CN113060117A

CN113060117A - Steering brake control method, steering brake control device, medium, and electronic apparatus

Info

Publication number: CN113060117A
Application number: CN201911285011.8A
Authority: CN
Inventors: 杨建民
Original assignee: Beijing Jingbangda Trade Co Ltd; Beijing Jingdong Zhenshi Information Technology Co Ltd
Current assignee: Beijing Jingbangda Trade Co Ltd; Beijing Jingdong Zhenshi Information Technology Co Ltd
Priority date: 2019-12-13
Filing date: 2019-12-13
Publication date: 2021-07-02
Anticipated expiration: 2039-12-13
Also published as: CN113060117B

Abstract

The present disclosure relates to a steering brake control method, a steering brake control device, a computer-readable medium, and an electronic apparatus. The method comprises the following steps: acquiring current motion parameters of a control object in a steering motion state, and acquiring motion interference information corresponding to the steering motion state; determining an ideal motion model associated with the control object; determining a parameter tracking error corresponding to the current motion parameter according to the current motion parameter and the ideal motion model; and determining braking control information for adjusting the steering motion state according to the motion interference information and the parameter tracking error. The method can effectively inhibit the interference introduced by the steering motion in a targeted manner, thereby ensuring the motion stability and safety of the control object.

Description

Steering brake control method, steering brake control device, medium, and electronic apparatus

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a steering brake control method, a steering brake control device, a computer-readable medium, and an electronic apparatus.

Background

The steering control performance and the braking control performance of the vehicle are key factors influencing the safety and the stability of the vehicle, and the steering braking working condition is the vehicle running working condition which is most prone to dangerous conditions such as tail flicking and side turning. Particularly, under the influence of interference factors such as uneven road surface or unbalanced wheel inflation pressure, the overall operation stability and safety performance of the vehicle have great problems.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

An object of the present disclosure is to provide a steering brake control method, a steering brake control device, a computer readable medium and an electronic device, which overcome the technical problems of poor interference response capability and poor safety and stability in vehicle control due to the limitations and disadvantages of the related art, at least to a certain extent.

According to an aspect of the present disclosure, there is provided a steering brake control method including:

acquiring current motion parameters of a control object in a steering motion state, and acquiring motion interference information corresponding to the steering motion state;

determining an ideal motion model associated with the control object;

determining a parameter tracking error corresponding to the current motion parameter according to the current motion parameter and the ideal motion model;

and determining braking control information for adjusting the steering motion state according to the motion interference information and the parameter tracking error.

In some exemplary embodiments of the present disclosure, based on the above technical solution, the determining braking control information for adjusting the steering motion state according to the motion disturbance information and the parameter tracking error includes:

determining a direct yaw moment and an overall braking moment according to the motion interference information and the parameter tracking error;

determining a plurality of power providing objects related to the control object, and determining a torque distribution algorithm for distributing torque for each power providing object;

inputting the direct yaw moment and the overall braking moment into the torque distribution algorithm to obtain braking moments of the respective power providing objects;

and determining braking control information for adjusting the steering motion state according to the braking torque of each power supply object.

In some exemplary embodiments of the present disclosure, based on the above technical solution, the inputting the direct yaw moment and the overall braking moment into the torque distribution algorithm to obtain the braking moment of each of the power providing objects includes:

determining the current state information of the control object according to the current motion parameters and the ideal motion model;

inputting the current state information, the direct yaw moment, and the overall braking moment into the torque distribution algorithm;

and according to the current state information and the direct yaw moment, carrying out torque distribution on the overall braking moment through the torque distribution algorithm to obtain the braking moment of each power supply object.

In some exemplary embodiments of the present disclosure, based on the above technical solutions, the determining a direct yaw moment and an overall braking moment according to the motion disturbance information and the parameter tracking error includes:

determining a coordination control law according to the motion interference information and the parameter tracking error;

and determining a direct yaw moment and an overall braking moment according to the coordinated control law.

In some exemplary embodiments of the present disclosure, based on the above technical solution, the determining a coordination control law according to the motion interference information and the parameter tracking error includes:

establishing a nonlinear system model for interference suppression and stabilization control according to the motion interference information and the parameter tracking error;

determining a dissipative inequality associated with the nonlinear system model;

and calculating a coordination control law meeting the dissipation inequality by using a back-stepping method.

In some exemplary embodiments of the present disclosure, based on the above technical solutions, the current motion parameters include a centroid slip angle, a steering angle, a motion speed, and a yaw rate of the control object.

In some exemplary embodiments of the present disclosure, based on the above technical solutions, the parameter tracking error includes a yaw angle tracking error corresponding to the centroid yaw angle, a velocity tracking error corresponding to the motion velocity, and an angular velocity tracking error corresponding to the yaw velocity; the determining a parameter tracking error corresponding to the current motion parameter according to the current motion parameter and the ideal motion model comprises:

inputting the steering angle and the motion speed into the ideal motion model to obtain a desired yaw angle value, a desired speed value and a desired angular speed value;

determining a slip angle tracking error according to the centroid slip angle and the slip angle expected value;

determining a speed tracking error according to the motion speed and the speed expected value;

and determining an angular velocity tracking error according to the yaw angular velocity and the expected angular velocity value.

According to an aspect of the present disclosure, there is provided a steering brake control device, including:

the parameter acquisition module is configured to acquire current motion parameters of a control object in a steering motion state and acquire motion interference information corresponding to the steering motion state;

a model determination module configured to determine an ideal motion model associated with the control object;

an error determination module configured to determine a parameter tracking error corresponding to the current motion parameter from the current motion parameter and the ideal motion model;

an information determination module configured to determine braking control information for adjusting the steering motion state based on the motion disturbance information and the parameter tracking error.

According to an aspect of the present disclosure, a computer-readable medium is provided, on which a computer program is stored, which is characterized in that the computer program realizes any of the above-described methods when executed by a processor.

According to one aspect of the present disclosure, there is provided an electronic device characterized by comprising a processor and a memory; wherein the memory is for storing executable instructions of the processor, the processor being configured to perform any of the methods described above via execution of the executable instructions.

In the technical scheme provided by the embodiment of the disclosure, the motion interference information corresponding to the steering motion state is input as the interference item rather than being considered as the system input item, so that the interference introduced by the steering motion can be effectively inhibited in a targeted manner, and the motion stability and the safety of the control object are ensured.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

Fig. 1 schematically shows a flow chart of steps of a steering brake control method in some exemplary embodiments of the invention.

Fig. 2 schematically illustrates a flowchart of steps for determining brake control information in some exemplary embodiments of the present disclosure.

Fig. 3 schematically illustrates a flow chart of steps for determining a direct yaw moment and an overall braking moment in some exemplary embodiments of the present disclosure.

Fig. 4 is a flow chart schematically illustrating steps of determining a coordination control law according to some exemplary embodiments of the present disclosure.

Fig. 5 schematically illustrates a flowchart of steps for determining braking torque of each power providing object in some exemplary embodiments of the present disclosure.

Fig. 6 schematically illustrates a linear two-degree-of-freedom model of an automobile used in an application scenario.

Fig. 7A schematically shows a state diagram in which the vehicle is under-steered.

Fig. 7B schematically shows a state diagram of the vehicle oversteer.

Fig. 8 schematically shows a block diagram of a control algorithm for implementing the steering brake control method in the technical solution of the present disclosure.

Fig. 9 schematically shows a block diagram of a steering brake control apparatus according to some exemplary embodiments of the present disclosure.

FIG. 10 schematically illustrates a schematic diagram of a program product in an exemplary embodiment of the disclosure.

Fig. 11 schematically illustrates a module diagram of an electronic device in an exemplary embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

The chassis structure of a four-wheel independent-drive (4-wheel-independent-drive, 4-wide) vehicle is greatly different from that of a traditional automobile, mature controller products (such as an anti-lock braking system (ABS), an anti-slip system (ASR), a driving force control system (TCS), an Electronic Stability Program (ESP), direct yaw moment control (DYC) and the like) cannot be directly applied to the 4-wide vehicle, and the products still have a great problem in the aspect of interference suppression.

In the related art of the present disclosure, the chassis integrated control scheme of the steering brake system for 4WID vehicles and other vehicles may mainly adopt two schemes of centralized integrated control and hierarchical coordinated control.

The centralized integrated control can be based on a neural network algorithm, a fuzzy algorithm, a sliding mode variable structure algorithm, an active disturbance rejection algorithm and the like, for example, a chassis integrated system controller is designed in a feedforward or feedback mode, decoupling of subsystems such as steering braking and the like is avoided during modeling, but the model is complex, and the difficulty in designing the controller is high. Although the robustness of the whole vehicle system can be ensured by adopting centralized integrated control, common internal and external interferences (such as inflation pressure of wheels, irregular stress of a road surface, uneven road surfaces on the left and the right, measurement errors of a sensor and the like) cannot be described, the pertinence is not enough, and the specific interference suppression effect and the control effect are reduced. In addition, the control algorithm model is too complex due to the high complexity of the whole vehicle model, and the control algorithm model is difficult to realize in engineering. Although the accuracy of the system model is not relied on, in practical application, the real-time performance cannot be guaranteed.

The hierarchical coordination control can perform coupling compensation or decoupling control on the system based on a blackboard rule fuzzy control algorithm, a coupling compensation algorithm of adaptive learning, a nonlinear decoupling internal model control algorithm and the like. However, decoupling control cannot meet the requirements of various aspects of the actual system; the control algorithm based on the fuzzy rule and the coupling compensation algorithm of the self-adaptive learning do not depend on the accuracy of the system model, but the real-time performance cannot be guaranteed.

The technical scheme disclosed by the invention is mainly used for designing the chassis integrated system interference suppression controller aiming at a certain specific interference introduced into a steering angle under a heavy steering scene caused by the vertical load imbalance of left and right wheels under the steering braking working condition of a vehicle so as to improve the operation stability and the safety performance of the vehicle under certain scenes (such as asymmetrical inflation pressure of the wheels, tire aging, irregular stress of a road surface, split road surface and the like).

The technical scheme of the disclosure is explained in detail with reference to the specific embodiments.

Fig. 1 schematically shows a flow chart of steps of a steering brake control method in some exemplary embodiments of the invention. As shown in fig. 1, the method may mainly include the following steps:

and S110, acquiring the current motion parameters of the control object in the steering motion state, and acquiring motion interference information corresponding to the steering motion state.

The control object is a moving object that needs to be subjected to steering brake control, and may be, for example, a running vehicle. The current motion parameters of the control object in the turning motion state may include, for example, a centroid slip angle β, a steering angle δ, a motion velocity V, a yaw rate γ, and the like. The motion disturbance information corresponding to the steering motion state may be, for example, a disturbance term related to the steering angle δ.

Step S120, determining an ideal motion model related to the control object.

The ideal motion model is a motion model of the control object that is created while ignoring influence factors that are less associated with the steering brake control, and with which a desired value of a control-object-related motion parameter can be determined.

And S130, determining a parameter tracking error corresponding to the current motion parameter according to the current motion parameter and the ideal motion model.

The expected value determined by the ideal motion model is a control target of each motion parameter, and the parameter tracking error corresponding to each current motion parameter can be determined according to the current motion parameter and the ideal motion model. The parametric tracking error may include, for example, a slip angle tracking error e corresponding to a centroid slip angle β_βVelocity tracking error e corresponding to the velocity of motion V_VAnd an angular velocity tracking error e corresponding to the yaw rate gamma_γ. In some alternative embodiments, this step may input the steering angle δ and the motion velocity V into an ideal motion model to obtain the desired slip angle β_dDesired speed value V_dAnd angular velocity desired value γ_dAnd then according to the centroid slip angle beta and the slip angle expected value beta_dDetermining a slip angle tracking error e_βAccording to the speed V and the desired speed V_dDetermining a velocity tracking error e_VAnd based on the yaw rate gamma and the angular rate expectation value gamma_dDetermining angular velocity tracking error e_γ。

And S140, determining braking control information for adjusting the steering motion state according to the motion interference information and the parameter tracking error.

The interference suppression algorithm can be used in combination with the motion interference information and the parameter tracking error to determine braking control information that enables the adjustment of the steering motion state of the control object while suppressing interference.

In the steering brake control method provided by the present exemplary embodiment, by inputting the motion disturbance information corresponding to the steering motion state as a disturbance item, rather than considering it as a system input item, it is possible to effectively suppress the disturbance introduced by the steering motion in a targeted manner, thereby ensuring the motion stability and safety of the control object.

Fig. 2 schematically illustrates a flowchart of steps for determining brake control information in some exemplary embodiments of the present disclosure. As shown in fig. 2, on the basis of the above embodiments, step s140. determining the braking control information for adjusting the steering motion state according to the motion disturbance information and the parameter tracking error may include the following steps:

and S210, determining a direct yaw moment and an integral braking moment according to the motion interference information and the parameter tracking error.

From the motion disturbance information acquired in step S110 and the parameter tracking error determined in step S130, this step can determine a direct yaw moment required to achieve the desired value of the parameter, and at the same time, can determine an overall braking moment for generating the direct yaw moment. In other embodiments, the direct yaw moment can also be generated using the total drive torque.

Step S220, determining a plurality of power providing objects related to the control object, and determining a torque distribution algorithm for distributing torque for each power providing object.

The overall braking torque determined in step S210 needs to be collectively provided by a plurality of power providing objects related to the control object, and this step may determine a torque distribution algorithm for distributing torque to the respective power providing objects. For example, if the control object is a four-wheel independent drive vehicle, the power supply object associated therewith may be in-wheel motors embedded in the four drive wheels, respectively.

And step S230, inputting the direct yaw moment and the integral braking moment into a torque distribution algorithm to obtain the braking moment of each power supply object.

The braking torque of each power providing object can be determined by calculating the direct yaw moment and the overall braking torque using a torque distribution algorithm.

And S240, determining braking control information for adjusting the steering motion state according to the braking torque of each power supply object.

The respective braking torque of each power supply object can be used for generating braking control information of the corresponding braking torque, so that the braking control of each power supply object is realized.

Fig. 3 schematically illustrates a flow chart of steps for determining a direct yaw moment and an overall braking moment in some exemplary embodiments of the present disclosure. As shown in fig. 3, on the basis of the above embodiments, step s210. determining the direct yaw moment and the overall braking moment according to the motion disturbance information and the parameter tracking error may include the following steps:

and S310, determining a coordination control law according to the motion interference information and the parameter tracking error.

And S320, determining a direct yaw moment and an integral braking moment according to the coordination control law.

The coordinated control law is an algorithm forming control commands for determining the functional relationship between the controlled state variables and the system input signals. The direct yaw moment and the overall braking moment can be determined as controlled state variables using the functional relationships determined by the coordinated control law.

Fig. 4 is a flow chart schematically illustrating steps of determining a coordination control law according to some exemplary embodiments of the present disclosure. As shown in fig. 4, on the basis of the above embodiments, step s310, determining a coordination control law according to the motion interference information and the parameter tracking error may include the following steps:

and S410, establishing a nonlinear system model for interference suppression and stabilization control according to the motion interference information and the parameter tracking error.

Firstly, a nonlinear system model is established according to the motion interference information and the parameter tracking error for interference suppression and stabilization control.

And S420, determining a dissipation inequality related to the nonlinear system model.

The nonlinear system model created by step S410 should achieve a strict dissipation with respect to a given supply law as a closed loop system, and this step determines the dissipation inequality associated with the nonlinear system model. Dissipation is the conversion of energy that is efficiently utilized to energy that is less efficiently utilized. The dissipation structure is a structural state existing in the system, is an open system far away from an equilibrium state, and forms a time, space and function ordered state when the elements in the system have complex nonlinear coherent effects by continuously exchanging substances, energy and information with the outside, wherein the ordered structure under the nonlinear equilibrium is called the dissipation structure. At any moment, the sum of the energy of the initial moment of the system and the externally supplied energy is always greater than the sum of the energies of the dissipated systems.

And S430, calculating a coordination control law meeting the dissipation inequality by using a back stepping method.

The essence of the dissipative system is that there is a non-negative energy function, i.e. a coordination control law, so that the energy supply rate of the system is always greater than the energy loss. For a given energy supply rate, if the coordination control law exists such that the dissipation inequality associated with the nonlinear system model holds, the nonlinear system model is the dissipation system.

The coordinated control law is calculated by adopting a Backstepping method (Backstepping), so that the solution of the HJI differential inequality (Hamilton Jacobi-Isaacs) can be avoided.

Fig. 5 schematically illustrates a flowchart of steps for determining braking torque of each power providing object in some exemplary embodiments of the present disclosure. As shown in fig. 5, on the basis of the above embodiments, step s230. inputting the direct yaw moment and the overall braking moment into the torque distribution algorithm to obtain the braking torques of the respective power providing objects, may include the steps of:

and S510, determining the current state information of the control object according to the current motion parameters and the ideal motion model.

The current state information of the control object can be determined by analyzing the current motion parameters of the control object by using the ideal motion model, and the current state information is used for judging the current stable state of the control object.

And S520, inputting the current state information, the direct yaw moment and the integral braking moment into a torque distribution algorithm.

And inputting the current state information, the direct yaw moment and the integral braking moment into a torque distribution algorithm together as input parameters, and performing calculation processing by the torque distribution algorithm.

And S530, according to the current state information and the direct yaw moment, carrying out torque distribution on the overall braking moment through a torque distribution algorithm to obtain the braking moment of each power providing object.

The torque distribution algorithm utilizes the input current state information and the direct yaw moment to carry out torque distribution on the overall braking torque, and determines the braking torque of each power supply object according to the distribution result.

The details and principles of the steering brake control according to the above embodiments will be described below with reference to specific application scenarios.

Taking four-wheel hub electric logistics vehicle as an example, the overall scheme principle of the technical scheme of the disclosure is as follows:

(1) the input term containing the steering angle of the front wheels is considered as a disturbance input rather than as a system input when modeling the entire vehicle. This allows for a targeted interference suppression controller design for such interference introduced by the steering angle.

(2) The control target selects the deviation of the centroid slip angle, the yaw angular velocity and the vehicle speed from an expected value, the expected value of the centroid slip angle and the yaw angular velocity can be calculated through a simplified model, and the centroid slip angle and the yaw angular velocity follow the expected value to ensure good steering following performance of the electric logistics vehicle; the good braking effect of the electric logistics vehicle can be ensured by the vehicle speed following the expected value.

(3) The method has the advantages that the specific interference is suppressed by adopting the interference suppression algorithm of the dissipation system L2, the algorithm can meet the multi-target control requirement for suppressing the interference while ensuring the control target, and the control law is simple in structure, good in real-time performance and easy in engineering practice.

(4) The method fully utilizes the characteristic that the four-wheel driving torque and the braking torque of the four-wheel hub electric logistics vehicle are independently controllable, adopts a layered design and is divided into a yaw moment algorithm layer and a torque distribution layer, and a distribution algorithm with good real-time performance is adopted to realize the distribution of the control force based on a direct yaw moment control scheme.

The specific implementation content of the above scheme is as follows:

modeling

Wherein a and b are respectively front wheelbase and rear axleDistance, beta is the centroid slip angle, delta is the steering angle, V is the motion speed, gamma is the yaw rate, alpha_rIs a rear wheel side slip angle, α_fIs front wheel side slip angle, u is longitudinal velocity, v is lateral velocity, F_xiLongitudinal force applied to the i-th wheel, F_yiThe i-th wheel is subjected to a lateral force.

Under the condition of steering braking of the automobile, combining a slip angle model to obtain an automobile yaw direction, longitudinal and lateral dynamic balance equation:

the dynamic equation of the yaw direction is as follows:

the lateral dynamics equation is:

the longitudinal kinetic equation is:

substituting the lateral force into the formula:

in the above formula, k_fIs the cornering coefficient, k, of the front wheel_rIs the cornering stiffness coefficient of the rear wheel; a and b are respectively a front wheel base and a rear wheel base, beta is a centroid slip angle, gamma is a yaw velocity, V is a motion velocity, and i is 1,2,3 and 4 to represent longitudinal braking forces of four wheels. I is_zIs the moment of inertia of the vehicle about the Z axis, and m is the weight of the vehicle

And (3) finishing to obtain an automobile steering brake chassis integrated system model:

y＝[β γ]^T

wherein M is_zFor directly balancing the pendulum moment, I is the moment of inertia of the vehicle about the Z axis, and y is the output

The ideal motion model of the vehicle is as follows:

wherein, beta_dFor desired value of slip angle, V_dFor desired speed value, γ_dIn order to be the desired value of the angular velocity,

v is vehicle speed, K ═ m (a/K)_f-b/k_r)V²/L²For the vehicle stability factor, L ═ a + b is the distance between the front and rear axles of the vehicle, τ is the time constant, and s is the frequency domain complex number.

Defining the parameter tracking error E as:

wherein e is_βFor slip angle tracking error, e_VFor velocity tracking error, e_γIs the angular velocity tracking error.

The chassis integration system model can be organized as:

in the above formula:

a₁₁＝-(k_f+k_r)/mv,a₁₂＝-(ak_f-bk_r)/(mv²-1)

a₂₁＝-(ak_f-bk_r)/I_z,a₂₂＝-(a²k_f+b²k_r)/I_zv

b₁₁＝0,b₂₁＝1/I_z,h₁₁＝k_f/mv,h₂₁＝ak_f/I_z

the inputs to the system are:

the interference amount of the system is:

the disturbance W of the system is changed according to the change of the front wheel steering angle, and can be observed

Second, L2 interference rejection controller design

1. Interference suppression and settling control problems for non-linear systems

If the ratio of the system output y to the disturbance input ω shows a decay, i.e. the ratio (gain level) μ is small enough, with μ < 1, we refer to the disturbance decay.

Given a non-linear system represented by the following formal equation

It is desirable to find a feedback control law u (u) (x) that achieves a tight dissipation of the resulting closed loop system with respect to the feed rate q.

q(ω,y)＝γ²ω²-y² (3)

Namely, under the action of the control law, the nonlinear system (2) is globally asymptotically stable, and the input L is₂L of norm and interference input omega₂The norm ratio is less than or equal to mu.

For the supply rate of equation (3), it is necessary to find a state feedback control law u ═ u (x), and to satisfy a smoothing function v (x) estimated in the following form for a certain positive and true number:

let the following dissipation inequality hold for all x ∈ R and all ω ∈ R:

wherein α (·) is a K_∞A function.

2. Steering brake chassis integrated system L of electric logistics vehicle₂Interference rejection controller design

A coordinated control law u ═ u needs to be designed₁ u₂]So that each state variable in the system (1) can track the expected value well (namely the tracking error model of the automobile chassis system is stable in a global asymptotic manner) and ensure the output L₂L of norm and interference w₂The ratio of the norms is less than or equal to a certain number mu.

First order x₁＝e_β,x₂＝[e_γ e_V]^TAnd is and

f(x₁,x₂,W)＝a₁₁e_β+a₁₂e_γ+w₁

the system (1) can be simplified to:

the design problem of the automobile chassis integrated system controller is as follows: design coordination control law u ═ u₁ u₂]The model (6) is set to the supply rate q (W, y) to γ²||W||²-||h(x₁,x₂)||²Strict dissipation, i.e. satisfying the dissipation inequality:

therefore, the design problem of the interference suppression and stabilization controller of the MIMO nonlinear system is shown, and the Backstepping method is adopted to obtain the control law u so as to avoid solving the HJI differential inequality.

Get

Then

In which the system is divided into a model-accurate known system and a feedback interconnection system containing an uncertainty systemSystem, in the above formula f (x)₁0, W) is x alone₁The model of the components is accurately known to the system,

f(x₁,x₂,W)＝f(x₁,0,W)+A(x₁,x₂)

q(x₁,x₂,W)＝f₁(x₁,x₂)+p₁(x₁,x₂,W)

combining equations (8) and (9) can obtain:

the system output can be deformed as:

suppose for a vector containing only x₁Component sub-systems, in which there is a number mu, a smooth positive function V (x) and a K_∞Function α (·), such that equation (12) holds for all x and W

Substituting equation (12) into equation (10) yields:

further processing of equation (13) can result in:

and (3) constructing a control law u:

u＝-Cx₂-x₂-A(x₁,x₂)-f₁(x₁,x₂)+v (15)

wherein C is a predetermined matrix defined as follows:

wherein, c_ij> 0, so the coordination control law u is written as:

substituting equation (15) into equation (14) yields:

for any ε > 0, we see the Schwarz inequality:

selecting an additional control law v:

equation (18) becomes:

and also

Combining equation (17), equation (20), and equation (21) yields:

due to alpha (| x)₁I) is a K_∞Class function, function α (| x)₁||)+c||x₂||²Is (x)₁,x₂) A positively determined strict function, so that there is one K_∞Class function α (·) such that

α(||(x₁,x₂)||)≤α(||x₁||)+c||x₂||² (23)

The dissipation inequality (7) can be derived from equation (22) and equation (23). The formula (16) and the formula (19) are control law expressions, namely, electric automobile chassis integrated controller models.

Third, direct balance pendulum moment formulation layer based on braking force

The control idea of Direct Yaw moment control (DYC) is as follows: the expected value of the control variable during the running of the automobile is calculated through the automobile running information (steering wheel angle, vehicle speed, road surface friction coefficient and the like) obtained by the sensor, and the additional yaw moment required by tracking the expected value is calculated, so that the yaw motion control of the automobile is realized. The direct yaw moment control is divided into a direct yaw moment control based on the driving force and a direct yaw moment control based on the braking force, and fig. 7A schematically shows a state where the vehicle is under-steered, and when a yaw moment M in the same direction as the steering is applied to the vehicle, the vehicle returns to a desired trajectory. Fig. 7B schematically shows a state in which the vehicle is oversteered, and the vehicle returns to the desired trajectory if a yaw moment M is applied to the vehicle in the opposite direction to the steering direction.

Fig. 8 schematically shows a block diagram of a control algorithm for implementing the steering brake control method in the technical solution of the present disclosure. As shown in fig. 8, the overall control algorithm of the disclosed solution includes a direct yaw moment algorithm layer and a torque distribution layer.

In the figure, e_βIs steamDeviation of the vehicle mass center slip angle; e.g. of the type_VThe vehicle speed deviation is obtained; e.g. of the type_γThe yaw angular speed deviation of the automobile; flag is a state quantity used for judging the current stable state of the automobile; m_zIn order to directly yaw the moment,

the integral braking torque is obtained; t is_iThe braking torque of each wheel is used for generating corresponding yaw moment for the four-wheel independent braking electric automobile. In the torque distribution layer, according to the direct yaw moment calculated by the current stable state of the automobile and the upper direct yaw moment algorithm layer and the braking moment of each wheel, the braking moment T of each wheel can be calculated by the constrained optimal distribution algorithm_i。

The technical scheme provided by the disclosure makes up the defect of research of the four-wheel hub electric automobile controller in the field of interference suppression, and enriches the modules of the whole four-wheel hub electric automobile controller. Compared with a large and complete control target of improving the robustness of the whole vehicle system, the steering angle control method has the advantages that one kind of interference introduced by the steering angle is restrained in a more targeted manner aiming at the specific scene of the steering braking condition of the electric logistics vehicle, and the control effect is more ideal. The driving safety of the electric automobile is improved in certain specific scenes through the combination of an interference suppression algorithm and a direct balance pendulum moment control algorithm.

It should be noted that although the above exemplary embodiments describe the various steps of the methods of the present disclosure in a particular order, this does not require or imply that these steps must be performed in that particular order, or that all of the steps must be performed, to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.

Fig. 9 schematically shows a block diagram of a steering brake control apparatus according to some exemplary embodiments of the present disclosure. As shown in fig. 9, the steering brake control device 900 may mainly include:

a parameter obtaining module 910 configured to obtain a current motion parameter of the control object in the steering motion state, and obtain motion interference information corresponding to the steering motion state;

a model determination module 920 configured to determine an ideal motion model associated with the control object;

an error determination module 930 configured to determine a parameter tracking error corresponding to the current motion parameter based on the current motion parameter and the ideal motion model;

an information determination module 940 configured to determine braking control information for adjusting the steering motion state based on the motion disturbance information and the parameter tracking error.

In some exemplary embodiments of the present disclosure, based on the above embodiments, the information determining module 940 may further include:

a moment determination unit configured to determine a direct yaw moment and an overall braking moment according to the motion disturbance information and the parameter tracking error;

an algorithm determination unit configured to determine a plurality of power providing objects related to a control object, and determine a torque distribution algorithm for distributing torque to the respective power providing objects;

a torque distribution unit configured to input the direct yaw moment and the overall braking moment into a torque distribution algorithm to obtain braking moments of the respective power providing objects;

a control information determination unit configured to determine braking control information for adjusting a steering movement state according to braking torques of the respective power providing objects.

In some exemplary embodiments of the present disclosure, based on the above embodiments, the torque distribution unit includes:

a current state determination unit configured to determine current state information of the control object according to the current motion parameter and the ideal motion model;

an algorithm input unit configured to input the current state information, the direct yaw moment, and the overall braking moment into a torque distribution algorithm;

and the algorithm distribution unit is configured to perform torque distribution on the overall braking torque through a torque distribution algorithm according to the current state information and the direct yaw moment so as to obtain the braking torque of each power supply object.

In some exemplary embodiments of the present disclosure, based on the above embodiments, the moment determination unit includes:

a control law determination unit configured to determine a coordination control law according to the motion interference information and the parameter tracking error;

a control law calculation unit configured to determine a direct yaw moment and an overall braking moment according to a coordinated control law.

In some exemplary embodiments of the present disclosure, based on the above embodiments, the control law determining unit includes:

a model establishing unit configured to establish a nonlinear system model for suppressing interference and stabilizing control according to the motion interference information and the parameter tracking error;

an inequality determination unit configured to determine a dissipative inequality associated with the non-linear system model;

and the backstepping calculation unit is configured to calculate the coordination control law which meets the dissipation inequality by using a backstepping method.

In some exemplary embodiments of the present disclosure, based on the above embodiments, the current motion parameters include a centroid slip angle, a steering angle, a motion speed, and a yaw rate of the control object.

In some exemplary embodiments of the present disclosure, based on the above embodiments, the parameter tracking error includes a yaw angle tracking error corresponding to a centroid yaw angle, a velocity tracking error corresponding to a motion velocity, and an angular velocity tracking error corresponding to a yaw velocity; the error determination module 930 includes:

a model input unit configured to input the steering angle and the motion speed into an ideal motion model to obtain a desired yaw angle value, a desired speed value and a desired angular speed value;

a first error determination unit configured to determine a slip angle tracking error from the centroid slip angle and a slip angle desired value;

a second error determination unit configured to determine a velocity tracking error from the motion velocity and the desired velocity value;

a third error determination unit configured to determine an angular velocity tracking error from the yaw rate and the desired angular velocity.

The details of the steering brake control device are described in detail in the corresponding steering brake control method, and therefore, the details are not described herein.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

In an exemplary embodiment of the present disclosure, there is also provided a computer readable medium having stored thereon a computer program which, when executed by a processor, may implement the above-mentioned method of the present disclosure. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code; the program product may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, or a removable hard disk, etc.) or on a network; when the program product is run on a computing device (which may be a personal computer, a server, a terminal apparatus, or a network device, etc.), the program code is configured to cause the computing device to perform the method steps in the above exemplary embodiments of the disclosure.

Referring to fig. 10, a program product 1000 for implementing the above method according to an embodiment of the present disclosure may employ a portable compact disc read only memory (CD-ROM) and include program codes, and may be run on a computing device (e.g., a personal computer, a server, a terminal device, or a network device, etc.). However, the program product of the present disclosure is not limited thereto. In the exemplary embodiment, the computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium.

The readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the C language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device over any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), etc.; alternatively, the connection may be to an external computing device, such as through the Internet using an Internet service provider.

In an example embodiment of the present disclosure, there is also provided an electronic device comprising at least one processor and at least one memory for storing executable instructions of the processor; wherein the processor is configured to perform the method steps in the above-described exemplary embodiments of the disclosure via execution of the executable instructions.

The electronic device 1100 in this exemplary embodiment is described below with reference to fig. 11. The electronic device 1100 is merely an example, and should not impose any limitations on the functionality or scope of use of embodiments of the present disclosure.

Referring to FIG. 11, an electronic device 1100 is shown in the form of a general purpose computing device. The components of the electronic device 1100 may include, but are not limited to: at least one processing unit 1110, at least one memory unit 1120, a bus 1130 that connects the various system components (including the processing unit 1110 and the memory unit 1120), and a display unit 1140.

Wherein the storage unit 1120 stores program code which can be executed by the processing unit 1110 such that the processing unit 1110 performs the method steps in the above exemplary embodiments of the present disclosure.

The storage unit 1120 may include readable media in the form of volatile storage units, such as a random access storage unit 1121(RAM) and/or a cache storage unit 1122, and may further include a read-only storage unit 1123 (ROM).

Storage unit 1120 may also include a program/utility 1124 having a set (at least one) of program modules 1125, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 1130 may be representative of one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 1100 may also communicate with one or more external devices 1200 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that allow a user to interact with the electronic device 1100, and/or with any devices (e.g., router, modem, etc.) that allow the electronic device 1100 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 1150. Also, the electronic device 1100 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 1160. As shown in FIG. 11, the network adapter 1160 may communicate with the other modules of the electronic device 1100 via the bus 1130. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software may be referred to herein generally as a "circuit," module "or" system.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments, and the features discussed in connection with the embodiments are interchangeable, if possible. In the above description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the embodiments of the disclosure may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.

Claims

1. A steering brake control method characterized by comprising:

determining an ideal motion model associated with the control object;

2. The steering brake control method according to claim 1, wherein the determining brake control information for adjusting the steering motion state based on the motion disturbance information and the parameter tracking error includes:

3. The steering brake control method according to claim 2, wherein the inputting the direct yaw moment and the overall braking moment into the torque distribution algorithm to obtain the braking moment of each of the power-supplying objects includes:

4. The steering brake control method according to claim 2, wherein the determining a direct yaw moment and an overall braking moment based on the motion disturbance information and the parameter tracking error comprises:

5. The steering brake control method according to claim 4, wherein the determining a coordination control law according to the motion disturbance information and the parameter tracking error comprises:

6. The steering brake control method according to claim 1, characterized in that the current motion parameters include a centroid slip angle, a steering angle, a motion speed, and a yaw rate of the control object.

7. The steering brake control method according to claim 6, characterized in that the parameter tracking error includes a yaw angle tracking error corresponding to the centroid yaw angle, a velocity tracking error corresponding to the motion velocity, and an angular velocity tracking error corresponding to the yaw velocity; the determining a parameter tracking error corresponding to the current motion parameter according to the current motion parameter and the ideal motion model comprises:

8. A steering brake control device characterized by comprising:

9. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 7.

10. An electronic device, comprising:

a processor;

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the method of any one of claims 1-7 via execution of the executable instructions.