Lateral control method, device and equipment in automatic driving mode and storage medium
By constructing a sliding mode controller based on the vehicle error state space equation and a linear sliding mode function, the problems of control accuracy and robustness under the influence of external disturbances in autonomous driving are solved, the computational complexity and hardware cost are reduced, and efficient lateral control is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG GEELY HLDG GRP CO LTD
- Filing Date
- 2023-12-20
- Publication Date
- 2026-07-31
AI Technical Summary
Existing autonomous driving path tracking control algorithms lack accuracy and robustness when facing external disturbances, and the MPC algorithm has high computational complexity, increasing hardware costs.
By constructing the vehicle error state space equation, calculating the sliding surface parameters and generating a linear sliding function, a sliding mode controller is constructed to output steering data for lateral control, reducing computational complexity and hardware requirements.
It improves anti-interference capability and control accuracy, reduces computational cost, and has good robustness and interpretability, making it suitable for practical applications.
Smart Images
Figure CN117762017B_ABST
Abstract
Description
Technical Field
[0001] This specification relates to the field of lateral control technology for intelligent vehicles, and in particular to a lateral control method, device, equipment, and storage medium in an autonomous driving mode. Background Technology
[0002] In recent years, the research and application of artificial intelligence and the implementation of 5G communication technologies have laid a solid foundation for the development of intelligent vehicles towards autonomous driving and connectivity. Vehicle control technology for intelligent vehicles, based on environmental perception technology, plans a target trajectory according to decisions, and through the cooperation of longitudinal and lateral control systems, enables the vehicle to drive accurately and stably along the target trajectory. Simultaneously, it enables the vehicle to perform basic operations such as speed adjustment, distance maintenance, lane changing, and overtaking during driving. The core technologies of autonomous driving control are longitudinal and lateral vehicle control technologies. Lateral control refers to the adjustment of the steering wheel angle and the control of tire force.
[0003] In related technologies, some control algorithms for autonomous driving path tracking suffer from drawbacks such as poor control accuracy and robustness, making them susceptible to external disturbances and leading to unstable tracking performance. Although using the MPC algorithm can improve control accuracy and enhance robustness to some extent, the MPC algorithm is computationally complex, requires significant computing power, and increases hardware costs. Summary of the Invention
[0004] To overcome the problems existing in related technologies, this specification provides a lateral control method, device, equipment, and storage medium in autonomous driving mode.
[0005] According to a first aspect of the embodiments of this specification, a lateral control method in an autonomous driving mode is provided, the method comprising:
[0006] Based on the real-time motion data of the vehicle, a vehicle error state space equation is constructed for lateral error, lateral error rate of change, heading error, heading error rate of change, and steering data.
[0007] The sliding surface parameters are calculated based on the vehicle error state space equation, and linear sliding functions are generated with each sliding surface parameter as a weighting coefficient, relating to the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error.
[0008] A sliding mode controller is constructed based on the linear sliding mode function and the vehicle error state space equation, and the steering wheel angle is calculated based on the steering data output by the sliding mode controller to perform lateral control of the vehicle.
[0009] Optionally, obtaining the sliding surface parameters based on the vehicle error state space equation includes: converting the vehicle error state space equation into a controllable canonical form and constructing a reduced-order system; and designing a state feedback control law for the reduced-order system using the pole placement method and calculating the sliding surface parameters.
[0010] Optionally, constructing the reduced-order system includes: decomposing the vehicle error state-space equation under the controllable canonical form into a first reduced-order subsystem independent of the sliding mode control law and a second reduced-order subsystem related to the sliding mode control law; and constructing the reduced-order system based on the first reduced-order subsystem.
[0011] Optionally, constructing the reduced-order system based on the first reduced-order subsystem includes: constructing a convergent relation for the sliding surface plane formula; and merging the convergent relation and the first reduced-order subsystem to obtain the reduced-order system.
[0012] Optionally, calculating the sliding surface parameters based on the vehicle error state space equation includes: if the vehicle error state space equation is constructed based on the vehicle dynamics model of the vehicle, determining whether the vehicle dynamics model is controllable; if the vehicle dynamics model is controllable, then calculating the sliding surface parameters based on the vehicle error state space equation.
[0013] Optionally, the step of constructing a sliding mode controller based on the linear sliding mode function and the vehicle error state space equation includes: constructing a linear sliding mode surface according to the linear sliding mode function; calculating an exponential reaching law for the linear sliding mode surface; substituting the vehicle error state space equation into the exponential reaching law to calculate a sliding mode control law, wherein the output value of the sliding mode control law is the steering data.
[0014] Optionally, the step of calculating the steering wheel angle based on the steering data output by the sliding mode controller to perform lateral control of the vehicle includes: acquiring the steering data output by the sliding mode controller, multiplying the steering data by the vehicle steering ratio to obtain the steering wheel radian; converting the steering wheel radian into a steering wheel angle and outputting it to the control module, and controlling the steering wheel through the control module.
[0015] According to a second aspect of the embodiments of this specification, a lateral control device in an autonomous driving mode is provided, the device comprising:
[0016] The acquisition unit is used to construct a vehicle error state space equation based on the vehicle's real-time motion data, including lateral error, lateral error rate of change, heading error, heading error rate of change, and steering data.
[0017] The processing unit is configured to calculate the sliding surface parameters according to the vehicle error state space equation, and generate linear sliding functions with respect to the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error, respectively, using each sliding surface parameter as a weighting coefficient.
[0018] The control unit is used to construct a sliding mode controller based on the linear sliding mode function and the vehicle error state space equation, and to calculate the value of the steering wheel angle according to the steering data output by the sliding mode controller, so as to perform lateral control on the vehicle.
[0019] According to a third aspect of the embodiments of this specification, an electronic device is provided, comprising:
[0020] processor;
[0021] Memory used to store processor-executable instructions;
[0022] The processor executes the executable instructions to implement the method described in the embodiments of the first aspect above.
[0023] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer instructions thereon, which, when executed by a processor, implement the steps of the method as described in the embodiments of the first aspect above.
[0024] The technical solutions provided in the embodiments of this specification may include the following beneficial effects:
[0025] In the embodiments of this specification, the sliding surface parameters are calculated by constructing a vehicle error state space, and a linear sliding mode function is generated with each sliding surface parameter as a weighting coefficient, relating to the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error. Based on the linear sliding mode function and the vehicle error state space equation, a sliding mode controller is constructed to output steering data to calculate the steering wheel angle for lateral control. This ensures that the vehicle's state is only related to the parameters of the sliding surface and is independent of external environmental disturbances, exhibiting strong robustness to external environmental interference and system parameter perturbations. Furthermore, it has low computational cost and low requirements for computing resources. While improving anti-interference capability and control accuracy, it also reduces the computational load. In addition, the sliding mode controller constructed with the linear sliding mode function has good interpretability and relatively simple stability analysis, which is beneficial for problem analysis and solution in practical applications.
[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description
[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this specification and, together with the description, serve to explain the principles of this specification.
[0028] Figure 1 This is a diagram illustrating a sliding mode control structure according to an exemplary embodiment.
[0029] Figure 2 This is a flowchart illustrating a lateral control method in an autonomous driving mode according to an exemplary embodiment of this specification.
[0030] Figure 3 This is a flowchart illustrating another lateral control method in an autonomous driving mode according to an exemplary embodiment of this specification.
[0031] Figure 4 This is a block diagram illustrating a lateral control device in an autonomous driving mode according to an exemplary embodiment of this specification.
[0032] Figure 5 This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of this specification. Detailed Implementation
[0033] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this specification as detailed in the appended claims.
[0034] The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of this specification. The singular forms “a,” “the,” and “the” as used in this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.
[0035] It should be understood that although the terms first, second, third, etc., may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this specification, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."
[0036] The main research direction of lateral control technology for intelligent vehicles is how to control the front wheel steering angle of intelligent vehicles so that they can follow a specified path and ensure driving safety and stability. This is a decisive factor affecting the quality of autonomous navigation of intelligent vehicles.
[0037] For lateral control, many algorithms exist to control steering wheel angle, such as Stanley, Pure Tracking, LQR, and MPC. However, Stanley and Pure Tracking algorithms calculate steering wheel angle based solely on road geometry, neglecting vehicle properties, resulting in poor tracking accuracy and sensitivity to external disturbances. While LQR considers vehicle dynamics, it only considers current-moment information. External disturbances affect the calculated angle, making LQR less resistant to interference. MPC considers both future and future vehicle dynamics, offering strong interference resistance. However, its complexity and high computational cost for steering wheel angle calculation increase hardware requirements and costs.
[0038] Therefore, this specification provides a lateral control method in autonomous driving mode, which generates a linear sliding mode function and constructs a sliding mode controller to output steering data, thereby improving anti-interference performance and control accuracy while reducing computational load.
[0039] Among them, the sliding mode controller is a nonlinear control strategy that can achieve precise control of the system state. Figure 1 This specification illustrates a sliding mode control structure according to an exemplary embodiment, such as... Figure 1 As shown, the sliding mode controller mainly consists of three parts: the sliding surface, the approaching motion segment, and the sliding motion segment. When the initial state of the system is not on the sliding surface, the approaching law causes the system state to move towards the sliding surface and reach it within a finite time. When the system state reaches the sliding surface, the sliding control law causes the system state to move along the sliding surface and converge to the origin (equilibrium point). Therefore, the design of the sliding mode controller includes three parts: sliding surface design, approaching law design, and control law design. The embodiments in this specification will construct a sliding mode controller to output the steering data required for lateral control.
[0040] The embodiments described in this specification will now be described in detail.
[0041] like Figure 2 As shown, Figure 2 This is a flowchart illustrating a lateral control method in autonomous driving mode according to an exemplary embodiment, specifically including the following steps:
[0042] S201, based on the vehicle's real-time motion data, constructs a vehicle error state-space equation for lateral error, lateral error rate of change, heading error, heading error rate of change, and steering data.
[0043] Specifically, in the autonomous driving mode of an intelligent vehicle, to perform lateral control, the vehicle's basic parameters must first be identified, obtaining the vehicle's mass m and front overhang length l. f Rear overhang length l r Moment of inertia I about the z-axis z Front wheel lateral stiffness C αf Rear wheel lateral stiffness C ar .
[0044] Secondly, it receives signals from speed sensors, accelerometers, RTK, and gyroscopes to measure the vehicle's longitudinal velocity v in real time during vehicle movement. x The position (x, y) and heading angle θ.
[0045] Based on the above vehicle parameters and real-time motion data, and combined with the vehicle's linear two-degree-of-freedom dynamics model, the following vehicle error state-space equations are constructed:
[0046]
[0047] in:
[0048]
[0049]
[0050] In the above vehicle error state-space equation, e1 represents the lateral error. e1 is the rate of change of lateral error, and e2 is the heading error. Let u = δ be the rate of change of heading error, and δ be the steering data.
[0051] S202, calculate the sliding surface parameters according to the vehicle error state space equation, and generate linear sliding functions with respect to the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error, respectively, using each sliding surface parameter as a weighting coefficient.
[0052] The design of sliding surface parameters is the foundation of sliding mode control. After obtaining the vehicle error state space equation, it is necessary to calculate the parameters of the sliding surface according to the vehicle error state space equation to obtain the coefficients of various vehicle state errors. In the embodiment of this specification, there are four state error coefficients c1, c2, c3, and c4 corresponding to the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error.
[0053] In an exemplary embodiment, obtaining the sliding surface parameters based on the vehicle error state space equation includes: converting the vehicle error state space equation into a controllable canonical form and constructing a reduced-order system; and designing a state feedback control law for the reduced-order system using the pole placement method and calculating the sliding surface parameters.
[0054] In an exemplary embodiment, when the vehicle error state space equation is constructed based on the vehicle dynamics model of the vehicle, it is necessary to determine the controllability of the vehicle dynamics model before obtaining the sliding surface parameters. Here, the controllability determination is to evaluate the controllability of the system.
[0055] In control theory, controllability refers to the ability to move a system from a given initial state to a target state by applying external control input. In sliding mode control, it is necessary to evaluate the controllability of the system to ensure that the sliding mode controller can effectively control the system and stabilize it in the desired state.
[0056] In the embodiments of this specification, the rank criterion is used to determine whether the vehicle error state space equation is controllable. If the vehicle dynamics model is controllable, the sliding surface parameters are calculated based on the vehicle error state space equation. If the vehicle dynamics model is uncontrollable, the autonomous driving mode is immediately terminated and switched to manual driving mode.
[0057] In this illustrated embodiment, two prerequisites exist for constructing the sliding surface: the first prerequisite is that the system state reaches the sliding surface, at which point s = 0; the second prerequisite is that the system state moves along the sliding surface, at which point... Since the vehicle dynamics model is controllable, there must exist a non-singular transformation matrix T that transforms the vehicle error state-space equations into controllable canonical form:
[0058]
[0059] Wherein, the non-singular transformation matrix is:
[0060] T = [A 3 BA 2 B AB B]
[0061] Since z = Tx and H = TAT -1 M = TB = [0, I1] T We can obtain:
[0062]
[0063] After conversion, the error state-space equation of the vehicle under the controllable standard model can be obtained:
[0064]
[0065] In summary, once the vehicle error state-space equation is converted into a controllable canonical form, a reduced-order system is constructed.
[0066] In an exemplary embodiment, constructing the reduced-order system includes: decomposing the vehicle error state-space equation under the controllable canonical form into a first reduced-order subsystem independent of the sliding mode control law and a second reduced-order subsystem related to the sliding mode control law; and constructing the reduced-order system based on the first reduced-order subsystem.
[0067] Specifically, the vehicle error state-space equation under the controllable standard form is decomposed into a first reduced-order subsystem that is independent of the sliding mode control law and a second reduced-order subsystem that is related to the sliding mode control law.
[0068] First reduced-order subsystem:
[0069] Second reduced-order subsystem:
[0070] At this point, since there is a control variable u in the second reduced-order subsystem, the second precondition is met. Find the control quantity u, and denote this control quantity u as u eq This is also known as the equivalent control law.
[0071] Specifically, according to:
[0072]
[0073] The equivalent control law u can be derived. eq :
[0074]
[0075] Find the equivalent control law u eq After that, the state variables of the second reduced-order subsystem are in u eq Under its influence, it moves along the sliding surface, thus ensuring the second prerequisite. At this point, the action of the second reduced-order subsystem ends, but it cannot be guaranteed that the system state converges to 0.
[0076] Next, we only need to ensure that the system state converges to 0 and the first premise, s = 0, based on the first reduced-order subsystem. The process of constructing the sliding surface is the process of ensuring that the system state converges to 0 and ensuring that s = 0. Therefore, we only need to construct the reduced-order system based on the first reduced-order subsystem.
[0077] In an exemplary embodiment, constructing the reduced-order system based on the first reduced-order subsystem includes: constructing a convergent relation for the sliding surface plane formula; and merging the convergent relation and the first reduced-order subsystem to obtain the reduced-order system.
[0078] Specifically, from s=0, we can obtain the formula for the sliding surface plane:
[0079] s=C1z1+C2z2=0
[0080] Convergence relation is constructed based on the sliding surface plane formula:
[0081]
[0082] By combining the convergence relation and the first reduced-order subsystem, the reduced-order system is obtained:
[0083]
[0084] For the reduced-order system, the state feedback control law is calculated using the pole placement method, and the sliding surface parameters are calculated. The specific steps are as follows:
[0085] (1) Convert the new reduced-order system into a controllable standard form.
[0086] (2) Calculate the characteristic polynomial coefficients of the state transition matrix of the reduced-order system.
[0087] (3) Given the desired pole, calculate the characteristic polynomial coefficients of the desired pole.
[0088] (4) Calculate the state feedback gain matrix under the controllable canonical form.
[0089] (5) Calculate the controllable canonical form transformation matrix.
[0090] (6) Calculate the state feedback gain matrix of the original reduced-order system.
[0091] After obtaining the state feedback gain matrix of the reduced-order system, the sliding surface parameters can be obtained:
[0092] C = [KI]T
[0093] Where C is a matrix composed of the error coefficients c1, c2, c3, and c4 for each state of the vehicle.
[0094] In some other embodiments, a linear quadratic regulator (LQR) can also be used to calculate the state feedback control law and the sliding surface parameters, which is not limited in this specification.
[0095] By reducing the order of the vehicle error state space equation, a reduced-order system is obtained, which reduces the system complexity. The sliding surface parameters are then calculated based on the reduced-order system, simplifying the computational complexity, reducing the computational power requirements, and decreasing the computational cost.
[0096] In some other embodiments, the sliding surface parameters can also be obtained by using the Lyapunov direct method based on the vehicle state error space equation, which is not limited in this specification.
[0097] After obtaining the parameters of the sliding surface, the vehicle's lateral error e1 and the rate of change of lateral error are... Heading error e2, rate of change of heading error Multiplying each by the respective state error coefficients c1, c2, c3, and c4, and then summing them, yields the cost function for the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error. That is, the linear sliding mode function.
[0098] By constructing a vehicle error state space to calculate the sliding mode surface parameters, and using each sliding mode surface parameter as a weighting coefficient, a linear sliding mode function is generated for the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error. This ensures that the vehicle's state depends only on the sliding mode surface parameters and is independent of external environmental disturbances, exhibiting strong robustness to external environmental interference and system parameter perturbations, while requiring lower computational costs and resources. Furthermore, the linear sliding mode function structure has good interpretability, which is beneficial for problem analysis and solution in practical applications.
[0099] S203, a sliding mode controller is constructed based on the linear sliding mode function and the vehicle error state space equation, and the steering wheel angle is calculated based on the steering data output by the sliding mode controller to perform lateral control on the vehicle.
[0100] In an exemplary embodiment, constructing a sliding mode controller based on the linear sliding mode function and the vehicle error state space equation includes:
[0101] (1) Construct a linear sliding surface based on the linear sliding function.
[0102] Based on linear sliding mode function The sliding surface s = 0 is the plane with a vehicle error cost function value of 0.
[0103] (2) Calculate the exponential approach law for the linear sliding surface.
[0104] like Figure 1 As shown, the entire plane is divided into three parts: s>0, s=0, and s<0. When s>0, it is necessary to ensure that... (The sliding mode function must be monotonically decreasing) to ensure that the system state reaches the sliding surface in a finite time. When s < 0, it is necessary to ensure that... Only when the sliding mode function is monotonically increasing can the system state reach the sliding surface within a finite time, that is, complete the approaching motion within a specified time.
[0105] In the embodiments of this specification, the exponential reaching law is used. As a reaching law, where ε>0, k>0, and sgn(s) is the sign function. When s>0, sgn(s)=1; when s<0, sgn(s)=-1; and when s=0, sgn(s)=0.
[0106] By employing the exponential approach law, the vehicle error cost function can be made to quickly approach 0.
[0107] In some other embodiments, those skilled in the art may use other approximation laws such as the constant-rate approximation law or the power approximation law according to their own needs, and this specification does not limit them.
[0108] (3) Substitute the vehicle error state space equation into the exponential approach law to calculate the sliding mode control law.
[0109] When the vehicle's state error reaches the sliding surface s=0 within a finite time under the action of the approaching law, it will move along the sliding surface s=0 under the action of the sliding control law, causing the vehicle's lateral error e1 and the rate of change of the lateral error to... Heading error e2, rate of change of heading error They converge to 0 respectively.
[0110] Specifically, substituting the vehicle error state-space equation into the exponential reaching law yields the sliding mode control law, which takes the following form:
[0111] u = -(CB) -1 [CAx+εsgn(s)+ks]
[0112] Where C is a matrix composed of the error coefficients c1, c2, c3, and c4 for each state of the vehicle.
[0113] In an exemplary embodiment, after obtaining the steering data u output by the sliding mode controller, the steering data u is multiplied by the vehicle steering ratio to obtain the steering wheel steering radian, wherein the steering ratio is a fixed parameter of the vehicle.
[0114] The steering wheel radian is converted into a steering wheel angle and output to the vehicle's control module, which then controls the steering wheel based on the steering wheel angle.
[0115] In the embodiments of this specification, the sliding surface parameters are calculated by constructing a vehicle error state space, and a linear sliding mode function is generated with each sliding surface parameter as a weighting coefficient, relating to the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error. Based on the linear sliding mode function and the vehicle error state space equation, a sliding mode controller is constructed to output steering data to calculate the steering wheel angle for lateral control. This ensures that the vehicle's state is only related to the parameters of the sliding surface and is independent of external environmental disturbances, exhibiting strong robustness to external environmental interference and system parameter perturbations. Furthermore, it has low computational cost and low requirements for computing resources. While improving anti-interference capability and control accuracy, it also reduces the computational load. In addition, the sliding mode controller constructed with the linear sliding mode function has good interpretability and relatively simple stability analysis, which is beneficial for problem analysis and solution in practical applications.
[0116] In the embodiments described in this specification, since the vehicle's sliding mode control system periodically collects the vehicle's actual motion data every 10 milliseconds, it cyclically calculates the value of the steering wheel angle. For example... Figure 3 As shown, Figure 3 This is a flowchart illustrating another lateral control method in autonomous driving mode according to an exemplary embodiment of this specification. The specific steps are as follows:
[0117] S301, Construct the vehicle error state space equation: Based on the real-time motion data of the vehicle, construct the vehicle error state space equation regarding lateral error, lateral error rate of change, heading error, heading error rate of change, and steering data.
[0118] S302, Determine if the equation is controllable: Determine the controllability of the vehicle dynamics model. If it is controllable, proceed to step S303. If it is not controllable, end the autonomous driving mode.
[0119] S303, Convert Controllable Standard Form: Converts the vehicle error state space equation into a controllable standard form.
[0120] S304, System Degradation: Constructing a Degraded System.
[0121] S305, Calculation parameters: Calculate the sliding surface parameters.
[0122] S306, Construct a linear sliding surface: Generate a linear sliding function with respect to the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error using each sliding surface parameter as a weighting coefficient, and construct a linear sliding surface.
[0123] S307, Calculation of the reaching law: Calculation of the reaching law based on the linear sliding surface.
[0124] S308, Calculate the sliding mode control law: Substitute the vehicle error state space equation into the approach law, calculate the sliding mode control law, and output the steering data.
[0125] S309, End autonomous driving: Determine whether to manually end the autonomous driving mode. If yes, end the autonomous driving mode; otherwise, return to step S301.
[0126] For details on the implementation process of the above steps, please refer to [link / reference]. Figure 2 The implementation process of the corresponding steps in the corresponding embodiments will not be described again here.
[0127] Corresponding to the embodiments of the methods described above, this specification also provides embodiments of the apparatus.
[0128] like Figure 4 As shown, Figure 4 This is a block diagram illustrating a lateral control device in an autonomous driving mode according to an exemplary embodiment of this specification, the device comprising:
[0129] The acquisition unit is used to construct a vehicle error state space equation based on the vehicle's real-time motion data, including lateral error, lateral error rate of change, heading error, heading error rate of change, and steering data.
[0130] The processing unit is configured to calculate the sliding surface parameters according to the vehicle error state space equation, and generate linear sliding functions with respect to the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error, respectively, using each sliding surface parameter as a weighting coefficient.
[0131] The control unit is used to construct a sliding mode controller based on the linear sliding mode function and the vehicle error state space equation, and to calculate the value of the steering wheel angle according to the steering data output by the sliding mode controller, so as to perform lateral control on the vehicle.
[0132] In an exemplary embodiment, obtaining the sliding surface parameters based on the vehicle error state space equation includes:
[0133] The vehicle error state-space equations are converted into a controllable canonical form, and a reduced-order system is constructed.
[0134] For the reduced-order system, a state feedback control law is designed using the pole placement method, and the sliding surface parameters are calculated.
[0135] In one exemplary embodiment, constructing the reduced-order system includes:
[0136] The vehicle error state space equation under the controllable standard form is decomposed into a first reduced-order subsystem that is independent of the sliding mode control law and a second reduced-order subsystem that is related to the sliding mode control law.
[0137] The reduced-order system is constructed based on the first reduced-order subsystem.
[0138] In an exemplary embodiment, constructing the reduced-order system based on the first reduced-order subsystem includes:
[0139] Convergence relations are constructed for the sliding surface plane formula;
[0140] The reduced-order system is obtained by combining the convergence relation and the first reduced-order subsystem.
[0141] In an exemplary embodiment, calculating the sliding surface parameters based on the vehicle error state space equation includes:
[0142] When the vehicle error state space equation is constructed based on the vehicle dynamics model, determine whether the vehicle dynamics model is controllable;
[0143] If the vehicle dynamics model is controllable, then the sliding surface parameters are calculated based on the vehicle error state space equation.
[0144] In an exemplary embodiment, constructing a sliding mode controller based on the linear sliding mode function and the vehicle error state space equation includes:
[0145] Construct a linear sliding surface based on the linear sliding function;
[0146] The exponential reaching law is calculated for the linear sliding surface;
[0147] Substituting the vehicle error state-space equation into the exponential approach law, the sliding mode control law is calculated.
[0148] In an exemplary embodiment, calculating the steering wheel angle based on the steering data output by the sliding mode controller to perform lateral control of the vehicle includes:
[0149] Obtain the steering data output by the sliding mode controller, multiply the steering data by the vehicle steering ratio, and obtain the steering wheel steering radian.
[0150] The steering wheel radian is converted into a steering wheel angle and output to the control module, which then controls the steering of the steering wheel.
[0151] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.
[0152] Figure 5 This is a schematic diagram illustrating the structure of an electronic device according to an exemplary embodiment of this specification. (Reference) Figure 5At the hardware level, the electronic device includes a processor 501, an internal bus 502, a network interface 503, memory 504, and non-volatile memory 505, and may also include other hardware required for business operations. The processor 501 reads the corresponding computer program from the non-volatile memory 505 into the memory 504 and then runs it. Of course, in addition to software implementation, this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.
[0153] The apparatus or module described in the above embodiments can be implemented by a computer chip or entity, or by a product with a certain function. A typical implementation device is a computer, which can be a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.
[0154] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a lateral control method in an autonomous driving mode as shown in any of the foregoing embodiments.
[0155] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0156] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0157] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0158] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0159] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A lateral control method in an autonomous driving mode, characterized in that, include: Based on the real-time motion data of the vehicle, a vehicle error state space equation is constructed for lateral error, lateral error rate of change, heading error, heading error rate of change, and steering data. The sliding surface parameters are calculated based on the vehicle error state space equation, and linear sliding functions are generated with each sliding surface parameter as a weighting coefficient, relating to the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error. A sliding mode controller is constructed based on the linear sliding mode function and the vehicle error state space equation, and the steering wheel angle is calculated based on the steering data output by the sliding mode controller to perform lateral control of the vehicle.
2. The method according to claim 1, characterized in that, The calculation of sliding surface parameters based on the vehicle error state space equation includes: The vehicle error state-space equations are converted into a controllable canonical form, and a reduced-order system is constructed. For the reduced-order system, a state feedback control law is designed using the pole placement method, and the sliding surface parameters are calculated.
3. The method according to claim 2, characterized in that, The construction of the reduced-order system includes: The vehicle error state space equation under the controllable standard form is decomposed into a first reduced-order subsystem that is independent of the sliding mode control law and a second reduced-order subsystem that is related to the sliding mode control law. The reduced-order system is constructed based on the first reduced-order subsystem.
4. The method according to claim 3, characterized in that, The construction of the reduced-order system based on the first reduced-order subsystem includes: Convergence relations are constructed for the sliding surface plane formula; The reduced-order system is obtained by combining the convergence relation and the first reduced-order subsystem.
5. The method according to claim 1, characterized in that, The calculation of sliding surface parameters based on the vehicle error state space equation includes: When the vehicle error state space equation is constructed based on the vehicle dynamics model, determine whether the vehicle dynamics model is controllable; If the vehicle dynamics model is controllable, then the sliding surface parameters are calculated based on the vehicle error state space equation.
6. The method according to claim 1, characterized in that, The construction of a sliding mode controller based on the linear sliding mode function and the vehicle error state space equation includes: Construct a linear sliding surface based on the linear sliding function; The exponential reaching law is calculated for the linear sliding surface; Substituting the vehicle error state-space equation into the exponential approach law, the sliding mode control law is calculated.
7. The method according to claim 1, characterized in that, The step of calculating the steering wheel angle based on the steering data output by the sliding mode controller to perform lateral control of the vehicle includes: Obtain the steering data output by the sliding mode controller, multiply the steering data by the vehicle steering ratio, and obtain the steering wheel steering radian. The steering wheel radian is converted into a steering wheel angle and output to the control module, which then controls the steering of the steering wheel.
8. A lateral control device in automatic driving mode, characterized in that, include: The acquisition unit is used to construct a vehicle error state space equation based on the vehicle's real-time motion data, including lateral error, lateral error rate of change, heading error, heading error rate of change, and steering data. The processing unit is used to calculate the sliding surface parameters according to the vehicle error state space equation, and generate linear sliding functions with each sliding surface parameter as a weighting coefficient for the lateral error, the rate of change of the lateral error, the heading error, and the rate of change of the heading error. The control unit is used to construct a sliding mode controller based on the linear sliding mode function and the vehicle error state space equation, and to calculate the value of the steering wheel angle according to the steering data output by the sliding mode controller, so as to perform lateral control on the vehicle.
9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor implements the method as described in any one of claims 1-7 by executing the executable instructions.
10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-7.