A control method and device for a vehicle
By constructing a target robust controller model and treating the quantization error as a bounded disturbance, the problem of uncontrollable quantization error of the quantizer is solved, thereby reducing the accuracy and design difficulty of vehicle control.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG GEELY HLDG GRP CO LTD
- Filing Date
- 2024-12-23
- Publication Date
- 2026-06-26
AI Technical Summary
In vehicle control systems, when a quantizer quantizes a signal, the quantization error is uncontrollable, making it impossible to use existing controller design methods, increasing the difficulty of controller design, and failing to guarantee the accuracy of vehicle control.
By constructing a target robust controller model, the quantization error is treated as a bounded disturbance. Based on the standard robust controller model and the open-loop system state-space equation of the vehicle, the state-space equation of the closed-loop system is constructed, and lateral and longitudinal control is performed.
By controlling the quantization error within a bounded range, the design difficulty of the controller is reduced, and the accuracy of vehicle control is guaranteed.
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Figure CN119739085B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicle technology, and more particularly to a vehicle control method and device. Background Technology
[0002] In control system theory, it is generally assumed that the signal output by the controller goes directly to the actuator without any signal loss during transmission. However, in real-world scenarios, considering factors such as the actuator's signal requirements and the bandwidth of the transmission link, the transmitted signal needs to be quantized using a quantizer. This results in an error between the signal output by the controller and the signal received by the actuator.
[0003] When using quantizers in related technologies to quantize signals, the quantization error is uncontrollable. This makes it impossible to design a controller model using current controller design methods after quantizing the signal, increasing the difficulty of controller design and making it impossible to guarantee the control accuracy of the vehicle. Summary of the Invention
[0004] In view of the above, this application provides a vehicle control method and apparatus to solve the deficiencies existing in the related art. The technical solution of this application is as follows:
[0005] According to an embodiment of the first aspect of this application, a vehicle control method is provided, comprising:
[0006] The structural parameters and motion parameters of the vehicle, as well as the digital signal output by the quantizer, are obtained, and the state-space equation of the open-loop system of the vehicle is constructed based on the structural parameters, the motion parameters, and the digital signal.
[0007] The quantization error of the quantizer is calculated based on the digital signal, and the quantization error is a bounded value.
[0008] The quantization error is used as a bounded disturbance of the target robust controller model to be constructed. Based on the standard robust controller model and the state space equation of the open-loop system, the state space equation of the closed-loop system of the vehicle is constructed. The closed-loop transfer function corresponding to the state space equation of the closed-loop system is determined, and the target robust controller model is constructed.
[0009] The vehicle is controlled laterally and longitudinally based on the target robust controller model.
[0010] According to an embodiment of the second aspect of this application, a vehicle control device is provided, comprising:
[0011] An acquisition unit is used to acquire the vehicle's structural parameters and motion parameters, as well as the digital signal output by the quantizer; and to construct the state-space equation of the vehicle's open-loop system based on the structural parameters, motion parameters, and digital signal.
[0012] An error determination unit is used to calculate the quantization error of the quantizer based on the digital signal, wherein the quantization error is a bounded value.
[0013] The controller construction unit takes the quantization error as a bounded disturbance of the target robust controller model to be constructed, constructs the state space equation of the closed-loop system of the vehicle based on the standard robust controller model and the state space equation of the open-loop system, and determines the closed-loop transfer function corresponding to the state space equation of the closed-loop system, and constructs the target robust controller model.
[0014] The lateral and longitudinal control unit is used to perform lateral and longitudinal control of the vehicle based on the target robust controller model.
[0015] According to an embodiment of the third aspect of this application, an electronic device is provided, including a memory, a processor, and executable instructions stored in the memory and executable on the processor, wherein the processor executes the executable instructions to implement the method as described in the first aspect.
[0016] According to an embodiment of the fourth aspect of this application, a computer-readable storage medium is provided having computer instructions stored thereon that, when executed by a processor, implement the steps of the method described in the first aspect.
[0017] According to an embodiment of the fifth aspect of this application, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the steps of the method described in the first aspect.
[0018] In the technical solution provided in this application, the quantization error obtained after the quantizer quantizes the signal is a bounded value. Therefore, in the process of constructing the target robust controller model, this quantization error can be treated as a bounded disturbance existing in the controller design process, thereby constructing the target robust controller model and controlling the vehicle based on this model. By applying the technical solution of this application, the quantization error can be controlled within a bounded range, thus enabling the use of current controller design methods to design the controller model while quantizing the signal, reducing the design difficulty of the controller, and ensuring the control accuracy of the vehicle.
[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit the embodiments of this application. Attached Figure Description
[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.
[0021] Figure 1 This is a schematic diagram illustrating a vehicle networking architecture according to an exemplary embodiment of this application;
[0022] Figure 2 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment of this application;
[0023] Figure 3 This is a schematic diagram illustrating the quantization relationship of a quantizer according to an exemplary embodiment of this application;
[0024] Figure 4 This is a schematic diagram illustrating a controller model according to an exemplary embodiment of this application;
[0025] Figure 5 This is a schematic diagram of an electronic device illustrated in an exemplary embodiment of this application;
[0026] Figure 6 This is a schematic diagram of a vehicle control device illustrated in an exemplary embodiment of this application. Detailed Implementation
[0027] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of this application as detailed in the appended claims.
[0028] It should be noted that the steps of the corresponding methods in other embodiments are not necessarily performed in the order shown and described in this application. In some other embodiments, the methods may include more or fewer steps than those described in this application. Furthermore, a single step described in this application may be broken down into multiple steps in other embodiments; and multiple steps described in this application may be combined into a single step in other embodiments.
[0029] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.
[0030] In traditional control systems, it is typically assumed that the state signals of the controlled object can reach the controller without loss, and the controller's control signals can also be transmitted to the actuators without loss. However, in real-world environments, due to the extensive use of digital processors, analog and digital signals need to be converted to each other, and this conversion cannot achieve ideal accuracy due to limitations in the precision of the acquired data. Simultaneously, with the development of networks, traditional control is shifting towards network control. In network control systems, if the amount of transmitted signal data is too large, signal transmission may experience delays and congestion.
[0031] However, when intelligent vehicles are in operation, the precision and robustness of control are crucial to driving safety, and effective control strategies are key technologies for controlling vehicle trajectory and improving active safety performance. When signal conversion accuracy is unsatisfactory, or when network latency or congestion occurs, the vehicle's control precision will significantly decrease, and the safety of autonomous driving will decline accordingly. Therefore, incorporating control signal quantization into the signal transmission process is extremely necessary.
[0032] Understandably, since quantization is the process of converting a continuous analog signal into a piecewise constant signal of a finite set of numbers, an error, known as quantization error, will exist between the digital signal processed by the quantizer and the analog signal input to the quantizer. However, when using quantizers in related technologies to quantize signals, the quantization error of some quantizers is uncontrollable. This makes it impossible to design a controller model using current controller design methods after signal quantization, increasing the difficulty of controller design.
[0033] To address the aforementioned problems, this application proposes a vehicle control method. In the technical solution provided in this application, the quantization error obtained after quantizing the signal is a bounded value. Therefore, during the construction of the target robust controller model, this quantization error can be treated as a bounded disturbance existing in the controller design process, thereby constructing the target robust controller model and controlling the vehicle based on this model. Applying the technical solution of this application, the quantization error can be controlled within a bounded range, thus enabling the design of the controller model using current controller design methods while quantizing the signal, reducing the difficulty of controller design.
[0034] The technical solutions provided by the various embodiments of this application will now be described in detail with reference to the accompanying drawings.
[0035] Figure 1 This is a signal processing flowchart illustrating an exemplary embodiment of this application. The main processing nodes include a control module 11 and an execution module 12. Specifically, the control module 11 may include a controller 111 and a quantizer 112.
[0036] First, it should be noted that when the execution module 12 controls the target object based on the received control signal, there may be an error between the actual change in the target object and the expected change represented by the control signal. For example, if the execution module is the chassis module of a vehicle, the control signal received by the chassis module indicates that the vehicle's steering wheel needs to be turned 30° to the left, but after actual execution, the steering wheel may only turn 29° to the left, resulting in an error of 1°, which is the error between the expected value and the actual value, and can be called a state error.
[0037] In response, the control module 11 can readjust its output control signal based on the state error, enabling the execution module 12 to control the target object based on the new control signal. Specifically, the control module 11 can use the state error x as input to the controller 111, and the controller calculates an analog signal u that represents the desired value. Then, u can be input to the quantizer 112 for quantization, obtaining the digital signal Q(u) corresponding to the quantized analog signal u. Finally, Q(u) is transmitted to the execution module 12, causing the execution module 12 to control the target object according to Q(u). It can be understood that after the control module 12 actually performs the control operation, it can recalculate the state error x based on the desired value and the actual value, and use the state error x as input to the controller 111, repeating the above process until the actual value equals the desired value.
[0038] Figure 2 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment of this application, the method specifically including the following steps:
[0039] Step 201: Obtain the vehicle's structural parameters and motion parameters, as well as the digital signal output by the quantizer, and construct the state-space equation of the vehicle's open-loop system based on the structural parameters, motion parameters, and digital signal.
[0040] In one embodiment, the state-space equation of the vehicle's open-loop system can be constructed based on the acquired structural and kinematic parameters of the vehicle, as well as the digital signal output by the quantizer. The vehicle's structural parameters describe its inherent properties, typically determined during the vehicle design and manufacturing phases and remaining constant throughout the vehicle's lifecycle. These include the vehicle's mass, front overhang length, rear overhang length, moment of inertia about its axis, front wheel lateral stiffness, and rear wheel lateral stiffness. The vehicle's kinematic parameters, on the other hand, describe the real-time changes in the vehicle's motion, typically obtained through real-time measurements by sensors. These include the vehicle's speed, position, and heading angle. Both structural and kinematic parameters are important parameters describing vehicle performance. The difference lies in that structural parameters are inherent properties of the vehicle, usually determined during the design phase, while kinematic parameters change in real-time and are typically measured by sensors, reflecting the vehicle's current dynamic state.
[0041] The state-space equation of a vehicle is a mathematical model describing the dynamic behavior of the vehicle, and is usually used to describe the state and state changes of the vehicle 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 kinematics and dynamics of the vehicle, a vehicle motion parameter model is constructed. Considering that the analog signal u will be quantized in this application to obtain the quantized digital signal Q(u), the state-space equation of the open-loop system of the vehicle, ∑1, can be obtained based on the vehicle's structural parameters, motion parameters, state error, and the quantized digital signal, as shown in equation (1):
[0042]
[0043] in,
[0044]
[0045] Where A is the state transition matrix, B is the control matrix, and C is a variable in matrices A and B. 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 moment of inertia of the vehicle about the z-axis. x Let m be the vehicle's longitudinal velocity and 'm' be the vehicle's mass. 'x' represents the state error, which is the error between the expected and actual values mentioned in the previous embodiments, specifically including the lateral error e1 and the rate of change of the lateral error. Heading error e2, rate of change of heading error Position error e3 and velocity error e4.
[0046] Specifically, using 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.
[0047] In one embodiment, the analog signal u is quantized using the quantizer provided in this application to obtain the digital signal Q(u). Since the signal output by the controller includes analog signals of the steering wheel angle and acceleration, i.e., u = [δ, α]... T Here, δ is the analog signal of the steering wheel angle, and α is the analog signal of the acceleration. Therefore, the quantized signal Q(u) obtained after quantization is Q(u) = [q(δ), q(α)]. T q(δ) is the quantized digital signal of the steering wheel angle, and q(α) is the quantized digital signal of the acceleration.
[0048] Specifically, the quantizer provided in this application includes a first quantization unit and a second quantization unit, as shown in the following formula (2):
[0049]
[0050] Among them, u th The threshold value represents the threshold for switching between the first quantization unit and the second quantization unit. The specific value is determined by the technicians based on actual needs and experience, and this application does not impose any restrictions on it.
[0051] When the amplitude of the analog signal u to be quantized is greater than the threshold, i.e., |u|>u th In this case, the analog signal u is quantized using the first quantization unit, as shown in equation (3) below:
[0052]
[0053] For the first quantization unit, the quantization unit is constructed based on a uniform quantizer and adjustment parameters, wherein the form of the referenced uniform quantizer is shown in the following equation (4):
[0054]
[0055] Int[a] represents the largest integer not greater than a. The quantization density of the uniform quantizer is determined.
[0056] The q contained in this first quantization unit h (u th ) and K(u th ) is an adjustable parameter.
[0057] It is understandable that, since the quantizer provided in this application includes two quantization units, when the amplitude of the analog signal is the same as the threshold, the quantized values corresponding to both sides of the threshold may jump. Therefore, by combining this adjustment parameter with a uniform quantizer to construct a first quantization unit, a jump can be made when the amplitude of the analog signal u is equal to the threshold u. th In this case, the continuity of the quantized digital signal is maintained.
[0058] The amplitude of the analog signal u to be quantized is no greater than the threshold, i.e., |u|≤u th In this case, the analog signal u is quantized using the second quantization unit, as shown in equation (5) below:
[0059] q(u)=q h (u) (5)
[0060] The q involved in the first and second quantization units h (u) characterizes the form of the hysteresis quantizer, as shown in equation (6) below:
[0061]
[0062] In the above formula, u i =ρ 1-i u min i = 1, 2, ..., u min >0 and 0<ρ<1 determine q h (u) Size of the dead zone, q h (u) in the set U = {0, ±u} i , ±u i The value of q is taken within (1+σ)}, where ρ is the quantization density. h For details on (u), please refer to the design methods of hysteresis quantizers in related technologies, which will not be elaborated here.
[0063] It is understandable that regardless of the quantizer used, quantization error is inevitable in the process of converting a continuous analog signal into a piecewise constant signal of a finite set. This error is the difference between the digital signal processed by the quantizer and the analog signal input to it. Generally speaking, the working principle of a uniform quantizer is to divide the amplitude range of the input signal into intervals of equal width. All samples falling into the same interval are encoded into the same digital signal. If the amplitude of the input signal is small, the fixed interval range is difficult to meet the quantization signal-to-noise ratio requirements for small amplitude signals. While a hysteresis quantizer can meet the quantization signal-to-noise ratio requirements for small amplitude signals, the quantization error of the hysteresis quantizer also increases as the amplitude of the input signal increases, leading to uncontrollable quantization error.
[0064] The quantizer provided in this application incorporates both uniform quantizers and hysteresis quantizers from related technologies. When the signal amplitude is small, a hysteresis quantizer is used to quantize the analog signal; when the signal amplitude is large, an adjusted uniform quantizer is used. (The last two sentences appear to be incomplete and require further context.) In this case, the quantization schematic diagram of the quantizer provided in this application is as follows: Figure 3 As shown, the quantizer is a linearizable quantizer. Therefore, when using the quantizer provided in this application, the quantization signal-to-noise ratio requirement can be met regardless of the amplitude of the analog signal to be quantized, while keeping the quantization error controllable.
[0065] Step 202: Calculate the quantization error of the quantizer based on the digital signal. The quantization error is a bounded value.
[0066] In one embodiment, as described above, quantization error inevitably exists during the process of converting a continuous analog signal into a piecewise constant signal of a finite set. Therefore, the quantization error Δq(u) can be calculated based on the quantization result q(u) of any signal u to be quantized by the quantizer provided in this application, as shown in the following equation (7):
[0067]
[0068] As can be seen from the above formula, the quantization error Δq(u) of the quantizer provided in this application is bounded for any u, that is, there exists a positive constant. This ensures that |Δq(u)|≤d. In other words, the quantization error of the quantizer provided in this application is necessarily less than a positive value and greater than a negative value, i.e., the quantization error is a bounded value. When the analog signal represented by u is an analog signal of steering wheel angle, the quantization error corresponding to the steering wheel angle is Δq(δ), and when the analog signal represented by u is an analog signal of acceleration, the quantization error corresponding to acceleration is Δq(α). Both Δq(δ) and Δq(α) are bounded values.
[0069] Step 203: The quantization error is used as a bounded disturbance of the target robust controller model to be constructed. Based on the standard robust controller model and the state space equation of the open-loop system, the state space equation of the closed-loop system of the vehicle is constructed. The closed-loop transfer function corresponding to the state space equation of the closed-loop system is determined, and the target robust controller model is constructed.
[0070] In one embodiment, the controller design method in the related art has already taken into account the impact of bounded disturbances on the control signal. Therefore, when the quantization error of the quantizer provided in this application is a bounded value, the quantization error can be directly regarded as a bounded disturbance based on the standard robust controller model H. ∞ Based on the aforementioned state-space equation ∑1 of the open-loop system, the state-space equation ∑4 of the closed-loop system of the vehicle is constructed.
[0071] Specifically, from Δq(u) = q(u) - u, we can obtain q(u) = u + Δq(u). Therefore, when the quantization error corresponding to the steering wheel angle is Δq(δ) and the quantization error corresponding to the acceleration is Δq(α), then ΔQ(u) = [Δq(δ), Δq(α)]. T This represents the quantization error matrix of the steering wheel angle and acceleration.
[0072] Based on this, the state-space equation ∑1 of the open-loop system can first be transformed into the state-space equation ∑2 of the following form, as shown in equation (8):
[0073]
[0074] At this point, in order to construct the state-space equation of the closed-loop system using the standard robust controller model, the state-space equation ∑2 can be transformed into the state-space equation ∑3, as shown in equation (9) below:
[0075]
[0076] In the above formula, z is the adjustable output, i.e., the theoretical output signal vector. y is the measured output, i.e., the measured output signal vector. A and B are the state transition matrix and control matrix mentioned above, respectively. 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.
[0077] Then, the state feedback matrix of the standard robust controller model can be obtained, and the state feedback matrix can be substituted into the state space equation of the open-loop system to construct the state space equation ∑4 of the closed-loop system of the vehicle.
[0078] It should be noted that, in this embodiment, a robust H∞ controller model is used to implement the lateral and longitudinal control of the vehicle, such as... Figure 4 As shown.
[0079] exist Figure 4 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.
[0080] 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 H∞ norm of the closed-loop transfer function from the disturbance input w to the adjustable output z is less than a given performance index γ > 0, i.e., ||T|. wz (s)|| ∞ <γ.
[0081] It is understood that the aforementioned embodiments construct the state-space equation of the vehicle's open-loop system. Introducing the standard robust controller model into this open-loop system is actually introducing an external control action into the open-loop system, thereby transforming the original open-loop system into a closed-loop system. Therefore, the state-space equation of the closed-loop system can be obtained accordingly, enabling the closed-loop system to maintain its stability when facing various uncertainties or disturbances.
[0082] In this embodiment, the standard robust controller is u = Kx, where K is a 2x6 matrix, which is the state feedback matrix of the standard robust controller model. Substituting the state feedback matrix K into the state space equation ∑3, we can obtain the state space equation ∑4 of the closed-loop system, as shown in equation (10) below:
[0083]
[0084] Similar to the state-space equations of the aforementioned open-loop system, C1 and C2 are 6x6 identity matrices, and 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.
[0085] Furthermore, by performing a Laplace transform on the state-space equation ∑4 of the closed-loop system, the corresponding closed-loop transfer function, T, is obtained. wz =(C1+D 11 K)[sI-(A+BK)] -1 B+D 12 T wz The H∞ norm index of (s) is ||T wz (s)|| ∞ <γ, where γ is a constant given by the technician.
[0086] Based on this, the target robust controller model can be constructed according to the closed-loop transfer function corresponding to the determined state-space equation of the closed-loop system.
[0087] Specifically, a convex optimization inequality can be constructed based on the state-space equations and performance indicators of the closed-loop transfer function of the closed-loop system, and a target robust controller model can be constructed based on the solution of the convex optimization inequality.
[0088] It is understandable that the closed-loop transfer function describes the relationship between the output and input of a control system, and 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 difficult to solve. Therefore, transforming the closed-loop transfer function parameter tuning problem into a convex optimization problem allows us to quickly find the optimal solution by leveraging the superior properties of convex optimization.
[0089] Furthermore, from the state-space equation ∑4 of the closed-loop system, the performance index γ, and the necessary and sufficient condition for the robust H∞ control problem to have a solution, we can obtain the linear matrix inequality shown in equation (11):
[0090]
[0091] Where X is an unknown symmetric positive definite matrix, W is an unknown matrix, and γ is a performance index.
[0092] It should be noted that for the performance index γ, we generally seek its minimum value. Therefore, the above robust H∞ control problem can be transformed into the following optimization problem, that is, constructing the original linear matrix as shown in equation (12):
[0093]
[0094] Same as described in the previous embodiments, A is the state transition matrix, B is the control matrix, C1 and C2 are 6x6 identity matrices, and D... 11 D 12 D21 D 22 It is a zero matrix with 6 rows and 1 column.
[0095] Then, by continuing to solve the above inequalities using the interior point penalty function method, we can obtain the symmetric positive definite matrix X, matrix W, and performance index γ as the solution to the convex optimization inequality, and thus obtain the target robust controller, as shown in the following equation (13):
[0096] u = Kx = WX -1 x (13)
[0097] in, 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, which is the analog signal output by the controller.
[0098] Step 104: Perform lateral and longitudinal control on the vehicle based on the target robust controller model.
[0099] In one embodiment, lateral control of 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 to maintain stable vehicle operation within the lane. In this embodiment, lateral and longitudinal control of the vehicle can be performed based on the output of the target robust controller model, specifically manifested in the control of steering wheel angle and acceleration.
[0100] Specifically, the target robust controller model, taking the state error x as input, can output analog signals of steering wheel angle and acceleration. Then, the quantizer provided in the above embodiments of this application can be used to quantize the analog signals of steering wheel angle and acceleration output by the target robust controller model, obtaining quantized steering wheel angle and quantized acceleration. This quantization process is the aforementioned process of converting analog signals into piecewise constant signals of a finite set; therefore, both the quantized steering wheel angle and quantized acceleration are digital signals. Subsequently, the quantized steering wheel angle and quantized acceleration can be transmitted to the vehicle's execution module, such as the chassis module, so that the execution module adjusts its operating state according to the obtained digital signals, enabling the vehicle to respond according to the desired operation.
[0101] As can be seen from the above embodiments, the quantization error of the quantizer provided in this application is regarded as a bounded disturbance, and then the target robust controller is constructed, which makes the controller design process simpler and more efficient, thereby reducing the design difficulty of the controller while ensuring the control accuracy of the vehicle.
[0102] Figure 5 This is a schematic structural diagram of a device provided in an exemplary embodiment. Please refer to... Figure 5 At the hardware level, the electronic device includes a processor 502, an internal bus 504, a network interface 506, memory 508, and non-volatile memory 510, and may also include other hardware required for business operations. The processor 502 reads the corresponding computer program from the non-volatile memory 510 into the memory 508 and then runs it, forming a health monitoring device at the logical level. Of course, in addition to software implementation, this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.
[0103] Corresponding to the embodiments of the methods described above, this application also provides embodiments of the apparatus. Please refer to... Figure 6 The vehicle's control unit can be applied to, for example... Figure 6 The device shown is used to implement the technical solution of this application. The vehicle control unit may include an acquisition unit 61, an error determination unit 62, a controller construction unit 63, and a lateral and longitudinal control unit 64, wherein:
[0104] Acquisition unit 61 is used to acquire the vehicle's structural parameters and motion parameters, as well as the digital signal output by the quantizer; and to construct the state-space equation of the vehicle's open-loop system based on the structural parameters, motion parameters, and digital signal.
[0105] Error determination unit 62 is used to calculate the quantization error of the quantizer based on the digital signal, wherein the quantization error is a bounded value;
[0106] The controller construction unit 63 takes the quantization error as a bounded disturbance of the target robust controller model to be constructed, constructs the state space equation of the closed-loop system of the vehicle based on the standard robust controller model and the state space equation of the open-loop system, and determines the closed-loop transfer function corresponding to the state space equation of the closed-loop system, and constructs the target robust controller model.
[0107] The lateral and longitudinal control unit 64 is used to perform lateral and longitudinal control on the vehicle based on the target robust controller model.
[0108] Optionally, the longitudinal and transverse control unit 64 is specifically used for:
[0109] The vehicle's state error is input into the target robust controller model, and the steering wheel angle and acceleration of the vehicle output by the model are obtained.
[0110] The steering wheel angle and acceleration are quantized by the quantizer to obtain the quantized steering wheel angle and quantized acceleration.
[0111] Based on the quantized steering wheel angle and the quantized acceleration, the vehicle is controlled laterally and longitudinally.
[0112] Optionally, the quantizer includes a first quantization unit and a second quantization unit. The quantizer performs quantization processing on any one of the signals, namely the steering wheel angle and acceleration, to obtain the corresponding quantized signal, including:
[0113] If the amplitude of any of the signals is greater than the threshold, the signal is quantized based on the first quantization unit;
[0114] If the amplitude of any of the signals is not greater than the threshold, the signal is quantized based on the second quantization unit.
[0115] Optionally, the first quantization unit is constructed based on a uniform quantizer and adjustment parameters, wherein the adjustment parameters are used to maintain the continuity of the quantized digital signal when the amplitude of the analog signal is equal to the boundary threshold.
[0116] The second quantization unit includes a hysteresis quantizer.
[0117] Optionally, the controller building unit 63 is specifically used for:
[0118] Obtain the state feedback matrix of the standard robust controller model;
[0119] 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.
[0120] Optionally, the controller building unit 63 is specifically used for:
[0121] 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.
[0122] Based on the solution of the convex optimization inequality, the target robust controller model is constructed.
[0123] Optionally, the solution to the convex optimization inequality is obtained by solving the convex optimization inequality using the interior point penalty function method.
[0124] Optional,
[0125] The 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.
[0126] The motion parameters include: velocity, position, and heading angle;
[0127] The state errors include: lateral error, lateral error rate of change, heading error, heading error rate of change, position error, and velocity error.
[0128] 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.
[0129] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.
[0130] Accordingly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the operation control method of the vehicle-mounted functional module as described in any of the above embodiments.
[0131] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.
[0132] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0133] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0134] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
Claims
1. A method for controlling a vehicle, characterized in that, include: The structural parameters and motion parameters of the vehicle, as well as the digital signal output by the quantizer, are obtained, and the state-space equation of the open-loop system of the vehicle is constructed based on the structural parameters, the motion parameters, and the digital signal. The quantization error of the quantizer is calculated based on the digital signal. The quantization error is a bounded value used to characterize the error between the digital signal output by the quantizer and the analog signal input to the quantizer. The analog signal is obtained based on the error between the actual change of the target object and the expected change represented by the control signal. The quantization error is used as a bounded disturbance of the target robust controller model to be constructed. Based on the standard robust controller model and the state space equation of the open-loop system, the state space equation of the closed-loop system of the vehicle is constructed. The closed-loop transfer function corresponding to the state space equation of the closed-loop system is determined, and the target robust controller model is constructed. The vehicle is controlled laterally and longitudinally based on the target robust controller model. The quantizer includes a first quantization unit and a second quantization unit. The first quantization unit is constructed based on a uniform quantizer and adjustment parameters. The second quantization unit includes a hysteresis quantizer. The first quantization unit is used to quantize the analog signal when the amplitude of the analog signal is greater than a threshold, and the second quantization unit is used to quantize the analog signal when the amplitude of the analog signal is not greater than the threshold.
2. The method according to claim 1, characterized in that, The lateral and longitudinal control of the vehicle based on the target robust controller model includes: The vehicle's state error is input into the target robust controller model, and the steering wheel angle and acceleration of the vehicle output by the model are obtained. The steering wheel angle and acceleration are quantized by the quantizer to obtain the quantized steering wheel angle and quantized acceleration. Based on the quantized steering wheel angle and the quantized acceleration, the vehicle is controlled laterally and longitudinally.
3. The method according to claim 2, characterized in that, The quantizer quantizes either the steering wheel angle or the acceleration signal to obtain the corresponding quantized signal, including: If the amplitude of any of the signals is greater than the threshold, the signal is quantized based on the first quantization unit. If the amplitude of any signal is not greater than the threshold, the signal is quantized based on the second quantization unit.
4. The method according to claim 3, characterized in that, The adjustment parameter is used to maintain the continuity of the quantized digital signal when the amplitude of the analog signal is equal to the threshold value.
5. The method according to claim 1, characterized in that, The construction of the state-space equations for the closed-loop system of the vehicle, based on the standard robust controller model and the state-space equations of the open-loop system, includes: Obtain the state feedback matrix of the standard robust controller model; 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.
6. The method according to claim 1, characterized in that, The construction of the target robust controller model includes: 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. Based on the solution of the convex optimization inequality, the target robust controller model is constructed.
7. The method according to claim 6, characterized in that, The solution to the convex optimization inequality is obtained by solving the convex optimization inequality using the interior point penalty function method.
8. The method according to claim 2, characterized in that, The 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 motion parameters include: velocity, position, and heading angle; The vehicle's state errors include: lateral error, lateral error rate of change, heading error, heading error rate of change, position error, and speed error.
9. A vehicle control device, characterized in that, The device includes: An acquisition unit is used to acquire the vehicle's structural parameters and motion parameters, as well as the digital signal output by the quantizer; and to construct the state-space equation of the vehicle's open-loop system based on the structural parameters, motion parameters, and digital signal. An error determination unit is used to calculate the quantization error of the quantizer based on the digital signal. The quantization error is a bounded value used to characterize the error between the digital signal output by the quantizer and the analog signal input to the quantizer. The analog signal is obtained based on the error between the actual change of the target object and the expected change represented by the control signal. The controller construction unit takes the quantization error as a bounded disturbance of the target robust controller model to be constructed, constructs the state space equation of the closed-loop system of the vehicle based on the standard robust controller model and the state space equation of the open-loop system, and determines the closed-loop transfer function corresponding to the state space equation of the closed-loop system, and constructs the target robust controller model. The lateral and longitudinal control unit is used to perform lateral and longitudinal control of the vehicle based on the target robust controller model; The quantizer includes a first quantization unit and a second quantization unit. The first quantization unit is constructed based on a uniform quantizer and adjustment parameters. The second quantization unit includes a hysteresis quantizer. The first quantization unit is used to quantize the analog signal when the amplitude of the analog signal is greater than a threshold, and the second quantization unit is used to quantize the analog signal when the amplitude of the analog signal is not greater than the threshold.
10. An electronic device, characterized in that, It includes a memory, a processor, and executable instructions stored in the memory and executable on the processor, wherein the processor executes the executable instructions to implement the method as described in any one of claims 1-8.
11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions that, when invoked and executed by a processor, implement the method described in any one of claims 1-8.
12. A computer program product, characterized in that, Includes a computer program / instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1-8.