A distributed vehicle in-situ steering control method, device, equipment and vehicle

Abstract

The present invention discloses a distributed vehicle pivot steering control method, device, equipment, and vehicle. The method comprises: obtaining a maximum yaw rate based on motor output performance and energy system power output capacity; determining a yaw acceleration limit for the maximum yaw rate based on the correlation between the vehicle's longitudinal force and lateral force and the road adhesion limit; determining a desired yaw rate based on the limiting relationship between the yaw acceleration and the maximum yaw rate using a preset MPC control algorithm; determining the yaw torque required to achieve the desired yaw rate using a preset sliding mode control algorithm; and distributing the yaw torque value among the vehicle's four wheels based on the motor's maximum output torque using a preset quadratic programming constraint solving algorithm to determine the desired driving force for each wheel. The present invention achieves precise control of the vehicle's yaw rate through motor speed, thereby enabling pivot steering.

Description

Technical Field

[0001] The present invention relates to the field of vehicle steering technology, and in particular to a distributed vehicle in-situ steering control method, device, on-board equipment and vehicle. Background Art

[0002] A vehicle's steering ability has always been an important research area in automotive development. The minimum turning radius, a key evaluation parameter for vehicle steering performance, largely characterizes a vehicle's ability to navigate winding, narrow terrain or bypass insurmountable obstacles. The smaller the vehicle's turning radius, the better its maneuverability during steering. Currently, passenger cars on the market have a minimum turning radius of 5-8 meters, resulting in insufficient steering flexibility. To address this issue, pivoting with a minimum turning radius of zero has emerged. Pivoting can significantly improve a vehicle's steering maneuverability, especially for special-purpose off-road vehicles, enabling them to turn or make U-turns in narrow spaces like streets, bridges, and those blocked by obstacles.

[0003] In current research, traditional wheeled vehicles are mostly centralized drive forms, and they mostly achieve on-the-spot steering by changing the mechanical structure. There is no specific on-the-spot steering control scheme. The control objects of on-the-spot steering are mostly concentrated on tracked vehicles. For the on-the-spot steering control of tracked vehicles, in order to achieve the driver's controllability of the vehicle's on-the-spot steering function, the existing control methods mostly use the vehicle steering wheel angle or accelerator pedal opening as the control input to determine the steering yaw torque or the desired yaw angular velocity, and track the expected value of the control target by adjusting the driving torque of each wheel in real time, and finally achieve the on-the-spot steering function.

[0004] Since the research object is a tracked vehicle, its in-situ steering control method cannot be applied to wheeled vehicles. The control quantity is the motor output torque, and there is no clear mathematical relationship between it and the state quantity yaw angular velocity. It is impossible to accurately control the current vehicle's yaw speed by controlling the motor torque. At the same time, the external characteristic constraints of the motor are not considered. When the motor speed gradually increases, the maximum output torque of the motor decreases, and the yaw responsiveness of the entire vehicle decreases. Summary of the Invention

[0005] The purpose of the present invention is to overcome the above-mentioned technical deficiencies and provide a distributed vehicle in-situ steering control method, device, on-board equipment and vehicle to solve the technical problem in the prior art that it is impossible to accurately control the yaw speed of the current vehicle by controlling the motor torque, thereby achieving in-situ steering.

[0006] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a distributed vehicle in-situ steering control method, comprising:

[0008] Obtaining the maximum yaw rate based on the motor output performance of the distributed vehicle and the power output capability of the energy system;

[0009] determining a yaw acceleration limit value of the maximum yaw angular velocity according to a correlation between the vehicle longitudinal force and lateral force and the road adhesion limit value;

[0010] Determining a desired yaw rate based on a preset MPC control algorithm and a limiting relationship between the yaw acceleration limit value and the maximum yaw rate;

[0011] Determining a yaw moment value required to achieve the desired yaw angular velocity according to a preset sliding mode control algorithm;

[0012] Based on the maximum output torque of the motor, a preset quadratic programming constraint solving algorithm is used to distribute the yaw moment value to the four wheels of the vehicle to determine the expected driving force of each wheel of the vehicle.

[0013] In some embodiments, determining the yaw acceleration limit value of the maximum yaw angular velocity based on a correlation between the vehicle longitudinal force and lateral force and the road adhesion limit value includes:

[0014] The preset least squares algorithm is used to fit the relationship between the vehicle longitudinal force and lateral force and the road adhesion limit value to determine the adhesion ellipse;

[0015] determining the adhesion limit longitudinal force according to the adhesion ellipse;

[0016] determining a current state longitudinal force of the vehicle based on the limit longitudinal force, a maximum output torque under the external characteristics of the vehicle motor, and a minimum value of the power output capability of the energy system;

[0017] Based on the current longitudinal force, the position of the yaw angular acceleration limit value is determined according to the adhesion ellipse, and the yaw angular acceleration limit value is determined using a preset yaw motion equation.

[0018] In some embodiments, the method of fitting the relationship between the vehicle longitudinal force and lateral force and the road adhesion limit using a preset least squares algorithm to determine the adhesion ellipse includes:

[0019] Constructing a standard ellipse model according to the relationship curve between the longitudinal force and the lateral force of the vehicle;

[0020] Constructing a set of sample points having a predetermined algebraic distance from the polynomial to the standard ellipse model;

[0021] The preset Lagrangian function is used to fit the standard ellipse model with the minimum sum of algebraic distances in the sample point set as the goal, so as to determine the attachment ellipse.

[0022] In some embodiments, determining the desired yaw rate based on a preset MPC control algorithm and according to a limiting relationship between the yaw acceleration and the maximum yaw rate includes:

[0023] Constructing a coordinate system with the center of mass of the vehicle as the coordinate origin, and establishing a vehicle kinematic model based on the offset of the vehicle center of mass relative to the horizontal and vertical coordinates of the coordinate system and the vehicle yaw angle based on the MPC controller;

[0024] Deforming the vehicle kinematic model using a preset Taylor expansion method to obtain an error model of the stationary steering vehicle;

[0025] Based on the principle of minimizing the center of mass offset of the vehicle, a preset iterative method is used to determine the vehicle's on-the-spot turning prediction model;

[0026] According to the control amount limit constraint and the control increment constraint, a preset quadratic programming algorithm is used to plan the vehicle pivot turn prediction model to determine the expected yaw angular velocity.

[0027] In some embodiments, before determining the vehicle pivot turn prediction model, the method further includes:

[0028] determining a steering control model between the steering wheel angle input and the desired yaw rate according to the degree of influence of the steering wheel angle input on the desired yaw rate;

[0029] Integrating the steering control model and the pivot point steering vehicle error model to obtain a pivot point steering state quantity model;

[0030] The pivot point turning state quantity model is iterated to obtain a vehicle pivot point turning prediction model.

[0031] In some embodiments, determining the yaw moment value required to achieve the desired yaw angular velocity according to a preset sliding mode control algorithm includes:

[0032] According to the speed at which the state point approaches the sliding surface, a preset saturation function is used to determine the correlation between the yaw rate and the sliding surface;

[0033] A yaw moment value required to achieve the desired yaw angular velocity is determined according to the influence of the correlation between the yaw angular velocity and the sliding mode surface on the yaw motion of the vehicle.

[0034] In some embodiments, the method of using a preset quadratic programming constraint solving algorithm to determine the expected driving force of each wheel of the vehicle includes:

[0035] Based on the adhesion ellipse, determine the adhesion rate of the tire;

[0036] Based on the minimization of the sum of the tire utilization variance and the four-wheel utilization as the optimization goal, and the four-wheel longitudinal force as the control variable, a preset quadratic programming model is used to determine the expected driving force of each wheel of the vehicle.

[0037] In a second aspect, the present invention further provides a distributed vehicle in-situ steering control device, comprising:

[0038] An acquisition module, used to obtain a maximum yaw angular velocity based on motor output performance and energy system power output capability;

[0039] a yaw acceleration determination module, configured to determine a yaw acceleration limit value of the maximum yaw angular velocity based on a correlation between the vehicle longitudinal force and lateral force and the road adhesion limit value;

[0040] a desired yaw rate determination module, configured to determine a desired yaw rate based on a preset MPC control algorithm and according to a limiting relationship between the yaw angular acceleration and the maximum yaw rate;

[0041] a yaw moment value determination module, configured to determine a yaw moment value required to achieve the desired yaw angular velocity according to a preset sliding mode control algorithm;

[0042] The expected driving force determination module is used to distribute the yaw moment value to the four wheels of the vehicle based on the maximum output torque of the motor and adopt a preset quadratic programming constraint solving algorithm to determine the expected driving force of each wheel of the vehicle.

[0043] In a third aspect, the present invention further provides an in-vehicle device, comprising: a processor and a memory;

[0044] The memory stores a computer-readable program executable by the processor;

[0045] When the processor executes the computer-readable program, the steps in the distributed vehicle in-situ steering control method as described above are implemented.

[0046] In a fourth aspect, the present invention further provides a vehicle comprising the distributed vehicle in-situ steering control device as described above, and / or the vehicle-mounted equipment as described above.

[0047] Compared with the prior art, the distributed vehicle in-situ steering control method, device, on-board equipment and vehicle provided by the present invention first obtain the maximum yaw velocity of the vehicle based on the motor output performance and the power output capacity of the energy system, and then determine the yaw acceleration limit value of the maximum yaw velocity based on the correlation between the vehicle longitudinal force and lateral force and the road adhesion limit value. Subsequently, a preset MPC control algorithm is adopted to determine the desired yaw velocity based on the limiting relationship between the yaw acceleration and the maximum yaw velocity, and the yaw moment value required to achieve the desired yaw velocity is determined through a preset sliding mode control algorithm. Then, the yaw moment is distributed to the four wheels of the vehicle through a quadratic programming constraint solving algorithm to determine the desired driving force of each wheel, thereby obtaining the corresponding motor output torque. The present invention realizes the precise control of the vehicle's yaw velocity through the motor speed, thereby achieving the purpose of vehicle in-situ steering. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of an embodiment of a distributed vehicle in-situ steering control method provided by the present invention;

[0049] Figure 2 This is a flow chart of an embodiment of step S102 in the distributed vehicle in-situ steering control method provided by the present invention;

[0050] Figure 3 This is a flow chart of an embodiment of step S201 in the distributed vehicle in-situ steering control method provided by the present invention;

[0051] Figure 4 This is a flow chart of an embodiment of step S103 in the distributed vehicle in-situ steering control method provided by the present invention;

[0052] Figure 5 This is a flow chart of an embodiment of step S104 in the distributed vehicle in-situ steering control method provided by the present invention;

[0053] Figure 6 1 is a schematic diagram of an embodiment of a distributed vehicle in-situ steering control device provided by the present invention;

[0054] Figure 7 It is a schematic diagram of the operating environment of an embodiment of the vehicle-mounted device provided by the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] Steering in place is a mode of action for some tracked vehicles. Steering in place can save parking space and reduce the difficulty of parking or steering. It is of great significance in the use of vehicles in urban spaces. In the existing technology, the control of steering in place for electric drive tracked vehicles is studied. The maximum output torque of the motor is used as the vehicle starting torque to improve steering responsiveness. The yaw rate negative feedback gain is adjusted in real time according to the steering wheel angle signal, and the stability and responsiveness of the steering in place are comprehensively optimized. However, because the research object is a tracked vehicle, the steering in place control method cannot be applied to wheeled vehicles. There is no clear mathematical relationship between the control quantity (motor output torque) and the state quantity (yaw rate), making it impossible to accurately control the current vehicle's yaw rate by controlling the motor torque. At the same time, the external characteristic constraints of the motor are not considered. When the motor speed gradually increases, the maximum output torque of the motor decreases, and the yaw responsiveness of the entire vehicle is reduced. Alternatively, a tire model with a parabolic distribution of ground normal force was constructed using the friction circle theory, and the tire force during the on-the-spot steering process was derived and calculated. An integral algorithm for the steering resistance torque was proposed to control the vehicle's on-the-spot steering. However, this method only analyzes the dynamic process of a six-wheeled vehicle during the on-the-spot steering process, providing a certain theoretical basis for the on-the-spot steering control process, but does not propose a practical and effective on-the-spot steering control method.

[0057] The distributed vehicle on-the-spot steering control method, device, equipment or vehicle involved in the present invention can be used for various types of vehicles to perform on-the-spot steering in narrow areas, thereby saving the space required for vehicle steering and greatly reducing the difficulty of steering in narrow areas.

[0058] The present invention first establishes a vehicle dynamics model based on the goal of pivoting control. Since there is no obvious vertical motion during vehicle steering and its effect on pivoting is minimal, the vehicle's roll, pitch, and vertical degrees of freedom can be ignored. A seven-degree-of-freedom vehicle dynamics model suitable for pivoting is established, and the following assumptions are made:

[0059] 1) The road surface is in good condition and there is no tire sinking;

[0060] 2) The vehicle's steering center is set as the center of mass, the dynamic characteristics of the suspension are ignored, and the effect of the suspension on the center of mass position during steering is not considered;

[0061] 3) Since the vehicle speed is very low when turning, air resistance can be ignored;

[0062] The equations of motion of the vehicle along the x and y axes can be calculated using Newton's second law:

[0063]

[0064]

[0065] The vehicle's yaw motion equation is:

[0066]

[0067] The longitudinal and lateral accelerations of the vehicle are:

[0068]

[0069] Among them, m is the mass of the vehicle; a x 、a y are longitudinal and lateral acceleration respectively; v x 、v y are longitudinal and lateral velocities respectively; F xi 、F yi are the longitudinal and lateral forces of each wheel (i=1, 2, 3, 4 represent 0.0 for the left front, right front, left rear, and right rear wheels respectively); I z is the vehicle's yaw moment of inertia; ω r 、 are the yaw rate and yaw acceleration at the center of mass respectively; B is the vehicle wheelbase; L f 、L r are the distances from the center of mass to the front and rear axles, respectively.

[0070] This embodiment provides a method for controlling stationary steering of distributed vehicles. This method addresses the issues of large turning radius and insufficient steering maneuverability, and aims to fully leverage the advantages of independently adjustable torque and direction of each wheel of a distributed drive vehicle. Specifically, within a human-vehicle-road system, a stationary steering control method suitable for distributed drive vehicles is designed and developed based on the driver's stationary steering intention and combined with road surface characteristics. The control method primarily aims to achieve zero turning radius, while also considering vehicle stability during stationary steering. This method achieves stationary steering while ensuring safety, thereby significantly improving the vehicle's steering maneuverability. (See ) Figure 1 The distributed vehicle in-situ steering control method includes the following steps:

[0071] S101, obtaining a maximum yaw angular velocity based on motor output performance and energy system power output capability;

[0072] S102, determining a yaw acceleration limit value of the maximum yaw angular velocity based on a correlation between the vehicle longitudinal force and lateral force and the road adhesion limit value;

[0073] S103, determining a desired yaw rate based on a preset MPC control algorithm and according to a limiting relationship between the yaw acceleration and the maximum yaw rate;

[0074] S104, determining a yaw moment value required to achieve the desired yaw angular velocity according to a preset sliding mode control algorithm;

[0075] S105 , based on the maximum output torque of the motor, a preset quadratic programming constraint solving algorithm is used to distribute the yaw moment value to the four wheels of the vehicle to determine the expected driving force of each wheel of the vehicle.

[0076] In this embodiment, the maximum yaw rate of the vehicle is first obtained based on the motor output performance and the power output capacity of the energy system. Then, based on the correlation between the vehicle longitudinal force and lateral force and the road adhesion limit, the yaw acceleration limit of the maximum yaw rate is determined. Then, using a preset MPC control algorithm, based on the limiting relationship between the yaw acceleration and the maximum yaw rate, the desired yaw rate is determined. Furthermore, using a preset sliding mode control algorithm, the yaw moment value required to achieve the desired yaw rate is determined. Then, using a quadratic programming constraint solving algorithm, the yaw moment is distributed among the four wheels of the vehicle to determine the desired driving force for each wheel, thereby obtaining the corresponding motor output torque. The present invention achieves precise control of the vehicle's yaw rate through the motor speed, thereby achieving the purpose of turning the vehicle on the spot.

[0077] It should be noted that the present invention first constructs a seven-degree-of-freedom vehicle dynamics model suitable for pivoting and a Dugoff tire model to analyze the influence of wheel loads and adhesion conditions on pivoting yaw velocity. This leads to a hierarchical control architecture. The upper layer, the decision-making layer, employs a model predictive control algorithm based on the motor output performance and power output capacity of the energy system, combined with the tire adhesion ellipse, to design a yaw rate decision method with a desired center of mass offset of zero. The middle layer, the control layer, develops a yaw motion tracking algorithm based on PI sliding mode control to compensate for the steering yaw moment, thereby improving the robustness and stability of directional control. The lower layer, with optimal tire utilization as the goal, designs a quadratic programming algorithm to optimize the distribution of the additional yaw moment to each wheel. The proposed method minimizes the vehicle's steering center offset while maintaining a maximum center of mass offset of approximately 0.5 meters, meeting the requirements for steering in confined spaces and significantly improving the vehicle's steering maneuverability.

[0078] In some embodiments, see Figure 2 The determining of the yaw acceleration limit value of the maximum yaw angular velocity based on the correlation between the vehicle longitudinal force and lateral force and the road adhesion limit value includes:

[0079] S201, using a preset least squares algorithm to fit the relationship between the vehicle longitudinal force and lateral force and the road adhesion limit value to determine an adhesion ellipse;

[0080] S202, determining the adhesion limit longitudinal force according to the adhesion ellipse;

[0081] S203, determining the current longitudinal force of the vehicle based on the limit longitudinal force, the maximum output torque under the external characteristics of the vehicle motor, and the minimum power output capacity of the energy system;

[0082] S204 : Based on the current longitudinal force, determine the position of the yaw angular acceleration limit value according to the adhesion ellipse, and determine the yaw angular acceleration limit value using a preset yaw motion equation.

[0083] In this embodiment, according to the adhesion ellipse theory, at a certain slip angle, as the longitudinal force increases, the lateral force gradually decreases, resulting in a change in the tire's lateral characteristics. When the longitudinal force reaches a certain value, the lateral force decreases significantly. This is because the tire approaches its adhesion limit, with the longitudinal force dominating the majority of the adhesion, leaving minimal lateral adhesion. Extensive testing has demonstrated that the envelope of the longitudinal and lateral force curves forms an adhesion ellipse, reflecting the limit of the combined longitudinal and lateral forces under certain adhesion conditions. During pivoting, the vehicle must exceed the road's adhesion limit, resulting in significant sideways deviation and slip. The tire is likely to be in a nonlinear range, with the forces acting on the tire outside the adhesion ellipse. Therefore, the yaw angular acceleration at this point is designed to be a limit value.

[0084] In some embodiments, see Figure 3 The method of fitting the relationship between the vehicle longitudinal force and lateral force and the road adhesion limit value using a preset least squares algorithm to determine the adhesion ellipse includes:

[0085] S301, constructing a standard ellipse model according to the relationship curve between the longitudinal force and the lateral force of the vehicle;

[0086] S302, constructing a set of sample points having a preset algebraic distance from the polynomial to the standard ellipse model;

[0087] S303 , using a preset Lagrangian function and taking the minimum sum of algebraic distances in the sample point set as a goal, fitting the standard ellipse model to determine the attachment ellipse.

[0088] In this embodiment, the attachment ellipse is fitted using the nonlinear least squares method. This method is simple and practical, and is a common method for data fitting. Its basic concept is to minimize the systematic error while taking into account the influence of random noise. The least squares method assumes that the random noise error generated by the data points is normally distributed and uses maximum likelihood estimation to find the optimal value to minimize the variance of the error and minimize the distance metric between the measurement point and the fitted ellipse. The specific steps for fitting the attachment ellipse using the least squares method are as follows:

[0089] (1) Ellipse equation expression:

[0090] The equation of an ellipse is expressed in general form:

[0091]

[0092] (2) Simplification of fitting problem:

[0093] The polynomial F(x,y) represents the algebraic distance from the point (x,y) to the given ellipse, and the fitting goal is to minimize the sum of the algebraic distances of the data sample point set.

[0094] F(x,y)=ax 2 +bxy+cy 2 +dx+ey+f

[0095] The inequality constraint can be converted into an equality constraint by minimizing the distance deviation: 4ac-b 2 =1.

[0096] Let A = [a, b, c, d, e, f] T ,X=[x 2 ,xy,y 2 ,x,y,1], the ellipse fitting problem can be further expressed as:

[0097]

[0098] Where D is the n×6 data sample point set matrix:

[0099]

[0100] C is a 6×6 constant matrix:

[0101]

[0102] (3) Fitting calculation:

[0103] Design the Lagrangian function:

[0104] L(D,λ)=DAA T D T -λ(A T CA-1)

[0105] Let its partial derivative be zero:

[0106]

[0107] We can get:

[0108] D T DA=λCA

[0109] Let D T D=S,D T DA=λCA is transformed into SA=λCA, which is simplified to the form of solving eigenvalue:

[0110]

[0111] Solve to get its eigenvalue and eigenvector (λ i ,u i ), λ i >0 corresponding eigenvector u i These are the final attachment ellipse fitting parameters.

[0112] According to the fitted adhesion ellipse, the relationship between the longitudinal force and the lateral force under the adhesion limit condition can be known:

[0113] F y =G(F x )

[0114] The tire forces satisfy the constraints:

[0115]

[0116] When the vehicle's available adhesion reaches its limit, the inequality constraint can be converted into an equality constraint:

[0117]

[0118] Further simplification can be obtained as the longitudinal force F under the adhesion limit x_lim1 :

[0119]

[0120] At the same time, the driving force of the current vehicle operation state is affected by the maximum output torque F under the external characteristics of the motor. x_lim2 and the power output capacity F of the energy system x_lim3 Decide.

[0121] Among them, F x_lim2 The motor external characteristic curve is obtained and calculated by the following formula:

[0122]

[0123] In the above formula, I discharge is the maximum discharge current allowed by the BMS, U is the bus voltage of the motor, I incharge is the generator output current, n is the sum of the four-wheel motor speeds, and i0 is the reduction ratio of the hub motor reducer.

[0124] Therefore, the peak yaw angular acceleration is set to a point on the attachment ellipse, and satisfies:

[0125]

[0126] Combined with the vehicle yaw motion equation, the peak yaw angular acceleration is:

[0127]

[0128] This is used as the maximum change increment of the current control variable ω in the MPC controller.

[0129] In some embodiments, see Figure 4 The method of determining the desired yaw rate based on the preset MPC control algorithm and the limiting relationship between the yaw acceleration and the maximum yaw rate comprises:

[0130] S401: Construct a coordinate system with the center of mass of the vehicle as the origin, and establish a vehicle kinematic model based on the offset of the center of mass of the vehicle relative to the horizontal and vertical coordinates of the coordinate system and the yaw angle of the vehicle using an MPC controller;

[0131] S402: deforming the vehicle kinematic model using a preset Taylor expansion method to obtain an error model of the stationary steering vehicle;

[0132] S403: Determine a vehicle turning-in-place prediction model using a preset iterative method based on the principle of minimizing the vehicle's center of mass offset;

[0133] S404 : According to the control amount limit constraint and the control increment constraint, a preset quadratic programming algorithm is used to program the vehicle pivot turn prediction model to determine the desired yaw angular velocity.

[0134] In this embodiment, the upper controller includes a desired control input decider and a desired control input tracker. Based on the MPC controller, the vehicle's current center of mass position X, Y, γ is used as the state variable, and the center of mass offset e is minimized. y =YY r , e x =xx r For the control target, the motor external characteristics, the power output capacity of the energy system and the tire friction ellipse are used as the maximum yaw rate limit, and the current design expected yaw rate ω is calculated. rd .

[0135] In the ground fixed coordinate system XOY, the vehicle kinematic equation is:

[0136] Where x and y are the longitudinal and lateral displacements of the center of mass in the vehicle coordinate system, respectively, and γ is the vehicle yaw angle. r) and the state variables are χ(x, y, γ), the general form of the control system is Among them, the general form is Taylor expanded at the reference trajectory point and the high-order terms are ignored, and the error model of the stationary steering vehicle is obtained as follows:

[0137]

[0138] In order to enable the driver to control the desired yaw rate in the steering process in real time, the steering wheel angle input is added to the MPC controller. The desired yaw angle in the process of turning in place is controlled by the steering wheel angle to achieve the purpose of the driver in the loop. The desired yaw angle γ r The relationship between δ and the steering wheel angle is shown in the following formula, where δ is the driver's current steering wheel angle input; δ max is the maximum steering wheel angle, γ max is the maximum desired yaw angle, which is 2pi here. To ensure that the desired yaw rate remains unchanged under a fixed steering wheel angle input, that is, the yaw angle error remains constant under a fixed angle, the current yaw angle γ is added to the equation.

[0139]

[0140] At the same time, considering the effectiveness of turning in place, the center of mass offset of the vehicle should be small enough, and the expected center of mass offset should be close to zero, that is, x r =0,y r =0.

[0141] The forward Euler method is used to discretize the error model of the stationary steering vehicle:

[0142] χ(k+1)=Aχ(k)+Bu(k)

[0143] in T is the sampling time

[0144] Integrate the position error and control error into a new state quantity:

[0145]

[0146] The state space expression of the control increment at the next moment is obtained as follows:

[0147]

[0148] Where: C=[I Nx 0]; Nu is the number of control quantities; Nx is the number of state quantities.

[0149] By iterating the obtained state space expression based on the control increment, the future prediction equation of the system can be obtained as follows:

[0150] Y=ψξ(k)+ΘΔU

[0151] Where:

[0152] The problem is transformed into a quadratic programming problem with control quantity limit constraints and control increment constraints:

[0153]

[0154] stΔu min ≤Δu t ≤Δu max

[0155] u min ≤u t +A I Δu t ≤u max

[0156] Where: N p , N c Represent the output prediction range and control range respectively, and N p >N c ; ρ is the weight factor; ε is the relaxation factor; Q and R are weight matrices of a certain dimension; Δu t , Δu min , Δu max are the control increment and the upper and lower limits of the control increment at time t; u t 、u min 、u max is the control quantity and its upper and lower limits at time t; the first sum in the objective function reflects the expected performance of target tracking, and the second reflects the constraint on the control quantity.

[0157] Define the system output reference value as

[0158] Y r =[η r (k+1) η r (k+2) … η r (k+N c ) … η r (k+N p )] T =[0 0 … 0 … 0] T

[0159] Since the vehicle will become unstable when the lateral acceleration reaches 0.85μg, the upper and lower limits of the yaw rate are set to

[0160]

[0161] Solve the quadratic programming problem and obtain a series of control increments of longitudinal vehicle speed and yaw angular velocity in the control time domain. The first sample is the optimal control increment actually acting on the system, and the state feedback control law at the current moment is obtained as follows:

[0162]

[0163] u(t) is the desired vehicle speed and desired yaw rate required to achieve on-the-spot steering at the current moment.

[0164] In some embodiments, before determining the vehicle pivot turn prediction model, the method further includes:

[0165] determining a steering control model between the steering wheel angle input and the desired yaw rate according to the degree of influence of the steering wheel angle input on the desired yaw rate;

[0166] Integrating the steering control model and the pivot point steering vehicle error model to obtain a pivot point steering state quantity model;

[0167] The pivot point turning state quantity model is iterated to obtain a vehicle pivot point turning prediction model.

[0168] In some embodiments, see Figure 5 , determining the yaw moment value required to achieve the desired yaw angular velocity according to a preset sliding mode control algorithm includes:

[0169] S501, determining a correlation between the desired yaw rate and the sliding mode surface using a preset saturation function according to a speed at which the state point approaches the sliding mode surface;

[0170] S502 : Determine a yaw moment value required to achieve the desired yaw angular velocity according to the influence of the correlation between the desired yaw angular velocity and the sliding mode surface on the yaw motion of the vehicle.

[0171] In this embodiment, in order to improve the robustness and tracking performance of the system, the integral term of the yaw angular velocity is added to the system representation, and the sliding surface function is set to

[0172]

[0173] Where K is the parameter of the sliding surface function, and the derivative of the sliding surface function is obtained

[0174]

[0175] Using the constant velocity approach law

[0176]

[0177] Where ζ is the reaching law constant, indicating the rate at which the state point of the system approaches the sliding mode surface. In order to weaken the chattering phenomenon in the sliding mode control, the saturation function sat(s) is used instead of sgn(s).

[0178]

[0179] Where H is the boundary layer thickness.

[0180] Combining the above formula and the vehicle dynamics model, the additional yaw moment value is obtained as

[0181]

[0182] In some embodiments, the method of using a preset quadratic programming constraint solving algorithm to determine the expected driving force of each wheel of the vehicle includes:

[0183] Based on the adhesion ellipse, determine the adhesion rate of the tire;

[0184] Based on the minimization of the sum of the tire utilization variance and the four-wheel utilization as the optimization goal, and the four-wheel longitudinal force as the control variable, a preset quadratic programming model is used to determine the expected driving force of each wheel of the vehicle.

[0185] In this embodiment, the determined yaw moment is optimally distributed based on a quadratic programming algorithm. Based on the tire friction circle theory, the horizontal resultant force within the tire-ground contact surface is always less than the product of its vertical load and the friction coefficient. The tire adhesion rate is expressed by the following formula:

[0186]

[0187] Since the lateral force is uncontrollable in actual situations, the above formula is simplified to:

[0188]

[0189] In order to make full use of the adhesion performance of each wheel, the minimum sum of the tire utilization variance and the four-wheel utilization is selected as the optimization goal:

[0190]

[0191] Where: η i is the utilization rate of each tire, η ave is the average value of each tire utilization rate, and λ is the optimization target weighting coefficient.

[0192] Taking the four-wheel longitudinal force as the control variable, the optimization objective is rewritten into the form of a standard quadratic programming problem:

[0193]

[0194]

[0195] In the formula, the control quantity x=[F x1 F x2 F x3 F x4 ] T ;

[0196] The following constraints are introduced:

[0197] (1) Equality constraints

[0198] In order to minimize the center of mass offset during pivoting, the longitudinal acceleration should be close to 0. Taking the yaw moment constraint into consideration, the torque distribution equation constraint is obtained as follows:

[0199]

[0200] (2) Inequality constraints

[0201] The driving force of the four wheels is mainly related to the tire friction and the maximum torque that the motor can provide, so:

[0202] -min{μ i F Zi ,T lim i0 / r}≤F xi ≤min{μ i F Zi ,T lim i0 / r}

[0203] Where: i0 is the reduction ratio of the reducer, T lim Determined by the following formula:

[0204]

[0205] n i is the current motor speed, n b is the motor base speed.

[0206] The interior point method is used to solve the above quadratic programming problem, solve the optimal driving force of each wheel, and then obtain the output torque of the motor.

[0207] The technical solution of the present invention realizes the need of vehicle steering in a narrow space through a model predictive control algorithm based on the desired yaw angular velocity, greatly improving the steering maneuverability of the vehicle. Taking into account the driver's controllability of the vehicle steering speed during the vehicle's on-the-spot steering process, steering wheel angle control is added to the model predictive algorithm, and the maximum yaw angular velocity is limited by considering the motor output performance and the power output capacity of the energy system. At the same time, a yaw moment tracker based on the PI sliding mode enables the vehicle's yaw angular velocity to respond quickly and maintain a steady state at the desired yaw angular velocity. The tracking effect is relatively good, and the entire on-the-spot steering process has good robustness and stability. The driver can control the yaw angular velocity during the on-the-spot steering process in real time.

[0208] Based on the above-mentioned distributed vehicle in-situ steering control method, the embodiment of the present invention also provides a distributed vehicle in-situ steering control device 600. Figure 6 The distributed vehicle in-situ steering control device 600 includes an acquisition module 61, a yaw angular acceleration determination module 620, a desired yaw angular velocity determination module 630, a yaw moment value determination module 640 and a desired driving force determination module 650.

[0209] An acquisition module 610 is configured to acquire a maximum yaw angular velocity based on the motor output performance and the power output capability of the energy system;

[0210] a yaw acceleration determination module 620, configured to determine a yaw acceleration limit value of the maximum yaw angular velocity based on a correlation between the vehicle longitudinal force and lateral force and the road adhesion limit value;

[0211] a desired yaw rate determination module 630, configured to determine a desired yaw rate based on a preset MPC control algorithm and according to a limiting relationship between the yaw acceleration and the maximum yaw rate;

[0212] a yaw moment value determination module 640, configured to determine a yaw moment value required to achieve the desired yaw angular velocity according to a preset sliding mode control algorithm;

[0213] The expected driving force determination module 650 is used to distribute the yaw moment value to the four wheels of the vehicle based on the maximum output torque of the motor and adopt a preset quadratic programming constraint solving algorithm to determine the expected driving force of each wheel of the vehicle.

[0214] like Figure 7 As shown, based on the above-mentioned distributed vehicle in-situ steering control method, a vehicle-mounted device is also provided, including: a processor and a memory;

[0215] The memory stores a computer-readable program executable by the processor;

[0216] When the processor executes the computer-readable program, the steps in the distributed vehicle in-situ steering control method as described above are implemented.

[0217] On-board equipment also includes engine and powertrain centralized control systems, chassis integrated control and safety systems, intelligent body electronic systems, and communication and information / entertainment systems.

[0218] The engine and powertrain centralized control system includes the engine centralized control system, automatic transmission control system, anti-lock braking and traction control system, etc.; the chassis integrated control and safety system includes the vehicle stability control system, active body posture control system, cruise control system, collision warning system, driver intelligent support system, etc.; the intelligent body electronic system includes the automatic seat adjustment system, intelligent headlight system, vehicle night vision system, electronic door lock and anti-theft system, etc.; the communication and information / entertainment system includes the intelligent vehicle navigation system, voice recognition system, "ON STAR" system (with automatic emergency call and query functions), vehicle maintenance data transmission system, vehicle audio system, real-time traffic information consultation system, dynamic vehicle tracking and management system, information service system (including network, etc.), etc. The above only introduces some of the components of the vehicle control system, but it should be understood that implementation of all the components shown is not required, and more or fewer components may be implemented instead.

[0219] The present invention also provides a vehicle, including a distributed vehicle in-situ steering control device, and / or an on-board equipment system, as well as components such as a brake switch, a clutch switch, a transmission neutral switch and an engine.

[0220] Of course, those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a memory, a magnetic disk, an optical disk, etc.

[0221] The specific embodiments of the present invention described above do not limit the scope of protection of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A distributed vehicle in-situ steering control method, characterized in that: include: Obtaining the maximum yaw rate based on the motor output performance of the distributed vehicle and the power output capability of the energy system; determining a yaw acceleration limit value of the maximum yaw angular velocity according to a correlation between the vehicle longitudinal force and lateral force and the road adhesion limit value; Determining a desired yaw rate based on a preset MPC control algorithm and a limiting relationship between the yaw acceleration limit value and the maximum yaw rate; Determining a yaw moment value required to achieve the desired yaw angular velocity according to a preset sliding mode control algorithm; Based on the maximum output torque of the motor, a preset quadratic programming constraint solving algorithm is used to distribute the yaw moment value to the four wheels of the vehicle to determine the expected driving force of each wheel of the vehicle.

2. The distributed vehicle in-situ steering control method according to claim 1, characterized in that: Determining the yaw acceleration limit value of the maximum yaw angular velocity based on the correlation between the vehicle longitudinal force and lateral force and the road adhesion limit value includes: The preset least squares algorithm is used to fit the relationship between the vehicle longitudinal force and lateral force and the road adhesion limit value to determine the adhesion ellipse; determining the adhesion limit longitudinal force according to the adhesion ellipse; determining a current state longitudinal force of the vehicle based on the limit longitudinal force, a maximum output torque under the external characteristics of the vehicle motor, and a minimum value of the power output capability of the energy system; Based on the current longitudinal force, the position of the yaw angular acceleration limit value is determined according to the adhesion ellipse, and the yaw angular acceleration limit value is determined using a preset yaw motion equation.

3. The distributed vehicle in-situ steering control method according to claim 2, characterized in that: The method of fitting the relationship between the vehicle longitudinal force and lateral force and the road adhesion limit value using a preset least squares algorithm to determine the adhesion ellipse includes: Constructing a standard ellipse model according to the relationship curve between the longitudinal force and the lateral force of the vehicle; Constructing a set of sample points having a predetermined algebraic distance from the polynomial to the standard ellipse model; The preset Lagrangian function is used to fit the standard ellipse model with the minimum sum of algebraic distances in the sample point set as the goal, so as to determine the attachment ellipse.

4. The distributed vehicle in-situ steering control method according to claim 1, characterized in that: The method of determining the desired yaw rate based on the preset MPC control algorithm and according to the limiting relationship between the yaw acceleration limit value and the maximum yaw rate includes: Constructing a coordinate system with the center of mass of the vehicle as the coordinate origin, and establishing a vehicle kinematic model based on the offset of the vehicle center of mass relative to the horizontal and vertical coordinates of the coordinate system and the vehicle yaw angle based on the MPC controller; Deforming the vehicle kinematic model using a preset Taylor expansion method to obtain an error model of the stationary steering vehicle; Based on the principle of minimizing the center of mass offset of the vehicle, a preset iterative method is used to determine the vehicle's on-the-spot turning prediction model; According to the control amount limit constraint and the control increment constraint, a preset quadratic programming algorithm is used to plan the vehicle pivot turn prediction model to determine the expected yaw angular velocity.

5. The distributed vehicle in-situ steering control method according to claim 4, characterized in that: Before determining the vehicle pivot turn prediction model, the method further includes: determining a steering control model between the steering wheel angle input and the desired yaw rate according to the degree of influence of the steering wheel angle input on the desired yaw rate; Integrating the steering control model and the pivot point steering vehicle error model to obtain a pivot point steering state quantity model; The pivot point turning state quantity model is iterated to obtain a vehicle pivot point turning prediction model.

6. The distributed vehicle in-situ steering control method according to claim 1, characterized in that: Determining the yaw moment value required to achieve the desired yaw angular velocity according to a preset sliding mode control algorithm includes: Determining the correlation between the desired yaw rate and the sliding mode surface using a preset saturation function according to the speed at which the state point approaches the sliding mode surface; A yaw moment value required to achieve the desired yaw angular velocity is determined according to the influence of the correlation between the desired yaw angular velocity and the sliding mode surface on the yaw motion of the vehicle.

7. The distributed vehicle in-situ steering control method according to claim 1, characterized in that: The method of using a preset quadratic programming constraint solving algorithm to determine the expected driving force of each wheel of the vehicle includes: Based on the adhesion ellipse, determine the adhesion rate of the tire; Based on the minimization of the sum of the tire utilization variance and the four-wheel utilization as the optimization goal, and the four-wheel longitudinal force as the control variable, a preset quadratic programming model is used to determine the expected driving force of each wheel of the vehicle.

8. A distributed vehicle in-situ steering control device, characterized in that: include: An acquisition module, used to acquire a maximum yaw angular velocity based on the motor output performance and the power output capability of the energy system of the distributed vehicle; a yaw acceleration determination module, configured to determine a yaw acceleration limit value of the maximum yaw angular velocity based on a correlation between the vehicle longitudinal force and lateral force and the road adhesion limit value; a desired yaw rate determination module, configured to determine a desired yaw rate based on a preset MPC control algorithm and according to a limiting relationship between the yaw acceleration limit value and the maximum yaw rate; a yaw moment value determination module, configured to determine a yaw moment value required to achieve the desired yaw angular velocity according to a preset sliding mode control algorithm; The expected driving force determination module is used to distribute the yaw moment value to the four wheels of the vehicle based on the maximum output torque of the motor and adopt a preset quadratic programming constraint solving algorithm to determine the expected driving force of each wheel of the vehicle.

9. A vehicle-mounted device, characterized in that: include: processor and memory; The memory stores a computer-readable program executable by the processor; When the processor executes the computer-readable program, the steps in the distributed vehicle in-situ steering control method as described in any one of claims 1 to 7 are implemented.

10. A vehicle, characterized in that: It includes a distributed vehicle in-situ steering control device as described in claim 8, or a vehicle-mounted device as described in claim 9.

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