A method and apparatus for vehicle control

Abstract

This specification discloses a method and apparatus for vehicle control. Based on the acquired vehicle structural parameters, vehicle motion parameters, lateral and longitudinal control saturation functions, and a standard robust controller model, the state-space equations of the vehicle's closed-loop system are constructed. Then, based on the state-space equations of the closed-loop system, the corresponding closed-loop transfer function is determined. Next, based on the performance indices of the closed-loop system's state-space equations and the closed-loop transfer function, convex optimization inequalities are constructed and solved to obtain solutions. Finally, based on the solutions to the convex optimization inequalities, an optimal robust controller model is constructed, and the vehicle's lateral and longitudinal control is performed based on this model. Thus, by considering the vehicle's hardware performance during the calculation process, calculation results that meet the vehicle's performance limits are obtained, solving the saturation nonlinearity problem related to the vehicle's steering wheel angle and acceleration / deceleration.

Description

Technical Field

[0001] This specification relates to the field of computer technology, and in particular to a method and apparatus for vehicle control. Background Technology

[0002] Lateral control in autonomous driving refers to the automatic adjustment of the steering wheel angle during vehicle operation, while longitudinal control refers to the automatic control of the vehicle's drive and braking during operation to maintain stable driving within the lane.

[0003] Currently, vehicle hardware performance has certain limitations. For example, there are limits on the maximum steering wheel angle, the maximum acceleration, and the maximum deceleration.

[0004] Current algorithms often fail to adequately consider the limitations of these vehicles during the calculation process. The calculation results usually need to be adjusted in subsequent steps based on the vehicle's performance limits. This not only reduces the accuracy of vehicle control, but also may accelerate hardware wear and tear and shorten the hardware's lifespan if the vehicle's hardware is kept under extreme conditions for an extended period. Summary of the Invention

[0005] This specification provides a method, apparatus, storage medium, and electronic device for vehicle control to improve the efficiency of vehicle control.

[0006] The following technical solution is adopted in this specification:

[0007] This specification provides a method for vehicle control, the method comprising:

[0008] Based on the obtained vehicle structural parameters, vehicle motion parameters, and lateral and longitudinal control saturation functions, the state-space equations of the vehicle's open-loop system are constructed; wherein, the lateral and longitudinal control saturation functions include: steering wheel angle saturation function and acceleration saturation function;

[0009] Based on the state-space equation of the open-loop system and the standard robust controller model, the state-space equation of the closed-loop system of the vehicle is constructed, and based on the state-space equation of the closed-loop system, the closed-loop transfer function corresponding to the state-space equation of the closed-loop system is determined.

[0010] Based on the state-space equation of the closed-loop system and the performance index of the closed-loop transfer function, the convex optimization inequality is constructed, and the solution of the convex optimization inequality is obtained by solving the convex optimization inequality.

[0011] Based on the solution of the convex optimization inequality, an optimal robust controller model is constructed, and the vehicle is controlled laterally and longitudinally based on the optimal robust controller model.

[0012] Optionally, based on the state-space equations of the open-loop system and the standard robust controller model, the state-space equations of the vehicle's closed-loop system are constructed, including:

[0013] Obtain the state feedback matrix of the standard robust controller model;

[0014] Based on the horizontal and vertical control saturation functions and the state feedback matrix, construct the horizontal and vertical control saturation matrix;

[0015] The lateral and longitudinal control saturation functions in the state space equation of the open-loop system are replaced with the lateral and longitudinal control saturation matrices, and the state feedback matrix is ​​substituted into the state space equation of the open-loop system to construct the state space equation of the closed-loop system of the vehicle.

[0016] Optionally, the horizontal and vertical control saturation matrix is ​​constructed based on the horizontal and vertical control saturation functions and the state feedback matrix, including:

[0017] The steering wheel angle saturation is determined based on the steering wheel angle saturation function and the state feedback matrix;

[0018] The acceleration saturation degree is determined based on the acceleration saturation function and the state feedback matrix;

[0019] Based on the steering wheel angle saturation and the acceleration saturation, construct the lateral and longitudinal control saturation matrix.

[0020] Optionally, before constructing the convex optimization inequality based on the state-space equation of the closed-loop system and the performance index of the closed-loop transfer function, the method further includes:

[0021] Based on the horizontal and vertical control saturation matrices in the state-space equation of the closed-loop system, determine the horizontal and vertical control vertex matrices corresponding to the horizontal and vertical control saturation matrices.

[0022] Based on the aforementioned horizontal and vertical control vertex matrices, the state-space equations of the closed-loop system are converted into state-space equations in vertex representation form.

[0023] Based on the state-space equations of the closed-loop system and the performance index of the closed-loop transfer function, the convex optimization inequality is constructed, including:

[0024] Based on the state-space equation of the vertex representation and the performance index of the closed-loop transfer function, the convex optimization inequality is constructed.

[0025] Optionally, solving the convex optimization inequality to obtain a solution to the convex optimization inequality includes:

[0026] The solution to the convex optimization inequality is obtained by solving for all vertices in the convex optimization inequality using the interior point penalty function method.

[0027] Optionally, the vehicle is subjected to lateral and longitudinal control based on the optimal robust controller model, including:

[0028] The vehicle motion parameters are input into the optimal robust controller model to obtain the vehicle's steering wheel angle and acceleration;

[0029] Based on the steering wheel angle and acceleration of the vehicle, the vehicle is controlled laterally and longitudinally.

[0030] Optionally, the vehicle structural parameters include: vehicle mass, front overhang length, rear overhang length, moment of inertia about the axis, front wheel lateral stiffness, and rear wheel lateral stiffness; the vehicle motion parameters include: lateral error, lateral error rate of change, heading error, heading error rate of change, position error, and speed error.

[0031] This specification provides a vehicle control device, the device comprising:

[0032] The construction module is used to construct the state-space equations of the vehicle's open-loop system based on the acquired vehicle structural parameters, vehicle motion parameters, and lateral and longitudinal control saturation functions; wherein, the lateral and longitudinal control saturation functions include: steering wheel angle saturation function and acceleration saturation function;

[0033] The determination module is used to construct the state space equation of the closed-loop system of the vehicle based on the state space equation of the open-loop system and the standard robust controller model, and to determine the closed-loop transfer function corresponding to the state space equation of the closed-loop system based on the state space equation of the closed-loop system.

[0034] The solution module is used to construct the convex optimization inequality based on the state-space equation of the closed-loop system and the performance index of the closed-loop transfer function, and to solve the convex optimization inequality to obtain the solution of the convex optimization inequality.

[0035] The control module is used to construct an optimal robust controller model based on the solution of the convex optimization inequality, and to perform lateral and longitudinal control on the vehicle based on the optimal robust controller model.

[0036] This specification provides an electronic device, including a communication interface, a processor, a memory, and a bus, wherein the communication interface, the processor, and the memory are interconnected via the bus;

[0037] The memory stores machine-readable instructions, and the processor executes the vehicle control method described above by invoking the machine-readable instructions.

[0038] This specification provides a machine-readable storage medium storing machine-readable instructions that, when invoked and executed by a processor, implement the aforementioned vehicle control method.

[0039] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects:

[0040] This specification incorporates lateral and longitudinal control saturation functions into the state-space equations of a traditional vehicle closed-loop system, constructing convex optimization inequalities. Then, based on the solutions to these convex optimization inequalities, an optimal robust controller model is constructed, and the vehicle's lateral and longitudinal control is performed based on this model. This approach considers the vehicle's hardware performance during calculation, yielding results that meet the vehicle's performance limits. It resolves the saturation nonlinearity issues related to steering wheel angle and acceleration / deceleration, improving the accuracy of vehicle control and extending hardware lifespan. Attached Figure Description

[0041] The accompanying drawings, which are included to provide a further understanding of this specification and form part of this specification, illustrate exemplary embodiments and are used to explain this specification, but do not constitute an undue limitation thereof. In the drawings:

[0042] Figure 1 This is a flowchart illustrating a vehicle control method as an exemplary embodiment;

[0043] Figure 2 This is a schematic diagram illustrating a controller model as an exemplary embodiment;

[0044] Figure 3 This is a schematic diagram illustrating a control flow in an exemplary embodiment;

[0045] Figure 4 This is a structural diagram of an electronic device containing a vehicle control device, as shown in an exemplary embodiment.

[0046] Figure 5 This is a structural diagram of a vehicle control device shown in an exemplary embodiment. Detailed Implementation

[0047] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0048] It should be noted that the steps of the corresponding methods are not necessarily performed in the order shown and described in this specification in other embodiments. In some other embodiments, the methods may include more or fewer steps than described in this specification. Furthermore, a single step described in this specification may be broken down into multiple steps in other embodiments; and multiple steps described in this specification may be combined into a single step in other embodiments.

[0049] Currently, vehicle hardware performance has certain limitations. For example, there are limits on the maximum steering wheel angle, the maximum acceleration, and the maximum deceleration.

[0050] Current algorithms often fail to adequately consider the limitations of these vehicles during the calculation process. The calculation results usually need to be adjusted in subsequent steps based on the vehicle's performance limits. This not only reduces the accuracy of vehicle control, but also may accelerate hardware wear and tear and shorten the hardware's lifespan if the vehicle's hardware is kept under extreme conditions for an extended period.

[0051] Based on this, this specification proposes a technical solution for constructing an optimal robust controller model based on lateral and longitudinal control saturation functions. This approach considers the vehicle's hardware performance during the calculation process, yielding results that meet the vehicle's performance limits. It solves the saturation nonlinearity problem related to steering wheel angle and acceleration / deceleration, improves the accuracy of vehicle control, and extends the lifespan of the hardware.

[0052] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0053] Figure 1 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment, the method specifically including the following steps:

[0054] S100: Based on the obtained vehicle structural parameters, vehicle motion parameters, and lateral and longitudinal control saturation functions, construct the state-space equation of the vehicle's open-loop system; wherein, the lateral and longitudinal control saturation functions include: steering wheel angle saturation function and acceleration saturation function.

[0055] In the embodiments of this specification, the state-space equations of the vehicle's open-loop system are constructed based on the obtained vehicle structural parameters, vehicle motion parameters, and lateral and longitudinal control saturation functions. The lateral and longitudinal control saturation functions mentioned here refer to the functions that limit the steering wheel angle and acceleration to a preset maximum and minimum value during vehicle motion. These lateral and longitudinal control saturation functions include: a steering wheel angle saturation function and an acceleration saturation function.

[0056] Among them, vehicle structural parameters are parameters that describe the inherent properties of a vehicle. They are usually determined during the vehicle design and manufacturing stages and remain unchanged throughout the vehicle's life cycle. These include, but are not limited to, the vehicle's front overhang length, rear overhang length, mass, z-axis moment of inertia, front wheel eccentric stiffness, rear wheel eccentric stiffness, and environmental factors.

[0057] Vehicle motion parameters describe the real-time changes of a vehicle during its movement. They are typically obtained through real-time measurements by sensors and include, but are not limited to, the vehicle's speed, position, and heading angle. In general, both vehicle structural parameters and vehicle motion parameters are important parameters describing vehicle performance. The difference lies in that vehicle structural parameters are inherent properties of the vehicle and are usually determined during the design phase, while vehicle motion parameters change in real time, are measured by sensors, and reflect the vehicle's current dynamic state.

[0058] The state-space equations of a vehicle are mathematical models describing its dynamic behavior, typically used to describe the vehicle's state and state changes at different time points. Based on the influence of vehicle structural parameters on the vehicle state, a vehicle structural parameter model is constructed. Then, based on the vehicle's kinematics and dynamics, a vehicle motion parameter model is constructed. Next, the vehicle structural parameter model, the vehicle motion parameter model, and the lateral and longitudinal control saturation functions are integrated to construct the state-space equations of the vehicle's open-loop system. The state-space equations of the open-loop system are shown below:

[0059] in,

[0060] In the above formula, z is the adjustable output, the theoretical output signal vector. y is the measured output, the measurable output signal vector. A is the state transition matrix. B1 and B2 are control matrices. C1 and C2 are 6x6 identity matrices. D 11 D 12 D 21 D 22 It is a zero matrix with 6 rows and 1 column. e1 represents the lateral error. e1 is the rate of change of lateral error, and e2 is the heading error. e3 is the rate of change of heading error, e4 is the position error, and e5 is the velocity error. Let u be the desired yaw rate, the specific value of which is unknown and is treated as an external perturbation. u = [δ, α] T δ is the steering wheel angle, and α is the acceleration.

[0061] Among them, sat(u)=[sat(δ), sat(α)] T . sat(δ) is the saturation function of the steering wheel angle, δ max δ represents the maximum steering wheel angle.min This represents the minimum steering wheel angle. It should be noted that the minimum steering wheel angle can be negative; for example, a 180-degree left turn is +180 degrees, and a 180-degree right turn is -180 degrees. sat(α) is the saturation function of acceleration, α max α is the maximum value of acceleration. min This represents the minimum value of acceleration. It should be noted that the minimum value of acceleration can be negative.

[0062] Furthermore, C af For the front wheel lateral stiffness, C ar For the rear wheel lateral stiffness, l f For the front overhang length, l r For rear overhang length, I z Let v be the vehicle's moment of inertia about the z-axis. x Let m be the longitudinal velocity of the vehicle, and m be the mass of the vehicle.

[0063] It should be noted that, through speed sensors, accelerometers, gyroscopes, and RTK (Real-Time Kinematic) technology, vehicle motion parameters, including longitudinal velocity v, are measured in real time during vehicle movement. x Position (x, y), heading angle θ, etc. Calculate the lateral error e1 and the rate of change of lateral error based on the position (x, y). Position error e3. Calculate heading error e2 based on heading angle θ, and the rate of change of heading error. Based on the longitudinal velocity v x Calculate the speed error e4.

[0064] S102: Based on the state-space equation of the open-loop system and the standard robust controller model, construct the state-space equation of the closed-loop system of the vehicle, and determine the closed-loop transfer function corresponding to the state-space equation of the closed-loop system.

[0065] In the embodiments of this specification, the state space equation of the vehicle's closed-loop system is constructed based on the state space equation of the open-loop system and the standard robust controller model, and the closed-loop transfer function corresponding to the state space equation of the closed-loop system is determined based on the state space equation of the closed-loop system.

[0066] It should be noted that the robust H∞ controller model is used in the embodiments of this specification to implement the lateral and longitudinal control of the vehicle. Specifically, as follows... Figure 2 As shown.

[0067] Figure 2 This is a schematic diagram of a controller model shown in an exemplary embodiment.

[0068] exist Figure 2 In the H∞ control problem, K(s) represents the controller, and G(s) represents the controlled object. G(s) is also called the generalized object. The generalized object has two outputs: a weighted output z representing the performance requirements, and an output y applied to the controller. z is the theoretical output signal vector, while y is the measured output signal vector. The generalized object G(s) also has two inputs: all external disturbances w acting on the object, and the controller output u applied to the object.

[0069] The closed-loop transfer function describes the relationship between the system's input and output and can be used to evaluate the system's performance and stability. Therefore, the goal of H∞ control is to design a controller u(s) = K(s)y(s) such that the closed-loop system is internally stable, and the closed-loop transfer function T from the disturbance input w to the adjustable output z is... wz The H∞ norm of (s) is less than a given performance index γ>0, i.e., ||T wz (s)|| ∞ <γ.

[0070] Understandably, based on vehicle structural parameters, vehicle motion parameters, and lateral and longitudinal control saturation functions, the state-space equation ∑1 of the vehicle's open-loop system is constructed. Introducing the standard robust controller model into the open-loop system is essentially introducing an external control action into the open-loop system, thus transforming the original open-loop system into a closed-loop system. Consequently, the state-space equation ∑2 of the closed-loop system can be obtained, enabling the closed-loop system to maintain its stability in the face of various uncertainties or disturbances.

[0071] In practical applications, due to the nonlinear characteristics of the horizontal and vertical control saturation functions, the state-space equations ∑1 of the open-loop system constructed based on these functions also exhibit nonlinear characteristics. This makes the state-space equations ∑1 complex and difficult to analyze and calculate further. Therefore, the nonlinear horizontal and vertical control saturation functions can be replaced with horizontal and vertical control saturation matrices to construct linear state-space equations, thereby simplifying and improving the efficiency of subsequent calculations and analysis.

[0072] In the embodiments described in this specification, the state feedback matrix of a standard robust controller model is obtained. The standard robust controller mentioned here is u = Kx.

[0073] Then, based on the horizontal and vertical control saturation functions and the state feedback matrix, the horizontal and vertical control saturation matrix is ​​constructed.

[0074] Specifically, the steering wheel angle saturation is determined based on the steering wheel angle saturation function and the state feedback matrix. Similarly, the acceleration saturation is determined based on the acceleration saturation function and the state feedback matrix.

[0075] Next, based on the steering wheel angle saturation and acceleration saturation, the lateral and longitudinal control saturation matrices are constructed. The specific lateral and longitudinal control saturation matrices are shown below:

[0076] in,

[0077] As can be seen from the above formula, M represents the lateral and longitudinal control saturation matrix. K represents the state feedback matrix. The state feedback matrix can be a 2x6 matrix. The first row of the state feedback matrix can be k1, and the second row can be k2. η1∈(0,1] represents the steering wheel angle saturation; the smaller η1 is, the higher the steering wheel angle saturation. η1=1 indicates that the steering wheel angle is still within the linear region. η2∈(0,1] represents the acceleration saturation; the smaller η2 is, the higher the acceleration saturation. η2=1 indicates that the acceleration is still within the linear region.

[0078] Next, the lateral and longitudinal control saturation functions in the state-space equations of the open-loop system are replaced with lateral and longitudinal control saturation matrices, and the state feedback matrix is ​​substituted into the state-space equations of the open-loop system to construct the state-space equations of the vehicle's closed-loop system. The state-space equations of the closed-loop system are shown below:

[0079]

[0080] In the above formula, K represents the state feedback matrix. M represents the horizontal and vertical control saturation matrices. A represents the state transition matrix. B1 and B2 are control matrices. C1 and C2 are 6-row, 6-column identity matrices. D 11 D 12 D 21 D 22 It is a zero matrix with 6 rows and 1 column. e1 represents the lateral error. e1 is the rate of change of lateral error, and e2 is the heading error. e3 is the rate of change of heading error, e4 is the position error, and e5 is the velocity error. The desired yaw rate, the specific value of which is unknown, is treated as an external disturbance.

[0081] Furthermore, by performing a Laplace transform on the state-space equation ∑2 of the closed-loop system, the corresponding closed-loop transfer function, T, is obtained. wz =(C1+D 11 K)[sI-(A+B1MK)] -1 B2+D 12 T wz The H∞ norm index of (s) is ||T wz (s)|| ∞ <γ.

[0082] As can be seen from the above embodiments, replacing the nonlinear horizontal and vertical control saturation functions with horizontal and vertical control saturation matrices and constructing linear state-space equations makes subsequent calculations and analyses simpler and more efficient.

[0083] S104: Based on the state-space equation of the closed-loop system and the performance index of the closed-loop transfer function, construct the convex optimization inequality and solve the convex optimization inequality to obtain the solution of the convex optimization inequality.

[0084] In the embodiments of this specification, a convex optimization inequality is constructed based on the state-space equation of the closed-loop system and the performance index of the closed-loop transfer function, and the solution of the convex optimization inequality is obtained by solving the convex optimization inequality.

[0085] Specifically, based on the horizontal and vertical control saturation matrices in the state-space equations of the closed-loop system, the horizontal and vertical control vertex matrices corresponding to the horizontal and vertical control saturation matrices are determined.

[0086] Here, consider any given compact convex set Ω, for any x∈Ω, η i There exists an infimum For any η i All have 0≤inf(η) i )≤1.

[0087] Therefore, there are four control vertex matrices in the horizontal and vertical directions, namely: It should be noted that M1, M2, M3, and M4 refer to four extreme cases during the vehicle's motion. Therefore, every situation the vehicle faces during its motion can be represented by these four extreme cases, i.e. in,

[0088] Then, based on the horizontal and vertical control vertex matrices, the state-space equations of the closed-loop system are transformed into state-space equations in vertex representation form.

[0089] In other words, substituting the horizontal and vertical control vertex matrices into the state-space equation ∑2 of the closed-loop system, we obtain the state-space equation ∑3 of the horizontal and vertical control vertex matrices as shown below:

[0090]

[0091] Finally, based on the state-space equations in vertex representation and the performance metrics of the closed-loop transfer function, convex optimization inequalities are constructed.

[0092] Understandably, the closed-loop transfer function describes the relationship between the system's output and input. By adjusting the parameters of the closed-loop transfer function, the system's performance can be optimized. However, the parameter tuning problem of the closed-loop transfer function is usually a nonlinear optimization problem, which is quite difficult to solve. Therefore, transforming the closed-loop transfer function parameter tuning problem into a convex optimization problem allows us to leverage the superior properties of convex optimization to quickly find the optimal solution.

[0093] Furthermore, based on the closed-loop system ∑2, the performance index γ, and the necessary and sufficient condition for the robust H∞ control problem to have a solution, the following linear matrix inequality can be obtained:

[0094]

[0095] In the above formula, X is an unknown symmetric positive definite matrix, W is an unknown matrix, and γ is a performance index.

[0096] It is important to note that for the performance index γ, we generally seek its minimum value. Therefore, the robust H∞ control problem described above can be transformed into the following optimization problem, i.e., constructing the following original linear matrix:

[0097] minγ

[0098]

[0099] It should be noted that the above linear matrix inequality, i.e. the original linear matrix, can be directly used as the final convex optimization inequality for subsequent solution calculations.

[0100] Furthermore, since Ω is a convex set, and the closed-loop system is completely described by its vertices, when solving the optimization problem, it is not necessary to solve for all points within the convex set; only the vertices of the convex set need to be solved. Therefore, the above optimization problem can be transformed into the following form:

[0101]

[0102] In the above formula, A is the state transition matrix. B1 and B2 are control matrices. C1 and C2 are 6x6 identity matrices. D 11 D 12 D 21 D 22 M is a 6x1 zero matrix. j Let M1, M2, M3, and M4 be the control vertex matrix for the j-th horizontal and vertical axes, where j = 1, 2, 3, and 4. Solve the convex optimization inequality for the four vertices M1, M2, M3, and M4 to obtain the symmetric positive definite matrix X, matrix W, and performance index γ, which serve as the solution to the convex optimization inequality.

[0103] Specifically, by using the interior point penalty function method, we solve for all vertices in the convex optimization inequality to obtain the solution to the convex optimization inequality.

[0104] As can be seen from the above embodiments, the interior-point penalty function method finds the optimal solution within the feasible region, avoiding searches on the boundaries of the feasible region, thereby accelerating the convergence speed. Furthermore, the interior-point penalty function method searches within the feasible region in each iteration, which also contributes to improving the convergence speed.

[0105] It should be noted that the established convex optimization problem can also be solved using methods such as the interior point method and gradient descent to obtain solutions to the convex optimization inequalities, thereby satisfying the system's performance indicators and stability requirements.

[0106] S106: Based on the solution of the convex optimization inequality, construct the optimal robust controller model, and perform lateral and longitudinal control on the vehicle based on the optimal robust controller model.

[0107] In the embodiments of this specification, an optimal robust controller model is constructed based on the solution of the convex optimization inequality, and the vehicle is controlled laterally and longitudinally based on the optimal robust controller model. The specific formula of the optimal robust controller model is as follows:

[0108] u=WX -1 x.

[0109] In the above formula, the solution to the convex optimization inequality is a symmetric positive definite matrix X and a matrix W. e1 represents the lateral error. e1 is the rate of change of lateral error, and e2 is the heading error. Let e3 be the rate of change of heading error, e4 be the position error, and e5 be the velocity error. u = [δ, α] T δ is the steering wheel angle, and α is the acceleration.

[0110] In the embodiments of this specification, vehicle motion parameters are input into the optimal robust controller model to obtain the vehicle's steering wheel angle and acceleration.

[0111] Then, based on the vehicle's steering wheel angle and acceleration, the vehicle is controlled laterally and longitudinally.

[0112] For example, vehicle sensors, such as wheel speed sensors, steering wheel angle sensors, and yaw rate sensors, are used to collect real-time vehicle status and environmental information, providing necessary information for subsequent control. Based on an optimal robust controller model, control inputs, such as steering wheel angle or torque, braking and / or acceleration commands, are calculated. Then, based on the calculated control inputs, commands are sent to the vehicle's actuators, such as the steering wheel and brakes. Accordingly, upon receiving the commands, the actuators adjust their operating states to ensure the vehicle responds according to the control inputs.

[0113] Figure 3 This is a schematic diagram of a control flow shown in an exemplary embodiment.

[0114] exist Figure 3 In this process, based on the obtained vehicle structural parameters, vehicle motion parameters, and lateral and longitudinal control saturation functions, the state-space equations of the vehicle's open-loop system are constructed.

[0115] Secondly, based on the horizontal and vertical control saturation functions and the state feedback matrix, the horizontal and vertical control saturation matrix is ​​constructed.

[0116] Then, the lateral and longitudinal control saturation functions in the state-space equation of the open-loop system are replaced with lateral and longitudinal control saturation matrices, and the state feedback matrix is ​​substituted into the state-space equation of the open-loop system to construct the state-space equation of the vehicle's closed-loop system.

[0117] Determine whether the vehicle's controller can control it. If so, determine the closed-loop transfer function corresponding to the state-space equation of the closed-loop system.

[0118] Then, based on the horizontal and vertical control saturation matrices in the state-space equation of the closed-loop system, the horizontal and vertical control vertex matrices corresponding to the horizontal and vertical control saturation matrices are determined, and based on the horizontal and vertical control vertex matrices, the state-space equation of the closed-loop system is converted into a state-space equation in vertex representation form.

[0119] Next, based on the state-space equations in vertex representation and the performance index of the closed-loop transfer function, a convex optimization inequality is constructed and solved to obtain the solution to the convex optimization inequality.

[0120] Finally, based on the solution of the convex optimization inequality, an optimal robust controller model is constructed, and the vehicle is controlled laterally and longitudinally based on the optimal robust controller model.

[0121] As can be seen from the above method, lateral and longitudinal control saturation functions are added to the state-space equations of the traditional vehicle closed-loop system to construct convex optimization inequalities. Then, based on the solutions of the convex optimization inequalities, an optimal robust controller model is constructed, and the vehicle is controlled laterally and longitudinally based on the optimal robust controller model. Thus, the vehicle's hardware performance is considered during the calculation process, resulting in calculation results that meet the vehicle's performance limits. This solves the saturation nonlinearity problem in the vehicle's steering wheel angle and acceleration / deceleration, improves the accuracy of vehicle control, and extends the hardware lifespan.

[0122] Corresponding to the embodiments of the vehicle control method described above, this specification also provides an embodiment of a vehicle control device.

[0123] Please see Figure 4, Figure 4 This is a structural diagram of an electronic device containing a vehicle control apparatus, as illustrated in an exemplary embodiment. At the hardware level, the device includes a processor 402, an internal bus 404, a network interface 406, memory 408, and non-volatile memory 410, and may also include other necessary hardware. One or more embodiments of this specification can be implemented in software, for example, the processor 402 reads the corresponding computer program from the non-volatile memory 410 into memory 408 and then runs it. Of course, besides software implementation, one or more embodiments of this specification do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0124] Please see Figure 5 , Figure 5 This is a structural diagram illustrating an exemplary embodiment of a vehicle control device. This vehicle control device can be applied to, for example... Figure 4 The electronic device shown implements the technical solution of this specification. The device may include:

[0125] The construction module 500 is used to construct the state-space equation of the vehicle's open-loop system based on the acquired vehicle structural parameters, vehicle motion parameters, and lateral and longitudinal control saturation functions; wherein, the lateral and longitudinal control saturation functions include: steering wheel angle saturation function and acceleration saturation function;

[0126] The determination module 502 is used to construct the state space equation of the closed-loop system of the vehicle based on the state space equation of the open-loop system and the standard robust controller model, and to determine the closed-loop transfer function corresponding to the state space equation of the closed-loop system based on the state space equation of the closed-loop system.

[0127] The solver module 504 is used to construct the convex optimization inequality based on the state-space equation of the closed-loop system and the performance index of the closed-loop transfer function, and solve the convex optimization inequality to obtain the solution of the convex optimization inequality.

[0128] The control module 506 is used to construct the optimal robust controller model based on the solution of the convex optimization inequality, and to perform lateral and longitudinal control on the vehicle based on the optimal robust controller model.

[0129] Optionally, the determining module 502 is specifically used to obtain the state feedback matrix of the standard robust controller model; construct the lateral and longitudinal control saturation matrix based on the lateral and longitudinal control saturation functions and the state feedback matrix; replace the lateral and longitudinal control saturation functions in the state space equation of the open-loop system with the lateral and longitudinal control saturation matrix, and substitute the state feedback matrix into the state space equation of the open-loop system to construct the state space equation of the closed-loop system of the vehicle.

[0130] Optionally, the determining module 502 is specifically used to determine the steering wheel angle saturation degree based on the steering wheel angle saturation function and the state feedback matrix; determine the acceleration saturation degree based on the acceleration saturation function and the state feedback matrix; and construct the lateral and longitudinal control saturation matrices based on the steering wheel angle saturation degree and the acceleration saturation degree.

[0131] Optionally, the solution module 504 is further configured to determine the horizontal and vertical control vertex matrices corresponding to the horizontal and vertical control saturation matrices in the state space equation of the closed-loop system; convert the state space equation of the closed-loop system into a vertex representation based on the horizontal and vertical control vertex matrices; and construct the convex optimization inequality based on the vertex representation of the state space equation and the performance index of the closed-loop transfer function.

[0132] Optionally, the solution module 504 is specifically used to solve for all vertices in the convex optimization inequality using the interior point penalty function method to obtain the solution of the convex optimization inequality.

[0133] Optionally, the control module 506 is specifically used to input the vehicle motion parameters into the optimal robust controller model to obtain the vehicle's steering wheel angle and acceleration; and to perform lateral and longitudinal control on the vehicle based on the vehicle's steering wheel angle and acceleration.

[0134] Optionally, the vehicle structural parameters include: vehicle mass, front overhang length, rear overhang length, moment of inertia about the axis, front wheel lateral stiffness, and rear wheel lateral stiffness; the vehicle motion parameters include: lateral error, lateral error rate of change, heading error, heading error rate of change, position error, and speed error.

[0135] The specific implementation process of the functions and roles of each unit 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.

[0136] Based on the same concept as the methods described above, this specification also provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor performs the steps of the method as described in any of the above embodiments by executing the executable instructions.

[0137] Based on the same concept as the methods described above, this specification also provides a computer-readable storage medium having computer instructions stored thereon that, when executed by a processor, implement the steps of the methods as described in any of the above embodiments.

[0138] Based on the same concept as the methods described above, this specification also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods as described in any of the above embodiments.

[0139] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of this specification, such 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, first information may also be referred to as second information without departing from the scope of one or more embodiments of this specification, 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," "in response to a determination," or "when," or "in the event of a determination."

[0140] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.

Claims

1. A method for vehicle control, characterized in that, The method includes: Based on the obtained vehicle structural parameters, vehicle motion parameters, and lateral and longitudinal control saturation functions, the state-space equations of the vehicle's open-loop system are constructed; wherein, the lateral and longitudinal control saturation functions include: steering wheel angle saturation function and acceleration saturation function; Obtain the state feedback matrix of the standard robust controller model; Based on the horizontal and vertical control saturation functions and the state feedback matrix, construct the horizontal and vertical control saturation matrix; The lateral and longitudinal control saturation functions in the state space equation of the open-loop system are replaced with the lateral and longitudinal control saturation matrices, and the state feedback matrix is ​​substituted into the state space equation of the open-loop system to construct the state space equation of the closed-loop system of the vehicle. Based on the state-space equation of the closed-loop system, determine the closed-loop transfer function corresponding to the state-space equation of the closed-loop system; Based on the state-space equations of the closed-loop system and the performance index of the closed-loop transfer function, a convex optimization inequality is constructed, and the solution of the convex optimization inequality is obtained by solving the convex optimization inequality. Based on the solution of the convex optimization inequality, an optimal robust controller model is constructed, and the vehicle is controlled laterally and longitudinally based on the optimal robust controller model.

2. The method as described in claim 1, characterized in that, Based on the horizontal and vertical control saturation functions and the state feedback matrix, the horizontal and vertical control saturation matrices are constructed, including: The steering wheel angle saturation is determined based on the steering wheel angle saturation function and the state feedback matrix. The acceleration saturation degree is determined based on the acceleration saturation function and the state feedback matrix. Based on the steering wheel angle saturation and the acceleration saturation, construct the lateral and longitudinal control saturation matrix.

3. The method as described in claim 1, characterized in that, Before constructing the convex optimization inequality based on the state-space equation of the closed-loop system and the performance index of the closed-loop transfer function, the method further includes: Based on the horizontal and vertical control saturation matrices in the state-space equation of the closed-loop system, determine the horizontal and vertical control vertex matrices corresponding to the horizontal and vertical control saturation matrices. Based on the aforementioned horizontal and vertical control vertex matrices, the state-space equations of the closed-loop system are converted into state-space equations in vertex representation form. Based on the state-space equations of the closed-loop system and the performance index of the closed-loop transfer function, the convex optimization inequality is constructed, including: Based on the state-space equation of the vertex representation and the performance index of the closed-loop transfer function, the convex optimization inequality is constructed.

4. The method as described in claim 3, characterized in that, Solving the convex optimization inequality yields a solution, including: The solution to the convex optimization inequality is obtained by solving for all vertices in the convex optimization inequality using the interior point penalty function method.

5. The method as described in claim 1, characterized in that, Based on the optimal robust controller model, the vehicle is subjected to lateral and longitudinal control, including: The vehicle motion parameters are input into the optimal robust controller model to obtain the vehicle's steering wheel angle and acceleration; Based on the steering wheel angle and acceleration of the vehicle, the vehicle is controlled laterally and longitudinally.

6. The method as described in claim 1, characterized in that, The vehicle structural parameters include: vehicle mass, front overhang length, rear overhang length, moment of inertia about the axis, front wheel lateral stiffness, and rear wheel lateral stiffness; the vehicle motion parameters include: lateral error, lateral error rate of change, heading error, heading error rate of change, position error, and speed error.

7. A vehicle control device, characterized in that, The device includes: The construction module is used to construct the state-space equations of the vehicle's open-loop system based on the acquired vehicle structural parameters, vehicle motion parameters, and lateral and longitudinal control saturation functions; wherein, the lateral and longitudinal control saturation functions include: steering wheel angle saturation function and acceleration saturation function; The module is used to obtain the state feedback matrix of the standard robust controller model; construct the lateral and longitudinal control saturation matrix based on the lateral and longitudinal control saturation functions and the state feedback matrix; replace the lateral and longitudinal control saturation functions in the state space equation of the open-loop system with the lateral and longitudinal control saturation matrix, and substitute the state feedback matrix into the state space equation of the open-loop system to construct the state space equation of the vehicle's closed-loop system; and determine the closed-loop transfer function corresponding to the state space equation of the closed-loop system based on the state space equation of the closed-loop system. The solution module is used to construct convex optimization inequalities based on the state-space equations of the closed-loop system and the performance index of the closed-loop transfer function, and to solve the convex optimization inequalities to obtain the solution of the convex optimization inequalities. The control module is used to construct an optimal robust controller model based on the solution of the convex optimization inequality, and to perform lateral and longitudinal control on the vehicle based on the optimal robust controller model.

8. An electronic device, characterized in that, It includes a communication interface, a processor, a memory, and a bus, wherein the communication interface, the processor, and the memory are interconnected via the bus; The memory stores machine-readable instructions, and the processor executes the method according to any one of claims 1 to 6 by invoking the machine-readable instructions.

9. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores machine-readable instructions, which, when invoked and executed by a processor, implement the method described in any one of claims 1 to 6.

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