Vehicle control method and device

Abstract

The invention discloses a vehicle control method and device. According to the obtained vehicle structure parameters, the vehicle motion parameters, the transverse and longitudinal control saturation functions and the standard robust controller model, a state-space equation of a closed-loop system of the vehicle is constructed, and according to the state-space equation of the closed-loop system, a closed-loop transfer function corresponding to the state-space equation of the closed-loop system is determined. Thirdly, constructing a convex optimization inequality according to the state-space equation of the closed-loop system and the performance index of the closed-loop transfer function, and solving the convex optimization inequality to obtain a solution of the convex optimization inequality; and finally, based on the solution of the convex optimization inequality, constructing an optimal robust controller model, and performing transverse and longitudinal control on the vehicle based on the optimal robust controller model. Therefore, the hardware performance of the vehicle is considered in the calculation process, a calculation result conforming to the performance limit of the vehicle is obtained, and the problem of saturation nonlinearity existing in the steering wheel angle, acceleration and deceleration of the vehicle is solved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and more particularly to a method and device for controlling a vehicle. Background Art

[0002] The lateral control of autonomous driving refers to the automatic adjustment of the steering wheel angle during driving, and the longitudinal control refers to the automatic control of the vehicle's driving and braking during driving to keep the vehicle stable in the lane.

[0003] At present, there are certain limitations on the hardware performance of vehicles. For example, there is a maximum limit on the steering wheel angle, a maximum limit on acceleration, and a maximum limit on deceleration.

[0004] Current algorithms often fail to fully consider these vehicle constraints during calculations. The calculation results usually need to be adjusted in subsequent steps according to the vehicle's performance limits. This practice not only reduces the accuracy of vehicle control, but also may accelerate the vehicle's hardware wear and shorten its service life if the vehicle's hardware is in a state of extremes for a long time. Summary of the invention

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

[0006] This manual adopts the following technical solutions:

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

[0008] According to the obtained vehicle structure parameters, vehicle motion parameters and lateral and longitudinal control saturation functions, the state space equation of the open-loop system of the vehicle is constructed; wherein the lateral and longitudinal control saturation functions include: a steering wheel angle saturation function and an acceleration saturation function;

[0009] Constructing a state-space equation of a closed-loop system of the vehicle according to the state-space equation of the open-loop system and a standard robust controller model, and determining a closed-loop transfer function corresponding to the state-space equation of the closed-loop system according to the state-space equation of the closed-loop system;

[0010] According to 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 convex optimization inequality is solved to obtain a solution of 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 in the lateral and longitudinal directions based on the optimal robust controller model.

[0012] Optionally, constructing a state space equation of a closed loop system of the vehicle according to the state space equation of the open loop system and a standard robust controller model includes:

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

[0014] Constructing a lateral and longitudinal control saturation matrix according to the lateral and longitudinal control saturation function and the state feedback matrix;

[0015] The lateral and longitudinal control saturation function in the state space equation of the open loop system is replaced by the lateral and longitudinal control saturation matrix, 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, constructing a lateral and longitudinal control saturation matrix according to the lateral and longitudinal control saturation function and the state feedback matrix includes:

[0017] Determining the steering wheel angle saturation according to the steering wheel angle saturation function and the state feedback matrix;

[0018] Determining the acceleration saturation according to the acceleration saturation function and the state feedback matrix;

[0019] A lateral and longitudinal control saturation matrix is ​​constructed according to the steering wheel angle saturation and the acceleration saturation.

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

[0021] According to the transverse and longitudinal control saturation matrix in the state space equation of the closed-loop system, determining the transverse and longitudinal control vertex matrix corresponding to the transverse and longitudinal control saturation matrix;

[0022] Based on the horizontal and vertical control vertex matrix, the state space equation of the closed-loop system is converted into a state space equation in vertex representation;

[0023] According to 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, including:

[0024] The convex optimization inequality is constructed based on the state space equation in the vertex representation and the performance indicator of the closed-loop transfer function.

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

[0026] According to the interior point penalty function method, all vertices in the convex optimization inequality are solved to obtain a solution to the convex optimization inequality.

[0027] Optionally, performing lateral and longitudinal control on the vehicle based on the optimal robust controller model includes:

[0028] Inputting the vehicle motion parameters into the optimal robust controller model to obtain the steering wheel angle and acceleration of the vehicle;

[0029] The vehicle is controlled in the lateral and longitudinal directions based on the steering wheel angle and acceleration of the vehicle.

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

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

[0032] A construction module is used to construct a state space equation of an open-loop system of the vehicle according to the obtained vehicle structural parameters, vehicle motion parameters and lateral and longitudinal control saturation functions; wherein the lateral and longitudinal control saturation functions include: a steering wheel angle saturation function and an acceleration saturation function;

[0033] A determination module, configured to construct a state-space equation of a closed-loop system of the vehicle according to the state-space equation of the open-loop system and a standard robust controller model, and determine a closed-loop transfer function corresponding to the state-space equation of the closed-loop system according to the state-space equation of the closed-loop system;

[0034] A solving module, used for constructing the convex optimization inequality according to the state space equation of the closed-loop system and the performance index of the closed-loop transfer function, and solving the convex optimization inequality to obtain a solution of the convex optimization inequality;

[0035] A 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 of 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 above-mentioned vehicle control method by calling the machine-readable instructions.

[0038] This specification provides a machine-readable storage medium, which stores machine-readable instructions. When the machine-readable instructions are called and executed by a processor, the above-mentioned vehicle control method is implemented.

[0039] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0040] In this specification, a saturation function of transverse and longitudinal control is added to the state space equation of the closed-loop system of a traditional vehicle to construct a convex optimization inequality. Then, based on the solution of the convex optimization inequality, an optimal robust controller model is constructed, and the vehicle is controlled transversely and longitudinally based on the optimal robust controller model. Thus, the hardware performance of the vehicle is considered in the calculation process, and the calculation results that meet the performance limit of the vehicle are obtained, which solves the saturation nonlinearity problem of the steering wheel angle and acceleration and deceleration of the vehicle, improves the accuracy of vehicle control, and prolongs the service life of the hardware. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0042] Figure 1 is a flow chart of a vehicle control method shown in an exemplary embodiment;

[0043] Figure 2 is a schematic diagram of a controller model shown in an exemplary embodiment;

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

[0045] Figure 4 is a structural diagram of an electronic device in which a vehicle control device is located, showing an exemplary embodiment;

[0046] Figure 5 It is a structural diagram of a vehicle control device shown in an exemplary embodiment. DETAILED DESCRIPTION

[0047] In order 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0048] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0049] At present, there are certain limitations on the hardware performance of vehicles. For example, there is a maximum limit on the steering wheel angle, a maximum limit on acceleration, and a maximum limit on deceleration.

[0050] Current algorithms often fail to fully consider these vehicle constraints during calculations. The calculation results usually need to be adjusted in subsequent steps according to the vehicle's performance limits. This practice not only reduces the accuracy of vehicle control, but also may accelerate the vehicle's hardware wear and shorten its service life if the vehicle's hardware is in a state of extremes for a long time.

[0051] Based on this, this specification proposes a technical solution for constructing an optimal robust controller model based on the lateral and longitudinal control saturation function. Thus, the hardware performance of the vehicle is taken into account during the calculation process, and the calculation results that meet the performance limit of the vehicle are obtained, which solves the saturation nonlinearity problem of the vehicle's steering wheel angle and acceleration and deceleration, improves the accuracy of vehicle control, and prolongs the service life of the hardware.

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

[0053] Figure 1 FIG. 1 is a flow chart of a vehicle control method shown in an exemplary embodiment, wherein the method specifically comprises the following steps:

[0054] S100: constructing a state space equation of an open-loop system of the vehicle according to the acquired vehicle structural parameters, vehicle motion parameters and lateral and longitudinal control saturation functions; wherein the lateral and longitudinal control saturation functions include: a steering wheel angle saturation function and an acceleration saturation function.

[0055] In the embodiment of this specification, the state space equation of the open-loop system of the vehicle is 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 limiting the steering wheel angle and acceleration of the vehicle between a preset maximum value and a preset minimum value during the movement. The 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 the vehicle, which are usually determined during the vehicle design and manufacturing stages and remain unchanged throughout the vehicle life cycle, including but not limited to the vehicle's front overhang length, rear overhang length, mass, z-axis moment of inertia, front wheel angular stiffness, rear wheel angular stiffness, environmental factors, etc.

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

[0058] The state space equation of a vehicle is a mathematical model that describes the dynamic behavior of the vehicle. It is usually used to describe the state and state changes of the vehicle at different time points. According to the influence of the vehicle structural parameters on the vehicle state, a vehicle structural parameter model is constructed. Then, according to the kinematic and dynamic laws of the vehicle, 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 equation of the vehicle's open-loop system. The state space equation of the open-loop system ∑1 is as follows:

[0059] in,

[0060] In the above formula, z is the adjustable output, the theoretical output signal vector. y is the measured output, the output signal vector that can be measured. A is the state transfer matrix. B1 and B2 are control matrices. C1 and C2 are unit matrices with 6 rows and 6 columns. D 11 , D 12 , D 21 , D 22 is a zero matrix with 6 rows and 1 column. e1 is the lateral error, is the lateral error change rate, e2 is the heading error, is the heading error change rate, e3 is the position error, and e4 is the speed error. is the desired yaw rate, the specific value here is unknown and is taken as an external disturbance. 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 is the maximum steering wheel angle, δmin It should be noted that the minimum value of the steering wheel angle can be a negative value, for example, a steering wheel turning 180 degrees to the left is +180 degrees, and a steering wheel turning 180 degrees to the right is -180 degrees. sat(α) is the saturation function of acceleration, α max is the maximum value of acceleration, α min It should be noted that the minimum value of acceleration can be a negative value.

[0062] Furthermore, C af is the front wheel cornering stiffness, C ar is the rear wheel cornering stiffness, l f is the front overhang length, l r is the rear overhang length, I z is the moment of inertia of the vehicle around the z axis, v x is the longitudinal velocity of the vehicle, and m is the mass of the vehicle.

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

[0064] S102: constructing a state-space equation of a closed-loop system of the vehicle according to the state-space equation of the open-loop system and a standard robust controller model, and determining a closed-loop transfer function corresponding to the state-space equation of the closed-loop system according to the state-space equation of the closed-loop system.

[0065] In an embodiment of the present specification, the state-space equation of the closed-loop system of the vehicle 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 in the embodiments of this specification, a robust H∞ controller model is used to achieve lateral and longitudinal control of the vehicle. Figure 2 shown.

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

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

[0069] The closed-loop transfer function can describe the relationship between the input and output of the system and can be used to evaluate the performance and stability of the system. Therefore, the goal of H∞ control is to design a controller u(s) = K(s)y(s) so 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, that is, ||T wz (s)|| ∞ <γ.

[0070] It can be understood that the state space equation ∑1 of the vehicle's open-loop system is constructed based on the vehicle's structural parameters, vehicle motion parameters, and lateral and longitudinal control saturation functions. Introducing the standard robust controller model into the open-loop system is actually introducing an external control effect into the open-loop system, thereby turning the original open-loop system into a closed-loop system. Therefore, the state space equation ∑2 of the closed-loop system can be obtained accordingly, so that the closed-loop system can maintain its stability in the face of various uncertainties or disturbances.

[0071] In practical applications, since the lateral and longitudinal control saturation functions have nonlinear characteristics, the state space equation ∑1 of the open-loop system constructed based on the lateral and longitudinal control saturation functions also has nonlinear characteristics, which makes the state space equation ∑1 complicated and difficult to perform further analysis and calculation. Therefore, the nonlinear lateral and longitudinal control saturation functions can be replaced with lateral and longitudinal control saturation matrices to construct linear state space equations, making subsequent calculations and analysis simpler and more efficient.

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

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

[0074] The steering wheel angle saturation is determined according to the steering wheel angle saturation function and the state feedback matrix, and the acceleration saturation is determined according to the acceleration saturation function and the state feedback matrix.

[0075] Next, according to the steering wheel angle saturation and acceleration saturation, the lateral and longitudinal control saturation matrix is ​​constructed. The lateral and longitudinal control saturation matrix is ​​as follows:

[0076] in,

[0077] It can be seen from the above formula that M is used to represent the lateral and longitudinal control saturation matrix. K is used to represent the state feedback matrix. Among them, the state feedback matrix can be a matrix of 2 rows and 6 columns. The first row in the state feedback matrix can be k1, and the second row can be k2. η1∈(0,1] is used to represent the steering wheel angle saturation. The smaller η1 is, the higher the steering wheel angle saturation is. η1=1 is used to indicate that the steering wheel angle is still in the linear region. η2∈(0,1] is used to represent the acceleration saturation. The smaller η2 is, the higher the acceleration saturation is. η2=1 is used to indicate that the acceleration is still in the linear region.

[0078] Next, 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. The state space equation of the closed-loop system ∑2 is as follows:

[0079]

[0080] In the above formula, K is used to represent the state feedback matrix. M is used to represent the horizontal and vertical control saturation matrix. A is used to represent the state transfer matrix. B1 and B2 are control matrices. C1 and C2 are unit matrices with 6 rows and 6 columns. D 11 , D 12 , D 21 , D 22 is a zero matrix with 6 rows and 1 column. e1 is the lateral error, is the lateral error change rate, e2 is the heading error, is the heading error change rate, e3 is the position error, and e4 is the speed error. is the desired yaw rate, the specific value of which is unknown and is treated as an external disturbance.

[0081] Furthermore, by performing Laplace transform on the state space equation ∑2 of the closed-loop system, the corresponding closed-loop transfer function, T 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] It can be seen from the above embodiments that the nonlinear lateral and longitudinal control saturation functions are replaced by lateral and longitudinal control saturation matrices to construct linear state space equations, thereby making subsequent calculations and analyses simpler and more efficient.

[0083] S104: constructing the convex optimization inequality according to the state space equation of the closed-loop system and the performance index of the closed-loop transfer function, and solving the convex optimization inequality to obtain a solution to the convex optimization inequality.

[0084] In the embodiments of the present specification, a convex optimization inequality is constructed according to the state space equation of the closed-loop system and the performance index of the closed-loop transfer function, and the convex optimization inequality is solved to obtain a solution to the convex optimization inequality.

[0085] Specifically, according to the horizontal and vertical control saturation matrix in the state space equation of the closed-loop system, the horizontal and vertical control vertex matrix corresponding to the horizontal and vertical control saturation matrix is ​​determined.

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

[0087] Therefore, there are four horizontal and vertical control vertex matrices, namely It should be noted that M1, M2, M3, and M4 can refer to the four extreme conditions of the vehicle during its motion. Then, each condition of the vehicle during its motion can be represented by these four extreme conditions, namely in,

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

[0089] That is to say, the horizontal and vertical control vertex matrix is ​​substituted into the state space equation ∑2 of the closed-loop system, and the state space equation ∑3 of the horizontal and vertical control vertex matrix is ​​obtained as follows:

[0090]

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

[0092] It is understandable that the closed-loop transfer function describes the relationship between the output and input of the system. By adjusting the parameters of the closed-loop transfer function, the performance indicators of the system can be optimized. However, the parameter adjustment problem of the closed-loop transfer function is usually a nonlinear optimization problem, which is difficult to solve. Therefore, by converting the closed-loop transfer function parameter adjustment problem into a convex optimization problem, the excellent properties of convex optimization can be used to quickly find the optimal solution.

[0093] Furthermore, based on the closed-loop system ∑2, the performance index γ, and the necessary and sufficient conditions for the robust H∞ control problem to be solvable, 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 indicator.

[0096] It should be noted that for the performance index γ, its minimum value is generally sought. Therefore, the above robust H∞ control problem can be transformed into the following optimization problem, that is, constructing the following original linear matrix:

[0097] minγ

[0098]

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

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

[0101]

[0102] In the above formula, A is the state transfer matrix. B1 and B2 are control matrices. C1 and C2 are unit matrices with 6 rows and 6 columns. 11 , D 12 , D 21 , D 22 is a zero matrix with 6 rows and 1 column. j is the jth horizontal and vertical control vertex matrix, where j = 1, 2, 3, 4. The four vertices M1, M2, M3, and M4 in the convex optimization inequality are solved to obtain the symmetric positive definite matrix X, the matrix W, and the performance index γ as the solution to the convex optimization inequality.

[0103] Specifically, according to the interior point penalty function method, all vertices in the convex optimization inequality are solved to obtain the solution of the convex optimization inequality.

[0104] From the above embodiments, it can be seen that the interior point penalty function method avoids searching on the boundary of the feasible domain by searching for the optimal solution inside the feasible domain, thereby accelerating the convergence speed. In addition, the interior point penalty function method searches inside the feasible domain in each iteration, which also helps to improve the convergence speed.

[0105] It should be noted that the established convex optimization problem can also be solved according to the interior point method, gradient descent method, etc. to obtain the solution of the convex optimization inequality to meet the performance indicators and stability requirements of the system.

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

[0107] In the embodiment of this specification, an optimal robust controller model is constructed based on the solution of the convex optimization inequality, and the vehicle is controlled in the lateral and longitudinal directions based on the optimal robust controller model. The formula of the optimal robust controller model is as follows:

[0108] u=WX -1 x.

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

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

[0111] Then, the vehicle is controlled in the lateral and longitudinal directions based on the vehicle's steering wheel angle and acceleration.

[0112] For example, sensors on the vehicle, such as wheel speed sensors, steering wheel angle sensors, yaw rate sensors, etc., are used to collect the vehicle's state and environmental information in real time to provide necessary information for subsequent control. Based on the optimal robust controller model, control inputs, such as steering wheel angle or torque, braking and / or acceleration instructions, are calculated. Then, based on the calculated control inputs, instructions are sent to the vehicle's actuators, such as the steering wheel, brakes, etc. Correspondingly, after receiving the instructions, the actuators adjust their working states according to the instructions so that the vehicle responds according to the control inputs.

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

[0114] exist Figure 3 In the method, the state space equation of the open-loop system of the vehicle is constructed according to the obtained vehicle structure parameters, vehicle motion parameters and lateral and longitudinal control saturation functions.

[0115] Secondly, according to 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 by the lateral and longitudinal control saturation matrix, 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.

[0117] It is determined whether the controller of the vehicle is controllable, and if so, a 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.

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

[0119] Then, according to the state space equation 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 of the convex optimization inequality.

[0120] Finally, based on the solution of the convex optimization inequality, the optimal robust controller model is constructed, and the vehicle is controlled in the lateral and longitudinal directions based on the optimal robust controller model.

[0121] It can be seen from the above method that the saturation function of the lateral and longitudinal control is added to the state space equation of the closed-loop system of the traditional vehicle to construct a convex optimization inequality. Then, based on the solution of the convex optimization inequality, the optimal robust controller model is constructed, and the vehicle is controlled laterally and longitudinally based on the optimal robust controller model. Therefore, the hardware performance of the vehicle is considered in the calculation process, and the calculation results that meet the performance limit of the vehicle are obtained, which solves the saturation nonlinearity problem of the steering wheel angle and acceleration and deceleration of the vehicle, improves the accuracy of vehicle control, and prolongs the service life of the hardware.

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

[0123] See also Figure 4, Figure 4 It is a structural diagram of an electronic device in which a vehicle control device is located, which is shown as an exemplary embodiment. At the hardware level, the device includes a processor 402, an internal bus 404, a network interface 406, a memory 408, and a non-volatile memory 410, and of course may also include other required hardware. One or more embodiments of this specification can be implemented based on software, such as the processor 402 reading the corresponding computer program from the non-volatile memory 410 into the memory 408 and then running it. Of course, in addition to the software implementation, one or more embodiments of this specification do not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0124] See also Figure 5 , Figure 5 FIG. 1 is a structural diagram of a vehicle control device according to an exemplary embodiment. The vehicle control device can be applied to Figure 4 In the electronic device shown in the figure, the technical solution of this specification is implemented. The device may include:

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

[0126] A determination module 502 is used to construct a state-space equation of a closed-loop system of the vehicle according to the state-space equation of the open-loop system and a standard robust controller model, and determine a closed-loop transfer function corresponding to the state-space equation of the closed-loop system according to the state-space equation of the closed-loop system;

[0127] A solving module 504 is used to construct the convex optimization inequality according to 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 a 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 perform lateral and longitudinal control on the vehicle based on the optimal robust controller model.

[0129] Optionally, the determination module 502 is specifically used to obtain the state feedback matrix of the standard robust controller model; construct a lateral and longitudinal control saturation matrix based on the lateral and longitudinal control saturation function and the state feedback matrix; replace the lateral and longitudinal control saturation function 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 determination module 502 is specifically used to determine the steering wheel angle saturation according to the steering wheel angle saturation function and the state feedback matrix; determine the acceleration saturation according to the acceleration saturation function and the state feedback matrix; and construct a lateral and longitudinal control saturation matrix according to the steering wheel angle saturation and the acceleration saturation.

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

[0132] Optionally, the solving module 504 is specifically used to solve all vertices in the convex optimization inequality according to the interior point penalty function method to obtain a solution to 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 steering wheel angle and acceleration of the vehicle; and perform lateral and longitudinal control of the vehicle based on the steering wheel angle and acceleration of the vehicle.

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

[0135] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

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

[0137] Based on the same concept as the above method, this specification also provides a computer-readable storage medium on which computer instructions are stored. When the instructions are executed by a processor, the steps of the method described in any of the above embodiments are implemented.

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

[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, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

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

Claims

1. A vehicle control method, characterized in that: The method comprises: According to the obtained vehicle structure parameters, vehicle motion parameters and lateral and longitudinal control saturation functions, the state space equation of the open-loop system of the vehicle is constructed; wherein the lateral and longitudinal control saturation functions include: a steering wheel angle saturation function and an acceleration saturation function; Constructing a state-space equation of a closed-loop system of the vehicle according to the state-space equation of the open-loop system and a standard robust controller model, and determining a closed-loop transfer function corresponding to the state-space equation of the closed-loop system according to the state-space equation of the closed-loop system; According to the state space equation of the closed-loop system and the performance index of the closed-loop transfer function, a convex optimization inequality is constructed, and the convex optimization inequality is solved to obtain a solution of 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 in the lateral and longitudinal directions based on the optimal robust controller model.

2. The method according to claim 1, characterized in that According to 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, including: Obtaining a state feedback matrix of the standard robust controller model; Constructing a lateral and longitudinal control saturation matrix according to the lateral and longitudinal control saturation function and the state feedback matrix; The lateral and longitudinal control saturation function in the state space equation of the open loop system is replaced by the lateral and longitudinal control saturation matrix, 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.

3. The method according to claim 2, characterized in that According to the lateral and longitudinal control saturation function and the state feedback matrix, a lateral and longitudinal control saturation matrix is ​​constructed, including: Determining the steering wheel angle saturation according to the steering wheel angle saturation function and the state feedback matrix; Determining the acceleration saturation according to the acceleration saturation function and the state feedback matrix; A lateral and longitudinal control saturation matrix is ​​constructed according to the steering wheel angle saturation and the acceleration saturation.

4. The method according to claim 2, characterized in that Before constructing the convex optimization inequality according to the state space equation of the closed-loop system and the performance index of the closed-loop transfer function, the method further includes: According to the transverse and longitudinal control saturation matrix in the state space equation of the closed-loop system, determining the transverse and longitudinal control vertex matrix corresponding to the transverse and longitudinal control saturation matrix; Based on the horizontal and vertical control vertex matrix, the state space equation of the closed-loop system is converted into a state space equation in vertex representation; According to 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, including: The convex optimization inequality is constructed based on the state space equation in the vertex representation and the performance indicator of the closed-loop transfer function.

5. The method according to claim 4, characterized in that Solving the convex optimization inequality to obtain a solution to the convex optimization inequality includes: According to the interior point penalty function method, all vertices in the convex optimization inequality are solved to obtain a solution to the convex optimization inequality.

6. The method according to claim 1, characterized in that The method further comprises: performing lateral and longitudinal control on the vehicle based on the optimal robust controller model, comprising: Inputting the vehicle motion parameters into the optimal robust controller model to obtain the steering wheel angle and acceleration of the vehicle; The vehicle is controlled in the lateral and longitudinal directions based on the steering wheel angle and acceleration of the vehicle.

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

8. A vehicle control device, characterized in that: The device comprises: A construction module is used to construct a state space equation of an open-loop system of the vehicle according to the obtained vehicle structural parameters, vehicle motion parameters and lateral and longitudinal control saturation functions; wherein the lateral and longitudinal control saturation functions include: a steering wheel angle saturation function and an acceleration saturation function; A determination module, configured to construct a state-space equation of a closed-loop system of the vehicle according to the state-space equation of the open-loop system and a standard robust controller model, and determine a closed-loop transfer function corresponding to the state-space equation of the closed-loop system according to the state-space equation of the closed-loop system; A solving module, used for constructing a convex optimization inequality according to the state space equation of the closed-loop system and the performance index of the closed-loop transfer function, and solving the convex optimization inequality to obtain a solution of the convex optimization inequality; A 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 of the vehicle based on the optimal robust controller model.

9. 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 7 by calling the machine-readable instructions.

10. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores machine-readable instructions, and when the machine-readable instructions are called and executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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