Vehicle control method and storage medium

Abstract

Embodiments of the present application provide a vehicle control method and a storage medium, comprising: acquiring kingpin turning angle state data, steering motor operation data, motor current feedback data and a target kingpin turning angle; constructing a first target model according to the kingpin turning angle state data, the steering motor operation data and the motor current feedback data, and constructing an extended disturbance state observer according to the kingpin turning angle state data, the motor current feedback data and the first target model; determining a basic control amount based on the kingpin turning angle state data, the target kingpin turning angle and the first target model, and determining an output compensation control amount based on the kingpin turning angle state data, the motor current feedback data, the target kingpin turning angle and the extended disturbance state observer; determining a kingpin compensation torque based on the basic control amount and the output compensation control amount, and adjusting a real-time kingpin turning angle to the target kingpin turning angle based on the kingpin compensation torque. The present application solves the technical problem of low accuracy of kingpin turning angle tracking control in the related art.

Description

Technical Field

[0001] This application relates to the field of vehicle steer-by-wire control technology, and more specifically, to a vehicle control method and storage medium. Background Technology

[0002] In related technologies, kingpin angle tracking control is typically achieved by using a cascaded closed-loop structure (i.e., an outer loop for the steering angle position and an inner loop for velocity or acceleration) and superimposing a feedforward term for the target steering angle change, thereby improving the dynamic response speed and tracking accuracy of the vehicle's steer-by-wire control system. However, when the equivalent inertia of the kingpin steering actuator is large and varies significantly with operating conditions, the aforementioned methods often require increasing the inner loop bandwidth or feedforward gain to achieve rapid tracking, making the system abnormally sensitive to parameter perturbations and load fluctuations. Furthermore, when superimposing disturbance observers or extended state observers to suppress complex disturbances such as friction, self-aligning torque, and road surface excitation, sufficient disturbance reconstruction capability requires increasing the observation gain and estimation bandwidth, which easily introduces additional phase lag and compresses the phase margin of the closed-loop system. This makes it difficult to maintain sufficient stability margin while pursuing high dynamic angle tracking performance, resulting in a narrow controller tuning window, high sensitivity to sampling delay and filtering, and ultimately low accuracy of kingpin angle tracking control.

[0003] There is currently no good solution to the above problems. Summary of the Invention

[0004] This application provides a vehicle control method and storage medium to at least solve the technical problem of low accuracy in kingpin angle tracking control in related technologies.

[0005] According to one aspect of the embodiments of this application, a vehicle control method is provided, comprising: acquiring kingpin angle state data, steering motor operating data, motor current feedback data, and a target kingpin angle, wherein the kingpin angle state data includes a real-time kingpin angle, which represents the real-time rotation angle of the steering kingpin column relative to the kingpin axis; constructing a first target model based on the kingpin angle state data, steering motor operating data, and motor current feedback data; and constructing an extended disturbance state observer based on the kingpin angle state data, motor current feedback data, and the first target model, wherein the first target model ... steering motor operating data, and motor current feedback data. The system generates an equivalent dynamic relationship model corresponding to the motor operating data and motor current feedback data. Based on the kingpin angle state data, the target kingpin angle, and the first target model, it determines the basic control quantity and the output compensation control quantity based on the kingpin angle state data, motor current feedback data, the target kingpin angle, and the extended disturbance state observer. The output compensation control quantity represents the correction control quantity corresponding to the real-time kingpin angle, and the basic control quantity represents the reference control quantity corresponding to the real-time kingpin angle under the preset inertia condition. Based on the basic control quantity and the output compensation control quantity, it determines the kingpin compensation torque and adjusts the real-time kingpin angle to the target kingpin angle based on the kingpin compensation torque.

[0006] Optionally, the kingpin rotation angle state data further includes: real-time kingpin rotation angle velocity. Constructing an expanded disturbance state observer based on the kingpin rotation angle state data, motor current feedback data, and the first target model includes: constructing a first expanded state variable based on the real-time kingpin rotation angle; constructing a second expanded state variable based on the real-time kingpin rotation angle velocity, wherein the real-time kingpin rotation angle velocity is the first derivative corresponding to the real-time kingpin rotation angle; constructing a third expanded state variable based on preset composite disturbance parameters; and constructing the expanded disturbance state observer based on the first expanded state variable, the second expanded state variable, the third expanded state variable, the motor current feedback data, and preset equivalent control gain parameters.

[0007] Optionally, the extended disturbance state observer includes: a first state equation, a second state equation, and a third state equation. Constructing the extended disturbance state observer based on the first extended state variable, the second extended state variable, the third extended state variable, motor current feedback data, and preset equivalent control gain parameters includes: constructing the first state equation based on the second extended state variable; constructing the second state equation based on the third extended state variable, motor current feedback data, and preset equivalent control gain parameters; and constructing the third state equation based on the third extended state variable. The extended disturbance state observer is constructed based on the first state equation, the second state equation, the third state equation, and a preset observation error, wherein the preset observation error is determined based on the real-time kingpin rotation angle and the extended state estimate corresponding to the first extended state variable.

[0008] Optionally, the kingpin rotation angle state data further includes: real-time kingpin rotation angle acceleration. Determining the basic control quantity based on the kingpin rotation angle state data, the target kingpin rotation angle, and the first target model includes: determining error state information based on the real-time kingpin rotation angle and the target kingpin rotation angle, wherein the error state information is the difference between the real-time kingpin rotation angle and the target kingpin rotation angle; and determining the basic control quantity based on the real-time kingpin rotation angle acceleration, preset equivalent inertia parameters, preset error feedback gain, and error state information.

[0009] Optionally, the kingpin angle state data further includes: real-time kingpin angle acceleration; the extended disturbance state observer includes: a third extended state variable; determining the output compensation control quantity based on the kingpin angle state data, motor current feedback data, target kingpin angle, and the extended disturbance state observer includes: determining target inertia characteristic information based on motor current feedback data and the third extended state variable, and determining the target kingpin angle acceleration based on the target inertia characteristic information, real-time kingpin angle acceleration, and error state information, wherein the error state information is the difference between the real-time kingpin angle and the target kingpin angle; determining the inertial compensation feedforward control quantity based on the target kingpin angle acceleration and target inertia characteristic information; determining the error feedback control quantity based on the error state information and the first derivative corresponding to the error state information; determining the extended state estimate corresponding to the third extended state variable based on the extended disturbance state observer, and determining the disturbance suppression compensation control quantity based on the extended state estimate corresponding to the third extended state variable; and determining the output compensation control quantity based on the inertial compensation feedforward control quantity, the error feedback control quantity, and the disturbance suppression compensation control quantity.

[0010] Optionally, determining the target inertia characteristic information based on motor current feedback data and the third extended state variable includes: determining inertia state information based on motor current feedback data and the third extended state variable; determining initial inertia characteristic information based on the inertia state information and preset inertia characteristic weights; performing bounded mapping processing on the initial inertia characteristic information to obtain corrected inertia characteristic information; and performing filtering processing on the corrected inertia characteristic information to obtain the target inertia characteristic information.

[0011] Optionally, determining the kingpin compensation torque based on the basic control quantity and the output compensation control quantity includes: determining the motor current reference control quantity based on the basic control quantity and the output compensation control quantity; and determining the kingpin compensation torque based on the motor current reference control quantity.

[0012] Optionally, determining the kingpin compensation torque based on the motor current reference control quantity includes: constructing a first objective function based on the motor current reference control quantity; determining a target control sequence based on the first objective function and preset constraints, wherein the target control sequence includes multiple candidate motor current control quantities; determining a target motor current control quantity among the multiple candidate motor current control quantities based on the target control sequence; and determining the kingpin compensation torque based on the target motor current control quantity.

[0013] Optionally, determining the kingpin compensation torque based on the target motor current control quantity includes: determining the motor electromagnetic torque based on the target motor current control quantity; and determining the kingpin compensation torque based on the motor electromagnetic torque.

[0014] According to another aspect of the embodiments of this application, a vehicle control device is also provided, including: an acquisition module, configured to acquire kingpin angle state data, steering motor operating data, motor current feedback data, and a target kingpin angle, wherein the kingpin angle state data includes a real-time kingpin angle, which represents the real-time rotation angle of the steering kingpin column relative to the kingpin axis; and a construction module, configured to construct a first target model based on the kingpin angle state data, steering motor operating data, and motor current feedback data, and to construct an extended disturbance state observer based on the kingpin angle state data, motor current feedback data, and the first target model, wherein the first target model represents the real-time rotation angle of the steering kingpin column relative to the kingpin axis; and a construction module, configured to construct a first target model based on the kingpin angle state data, steering motor operating data, and motor current feedback data, and a target kingpin angle. The system includes: an equivalent dynamic relationship model corresponding to motor operating data and motor current feedback data; a determination module, used to determine the basic control quantity based on the kingpin angle state data, the target kingpin angle, and the first target model, and to determine the output compensation control quantity based on the kingpin angle state data, motor current feedback data, the target kingpin angle, and the extended disturbance state observer, wherein the output compensation control quantity represents the correction control quantity corresponding to the real-time kingpin angle, and the basic control quantity represents the reference control quantity corresponding to the real-time kingpin angle under preset inertia conditions; and a processing module, used to determine the kingpin compensation torque based on the basic control quantity and the output compensation control quantity, and to adjust the real-time kingpin angle to the target kingpin angle based on the kingpin compensation torque.

[0015] Optionally, the kingpin rotation angle state data also includes: real-time kingpin rotation angle velocity. The construction module is further used to: construct a first extended state variable based on the real-time kingpin rotation angle; construct a second extended state variable based on the real-time kingpin rotation angle velocity, wherein the real-time kingpin rotation angle velocity is the first derivative corresponding to the real-time kingpin rotation angle; construct a third extended state variable based on preset composite disturbance parameters; and construct an extended disturbance state observer based on the first extended state variable, the second extended state variable, the third extended state variable, the motor current feedback data, and preset equivalent control gain parameters.

[0016] Optionally, the extended disturbance state observer includes: a first state equation, a second state equation, and a third state equation. The construction module is further configured to: construct the first state equation based on the second extended state variable; construct the second state equation based on the third extended state variable, motor current feedback data, and preset equivalent control gain parameters; and construct the third state equation based on the third extended state variable; and construct the extended disturbance state observer based on the first state equation, the second state equation, the third state equation, and a preset observation error, wherein the preset observation error is determined based on the real-time kingpin rotation angle and the extended state estimate corresponding to the first extended state variable.

[0017] Optionally, the kingpin rotation angle state data also includes: real-time kingpin rotation angle acceleration. The determination module is also used to: determine error state information based on the real-time kingpin rotation angle and the target kingpin rotation angle, wherein the error state information is the difference between the real-time kingpin rotation angle and the target kingpin rotation angle; and determine the basic control quantity based on the real-time kingpin rotation angle acceleration, the preset equivalent inertia parameter, the preset error feedback gain, and the error state information.

[0018] Optionally, the kingpin rotation angle state data further includes: real-time kingpin rotation angle acceleration; the extended disturbance state observer includes: a third extended state variable; the determination module is further configured to: determine target inertia characteristic information based on motor current feedback data and the third extended state variable, and determine target kingpin rotation angle acceleration based on target inertia characteristic information, real-time kingpin rotation angle acceleration, and error state information, wherein the error state information is the difference between the real-time kingpin rotation angle and the target kingpin rotation angle; determine inertial compensation feedforward control quantity based on target kingpin rotation angle acceleration and target inertia characteristic information; determine error feedback control quantity based on error state information and the first derivative corresponding to error state information; determine the extended state estimate corresponding to the third extended state variable based on the extended disturbance state observer, and determine disturbance suppression compensation control quantity based on the extended state estimate corresponding to the third extended state variable; and determine output compensation control quantity based on inertial compensation feedforward control quantity, error feedback control quantity, and disturbance suppression compensation control quantity.

[0019] Optionally, the determining module is further configured to: determine inertia state information based on motor current feedback data and the third extended state variable; determine initial inertia characteristic information based on the inertia state information and preset inertia characteristic weights; perform bounded mapping processing on the initial inertia characteristic information to obtain corrected inertia characteristic information; and perform filtering processing on the corrected inertia characteristic information to obtain target inertia characteristic information.

[0020] Optionally, the processing module is also used to: determine the motor current reference control quantity based on the basic control quantity and the output compensation control quantity; and determine the kingpin compensation torque based on the motor current reference control quantity.

[0021] Optionally, the processing module is further configured to: construct a first objective function based on the motor current reference control quantity; determine a target control sequence according to the first objective function and preset constraints, wherein the target control sequence includes multiple candidate motor current control quantities; determine the target motor current control quantity among the multiple candidate motor current control quantities based on the target control sequence; and determine the kingpin compensation torque according to the target motor current control quantity.

[0022] Optionally, the processing module is also used to: determine the motor electromagnetic torque based on the target motor current control quantity; and determine the kingpin compensation torque based on the motor electromagnetic torque.

[0023] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0024] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0025] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0026] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0027] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0028] In this embodiment, by acquiring kingpin angle state data, steering motor operation data, motor current feedback data, and a target kingpin angle, and constructing a first target model based on the kingpin angle state data, steering motor operation data, and motor current feedback data, and constructing an extended disturbance state observer based on the kingpin angle state data, motor current feedback data, and the first target model, a basic control quantity is determined based on the kingpin angle state data, the target kingpin angle, and the first target model. An output compensation control quantity is also determined based on the kingpin angle state data, motor current feedback data, the target kingpin angle, and the extended disturbance state observer. Furthermore, a kingpin compensation torque is determined based on the basic control quantity and the output compensation control quantity. Finally, the real-time kingpin angle is adjusted to the target kingpin angle based on the kingpin compensation torque. This achieves the goal of high-precision kingpin angle tracking control under the coupling effect of equivalent large inertia and complex disturbances, thereby improving the technical effect of kingpin angle tracking control accuracy and solving the technical problem of low kingpin angle tracking control accuracy in related technologies. Attached Figure Description

[0029] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0030] Figure 1 This is a flowchart of a vehicle control method according to an embodiment of this application;

[0031] Figure 2 This is a schematic diagram of a vehicle control system according to an embodiment of this application;

[0032] Figure 3 This is a schematic diagram of a vehicle control method according to an embodiment of this application;

[0033] Figure 4 This is a schematic diagram of another vehicle control method according to an embodiment of this application;

[0034] Figure 5 This is a structural block diagram of a vehicle control device according to an embodiment of this application. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] In related technologies, kingpin angle tracking control is typically achieved by using a cascaded closed-loop structure (i.e., an outer loop for the steering angle position and an inner loop for velocity or acceleration) and superimposing a feedforward term for the target steering angle change, thereby improving the dynamic response speed and tracking accuracy of the vehicle's steer-by-wire control system. However, when the equivalent inertia of the kingpin steering actuator is large and varies significantly with operating conditions, the aforementioned methods often require increasing the inner loop bandwidth or feedforward gain to achieve rapid tracking, making the system abnormally sensitive to parameter perturbations and load fluctuations. Furthermore, when superimposing disturbance observers or extended state observers to suppress complex disturbances such as friction, self-aligning torque, and road surface excitation, sufficient disturbance reconstruction capability requires increasing the observation gain and estimation bandwidth, which easily introduces additional phase lag and compresses the phase margin of the closed-loop system. This makes it difficult to maintain sufficient stability margin while pursuing high dynamic angle tracking performance, resulting in a narrow controller tuning window, high sensitivity to sampling delay and filtering, and ultimately low accuracy of kingpin angle tracking control.

[0038] Specifically, in existing control architectures, there is severe dynamic coupling and gain conflict between the outer-loop position controller and the inner-loop speed / acceleration controller. When the equivalent inertia increases significantly due to changes in steering angle, vehicle speed, or road adhesion conditions, the inner loop needs to significantly increase its bandwidth to maintain the closed-loop response speed. However, this not only amplifies the disturbance amplification effect of model uncertainties (such as hysteresis of the deceleration mechanism and drift of motor parameters) on the control quantity, but also forces the feedforward term to rely excessively on the ideal dynamic model. Once the actual inertia deviates from the nominal value, it will cause tracking overshoot or even oscillation. At the same time, in order to compensate for the unmodeled dynamics and strong nonlinear disturbances accumulated under large inertia, the extended state observer usually adopts high observation gain to improve the estimation convergence speed. However, the high gain results in an excessively wide output bandwidth for the observer, making it extremely sensitive to sensor noise, current sampling delay, and mechanical structure resonance. The phase lag and amplitude attenuation introduced by this are superimposed at each stage in the cascaded structure, causing the phase margin of the overall closed-loop system to drop sharply in the mid-to-high frequency range. This can even trigger unstable oscillations at the moment of switching between typical operating conditions. The low-pass filter introduced to alleviate this problem further weakens the real-time performance of disturbance compensation, forming a vicious cycle of "improving response → aggravating sensitivity → relying on filtering → sacrificing bandwidth → reducing accuracy". Ultimately, this leads to multiple failure risks for the system under high dynamic steering requirements (such as low speed and large turning angle, emergency obstacle avoidance), including tracking delay, increased steady-state error, and collapse of disturbance rejection capability.

[0039] According to an embodiment of this application, a method embodiment for vehicle control is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0040] This method embodiment can be executed in an electronic device or similar computing device that includes memory and a processor. Taking operation on a computer terminal as an example, the computer terminal may include one or more processors (processors may include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), digital signal processing (DSP) chips, microcontroller units (MCUs), field-programmable gate arrays (FPGAs), neural network processors (NPUs), tensor processors (TPUs), artificial intelligence (AI) type processors, etc.) and memory for storing data. Optionally, the computer terminal may also include transmission devices, input / output devices, and display devices for communication functions. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the computer terminal. For example, the computer terminal may include more or fewer components than described above, or have a different configuration than described above.

[0041] The memory can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the vehicle control method in this embodiment. The processor executes various functional applications and data processing by running the computer program stored in the memory, thereby implementing the aforementioned vehicle control method. The memory may include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks (LANs), mobile communication networks, and combinations thereof.

[0042] The transmission device is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0043] Display devices can be, for example, touchscreen liquid crystal displays (LCDs) and touch displays (also referred to as "touchscreens" or "touch displays"). The LCD allows users to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), which allows users to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. Optional human-computer interaction functions include: creating web pages, drawing, word processing, creating electronic documents, playing games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital video, playing digital music, and / or web browsing, etc. Executable instructions for performing the above human-computer interaction functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0044] This embodiment provides a vehicle control method. Figure 1 This is a flowchart of a vehicle control method according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps:

[0045] Step S11: Obtain kingpin rotation angle status data, steering motor operation data, motor current feedback data, and target kingpin rotation angle. The kingpin rotation angle status data includes real-time kingpin rotation angle, which represents the real-time rotation angle of the vehicle's steering kingpin column relative to the kingpin axis.

[0046] The aforementioned kingpin angle state data refers to the motion state information of the vehicle's kingpin steering actuator at the current moment, which may include, but is not limited to, real-time kingpin angle and real-time kingpin angular velocity. Specifically, the real-time kingpin angle refers to the actual angle value of the steering kingpin column's rotation relative to the vehicle's zero-position reference plane within the current sampling period, used to characterize the wheel's current instantaneous steering position. The real-time kingpin angular velocity refers to the instantaneous derivative of the real-time kingpin angle with time, used to characterize the rate of rotation of the steering kingpin column at the current moment.

[0047] For example, the real-time kingpin rotation angle can be directly acquired by a high-resolution photoelectric encoder or rotary transformer mounted on the steering kingpin column, and output after digital filtering and zero-position calibration. The real-time kingpin rotation speed can be obtained by differential calculation of the real-time kingpin rotation angle and combined with a low-pass filter to suppress high-frequency noise.

[0048] The aforementioned steering motor operating data may include, but is not limited to, the angular position, angular velocity, and angular acceleration of the steering motor in the current sampling period, used to characterize the rotational state and dynamic change trend of the motor rotor relative to its initial zero position.

[0049] For example, the angular position of the motor rotor can be acquired in real time by a high-precision incremental encoder or absolute magnetic encoder installed on the motor shaft end, the angular velocity can be obtained by differential calculation within the sampling period, and the angular acceleration can be obtained by numerical differentiation and moving average filtering.

[0050] The aforementioned motor current feedback data can specifically be the instantaneous value of the three-phase current flowing through the stator winding of the steering motor in the current sampling period or its equivalent DC bus current, used to characterize the real-time working state of the motor's output electromagnetic torque and reflect the response strength of the drive system to load changes and the characteristics of electrical energy input.

[0051] For example, three-phase current signals can be obtained by combining shunt resistor sampling with a high-bandwidth current amplifier and analog-to-digital conversion circuit, or the DC bus current can be directly measured by a Hall effect current sensor. After synchronous sampling and Kalman filtering, switching noise and electromagnetic interference are suppressed, and finally stable and accurate motor current feedback data is output.

[0052] The aforementioned target kingpin angle refers to the angle reference value that the kingpin steering actuator should reach at the current moment, calculated by the on-board controller based on the desired steering trajectory, vehicle speed information, and vehicle dynamics model output by the autonomous driving path planning module. It is used to characterize the theoretical kingpin angle command required by the vehicle to achieve the predetermined trajectory tracking or control intention under the current driving conditions.

[0053] Step S12: Construct a first target model based on the kingpin rotation angle state data, steering motor operation data and motor current feedback data, and construct an extended disturbance state observer based on the kingpin rotation angle state data, motor current feedback data and the first target model. The first target model is used to represent the equivalent dynamic relationship model corresponding to the kingpin rotation angle state data, steering motor operation data and motor current feedback data.

[0054] For example, the first target model constructed based on the kingpin rotation angle state data, steering motor operating data, and motor current feedback data can be represented as:

[0055]

[0056] in, Main pin rotational acceleration The preset equivalent inertia parameter corresponding to the main steering actuator. This is the equivalent damping coefficient. To unify and merge non-inertia terms, used to represent road surface self-alignment torque, friction, clearance, nonlinear load, and modeling errors. is the electric moment constant.

[0057] Furthermore, the extended disturbance state observer constructed based on the kingpin rotation angle state data, motor current feedback data, and the first target model can be expressed as:

[0058]

[0059] in, These are the estimates of each extended state by the extended perturbation state observer. For observation error, For observer gain parameters, To preset composite disturbance parameters, It is a continuous nonlinear function. This is a nonlinear adjustment coefficient used to suppress the initial observation peak.

[0060] Step S13: Determine the basic control quantity based on the kingpin rotation angle state data, the target kingpin rotation angle and the first target model, and determine the output compensation control quantity based on the kingpin rotation angle state data, the motor current feedback data, the target kingpin rotation angle and the extended disturbance state observer. The output compensation control quantity is used to represent the correction control quantity corresponding to the real-time kingpin rotation angle, and the basic control quantity is used to represent the reference control quantity corresponding to the real-time kingpin rotation angle under the preset inertia condition.

[0061] The aforementioned basic control quantity refers to the benchmark control quantity generated based on the steering tracking error and its derivative, under the nominal equivalent inertia condition, used to drive the kingpin steering actuator to achieve the steering tracking function.

[0062] The aforementioned output compensation control quantity refers to the dynamic correction and enhancement control quantity applied to the basic control quantity after comprehensively considering the current actual equivalent inertia change characteristics of the kingpin steering actuator, the composite disturbance information estimated by the extended disturbance state observer, and the steering angle tracking error state. It is used to synergistically suppress the response hysteresis caused by sudden changes in inertia and the tracking deviation caused by external disturbances.

[0063] Step S14: Determine the kingpin compensation torque based on the basic control quantity and the output compensation control quantity, and adjust the real-time kingpin rotation angle to the target kingpin rotation angle based on the kingpin compensation torque.

[0064] The aforementioned kingpin compensation torque refers to the net output torque generated by the drive module in the stator winding of the steering motor through an electromagnetic coupling mechanism based on the final generated motor current reference control quantity. After being amplified by the transmission ratio and attenuated by the reduction mechanism, the torque is transmitted to the steering kingpin column to overcome the system's equivalent inertial resistance and external combined disturbances and drive the kingpin to achieve target angle tracking.

[0065] Specifically, adjusting the real-time kingpin angle to the target kingpin angle based on the kingpin compensation torque can be understood as follows: the kingpin compensation torque acts directly on the steering kingpin column through the reduction mechanism, overcoming the combined disturbances caused by the rotational inertia resistance, road self-alignment torque, mechanical friction and clearance, and tire return torque fluctuations caused by the equivalent large inertia. This drives the kingpin column to make a continuous and smooth angular displacement response along the target angle trajectory, so that the kingpin angle feedback signal collected in real time by the angle sensor gradually converges to the target angle signal commanded by the vehicle controller, thereby achieving high-precision and robust closed-loop angle tracking.

[0066] Based on steps S11 to S14 above, by acquiring kingpin angle state data, steering motor operation data, motor current feedback data, and target kingpin angle, and constructing a first target model based on the kingpin angle state data, steering motor operation data, and motor current feedback data, and constructing an extended disturbance state observer based on the kingpin angle state data, motor current feedback data, and the first target model, then determining the basic control quantity based on the kingpin angle state data, target kingpin angle, and the first target model, and determining the output compensation control quantity based on the kingpin angle state data, motor current feedback data, target kingpin angle, and extended disturbance state observer, and then determining the kingpin compensation torque based on the basic control quantity and the output compensation control quantity, and finally adjusting the real-time kingpin angle to the target kingpin angle based on the kingpin compensation torque, the goal of achieving high-precision tracking control of the kingpin angle under the coupling effect of equivalent large inertia and compound disturbance is achieved. This achieves the technical effect of improving the accuracy of kingpin angle tracking control, and thus solves the technical problem of low accuracy of kingpin angle tracking control in related technologies.

[0067] The vehicle control method in the embodiments of this application will be further described below.

[0068] Optionally, the kingpin rotation angle state data further includes: real-time kingpin rotation angle velocity. In step S12, constructing the extended disturbance state observer based on the kingpin rotation angle state data, motor current feedback data, and the first target model includes:

[0069] Step S121: Construct the first extended state variable based on the real-time kingpin rotation angle;

[0070] Step S122: Construct a second extended state variable based on the real-time kingpin angular velocity, wherein the real-time kingpin angular velocity is the first derivative corresponding to the real-time kingpin angular velocity;

[0071] Step S123: Construct a third extended state variable based on preset composite disturbance parameters;

[0072] Step S124: Construct an extended disturbance state observer based on the first extended state variable, the second extended state variable, the third extended state variable, the motor current feedback data, and the preset equivalent control gain parameters.

[0073] For example, based on the first objective model, we can obtain:

[0074]

[0075] Based on the above formula, the preset equivalent control gain parameter can be defined as:

[0076]

[0077] For example, the first extended state variable mentioned above can be represented as:

[0078]

[0079] The second extended state variable mentioned above can be expressed as:

[0080]

[0081] The third extended state variable mentioned above can be represented as:

[0082]

[0083] The extended state variable group consisting of the first extended state variable, the second extended state variable, and the third extended state variable can be represented as:

[0084]

[0085] Furthermore, an extended disturbance state observer can be constructed based on the aforementioned extended state variable set, motor current feedback data, and preset equivalent control gain parameters.

[0086] Based on steps S121 to S124 above, a first extended state variable is constructed based on the real-time kingpin rotation angle, a second extended state variable is constructed based on the real-time kingpin rotation velocity, and a third extended state variable is constructed based on preset composite disturbance parameters. Finally, an extended disturbance state observer is constructed based on the first extended state variable, the second extended state variable, the third extended state variable, motor current feedback data, and preset equivalent control gain parameters. This enables high-precision, real-time state estimation of the kingpin steering actuator under dynamic conditions, including composite disturbances such as equivalent inertia changes, nonlinear friction, road surface self-aligning torque, and modeling errors. It effectively separates and reconstructs extended disturbance components that are difficult to measure directly in the system. At the same time, by coupling the motor current feedback with the preset equivalent control gain parameters, the robustness of the observer to parameter perturbations and sampling delays is enhanced, improving the convergence speed and steady-state estimation accuracy of the extended state variables under high-frequency disturbances and large inertial delay conditions.

[0087] Optionally, the extended disturbance state observer includes: a first state equation, a second state equation, and a third state equation. In step S124, constructing the extended disturbance state observer based on the first extended state variable, the second extended state variable, the third extended state variable, the motor current feedback data, and the preset equivalent control gain parameter includes:

[0088] Step S1241: Construct the first state equation based on the second extended state variable;

[0089] Step S1242: Construct a second state equation based on the third extended state variable, motor current feedback data and preset equivalent control gain parameters, and construct a third state equation based on the third extended state variable.

[0090] Step S1243: Construct an extended disturbance state observer based on the first state equation, the second state equation, the third state equation and the preset observation error, wherein the preset observation error is determined based on the real-time kingpin rotation angle and the extended state estimate corresponding to the first extended state variable.

[0091] For example, the aforementioned preset observation error can be expressed as:

[0092]

[0093] in, This represents the estimated value of the expanded state corresponding to the first expanded state variable. For real-time main sales cornering.

[0094] For example, based on the first objective model, we can obtain:

[0095]

[0096] Based on the above formula, the preset equivalent control gain parameter can be defined as:

[0097]

[0098] For example, the first state equation constructed based on the second extended state variable can be expressed as:

[0099]

[0100] The second state equation, constructed based on the third extended state variable, motor current feedback data, and preset equivalent control gain parameters, can be expressed as:

[0101]

[0102] in, It controls the input motor current. This is the equivalent control gain parameter, which is affected by the equivalent inertia.

[0103] The third state equation constructed based on the third extended state variables can be expressed as:

[0104]

[0105] in, Let be the rate of change of the disturbance.

[0106] The expanded state equation is:

[0107]

[0108] Furthermore, based on the first state equation, the second state equation, the third state equation, and the preset observation error, an extended perturbation state observer can be constructed:

[0109]

[0110] Based on steps S1241 to S1243, the dynamic evolution characteristics of the turning position and velocity are accurately captured by the first state equation. The nonlinear coupling relationship between the motor current input and the equivalent control gain is modeled by the second state equation, effectively separating the dynamic response deviation component caused by the time-varying nature of inertia. At the same time, relying on the third state equation, the generalized disturbances such as unmodeled dynamics, road surface self-correcting torque, friction nonlinearity, and external interference are continuously integrated and converged. Then, the estimation deviation of each extended state is dynamically corrected by the feedback correction mechanism of the observation error. It can achieve synchronous, decoupled, and high-bandwidth estimation of the coordinated evolution of the turning state, the influence component of inertia change, and the composite disturbance without relying on accurate model parameters. This can improve the estimation stability of the observer under strong nonlinearity, parameter perturbation, and sampling delay conditions.

[0111] Optionally, the kingpin rotation angle state data further includes: real-time kingpin rotation angle acceleration. In step S13, the basic control variables determined based on the kingpin rotation angle state data, the target kingpin rotation angle, and the first target model include:

[0112] Step S1311: Determine error status information based on the real-time kingpin angle and the target kingpin angle, wherein the error status information is the difference between the real-time kingpin angle and the target kingpin angle;

[0113] Step S1312: Determine the basic control quantity based on the real-time kingpin rotation acceleration, preset equivalent inertia parameters, preset error feedback gain, and error state information.

[0114] For example, the above error status information can be represented as:

[0115]

[0116] in, The target is the main pin corner. Furthermore, it can be determined that... .

[0117] For example, based on real-time kingpin angular acceleration, preset equivalent inertia parameters, preset error feedback gain, and error state information, the basic control quantity can be determined using the following formula:

[0118]

[0119] in, To preset the error feedback gain, The basic control variables for achieving corner tracking under standard inertia conditions.

[0120] Based on steps S1311 to S1312 above, error state information is determined based on the real-time kingpin rotation angle and the target kingpin rotation angle. Then, based on the real-time kingpin rotation angle acceleration, preset equivalent inertia parameters, preset error feedback gain, and error state information, the basic control quantity is determined. Without relying on high-gain observation or increasing the inner loop bandwidth, a physically meaningful dynamic compensation reference control quantity can be generated directly based on the matching relationship between the real-time rotation angle dynamic response characteristics and the nominal inertia model. This can alleviate the control lag and overshoot caused by abrupt changes in equivalent inertia or parameter mismatch.

[0121] Optionally, the kingpin rotation angle state data further includes: real-time kingpin rotation angle acceleration, and the expanded disturbance state observer includes: a third expanded state variable. In step S13, the output compensation control quantity is determined based on the kingpin rotation angle state data, motor current feedback data, target kingpin rotation angle, and the expanded disturbance state observer, including:

[0122] Step S1321: Determine the target inertia characteristic information based on the motor current feedback data and the third extended state variable, and determine the target kingpin angular acceleration based on the target inertia characteristic information, the real-time kingpin angular acceleration and the error state information, wherein the error state information is the difference between the real-time kingpin angular acceleration and the target kingpin angular acceleration.

[0123] Step S1322: Determine the inertial compensation feedforward control quantity based on the target kingpin rotational acceleration and target inertia characteristic information;

[0124] Step S1323: Determine the error feedback control quantity based on the error state information and the first derivative corresponding to the error state information;

[0125] Step S1324: Determine the estimated value of the expanded state corresponding to the third expanded state variable based on the expanded disturbance state observer, and determine the disturbance suppression compensation control quantity based on the estimated value of the expanded state corresponding to the third expanded state variable.

[0126] Step S1325: Determine the output compensation control quantity based on the inertial compensation feedforward control quantity, the error feedback control quantity, and the disturbance suppression compensation control quantity.

[0127] The aforementioned inertial compensation feedforward control quantity refers to the feedforward compensation torque generated to offset the dynamic response lag caused by the change in the equivalent inertia of the kingpin steering actuator. Its essence is a dynamic feedforward control component constructed in collaboration with the target inertia characteristic information and the target kingpin angular acceleration, used to actively match the inertial characteristics of the system. It aims to enhance the speed and consistency of angular tracking without relying on the increase of inner loop bandwidth.

[0128] The aforementioned error feedback control quantity refers to the feedback adjustment component generated based on the tracking error and its rate of change between the real-time kingpin rotation angle and the target kingpin rotation angle. Its function is to stabilize the dynamic response of the system through the closed-loop error correction mechanism, suppress the rotation angle deviation caused by model uncertainty, parameter perturbation or external disturbance, and ensure the stable convergence of tracking accuracy.

[0129] The aforementioned disturbance suppression compensation control quantity refers to the disturbance rejection compensation component derived from the composite disturbance state variables estimated by the extended disturbance state observer. It is used to offset the effects of generalized disturbances, including road surface normalizing torque, friction, clearance, nonlinear load, and modeling errors, in real time, thereby improving the system's anti-interference capability and robust stability under complex working conditions.

[0130] For example, based on and The error dynamics equation can be constructed as follows:

[0131]

[0132] Furthermore, based on the above error dynamics equations and the first objective model, we can obtain:

[0133]

[0134] For example, based on the target inertia characteristics and the above formula, the expected error dynamic equation for adaptive scheduling with inertia variation can be constructed as follows:

[0135]

[0136] in, For target inertia characteristics information, These are the nominal error dynamic parameters. This is the inertia characteristic scheduling coefficient.

[0137] Furthermore, based on the above expected error dynamic equation, the target kingpin rotational acceleration can be derived:

[0138]

[0139] For example, based on the target kingpin angular acceleration and target inertia characteristics, the inertia compensation feedforward control quantity can be determined using the following formula:

[0140]

[0141] in, This is the feedforward control quantity for inertia compensation. The nominal equivalent inertia of the main steering actuator.

[0142] For example, based on the error state information and the corresponding first derivative, the error feedback control quantity can be determined using the following formula:

[0143]

[0144] in, For error feedback control quantity, The feedback gain is tuned by closed-loop stability analysis.

[0145] For example, based on the estimated value of the expanded state corresponding to the third expanded state variable, the disturbance suppression compensation control quantity can be determined by the following formula:

[0146]

[0147] in, This is the disturbance suppression compensation control quantity. , This is the estimated value of the extended state corresponding to the third extended state variable.

[0148] Furthermore, based on the inertial compensation feedforward control quantity, the error feedback control quantity, and the disturbance suppression compensation control quantity, the output compensation control quantity can be determined using the following formula:

[0149]

[0150] in, To output the compensation control quantity.

[0151] Based on steps S1321 to S1325 above, the output compensation control quantity is determined based on the inertial compensation feedforward control quantity, the error feedback control quantity, and the disturbance suppression compensation control quantity. This can effectively integrate the prior compensation for dynamic changes in system inertia, the real-time feedback correction of corner tracking error, and the observation and suppression capability of composite disturbances. It can achieve multi-dimensional collaborative optimization of control commands without relying on a single gain adjustment, improve the smoothness, consistency, and response accuracy of the control quantity under the conditions of sudden changes in inertia, load disturbances, and model uncertainties, and at the same time avoid command oscillation and overshoot caused by single control components due to gain overload or phase lag.

[0152] Optionally, in step S1321, determining the target inertia characteristic information based on the motor current feedback data and the third extended state variable includes:

[0153] Step S21: Determine the inertia state information based on the motor current feedback data and the third extended state variable;

[0154] Step S22: Determine the initial inertia characteristic information based on the inertia state information and the preset inertia characteristic weights;

[0155] Step S23: Perform bounded mapping processing on the initial inertia characteristic information to obtain the corrected inertia characteristic information;

[0156] Step S24: Filter the corrected inertia characteristic information to obtain the target inertia characteristic information.

[0157] Specifically, from the second state equation of the extended perturbation state observer, we can obtain:

[0158]

[0159] Combined with control input items:

[0160]

[0161] Therefore, the inertia state information can be defined as:

[0162]

[0163] in, For inertia state information, To prevent tiny positive numbers with a denominator of zero.

[0164] Furthermore, based on the inertia state information and the preset inertia characteristic weights, the following inertia characteristic function can be constructed:

[0165]

[0166] in, This is the initial inertia characteristic information. The preset inertia characteristic weights. This is inertia state information, used to reflect the relative dominance of inertia effects on the dynamic response of the system in the current system.

[0167] Furthermore, by performing bounded mapping on the initial inertia characteristic information, the corrected inertia characteristic information can be expressed as:

[0168]

[0169] In the formula, This refers to the corrected inertia characteristic information, which is the inertia characteristic information used for controller scheduling. These represent the lower and upper limits of inertia characteristic information, respectively. It is a saturation function used to prevent excessive inertia characteristic information from causing sudden changes in control commands.

[0170] To suppress the adverse effects of inertia characteristic information on the control system during rapid changes in operating conditions, it is possible to... Perform first-order filtering:

[0171]

[0172] in, Information on the target inertia characteristics. These are the inertia characteristic filtering coefficients, used to adjust the dynamic smoothness of inertia characteristic information.

[0173] Based on steps S21 to S24 above, the inertia state information is determined based on the motor current feedback data and the third extended state variable. The initial inertia characteristic information is determined according to the inertia state information and the preset inertia characteristic weight. Then, the initial inertia characteristic information is subjected to bounded mapping processing to obtain the corrected inertia characteristic information. Finally, the corrected inertia characteristic information is filtered to obtain the target inertia characteristic information. This can effectively decouple the nonlinear influence of inertia changes on the dynamic response of the system, eliminate the drastic fluctuations in control commands caused by sudden changes in inertia or estimation drift, and improve the smoothness and reliability of inertia characteristic information during the switching of operating conditions.

[0174] Optionally, in step S14, determining the kingpin compensation torque based on the basic control quantity and the output compensation control quantity includes:

[0175] Step S141: Determine the motor current reference control quantity based on the basic control quantity and the output compensation control quantity;

[0176] Step S142: Determine the kingpin compensation torque based on the motor current reference control quantity.

[0177] The aforementioned motor current reference control quantity refers to the corrected control quantity obtained by dynamically correcting the basic control quantity after the large inertia compensation feedforward control quantity, error feedback control quantity, and disturbance suppression compensation control quantity are synergistically integrated. This corrected control quantity is used to drive the steering motor and achieve high-precision tracking of the kingpin angle.

[0178] For example, based on the basic control quantity and the output compensation control quantity, the motor current reference control quantity can be determined by the following formula:

[0179]

[0180] in, This is the reference control quantity for motor current.

[0181] Specifically, determining the kingpin compensation torque based on the motor current reference control quantity can be understood as converting the motor current reference control quantity into the corresponding electromagnetic torque output through the motor electromagnetic torque equation, and then amplifying and transmitting the torque through the transmission efficiency and reduction ratio of the reduction mechanism, ultimately acting on the steering kingpin column to form the kingpin compensation torque.

[0182] Based on steps S141 to S142 above, the motor current reference control quantity is determined based on the basic control quantity and the output compensation control quantity. Then, the kingpin compensation torque is determined based on the motor current reference control quantity. While maintaining the structural integrity of the basic control quantity, the output compensation control quantity can dynamically correct the inertia change and compound disturbance, realize the accurate reconstruction of the motor current reference control quantity, and make the voltage and current response output by the drive module more in line with the actual torque requirements of the kingpin steering actuator under large inertia and nonlinear loads. This can improve the instantaneous following performance and working condition adaptability of the kingpin compensation torque.

[0183] Optionally, in step S142, determining the kingpin compensation torque based on the motor current reference control quantity includes:

[0184] Step S1421: Construct the first objective function based on the motor current reference control quantity;

[0185] Step S1422: Determine the target control sequence based on the first objective function and preset constraints, wherein the target control sequence includes multiple candidate motor current control quantities;

[0186] Step S1423: Determine the target motor current control quantity among multiple candidate motor current control quantities based on the target control sequence;

[0187] Step S1424: Determine the kingpin compensation torque based on the target motor current control quantity.

[0188] For example, a discrete prediction model for motor current can be constructed based on the motor current reference control quantity as shown below:

[0189]

[0190] in, For the first Motor current for each sampling period. For the first The driving voltage for each sampling period. It is the system parameter matrix obtained by discretizing the motor electrical model. This is the equivalent uncertain disturbance term, used to represent parameter uncertainty, load fluctuation, and modeling error.

[0191] Considering the uncertainty of system parameters, the above formula can be further expressed as:

[0192]

[0193] in, Here is the system uncertainty matrix. It is a set of uncertainties.

[0194] Furthermore, the first objective function can be constructed as follows:

[0195]

[0196] in, To predict the step size. For a moment The corresponding future The predicted value of the motor current for each step. This is the reference control quantity for motor current. It is a positive definite weight matrix. This indicates optimizing control performance under the most unfavorable disturbance conditions.

[0197] In the actual solution process, the minimum-maximum robust optimization problem in the first objective function can be equivalently transformed into a robust optimization problem that satisfies the constraints under all allowable uncertainties. Under the premise of ensuring that the performance index is minimized under the most unfavorable disturbance of the system, the objective control sequence is solved.

[0198] For example, motor current constraints and motor current rate of change constraints can be added during the model predictive control optimization process:

[0199]

[0200] in, This is a physical limitation on the motor current. To limit the rate of change of motor current.

[0201] To ensure robust stability of the closed loop, terminal constraints can be introduced:

[0202]

[0203] in, It is a robust positive invariant set. This is the preset robust feedback gain matrix. The set of allowed control inputs.

[0204] Furthermore, by solving the above robust model predictive control optimization problem, the target control sequence is obtained:

[0205]

[0206] in, For multiple candidate motor current control quantities.

[0207] For example, in each sampling period, the controller outputs only the first control quantity (i.e., the target motor current control quantity) among the multiple candidate motor current control quantities as the motor current control command at the current moment, and reconstructs the prediction model and optimization problem based on the updated system state information in the next sampling period, thereby forming a rolling time-domain robust model predictive control strategy:

[0208]

[0209] in, The timing of the control quantity's action.

[0210] Furthermore, the kingpin compensation torque can be determined based on the target motor current control quantity.

[0211] Based on steps S1421 to S1424 above, a first objective function is constructed based on the motor current reference control quantity, and a target control sequence is determined according to the first objective function and preset constraints. Then, the target motor current control quantity among multiple candidate motor current control quantities is determined based on the target control sequence. Finally, the kingpin compensation torque is determined based on the target motor current control quantity. Based on the physical limitations of motor current, the rate of change constraint and the robust stability boundary of the system, the impact of parameter uncertainty and external disturbance on the control output can be effectively suppressed, the real-time performance and safety of current command generation can be improved, and actuator instability or saturation caused by overload or sudden change can be avoided.

[0212] Optionally, in step S1424, determining the kingpin compensation torque based on the target motor current control quantity includes:

[0213] Step S31: Determine the electromagnetic torque of the motor based on the target motor current control quantity;

[0214] Step S32: Determine the kingpin compensation torque based on the motor electromagnetic torque.

[0215] The electromagnetic torque of the aforementioned motor refers to the electromagnetic torque generated by the interaction between current and magnetic field of the steering motor after it is energized, which is used to drive the kingpin steering actuator to rotate.

[0216] For example, the electromagnetic torque of the motor can be determined using the following formula based on the target motor current control quantity:

[0217]

[0218] in, For the first The actual motor current for each sampling period. For the first The electromagnetic torque of the motor in one sampling period. This refers to the inductance of the motor windings. This represents the resistance of the motor windings. This is the sampling control period.

[0219]

[0220] in, For the first Motor drive voltage control quantity per sampling period. This is the DC bus voltage of the drive module. For the first Voltage modulation ratio per sampling period. This is the motor current-to-voltage conversion coefficient. This refers to the dead zone compensation and nonlinear correction quantities.

[0221] After being transmitted through the reduction mechanism, the kingpin compensation torque output to the kingpin steering actuator is:

[0222]

[0223] in, For the first The main pin compensation torque for each sampling period. The transmission efficiency of the speed reduction mechanism. This refers to the reduction ratio of the reduction mechanism.

[0224] Furthermore, the steering angle sensor collects the steering kingpin column angle signal. After filtering, the kingpin angle feedback signal can be obtained. :

[0225]

[0226] in, For the first The main pin rotation angle feedback signal for each sampling period. It is the first The sensor's original output signal for one sampling period. These are the feedback filter coefficients.

[0227] Based on steps S31 to S32 above, the electromagnetic torque of the motor is determined according to the target motor current control quantity, and then the kingpin compensation torque is determined based on the electromagnetic torque of the motor. This enables precise closed-loop control of the kingpin steering actuator, ensuring that the electromagnetic torque output by the motor can be accurately mapped to the kingpin end. This overcomes the torque transmission distortion caused by the nonlinearity of the reduction mechanism, efficiency fluctuations, and mechanical backlash, improves the real-time performance and consistency of torque command execution, and thus improves the accuracy of kingpin angle tracking.

[0228] Figure 2 This is a schematic diagram of a vehicle control system according to an embodiment of this application, such as... Figure 2 As shown in the figure, the explanations for each label are as follows:

[0229] 1-On-board controller; 2-Steering-by-wire controller; 3-Drive module; 4-Motor current acquisition module; 5-Kingpin steering actuator; 6-Extended disturbance state observer; 7-Large inertia compensation angle tracker; 8-Communication harness; 9-Angle sensor; 10-Motor position sensor; a-Target angle signal; b-Vehicle speed signal; c-System status; d-Motor current control quantity; e-Error status information; f-Inertia status information; g-Complex disturbance status information; h-Output control quantity; j-Drive voltage control quantity; k-Kingpin angle feedback signal; m-Motor operating status signal; n-Motor current signal.

[0230] Specifically, the on-board controller 1 and the steer-by-wire controller 2 are connected via a communication harness 8. The on-board controller 1 outputs a target steering angle signal a and a vehicle speed signal b to the steer-by-wire controller 2. The steer-by-wire controller 2 sends a kingpin angle feedback signal k and a motor operating status signal m to the on-board controller 1. The steer-by-wire controller 2 is electrically connected to the drive module 3. Based on the target steering angle signal a, the vehicle speed signal b, and the feedback information, the steer-by-wire controller 2 generates a motor current control quantity d and sends it to the drive module 3. The drive module 3 generates a drive voltage control quantity j based on the motor current control quantity d and outputs it to the steering motor of the kingpin steering actuator 5. The motor current acquisition module 4 is installed in the bus or phase current sampling circuit of the drive module 3 and is signal-connected to the steer-by-wire controller 2. The motor current acquisition module 4 acquires the motor current signal n in real time and sends it to the steer-by-wire controller 2. The steering angle sensor 9 is installed on the steering kingpin column of the kingpin steering actuator 5 and is signal-connected to the steer-by-wire controller 2. It is used to acquire the kingpin angle feedback signal k and send it to the steer-by-wire controller 2. The motor position sensor 10 is installed at the steering motor end of the kingpin steering actuator 5 and is connected to the steer-by-wire controller 2 for signal acquisition of motor operating status signals m, such as motor angular position and angular velocity, and sends them to the steer-by-wire controller 2. The extended disturbance state observer 6 is located inside the steer-by-wire controller 2 and is connected to the kingpin angle feedback signal k (from the angle sensor 9), the motor operating status signal m (from the motor position sensor 10), and the motor current signal n (from the motor current acquisition module 4). It is used to estimate the extended state of the kingpin steering actuator 5 under different operating conditions and complex external disturbances, and output disturbance state information and inertia-related state information. The large inertia compensation angle tracker 7 is installed inside the steer-by-wire controller 2. Its inputs include at least: target angle signal a, kingpin angle feedback signal k, error state information e output by the extended disturbance state observer 6, inertia state information f, and composite disturbance state information g. Its output is the output control quantity h used to compensate the output of the drive module 3, so as to achieve fast and robust tracking control of the kingpin angle.

[0231] Figure 3 This is a schematic diagram of a vehicle control method according to an embodiment of this application, such as... Figure 3 As shown, the motor current feedback signal is obtained based on the sensor. and the kingpin angle feedback signal Then, firstly based on the motor current feedback signal and main pin angle feedback signal An extended state variable is constructed, and an extended state equation is generated based on the extended state variable. Then, an observer state equation is generated based on the extended state equation, thus constructing an extended perturbation state observer. Subsequently, inertial state information is determined based on the observer state equation. And based on the composite compensation controller, the disturbance suppression compensation amount and error feedback control amount are determined, and then based on the inertial state information... Generate inertial characteristic functions, and analyze inertial characteristic information based on these functions. By performing a bounded mapping, the corrected inertial property information is obtained. Furthermore, the corrected inertial characteristic information... Perform first-order filtering to obtain the filtered inertial characteristic information. And based on the filtered inertial characteristic information A dynamic equation for the desired error is constructed to obtain the desired acceleration of the target rotation angle. Based on this desired acceleration, the inertia compensation feedforward control quantity is determined. Subsequently, the output compensation control quantity is determined based on the disturbance suppression compensation quantity, the error feedback control quantity, and the inertia compensation feedforward control quantity. Finally, the kingpin compensation torque is determined based on the output compensation control quantity and the basic control quantity and sent to the actuator.

[0232] Figure 4 This is a schematic diagram of another vehicle control method according to an embodiment of this application, such as... Figure 4 As shown, after obtaining the motor current reference control quantity, a discrete prediction model for the motor current can be constructed based on the motor current reference control quantity. This model is then optimized to obtain the optimization objective function. Subsequently, based on the optimization objective function, motor current constraints, motor current rate of change constraints, and terminal robust invariant set constraints are introduced. The optimal control sequence is solved using a robust optimization solver, and then the final motor current control quantity is obtained through rolling optimization.

[0233] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0234] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0235] This application also provides a vehicle control device for implementing the above embodiments and preferred embodiments, which will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0236] Figure 5 This is a structural block diagram of a vehicle control device according to an embodiment of this application, such as... Figure 5 As shown, the device includes:

[0237] The acquisition module 501 is used to acquire kingpin rotation angle status data, steering motor operation data, motor current feedback data and target kingpin rotation angle. The kingpin rotation angle status data includes real-time kingpin rotation angle, which represents the real-time rotation angle of the vehicle's steering kingpin column relative to the kingpin axis.

[0238] The construction module 502 is used to construct a first target model based on the kingpin rotation angle state data, steering motor operation data and motor current feedback data, and to construct an extended disturbance state observer based on the kingpin rotation angle state data, motor current feedback data and the first target model. The first target model is used to represent the equivalent dynamic relationship model corresponding to the kingpin rotation angle state data, steering motor operation data and motor current feedback data.

[0239] The determination module 503 is used to determine the basic control quantity based on the kingpin rotation angle state data, the target kingpin rotation angle and the first target model, and to determine the output compensation control quantity based on the kingpin rotation angle state data, the motor current feedback data, the target kingpin rotation angle and the extended disturbance state observer. The output compensation control quantity is used to represent the correction control quantity corresponding to the real-time kingpin rotation angle, and the basic control quantity is used to represent the reference control quantity corresponding to the real-time kingpin rotation angle under the preset inertia condition.

[0240] The processing module 504 is used to determine the kingpin compensation torque based on the basic control quantity and the output compensation control quantity, and to adjust the real-time kingpin rotation angle to the target kingpin rotation angle based on the kingpin compensation torque.

[0241] Optionally, the kingpin rotation angle state data also includes: real-time kingpin rotation angle velocity. The construction module 502 is further used to: construct a first extended state variable based on the real-time kingpin rotation angle; construct a second extended state variable based on the real-time kingpin rotation angle velocity, wherein the real-time kingpin rotation angle velocity is the first derivative corresponding to the real-time kingpin rotation angle; construct a third extended state variable based on preset composite disturbance parameters; and construct an extended disturbance state observer based on the first extended state variable, the second extended state variable, the third extended state variable, the motor current feedback data, and preset equivalent control gain parameters.

[0242] Optionally, the extended disturbance state observer includes: a first state equation, a second state equation, and a third state equation. The construction module 502 is further configured to: construct the first state equation based on the second extended state variable; construct the second state equation based on the third extended state variable, motor current feedback data, and preset equivalent control gain parameters; and construct the third state equation based on the third extended state variable; and construct the extended disturbance state observer based on the first state equation, the second state equation, the third state equation, and a preset observation error, wherein the preset observation error is determined based on the real-time kingpin rotation angle and the extended state estimate corresponding to the first extended state variable.

[0243] Optionally, the kingpin rotation angle state data also includes: real-time kingpin rotation angle acceleration. The determining module 503 is further used to: determine error state information based on the real-time kingpin rotation angle and the target kingpin rotation angle, wherein the error state information is the difference between the real-time kingpin rotation angle and the target kingpin rotation angle; and determine the basic control quantity based on the real-time kingpin rotation angle acceleration, the preset equivalent inertia parameter, the preset error feedback gain, and the error state information.

[0244] Optionally, the kingpin rotation angle state data further includes: real-time kingpin rotation angle acceleration; the extended disturbance state observer includes: a third extended state variable; and the determination module 503 is further configured to: determine target inertia characteristic information based on motor current feedback data and the third extended state variable, and determine target kingpin rotation angle acceleration based on target inertia characteristic information, real-time kingpin rotation angle acceleration, and error state information, wherein the error state information is the difference between the real-time kingpin rotation angle and the target kingpin rotation angle; determine inertial compensation feedforward control quantity based on target kingpin rotation angle acceleration and target inertia characteristic information; determine error feedback control quantity based on error state information and the first derivative corresponding to error state information; determine the extended state estimate corresponding to the third extended state variable based on the extended disturbance state observer, and determine disturbance suppression compensation control quantity based on the extended state estimate corresponding to the third extended state variable; and determine output compensation control quantity based on inertial compensation feedforward control quantity, error feedback control quantity, and disturbance suppression compensation control quantity.

[0245] Optionally, the determining module 503 is further configured to: determine inertia state information based on motor current feedback data and the third extended state variable; determine initial inertia characteristic information based on the inertia state information and preset inertia characteristic weights; perform bounded mapping processing on the initial inertia characteristic information to obtain corrected inertia characteristic information; and perform filtering processing on the corrected inertia characteristic information to obtain target inertia characteristic information.

[0246] Optionally, the processing module 504 is also used to: determine the motor current reference control quantity based on the basic control quantity and the output compensation control quantity; and determine the kingpin compensation torque based on the motor current reference control quantity.

[0247] Optionally, the processing module 504 is further configured to: construct a first objective function based on the motor current reference control quantity; determine a target control sequence according to the first objective function and preset constraints, wherein the target control sequence includes multiple candidate motor current control quantities; determine the target motor current control quantity among the multiple candidate motor current control quantities based on the target control sequence; and determine the kingpin compensation torque according to the target motor current control quantity.

[0248] Optionally, the processing module 504 is also used to: determine the motor electromagnetic torque based on the target motor current control quantity; and determine the kingpin compensation torque based on the motor electromagnetic torque.

[0249] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0250] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods described in various embodiments of this application when it runs.

[0251] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0252] Step S11: Obtain kingpin rotation angle status data, steering motor operation data, motor current feedback data, and target kingpin rotation angle. The kingpin rotation angle status data includes real-time kingpin rotation angle, which represents the real-time rotation angle of the vehicle's steering kingpin column relative to the kingpin axis.

[0253] Step S12: Construct a first target model based on the kingpin rotation angle state data, steering motor operation data and motor current feedback data, and construct an extended disturbance state observer based on the kingpin rotation angle state data, motor current feedback data and the first target model. The first target model is used to represent the equivalent dynamic relationship model corresponding to the kingpin rotation angle state data, steering motor operation data and motor current feedback data.

[0254] Step S13: Determine the basic control quantity based on the kingpin rotation angle state data, the target kingpin rotation angle and the first target model, and determine the output compensation control quantity based on the kingpin rotation angle state data, the motor current feedback data, the target kingpin rotation angle and the extended disturbance state observer. The output compensation control quantity is used to represent the correction control quantity corresponding to the real-time kingpin rotation angle, and the basic control quantity is used to represent the reference control quantity corresponding to the real-time kingpin rotation angle under the preset inertia condition.

[0255] Step S14: Determine the kingpin compensation torque based on the basic control quantity and the output compensation control quantity, and adjust the real-time kingpin rotation angle to the target kingpin rotation angle based on the kingpin compensation torque.

[0256] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0257] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0258] Step S11: Obtain kingpin rotation angle status data, steering motor operation data, motor current feedback data, and target kingpin rotation angle. The kingpin rotation angle status data includes real-time kingpin rotation angle, which represents the real-time rotation angle of the vehicle's steering kingpin column relative to the kingpin axis.

[0259] Step S12: Construct a first target model based on the kingpin rotation angle state data, steering motor operation data and motor current feedback data, and construct an extended disturbance state observer based on the kingpin rotation angle state data, motor current feedback data and the first target model. The first target model is used to represent the equivalent dynamic relationship model corresponding to the kingpin rotation angle state data, steering motor operation data and motor current feedback data.

[0260] Step S13: Determine the basic control quantity based on the kingpin rotation angle state data, the target kingpin rotation angle and the first target model, and determine the output compensation control quantity based on the kingpin rotation angle state data, the motor current feedback data, the target kingpin rotation angle and the extended disturbance state observer. The output compensation control quantity is used to represent the correction control quantity corresponding to the real-time kingpin rotation angle, and the basic control quantity is used to represent the reference control quantity corresponding to the real-time kingpin rotation angle under the preset inertia condition.

[0261] Step S14: Determine the kingpin compensation torque based on the basic control quantity and the output compensation control quantity, and adjust the real-time kingpin rotation angle to the target kingpin rotation angle based on the kingpin compensation torque.

[0262] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0263] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0264] Optionally, in this embodiment, the above-mentioned computer program product can be configured as a computer program that performs the following steps:

[0265] Step S11: Obtain kingpin rotation angle status data, steering motor operation data, motor current feedback data, and target kingpin rotation angle. The kingpin rotation angle status data includes real-time kingpin rotation angle, which represents the real-time rotation angle of the vehicle's steering kingpin column relative to the kingpin axis.

[0266] Step S12: Construct a first target model based on the kingpin rotation angle state data, steering motor operation data and motor current feedback data, and construct an extended disturbance state observer based on the kingpin rotation angle state data, motor current feedback data and the first target model. The first target model is used to represent the equivalent dynamic relationship model corresponding to the kingpin rotation angle state data, steering motor operation data and motor current feedback data.

[0267] Step S13: Determine the basic control quantity based on the kingpin rotation angle state data, the target kingpin rotation angle and the first target model, and determine the output compensation control quantity based on the kingpin rotation angle state data, the motor current feedback data, the target kingpin rotation angle and the extended disturbance state observer. The output compensation control quantity is used to represent the correction control quantity corresponding to the real-time kingpin rotation angle, and the basic control quantity is used to represent the reference control quantity corresponding to the real-time kingpin rotation angle under the preset inertia condition.

[0268] Step S14: Determine the kingpin compensation torque based on the basic control quantity and the output compensation control quantity, and adjust the real-time kingpin rotation angle to the target kingpin rotation angle based on the kingpin compensation torque.

[0269] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0270] Optionally, in this embodiment, the above-mentioned computer program product can be configured as a computer program that performs the following steps:

[0271] Step S11: Obtain kingpin rotation angle status data, steering motor operation data, motor current feedback data, and target kingpin rotation angle. The kingpin rotation angle status data includes real-time kingpin rotation angle, which represents the real-time rotation angle of the vehicle's steering kingpin column relative to the kingpin axis.

[0272] Step S12: Construct a first target model based on the kingpin rotation angle state data, steering motor operation data and motor current feedback data, and construct an extended disturbance state observer based on the kingpin rotation angle state data, motor current feedback data and the first target model. The first target model is used to represent the equivalent dynamic relationship model corresponding to the kingpin rotation angle state data, steering motor operation data and motor current feedback data.

[0273] Step S13: Determine the basic control quantity based on the kingpin rotation angle state data, the target kingpin rotation angle and the first target model, and determine the output compensation control quantity based on the kingpin rotation angle state data, the motor current feedback data, the target kingpin rotation angle and the extended disturbance state observer. The output compensation control quantity is used to represent the correction control quantity corresponding to the real-time kingpin rotation angle, and the basic control quantity is used to represent the reference control quantity corresponding to the real-time kingpin rotation angle under the preset inertia condition.

[0274] Step S14: Determine the kingpin compensation torque based on the basic control quantity and the output compensation control quantity, and adjust the real-time kingpin rotation angle to the target kingpin rotation angle based on the kingpin compensation torque.

[0275] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0276] Optionally, in this embodiment, the computer program described above can be configured to perform the following steps when executed by the processor:

[0277] Step S11: Obtain kingpin rotation angle status data, steering motor operation data, motor current feedback data, and target kingpin rotation angle. The kingpin rotation angle status data includes real-time kingpin rotation angle, which represents the real-time rotation angle of the vehicle's steering kingpin column relative to the kingpin axis.

[0278] Step S12: Construct a first target model based on the kingpin rotation angle state data, steering motor operation data and motor current feedback data, and construct an extended disturbance state observer based on the kingpin rotation angle state data, motor current feedback data and the first target model. The first target model is used to represent the equivalent dynamic relationship model corresponding to the kingpin rotation angle state data, steering motor operation data and motor current feedback data.

[0279] Step S13: Determine the basic control quantity based on the kingpin rotation angle state data, the target kingpin rotation angle and the first target model, and determine the output compensation control quantity based on the kingpin rotation angle state data, the motor current feedback data, the target kingpin rotation angle and the extended disturbance state observer. The output compensation control quantity is used to represent the correction control quantity corresponding to the real-time kingpin rotation angle, and the basic control quantity is used to represent the reference control quantity corresponding to the real-time kingpin rotation angle under the preset inertia condition.

[0280] Step S14: Determine the kingpin compensation torque based on the basic control quantity and the output compensation control quantity, and adjust the real-time kingpin rotation angle to the target kingpin rotation angle based on the kingpin compensation torque.

[0281] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0282] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0283] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0284] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0285] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0286] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A vehicle control method, characterized in that, include: Acquire kingpin rotation state data, steering motor operation data, motor current feedback data, and target kingpin rotation angle. The kingpin rotation state data includes real-time kingpin rotation angle, which represents the real-time rotation angle of the vehicle's steering kingpin column relative to the kingpin axis. A first target model is constructed based on the kingpin angle state data, the steering motor operation data, and the motor current feedback data. An extended disturbance state observer is constructed based on the kingpin angle state data, the motor current feedback data, and the first target model. The first target model is used to represent the equivalent dynamic relationship model corresponding to the kingpin angle state data, the steering motor operation data, and the motor current feedback data. Based on the kingpin rotation angle state data, the target kingpin rotation angle, and the first target model, a basic control quantity is determined, and based on the kingpin rotation angle state data, the motor current feedback data, the target kingpin rotation angle, and the extended disturbance state observer, an output compensation control quantity is determined, wherein the output compensation control quantity is used to represent the correction control quantity corresponding to the real-time kingpin rotation angle, and the basic control quantity is used to represent the reference control quantity corresponding to the real-time kingpin rotation angle under preset inertial conditions; The kingpin compensation torque is determined based on the basic control quantity and the output compensation control quantity, and the real-time kingpin angle is adjusted to the target kingpin angle based on the kingpin compensation torque.

2. The method according to claim 1, characterized in that, The kingpin rotation angle state data further includes: real-time kingpin rotation angle velocity, and the step of constructing an extended disturbance state observer based on the kingpin rotation angle state data, the motor current feedback data, and the first target model includes: The first expansion state variable is constructed based on the real-time kingpin rotation angle; A second extended state variable is constructed based on the real-time kingpin rotation velocity, wherein the real-time kingpin rotation velocity is the first derivative corresponding to the real-time kingpin rotation angle; A third extended state variable is constructed based on preset composite disturbance parameters; The extended disturbance state observer is constructed based on the first extended state variable, the second extended state variable, the third extended state variable, the motor current feedback data, and the preset equivalent control gain parameter.

3. The method according to claim 2, characterized in that, The extended disturbance state observer includes: a first state equation, a second state equation, and a third state equation. Constructing the extended disturbance state observer based on the first extended state variable, the second extended state variable, the third extended state variable, the motor current feedback data, and the preset equivalent control gain parameter includes: The first state equation is constructed based on the second extended state variable; The second state equation is constructed based on the third extended state variable, the motor current feedback data, and the preset equivalent control gain parameter; and the third state equation is constructed based on the third extended state variable. The extended disturbance state observer is constructed based on the first state equation, the second state equation, the third state equation, and a preset observation error, wherein the preset observation error is determined based on the real-time kingpin rotation angle and the extended state estimate corresponding to the first extended state variable.

4. The method according to claim 1, characterized in that, The kingpin rotation angle state data further includes: real-time kingpin rotation angle acceleration, and the determination of the basic control quantity based on the kingpin rotation angle state data, the target kingpin rotation angle, and the first target model includes: Error status information is determined based on the real-time kingpin angle and the target kingpin angle, wherein the error status information is the difference between the real-time kingpin angle and the target kingpin angle; The basic control quantity is determined based on the real-time kingpin rotation acceleration, the preset equivalent inertia parameter, the preset error feedback gain, and the error state information.

5. The method according to claim 1, characterized in that, The kingpin rotation angle state data further includes: real-time kingpin rotation angle acceleration; the expanded disturbance state observer includes: a third expanded state variable; the determination of the output compensation control quantity based on the kingpin rotation angle state data, the motor current feedback data, the target kingpin rotation angle, and the expanded disturbance state observer includes: The target inertia characteristic information is determined based on the motor current feedback data and the third extended state variable, and the target kingpin angular acceleration is determined based on the target inertia characteristic information, the real-time kingpin angular acceleration and the error state information, wherein the error state information is the difference between the real-time kingpin angular acceleration and the target kingpin angular acceleration. The inertial compensation feedforward control quantity is determined based on the target kingpin rotational acceleration and the target inertia characteristic information; The error feedback control quantity is determined based on the error state information and the first derivative corresponding to the error state information. The extended state estimate corresponding to the third extended state variable is determined based on the extended disturbance state observer, and the disturbance suppression compensation control quantity is determined based on the extended state estimate corresponding to the third extended state variable. The output compensation control quantity is determined based on the inertial compensation feedforward control quantity, the error feedback control quantity, and the disturbance suppression compensation control quantity.

6. The method according to claim 5, characterized in that, The determination of the target inertia characteristic information based on the motor current feedback data and the third extended state variable includes: Inertia state information is determined based on the motor current feedback data and the third extended state variable. The initial inertia characteristic information is determined based on the inertia state information and the preset inertia characteristic weights; The initial inertia characteristic information is subjected to bounded mapping processing to obtain the corrected inertia characteristic information; The corrected inertia characteristic information is filtered to obtain the target inertia characteristic information.

7. The method according to claim 1, characterized in that, The determination of the kingpin compensation torque based on the basic control quantity and the output compensation control quantity includes: The motor current reference control quantity is determined based on the basic control quantity and the output compensation control quantity. The kingpin compensation torque is determined based on the motor current reference control value.

8. The method according to claim 7, characterized in that, Determining the kingpin compensation torque based on the motor current reference control quantity includes: A first objective function is constructed based on the motor current reference control quantity; A target control sequence is determined based on the first objective function and preset constraints, wherein the target control sequence includes multiple candidate motor current control quantities; The target motor current control quantity is determined from the plurality of candidate motor current control quantities based on the target control sequence; The kingpin compensation torque is determined based on the target motor current control quantity.

9. The method according to claim 8, characterized in that, Determining the kingpin compensation torque based on the target motor current control quantity includes: The electromagnetic torque of the motor is determined based on the target motor current control quantity; The kingpin compensation torque is determined based on the electromagnetic torque of the motor.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 9.

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