motion information input by the user is less than an error threshold; based on the adaptive iterative learning controller, the longitudinal operation of the bus is controlled.

2. The method according to claim 1, characterized in that, Establishing a linear discrete-time bus dynamics model includes: obtaining the linear form of the incremental product of higher-order pseudo-partial derivatives and the input of the bus dynamics model; using the linear form to represent the change of the output of the bus dynamics model between adjacent iterations, so as to realize the dynamic linearization of the nonlinear discrete-time bus dynamics model, wherein the higher-order pseudo-partial derivatives are time-varying parameters, and the higher-order pseudo-partial derivatives satisfy a preset boundedness condition.

3. The method according to claim 2, characterized in that, The linear discrete-time bus dynamics model satisfies: ， For vehicle speed, Sampling time, For vehicle quality, As the driving force, For braking force, For air resistance, For road resistance.

4. The method according to claim 1, characterized in that, The control rate of the controller satisfies: = + ， This is the total control input at time t during the k-th iteration. For the feedforward part, For the feedback section, the feedforward section The update is based on the pseudo-partial derivative estimate from the previous iteration and a higher-order learning algorithm; the feedback part... Adjustments are made based on the tracking error of the current iteration.

5. The method according to claim 3, characterized in that, An adaptive iterative learning controller for generating higher-order pseudo-partial derivative estimates includes: estimating the pseudo-partial derivatives using a higher-order estimation algorithm with a parameter reset mechanism to obtain pseudo-partial derivative estimates; and resetting the pseudo-partial derivative estimates to initial values ​​when the pseudo-partial derivative estimates meet a reset condition, wherein the update formula for the pseudo-partial derivatives satisfies: ， Let be the estimated value of the pseudo-partial derivative at time t in the k-th iteration. As the first weighting factor, As a regulating factor, For input increment, This is the output increment.

6. The method according to claim 4, characterized in that, The feedforward portion of the controller The update formula satisfies: ， For learning gain, As the second weighting factor, This represents the tracking error from the previous iteration.

7. The method according to claim 4, characterized in that, The feedback section The update formula satisfies: ， For feedback gain, For controller parameters, This represents the tracking error for the current iteration.

8. A smart driving bus control device, characterized in that, include: A module is established to establish a linear discrete-time bus dynamics model, wherein the bus dynamics model is used to describe the longitudinal motion information of the bus; a generation module is used to generate an adaptive iterative learning controller for estimating higher-order pseudo-partial derivatives based on the bus dynamics model, wherein the adaptive iterative learning controller is used to control the longitudinal motion information, and the error between the controller and the target longitudinal motion information input by the user is less than an error threshold; a control module is used to control the longitudinal operation of the bus based on the adaptive iterative learning controller.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the intelligent driving bus control method according to any one of claims 1 to 7 by running instructions in the memory.

10. A computer storage medium, characterized in that, The computer storage medium stores instructions that, when executed, implement the intelligent driving bus control method according to any one of claims 1 to 7.

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