A method for coupling control of air suspension and active stabilizer bar

Abstract

The application discloses a kind of air suspension and active stabilizer bar cross-domain coupling control method, comprising the following steps: signal input layer gathers and fuses data transmission to decision layer;Decision layer is based on model predictive control algorithm and forms control instruction, including constructing automobile three degrees of freedom coupling dynamics model, control instruction is obtained by iterative solution cost function minimum value in rolling time domain;Three degrees of freedom coupling dynamics model is quantified multidimensional motion correlation by stiffness matrix off-diagonal line item, can accurately predict the cross influence of air suspension air pressure adjustment on roll movement, provides nonlinear prediction basis for model predictive control.The application realizes the collaborative optimization of vehicle vertical comfort, roll stability, pitch dynamic characteristics by multidimensional dynamics signal, dynamic arbitration control logic, adaptation complex composite working condition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control systems, in particular to an air suspension and active stabilizer bar coupling control method. BACKGROUND

[0002] During vehicle driving, vertical comfort (such as shock suppression when passing through a deceleration strip or a bumpy road), roll stability (such as body roll control when turning), and pitch dynamic characteristics (such as nodding or lifting when accelerating / braking) are key performance indicators that affect the driving experience and driving safety. Air suspension can optimize vertical stiffness and height by adjusting the air chamber pressure, and active stabilizer bar can adjust the torsional stiffness in real time by relying on a motor / hydraulic system. Both of them act on the vertical, roll / pitch motion dimensions and are the core actuators for improving vehicle dynamics performance.

[0003] In the prior art, air suspension and active stabilizer bar are usually controlled independently, that is, air suspension focuses on vertical vibration damping and is adjusted according to body vertical acceleration, suspension travel, etc.; active stabilizer bar focuses on roll and pitch torque suppression. However, the actual motion of the vehicle is a multi-dimensional coupled dynamic process of vertical, roll, and pitch, and independent control has the following defects:

[0004] 1) Coupling characteristics are not coordinated: when turning, if the vertical adjustment of the air suspension is not linked with the roll control of the active stabilizer bar, the vertical load transfer caused by body roll will interfere with the suspension comfort optimization, and vice versa; in the braking nodding working condition, the roll motion and the vertical vibration are superimposed on each other, and independent control may lead to attitude out of control and comfort conflict.

[0005] 2) Control logic is isolated: there is a lack of information exchange and dynamic arbitration across systems, and the stiffness adjustment of the air suspension may weaken the roll suppression effect of the active stabilizer bar, or the target conflict may lead to frequent action of the actuator, increasing energy consumption and hardware wear.

[0006] 3) Performance boundary is limited: in the face of complex working conditions (such as asymmetric road + turning + braking composite scene), independent control is difficult to balance vertical comfort, roll stability, and pitch dynamic characteristics, either sacrificing comfort to ensure stability, or causing rollover risk due to excessive suspension softening, and it is impossible to achieve multi-performance coordinated optimization. SUMMARY

[0007] In view of the defects of the prior art, the present application provides an air suspension and active stabilizer bar cross-domain coupling control method, which solves the problems of comfort and stability conflict in multiple working conditions, control logic isolation causing system internal consumption, and performance boundary limitation in complex scenarios when the vehicle air suspension and active stabilizer bar are controlled independently due to ignoring the cross-domain coupling characteristics of vertical, roll, and pitch motion.

[0008] The technical solution of the present application is as follows:

[0009] A method for coupling control of air suspension and active stabilizer bar, comprising the following steps:

[0010] The signal input layer collects and fuses data and transmits the data to the decision layer, wherein the data transmitted to the decision layer includes road curvature, road roughness, longitudinal acceleration, lateral acceleration, vertical acceleration, roll angular velocity and pitch angular velocity of the vehicle;

[0011] The decision layer forms a control instruction based on a model predictive control algorithm, including constructing a three-degree-of-freedom coupled dynamics model of the vehicle, and iteratively solving a minimum value of a cost function in a rolling time domain to obtain the control instruction; the three-degree-of-freedom coupled dynamics model of the vehicle is:

[0012] ,

[0013] ,

[0014] , , ,

[0015] wherein, is the vertical displacement of the vehicle body mass center, is the vertical velocity, is the vertical acceleration of the vehicle body, is the roll angle of the vehicle body, is the roll angular velocity, is the roll angular acceleration, is the pitch angle of the vehicle body, is the pitch angular velocity, is the pitch angular acceleration, is the mass-inertia matrix, is the damping matrix, is the stiffness matrix, is the control distribution matrix, is an external excitation term, the damping matrix and the stiffness matrix are asymmetric matrices, the off-diagonal elements of the damping matrix include vertical- roll coupling damping, vertical-pitch coupling damping, roll angular velocity induced vertical force coupling damping and pitch angular velocity induced vertical force coupling damping, and the off-diagonal elements of the stiffness matrix include vertical-pitch coupling stiffness, vertical-roll coupling stiffness, roll angle induced vertical force coupling stiffness and pitch angle induced vertical force coupling stiffness; is the control instruction, is the air spring pressure change, is the active stabilizer bar output torque, is the pitch actuator control torque;

[0016] The cost function is a combination of vehicle state terms and control command terms. The vehicle state terms are a dynamically weighted sum of vehicle vertical acceleration, roll angle, pitch angle, vertical velocity, roll angular velocity, and pitch angular velocity, with the dynamic weights determined by the vehicle's operating conditions. By dynamically adjusting the priorities of objectives such as vertical comfort, roll stability, and pitch suppression, the system can achieve an optimal balance between comfort and stability under different operating conditions, adapting to all scenarios and alleviating the problems of isolated control logic and limited performance boundaries.

[0017] Furthermore, the cost function is

[0018] ,

[0019] in, To predict the time domain, To control the time domain, This represents the maximum vertical acceleration. This represents the maximum roll angle. This represents the maximum pitch angle. This represents the maximum vertical velocity. This represents the maximum roll rate. This represents the maximum pitch angular velocity. To dynamically adjust the weighting coefficients of each objective priority, This is the actuator energy consumption weight matrix. Representing the current moment, Represents an integer index variable. Represents the prediction of the future starting from the current moment. A specific point in time.

[0020] Furthermore, , , , , , , , , , , ,

[0021] in For the lateral acceleration of the vehicle, For vehicle speed, For the longitudinal acceleration of the vehicle, This represents the rate of change of air suspension pressure. This represents the maximum pressure change rate of the air suspension. This is the maximum output torque of the stabilizer bar. For the pitch coordinator torque variation rate, The maximum torque change rate of the pitch coordinator. , , , , are calibration coefficients.

[0022] Further, when iteratively solving the minimum value of the cost function in the rolling time domain, the following constraints are set: vehicle body roll angle constraint, vehicle body mass center vertical acceleration constraint, air spring pressure change constraint, air spring pressure change rate constraint, active stabilizer bar output torque constraint and active stabilizer bar output torque change rate constraint. Ensure that the output control instruction meets the physical limit and engineering safety constraint of all actuators, which can be directly and safely applied to the actuator. Prevent the actuator from overshooting or the vehicle from losing stability, ensure the safe and reliable operation of the system, avoid the problems of comfort decline, stability risk increase and other problems caused by control instructions exceeding constraints, and further solve the performance boundary limited problem.

[0023] Further, a feedforward compensation is set for the control instruction, which includes road preview compensation = , curve roll feedforward and pitch feedforward of braking / acceleration , wherein, is a calibration coefficient, is the road roughness previewed in the future time, t represents the current time, is the roll moment of inertia of the vehicle body, is the vehicle speed, is the previewed road curvature, and r is the tire radius, is the sprung mass, is the longitudinal acceleration, is the mass center height.

[0024] Further, when the signal input layer collects and fuses the data transmission to the decision layer, the original road curvature and the vehicle speed are collected to fuse and construct the road curvature observation value and perform Kalman filtering estimation to obtain the road curvature transmitted to the decision layer. The road curvature observation value is: , is the original road curvature, is the lateral acceleration, is the vehicle speed, is the dynamic weight based on visibility adjustment. This scheme realizes accurate curvature estimation in all working conditions through dynamic weight allocation and signal decoupling, and solves the inherent defects of single sensor in curvature detection.

[0025] Further, when the signal input layer collects and fuses data transmission to the decision layer, the side inclination angle velocity collected and the predicted side inclination angle output by the three-degree-of-freedom coupled dynamics model of the automobile are used to construct a side inclination angle observation value and perform Kalman filtering estimation to obtain a side inclination angle transmitted to the decision layer, the side inclination angle observation value is: , is the side inclination angle calculated by the collected side inclination angle velocity, is the predicted side inclination angle, is a fixed weight coefficient.

[0026] Further, the implementer layer is used to convert the control instruction into a physical quantity directly controllable by each implementer bottom layer through an optimization objective function, and then convert the physical quantity into a driving signal of each implementer for control.

[0027] The physical quantity directly controllable by each implementer bottom layer is:

[0028] ,

[0029] wherein, is a pressure change amount of the four air springs of the suspension, FL represents the left front, FR represents the right front, RL represents the left rear, and RR represents the right rear, is a main stabilizer bar motor control current, is a pitch hydraulic actuator pressure difference,

[0030] The objective function is:

[0031] ,

[0032] wherein, is a working condition adaptive weight matrix, B is a linear mapping matrix constructed based on an implementer dynamics model, is a matrix for punishing excessive action of the implementer, is a smoothing matrix, is a control instruction output by the decision layer, is a last period instruction of the directly controllable physical quantity.

[0033] Further, , , , , is a weight of vertical stiffness control, is a vertical impact frequency, is a vertical basic weight, is a frequency sensitivity coefficient, is a vertical resonance frequency of the vehicle, is a side inclination basic weight, is a side inclination sensitivity coefficient, a safety roll strength threshold, a roll strength coefficient, a deceleration coefficient, a safety deceleration threshold, a deceleration sensitivity coefficient, a pitch base weight;

[0034] weight coefficients of front and rear air suspension air pressure control quantities, respectively, satisfying a weight coefficient of active stabilizer bar motor current control quantity, a weight coefficient of hydraulic actuator pressure control quantity;

[0035] a smoothing weight coefficient of air suspension air pressure change quantity, which is a monotonic increasing function of vehicle speed a smoothing weight coefficient of active stabilizer bar motor current change quantity, which is a monotonic increasing function of vehicle speed a smoothing weight coefficient of hydraulic actuator pressure change quantity, which is a monotonic increasing function of vehicle speed

[0036] Compared with the prior art, the present application has the following advantages:

[0037] 1. Cross-domain collaborative control is realized: through a three-degree-of-freedom coupling model and an MPC algorithm, the target conflict problem when air suspension and stabilizer bar are independently controlled is fundamentally solved, and collaborative optimization of vertical, roll and pitch movements is realized.

[0038] 2. The system adaptive ability is improved: a dynamic arbitration mechanism is adopted, which can intelligently adjust the control target weight according to real-time working condition information such as vehicle speed and acceleration, so that the system can achieve the best balance between comfort and stability and adapt to all scene working conditions.

[0039] 3. Anti-interference and foresight ability are enhanced: feedforward compensation is provided by using visual preview information, combined with feedback control, which significantly improves the system's suppression ability for predictable disturbances such as road undulations, curves, braking / acceleration, etc., and reduces response delay.

[0040] 4. The robustness and reliability of the control system are guaranteed: detailed dynamic models, real-time constraint processing and fault tolerance strategies are designed at the actuator layer, which ensures the physical realizability of the control command, and when some components fail, the vehicle can maintain basic stability through redundant control.

[0041] ​​​​​​In summary, the application realizes the synergistic optimization of the vehicle vertical comfort, roll stability and pitch dynamic characteristics by synergizing multi-dimensional dynamic signals, dynamic arbitration control logic and adapting complex compound working conditions, breaks through the performance bottleneck of traditional independent control and improves the vehicle all-scene driving quality and system energy efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 A vertical displacement response simulation result diagram of the air suspension and active stabilizer bar cross-domain coupling control method of the embodiment.

[0043] Figure 2 A roll angle response simulation result diagram of the air suspension and active stabilizer bar cross-domain coupling control method of the embodiment.

[0044] Figure 3 A pitch angle response simulation result diagram of the air suspension and active stabilizer bar cross-domain coupling control method of the embodiment.

[0045] Figure 4 A vertical acceleration response simulation result diagram of the air suspension and active stabilizer bar cross-domain coupling control method of the embodiment. DETAILED DESCRIPTION

[0046] The application will be further described below in conjunction with the embodiments, but not as a limitation to the application.

[0047] The air suspension and active stabilizer bar cross-domain coupling control method of the embodiment mainly includes that the signal input layer collects and fuses data and transmits to the decision layer, the decision layer forms a control instruction based on a model predictive control algorithm, the actuator layer converts the control instruction into physical quantities directly controllable by each actuator bottom layer through an optimization objective function, and then converts the physical quantities into driving signals of each actuator for control. The processing process of each layer will be specifically described below.

[0048] The signal input layer includes a sensor layer and a data fusion layer.

[0049] The sensor layer includes a binocular vision system, a 6-axis IMU (inertial measurement unit), a stabilizer bar torque sensor, an air pressure sensor, a wheel speed sensor and a steering wheel angle sensor. The data measured by the sensor layer is transmitted to the decision layer as an input signal.

[0050] The binocular vision system is mainly used to measure the curvature of the road in front of the vehicle and the road unevenness . The vision system adjusts its preview distance and preview time according to the change of the vehicle speed. Therefore, its main function is to realize the advanced control optimization of the vehicle dynamic state by providing preview information to the system.

[0051] 6-axis IMU, mainly measures the longitudinal, lateral and vertical acceleration of the vehicle, as well as the roll and pitch angular velocity of the vehicle. The data measured by the 6-axis IMU is transmitted to the decision layer as the driving state of the vehicle.

[0052] Stabilizer bar torque sensor, mainly used to measure the actual output torque of the active stabilizer bar.

[0053] Air pressure sensor, mainly used to measure the pressure of the air spring chamber.

[0054] Wheel speed sensor, real-time monitoring of each wheel speed.

[0055] Data fusion layer, realized by Kalman filtering algorithm, output roll angle , road curvature and other decision parameters.

[0056] 1) Fusion method of road curvature K.

[0057] In order to solve the inherent defects of single sensor in curvature detection, this system adopts hierarchical confidence fusion strategy, realizes accurate curvature estimation in all working conditions through dynamic weight distribution and signal decoupling. Specifically, the following steps are included:

[0058] Step 1: receive the original curvature of binocular vision system and the original lateral acceleration signal of IMU;

[0059] Step 2: design dynamic weight distribution mechanism:

[0060] ,

[0061] is the weight of the original curvature signal of binocular vision system. When the visibility is < 50m, the vision sensor is greatly disturbed by the environment, take ; in other working conditions, the vision sensor is reliable, take . The above range is calibrated through multi-condition test, covering the reasonable parameter interval of similar scenes.

[0062] Step 3: mixed observation value construction:

[0063] ,

[0064] In the formula, is the measurement value of curvature fusion, is the lateral acceleration signal (m / s 2 ) output by IMU, is the wheel speed value (m / s) fused by wheel speed. The calculation formula of vehicle speed is as follows:

[0065]

[0066] where R represents the effective tire diameter (m), represents the rotation speed of each tire (rad / s).

[0067] Step 4: Optimization of Kalman Filter. Specifically includes the following steps:

[0068] (1) State prediction: ( = 1, i.e., using a first-order uniform speed model).

[0069] (2) Covariance prediction and Kalman gain calculation:

[0070] , ,

[0071] where, is the error covariance matrix, Kalman gain matrix, is the observation matrix ( ), is the covariance matrix of process noise, representing the suddenness of road curvature change, which can be calibrated through experiments, and the value range is generally . is the observation noise covariance matrix, representing the reliability difference between visual curvature and IMU dynamic curvature, which can be jointly calibrated through double moving line working condition simulation and real vehicle step input test, and the value range is , .

[0072] (3) State update and covariance matrix update.

[0073] , .

[0074] (4) Boundary constraints: ,

[0075] where, is the preset or real-time estimated road adhesion coefficient, which is taken as here, which can ensure more than 95% of the road working conditions, and reserves a safety boundary (good road adhesion coefficient can reach 0.7-0.8, taking the lower limit). is the acceleration of gravity.

[0076] 2) Observation fusion method of roll angle .

[0077] To solve the problem of IMU angular velocity integral drift and model prediction lag, a fixed weight based pre-fusion Kalman filter architecture is proposed. Specifically, the following steps are included:

[0078] Step 1: Observation construction.

[0079] ,

[0080] In the formula, is the roll angle obtained by integrating the roll angular velocity of the IMU, i.e. , is the roll angular velocity measured by the IMU (in practical applications, discrete equations can be used). is the roll angle predicted based on a three-degree-of-freedom vehicle model (this model is established in the later section). is the weight coefficient, the value range is 0.7 0.9, which is an empirical calibration weight, tending to let the IMU, which has good real-time performance but is prone to drift, dominate, and the predicted value based on the physical constraint model is supplemented. The weight can be adjusted according to the actual working conditions.

[0081] Step 2: Optimize state estimation through Kalman filter.

[0082] (1) State prediction: ( ),

[0083] where, is the prior roll angle at time , and is the roll angle predicted based on the above vehicle dynamics model.

[0084] (2) Covariance prediction and Kalman gain calculation:

[0085] ,

[0086] ,

[0087] In the formula, is the process noise covariance, representing the unpredictable disturbance of the vehicle body roll motion, which needs to be calibrated through experiments, the value range of is [0.02, 0.06]. is the observation matrix, which is also . is the observation noise covariance matrix, representing the difference in reliability between the IMU integral drift and the vehicle model prediction, which can be calibrated through experiments, and the value range is ,

[0088] (3) Posterior state update and posterior covariance update:

[0089] , ,

[0090] The decision layer includes a vehicle three-degree-of-freedom coupling model, a dynamic arbitration mechanism, an MPC controller, and a preview coordination decision.

[0091] The vehicle three-degree-of-freedom coupling model serves as a prediction model of the MPC algorithm, and is used to accurately predict the vehicle state in a rolling time domain, thereby providing a basis for optimization solution. The model quantifies the multi-dimensional motion correlation through the non-diagonal line items of the stiffness matrix, thereby providing a nonlinear prediction basis for the subsequent MPC (model predictive control). Unlike traditional linear models, the model can accurately predict the cross-influence of air suspension pressure adjustment on roll motion, so that the optimization sequence of the MPC is more in line with the real dynamics characteristics of the vehicle. The specific steps are as follows:

[0092] 1) Definition of system state vector.

[0093] A three-degree-of-freedom coupling model is constructed, and the vertical displacement of the vehicle body mass center , the roll angle of the vehicle body , and the pitch angle of the vehicle body are selected as the core motion parameters, and the system state vector and its derivative are defined as follows:

[0094] , , ,

[0095] Among them, reflects the compression or extension state of the vehicle suspension; characterizes the roll degree around the longitudinal axis when the vehicle is turning; embodies the pitch change around the transverse axis when the vehicle is accelerating or braking.

[0096] 2) Establishment of a three-degree-of-freedom coupling dynamics model of the automobile.

[0097] The traditional scheme decouples the vertical (air suspension), roll / pitch (stabilizer bar) control, and ignores the mechanical correlation of the three (such as the transfer of vertical load when rolling, which will react on the suspension stiffness). The equation unifies the three-degree-of-freedom motion, and quantifies the “vertical-roll coupling” (the correlation between vertical force and roll angle caused by the difference in left and right suspension stiffness) through the non-diagonal line items of the stiffness matrix, thereby revealing the root cause of the cross-domain dynamics conflict at the model level. The three-degree-of-freedom coupling dynamics model of the automobile is as follows:

[0098] ,

[0099] ,

[0100] where, represents the air spring pressure variation (kPa), which is the dynamic adjustment of the gas pressure in the air spring cavity, used to adjust the suspension vertical stiffness and vehicle body height (m); is the active stabilizer bar output torque (N·m), which directly suppresses the vehicle body roll angle and reduces the risk of cornering roll; is the pitch actuator control torque (N·m), which suppresses the pitch motion during acceleration / braking. is the mass-inertia matrix, is the damping matrix, is the stiffness matrix, is the control allocation matrix, is the external excitation term.

[0101] , , ,

[0102] , ,

[0103] where, is the sprung mass (kg), is the roll moment of inertia (kg·m²), is the pitch moment of inertia (kg·m²).

[0104] The off-diagonal terms in the stiffness matrix represent the coupled asymmetry. Among them, represents the total vertical stiffness (N / m), is the vertical-pitch coupling stiffness (N / rad); is the vertical-roll coupling stiffness (N / rad); is the roll angle-induced vertical force coupling stiffness (N / rad), is the pitch angle-induced vertical force coupling stiffness (N / rad), is the equivalent roll stiffness (N·m / rad), is the equivalent pitch stiffness (N·m / rad). and characterize the core ability of the suspension system to generate anti-roll and anti-pitch moments, which are the key to coupled control, so they are retained and accurately modeled. The terms and characterize the secondary feedback effect of the vehicle body posture on the vertical force, which is much smaller in magnitude than the main coupling terms and control objectives, so they can be approximately ignored ( , ) under the premise of ensuring accuracy to simplify the controller design.

[0105] Similarly, the off-diagonal terms of the damping matrix also follow the asymmetric coupling relationship. Among them, is the vertical damping (N·s / m), is the roll damping (N·m·s / rad), which describes the ratio of damping torque to angular velocity in roll motion. is the pitch damping (N·m·s / rad), which describes the ratio of damping torque to angular velocity in pitch motion. is the coupling damping coefficient between vertical and roll; is the coupling damping coefficient between vertical and pitch. is the vertical force coupling damping induced by roll angular velocity (N·s / rad), is the vertical force coupling damping induced by pitch angular velocity (N·s / rad). Based on the same simplification principle as the stiffness matrix, the coupling damping terms and can also be ignored (i.e. , ), while the core terms and are retained.

[0106] is the stabilizer bar force arm (m), is the pitch actuator force arm (m).

[0107] In the expression, represents the vertical excitation force of road unevenness (N), represents the vehicle mass center height (m), represents the roll excitation torque of road (N·m). is the pitch excitation torque of road (N·m).

[0108] ,

[0109] ,

[0110] ,

[0111] is the road vertical unevenness input is the lateral acceleration (m / s 2 ), is the longitudinal acceleration (m / s 2 ), is the pitch control efficiency coefficient. is the front axle suspension vertical force (N), is the rear axle suspension vertical force (N). is the air spring effective area (m2), is the stabilizer bar force arm length (m), is the pitch lever ratio, and .

[0112] ,

[0113] ,

[0114] ,

[0115] ,

[0116] ,

[0117] ,

[0118] ,

[0119] ,

[0120] ,

[0121] is the vertical critical damping ratio (dimensionless), which needs to be calibrated through experiments, and in engineering, it is taken as [0.2, 0.4]. is the roll critical damping ratio (dimensionless), which needs to be calibrated through experiments, and in engineering, it is taken as [0.1, 0.3]. is the pitch critical damping ratio, which needs to be calibrated through experiments. Generally it is taken as [0.15, 0.35]. is the additional roll stiffness coefficient provided by the active stabilizer bar (N·m / rad), is the front left suspension vertical stiffness (N·m), is the front right suspension vertical stiffness (N·m), is the rear left suspension vertical stiffness (N·m), is the rear right suspension vertical stiffness (N·m), is the front track (distance between the centers of the left and right wheels) (m), is the rear track (m), is the horizontal distance from the front axle to the center of mass (m), is the horizontal distance from the rear axle to the center of mass (m). is the moment of inertia of the vehicle sprung mass about the X axis (vehicle longitudinal axis) (kg / m 2 ), is the moment of inertia of the vehicle sprung mass about the Y axis (vehicle transverse axis) (kg / m 2 ), are damping coefficients of front left and right wheel suspensions (N·s / m), respectively; are damping coefficients of rear left and right wheel suspensions (N·s / m), respectively.

[0122] A dynamic arbitration mechanism is used to convert multiple targets such as vertical comfort, lateral handling, and tire load stability into quantifiable cost functions through the establishment of a unified mathematical model, and to realize a dynamic collaborative control paradigm by dynamically adjusting the target priority based on real-time working conditions.

[0123] Under different working conditions, the vehicle has different concerns for vertical comfort, roll stability, and pitch suppression, and the traditional control method is difficult to dynamically adjust the target priority, resulting in inconsistent control performance or resource waste. The present application designs a dynamic arbitration mechanism to drive the controller to realize real-time adjustment of the target weight, improving the control adaptability.

[0124] The dynamic arbitration module receives the vehicle state (vehicle speed, lateral acceleration, longitudinal acceleration) in real time, generates a state weight vector, and the cost function The following discrete form is used:

[0125] ,

[0126] In the formula, is the prediction time domain, is the control time domain. is the vertical acceleration of the vehicle body, is the maximum vertical acceleration, which is taken as 1.5 m / s 2 ; is the roll angle, reflecting the degree of roll in a curve, is the maximum roll angle, taken as =5°; is the pitch angle, representing the degree of lifting the head when accelerating or lowering the head when decelerating, is the maximum pitch angle, taken as =4°; is the vertical velocity, is the maximum vertical velocity, taken as =0.5 m / s; is the roll angular velocity, is the maximum roll angular velocity, taken as ; is the pitch angular velocity, is the maximum pitch angular velocity, taken as ; is the aforementioned control input, corresponding to the air suspension pressure change, stabilizer bar torque, and pitch control torque, respectively. To dynamically adjust the weight coefficients of each target priority, To suppress invalid actions for the actuator energy consumption weight matrix. represents the current time, represents an integer index variable, represents the first time point predicted from the current time to the future.

[0127] The weight value adopts an adaptive mechanism:

[0128] The formula indicates decreases with the increase of lateral acceleration (intense driving temporarily reduces the priority of comfort).

[0129] The formula indicates increases with the increase of vehicle speed (high-speed curve needs to strengthen roll suppression).

[0130] , where is the longitudinal acceleration of the vehicle, is the calibration coefficient, which is verified by simulation and has a value range of 3-5. The formula indicates the larger , the closer to 1 (pitch priority); when driving smoothly, , release the weight to other targets.

[0131] characterizes the square weighting of the vertical speed, is the vertical speed weight coefficient, which optimizes the suspension dynamic travel and tire ground performance.

[0132] is the square weighting of roll speed, is the roll speed weight coefficient, which reduces the risk of rollover.

[0133] is the square weighting of pitch speed, is the pitch speed weight coefficient, which improves the stability of the attitude.

[0134] The value of

[0135] , , Here is designed as an adaptive coefficient dynamically adjusted according to the working conditions, i.e.: , which indicates that should be increased to strengthen speed constraints when the speed is large.

[0136] Energy consumption weight The physical characteristics of the actuators to be fitted (air suspension, stabilizer bar motor, pitch coordinator) have different energy consumption modes. The design core is to prioritize low-power control and punish invalid actions to balance control performance and energy consumption. The specific rules include:

[0137] 1) Air suspension energy consumption weight (pressure regulation).

[0138] Air compressor power consumption and pressure change rate Strong correlation (high frequency / large pressure adjustment will sharply increase energy consumption), therefore design:

[0139] ,

[0140] In the formula, is the maximum pressure change rate of the air suspension, which is determined by the air suspension hardware and can be calibrated by experiment. , is the calibration coefficient, which is verified by simulation and has a value range of 0.8, 0.2.

[0141] 2) Stabilizer bar motor energy consumption weight (torque output).

[0142] Stabilizer bar motor power consumption and torque square Positive correlation (the larger the torque, the higher the heat / energy consumption), therefore design :

[0143] ,

[0144] In the formula, is the maximum output torque of the stabilizer bar, which is determined by the technical parameters of the stabilizer bar and can be calibrated by experiment. is the calibration coefficient, which can be optimized according to the energy consumption-performance balance. Here =1.

[0145] 3) Pitch coordinator energy consumption weight (torque output).

[0146] Pitch coordinator (such as active suspension cylinder, torque motor) energy consumption and torque change rate Strong correlation (high frequency torque adjustment will increase hydraulic or motor loss), therefore design :

[0147] ,

[0148] In the formula, The maximum moment change rate of the pitch coordinator is determined by the system hardware and can be calibrated through experiments. The calibration coefficient is mainly used to balance energy consumption and response speed, which can be optimized through simulation or experiments. Here 0.6 is taken.

[0149] Based on the MPC rolling optimization strategy, the dynamic coupling relationship of vehicle vertical-roll-pitch motion is accurately predicted based on the three-degree-of-freedom coupling model, the working condition adaptive weight output by the dynamic arbitration mechanism is fused, and the minimum value of the cost function is iteratively solved in the rolling time domain to realize the cooperative optimization of air suspension pressure regulation and active stabilizer bar torque distribution. Through the closed-loop correction of feedforward preview information and feedback state error, the control strategy can not only anticipate road disturbances, but also adapt to changes in vehicle dynamics in real time, breaking through the adaptive limitations of traditional control of multi-actuator coupling conflicts and dynamic switching of working conditions.

[0150] The execution process of the MPC rolling optimization strategy is as follows:

[0151] Step 1, discretization and prediction initialization of three-degree-of-freedom coupling model.

[0152] 1) Establish a continuous-time model.

[0153] First, define the extended variable . From the three-degree-of-freedom coupled dynamics equation of the vehicle , a continuous-time state space model is established:

[0154] ,

[0155] where, is the system matrix, is the control input matrix, is the disturbance vector.

[0156] , ,

[0157] , ,

[0158] ,

[0159] , .

[0160] 2) Use the Euler discretization method to convert the continuous-time model to a discrete state space form.

[0161] ,

[0162] is the discretized state vector (contains displacement, angular velocity information), is the system state transition matrix, and , is the sampling period, which is taken as . is the model prediction noise.

[0163] ,

[0164] .

[0165] 3) Substitute the above cost function again.

[0166] ,

[0167] The vertical acceleration in the cost function is processed by the velocity difference:

[0168] .

[0169] 4) Recurrence time domain optimization solution (optimal control quantity generation).

[0170] (1) Constraint setting.

[0171] To avoid actuator overshoot or vehicle instability, the following constraints are set:

[0172] State constraints: roll angle (prevent rollover); vertical acceleration (comfort threshold);

[0173] Control constraints: air suspension air pressure change (actuator physical limit); stabilizer bar torque (motor power limit);

[0174] Rate constraints: air pressure change rate (avoid suspension impact); torque change rate (motor response limit).

[0175] (2) Quadratic programming (QP) to solve the optimal control sequence.

[0176] Let be the predicted state variable, and construct the state sequence and control sequence within the prediction horizon.

[0177] Predicted state sequence: .

[0178] Control sequence: .

[0179] where, As before, .

[0180] By recursively unfolding the prediction equation:

[0181] ,

[0182] ,

[0183] ,

[0184] The cost function can be reorganized as:

[0185] ,

[0186] where the weight matrix = blkdiag( ) is the state weight matrix in the prediction horizon, realizing real-time switching of multi-objective priorities, where contains dynamic weights (updated in real time by the arbitration mechanism). = blkdiag( ) is the control weight block diagonal matrix, as above.

[0187] Feedforward compensation integration.

[0188] Preview cooperative decision-making uses preview information (road curvature, unevenness) to adjust control parameters in advance. The output of the preview cooperative decision-making is used as a known feedforward term to offset disturbances in the prediction equation.

[0189] The traditional technical solution relies on real-time feedback (such as adjusting the stabilizer bar torque after the roll angle exceeds the threshold), which cannot respond to predictable disturbances (such as a curve ahead or a bumpy road) in advance, resulting in delayed response (control action lags behind disturbance arrival). Facing complex working conditions, independent control is difficult to balance multiple objectives (comfort, stability, safety), often resulting in frequent actuator action or performance compromise (such as sacrificing comfort to maintain stability) due to priority conflicts. Therefore, a preview cooperative technology is designed.

[0190] Through binocular vision and vehicle dynamics sensing, the future disturbance of the road is generated to generate a feedforward control quantity to offset the dynamic effects of the road, curve, and braking in advance, including:

[0191] 1) Road preview compensation (vertical comfort).

[0192] = ,

[0193] where is the feedforward compensation term for the air spring pressure change; For calibration coefficient, it needs to be calibrated by real vehicle, and the value is (50~100kPa / m); For binocular vision preview future The road roughness (m) at the moment is processed by low-pass filtering. The meaning of this formula is to adjust the air suspension pressure in advance to offset the road impact that will arrive soon and reduce the vertical acceleration 。

[0194] 2) Bend roll feedforward (roll stability).

[0195] ,

[0196] In the formula, is the feedforward compensation item of the output torque of the active stabilizer bar; is the roll moment of inertia of the vehicle body (Iy) , determined by the three-degree-of-freedom model of the rear section); is the vehicle speed, is the aforementioned fused preview road curvature; r is the tire radius.

[0197] 3) Braking / acceleration pitch feedforward (pitch dynamic characteristic).

[0198] ,

[0199] In the formula, is the pitch feedforward torque compensation item of braking / acceleration; is the sprung mass, is the longitudinal acceleration (sensed in real time by the IMU); is the height of the center of mass (m).

[0200] The actuator layer is a key link for converting the control instructions output by the decision layer into physical actions, and includes the following core modules: actuator dynamic characteristic modeling, control quantity distribution algorithm, real-time constraint processing, actuator interface conversion module, and fault tolerance strategy.

[0201] Actuator dynamic characteristic modeling is mainly to clarify the response delay, physical constraints and nonlinear characteristics of each actuator. Through the above three-degree-of-freedom coupled model, the control requirements of the vehicle roll, vertical and pitch motion on the suspension / stabilizer bar actuator (such as the need to dynamically adjust the vertical stiffness with the roll angle) are clarified. The following models the air suspension actuator, active stabilizer bar actuator and pitch control actuator, establishes their physical characteristic models, and provides a basis for the physical implementation of the control instructions.

[0202] 1. Actuator dynamic characteristic modeling.

[0203] 1) Air suspension actuator modeling.

[0204] (1) Vertical stiffness model.

[0205] Air spring is a typical nonlinear spring structure, total vertical stiffness can be calculated by the following formula:

[0206] ,

[0207] represents the basic gas stiffness of the i-th air spring at the initial air pressure , represents the basic mechanical stiffness of the rubber base and sealing structure of the i-th air spring, represents the air pressure change of the i-th air spring, represents the real-time air pressure, is the polytropic index of the gas, and here =1.3. These parameters of the air spring can be calibrated by the laboratory.

[0208] (2) Air pressure dynamic response model.

[0209] The air pressure change rate is limited by the air compressor power, and satisfies the first-order lag characteristic:

[0210] ,

[0211] In the formula, is the air pressure change of the i-th air spring (kPa), that is, the difference between the target air pressure and the initial air pressure output by the decision layer; is the response time constant, usually 0.5s~1.5s (large flow compressor takes a large value, and small flow takes a small value), is the gain coefficient, which is 200~500kPa / V by experimental calibration, is the control voltage (V) of the actuator driving circuit, the range is 0~5V, and the target air pressure change quantity obtained by the decision layer MPC optimization is backstepped.

[0212] (3) Physical constraints

[0213] Air pressure range: [-0.5bar, 0.5bar] to avoid air chamber overpressure rupture or negative pressure collapse;

[0214] Air pressure change rate: [-0.3bar / s, 0.5bar / s]) to prevent suspension impact or compressor overload.

[0215] 2) Active stabilizer bar actuator modeling.

[0216] The active stabilizer bar is driven by "permanent magnet synchronous motor + reduction mechanism", which suppresses the body roll by outputting torque. The core is the modeling of torque transmission and dynamic response:

[0217] (1) Torque output model.

[0218] The relationship between the actual output torque of the stabilizer bar and the motor control current is as follows, considering the transmission efficiency and friction torque:

[0219] ,

[0220] In the formula, is the transmission efficiency of the reduction mechanism, considering gear meshing and bearing friction, taking 0.85~0.95; is the reduction ratio, determined by the mechanical structure, usually taking 15~30 (converts the motor's high speed and low torque into the stabilizer bar's low speed and high torque); is the motor torque constant (N・m / A), determined by the motor parameters, taking 2.5~4.0 N・m / A; is the motor control current command (A), determined by the decision-making layer target roll torque backstepping ( ); is the stabilizer bar torsional friction torque (N・m), generated by the bushing and bearing resistance, taking 5~15 N・m; is the roll angular velocity direction symbol (6-axis IMU measurement), ensuring that the friction torque and the torsional direction are opposite.

[0221] (2) Dynamic response model.

[0222] The motor inductance and the inertia of the reduction mechanism cause the torque output to have a first-order lag:

[0223] ,

[0224] In the formula, is the response time constant (s), affected by the motor speed and the reduction ratio, taking 0.2~0.8 s; is the target roll suppression torque (N・m) output by the decision-making layer MPC, which needs to satisfy [−800N m,800N m] (motor power limit).

[0225] (3) Nonlinear correction.

[0226] Motor current saturation: when, ( is the maximum allowable current of the motor, taking 11A, ensuring 800N·m);

[0227] Torsion angle limit: when the stabilizer bar torsion angle is saturated, the torque output is saturated, i.e. , , is the mechanical structure limit.

[0228] 3) Pitch control actuator modeling.

[0229] The pitch control actuator uses a hydraulic actuator to generate a pitch suppression torque through telescopic motion, adapting to the acceleration lifting head / braking nodding working condition:

[0230] (1) Torque output model.

[0231] The pitch torque is directly related to the hydraulic system pressure difference, which needs to be combined with the installation position force arm:

[0232] ,

[0233] where, is the actuator arm (m), i.e. the horizontal distance from the actuator mounting point to the vehicle body center of mass, which is measured by geometry to be 0.8~1.2m (consistent with the three-degree-of-freedom coupling model in ); is the effective area of the actuator piston (m²), which is determined by the actuator specification, and is taken as 0.01~0.02m²; is the pressure difference between the rodless cavity and the rod cavity of the actuator (Pa), which is adjusted by the hydraulic valve control command. The maximum pressure difference Pa (rated value of the hydraulic system).

[0234] (2) Dynamic response model.

[0235] The compressibility of the hydraulic system oil and the valve port flow delay cause the pressure difference response to lag, which conforms to the first-order dynamic characteristics:

[0236] ,

[0237] where, is the response time constant (s), which is affected by the oil viscosity and pipe length, and is taken as 0.3~1.0s (at low temperature, the viscosity is high, which can be compensated by oil temperature compensation: , C, is the oil temperature sensor measurement value. is the hydraulic system gain (Pa / V), taken as 5×10 6 ~1×10 7 Pa / V; is the hydraulic valve control voltage command (V), ranging from 0~5V, corresponding to a pressure difference of 0- .

[0238] (3) Physical constraints.

[0239] Maximum pitch moment: [-1000 N.m, 1000 N.m]);

[0240] Flow limit: maximum flow of hydraulic pump (L / min) determines the actuator extension speed, which needs to satisfy , is the actuator extension speed (m / s) to avoid response delay caused by insufficient flow.

[0241] 2. Control allocation algorithm.

[0242] The control allocation algorithm is to decompose the generalized virtual control quantity (total demand) calculated by the decision layer into specific physical instructions for each actuator by considering the dynamic characteristics, physical limits, and multi-objective priorities of each actuator through a real-time optimization allocation framework.

[0243] 1) Optimization variable definition.

[0244] The physical variables that can be directly controlled by each actuator are used as optimization variables:

[0245] ,

[0246] wherein is the pressure change of the four air springs of the suspension (kPa), with constraints [-0.5 bar, 0.5 bar], FL represents the left front, FR represents the right front, RL represents the left rear, and RR represents the right rear; is the motor control current of the active stabilizer bar (A), with constraints [0, 11 A]; is the pitch hydraulic actuator pressure difference (Pa), with constraints Pa.

[0247] 2) Control efficiency matrix (B).

[0248] Based on the actuator dynamics model, a linear mapping matrix B is constructed:

[0249] ,

[0250] wherein is the initial vertical stiffness of the front left air spring (N / m), is the initial vertical stiffness of the front right air spring (N / m), is the initial vertical stiffness of the rear left air spring (N / m), and is the initial vertical stiffness of the rear right air spring (N / m). has the same meaning as before, is the gas variability index. is the reference air pressure of the left front air spring, is the reference air pressure of the right front air spring, is the reference air pressure of the left rear air spring, is the reference air pressure of the right rear air spring, all four parameters are set to (initial air pressure value). , are the front and rear wheel track (m) respectively, , are the front and rear wheel track (m) respectively. is the effective area of the piston, is the force arm, is the motor torque constant.

[0251] 2) Objective function design.

[0252] Design a weighted quadratic objective function to achieve multi-objective optimization:

[0253] ,

[0254] The formula contains three sub-items:

[0255] (1) Tracking error term , is the total demand vector of the decision layer (vertical stiffness increment, roll torque, pitch torque); is the working condition adaptive weight matrix (diagonal matrix), . Among them, , , . is the weight of vertical stiffness control (priority), the larger the value, the more the algorithm prioritizes adjusting the vertical stiffness. is the vertical impact frequency (such as when passing through a deceleration strip, the frequency of the vehicle body's vertical vibration), is the vertical base weight (empirical value, determines the basic priority of vertical control), is the frequency sensitivity coefficient (determines the response strength of the weight to the change of the frequency, the larger the value, the faster the weight decreases when the frequency deviates from the resonance point), is the vertical resonance frequency of the vehicle (usually 1.5~2.5Hz), which is a inherent characteristic of the vehicle and is calibrated through experiments). is the roll base weight (empirical value, determines the basic priority of roll control), is the roll sensitivity coefficient (calibrated through experiments), is the safety roll intensity threshold (an empirical value, which can be 0.2). is the roll strength coefficient, is the vehicle speed (m / s), is the turning radius (m), is the gravity acceleration m / s 2 is the deceleration coefficient, is the safety deceleration threshold, is the deceleration sensitivity coefficient (numerical value needs to be experimentally calibrated), is the pitch basic weight (empirical value, determines the basic priority of pitch control).

[0256] (2) Control cost term , used to punish excessive actuator action, reduce energy consumption and wear, is a diagonal matrix, the element value is based on the actuator energy consumption characteristics; . In the formula, and are the weight coefficients of the front and rear air suspension air pressure control variables, respectively, representing the unit air pressure change cost of the front and rear air springs, and satisfying to reflect the energy consumption difference of the front and rear actuators. is the weight coefficient of the active stabilizer bar motor current control variable, is the weight coefficient of the hydraulic actuator pressure control variable. These four parameters need to be calibrated through bench test or road test.

[0257] (3) Smoothing term , mainly used to suppress command mutation and improve comfort, is the last period command of the directly controllable physical quantity. is the weight matrix, and is also a diagonal matrix:

[0258] ,

[0259] is the smoothing weight coefficient of the air suspension air pressure change variable, which is a monotonically increasing function of the vehicle speed , is the smoothing weight coefficient of the active stabilizer bar motor current change variable, which is also a monotonically increasing function of the vehicle speed . is the smoothing weight coefficient of the hydraulic actuator pressure change variable, which is a monotonically increasing function of the vehicle speed . These three parameters also need to be calibrated through bench test or road test.

[0260] 3) Constraint conditions.

[0261] Convert the physical limits of the actuator into inequality constraints of the QP problem:

[0262] Amplitude constraint:​​ , = [−50,−50,−50,−50,0,0] T , = [50,50,50,50,11,2×10 7 ] T .

[0263] 3. Real-time constraint handling strategy.

[0264] Real-time constraint handling, responsible for ensuring that the control commands meet all the physical limits and engineering safety constraints of actuators before they are issued, is the key to guarantee the safe and reliable operation of the system. This module receives the original control sequence from the MPC controller and compares it with the pre-set constraints, modifies it or uses it as a boundary condition for the optimization problem. It specifically handles the following three types of constraints:

[0265] 1) Actuator amplitude and range constraints: Ensure that the control commands of each actuator do not exceed the physical limits allowed by the hardware. This includes: air suspension pressure change [−50, 50] kPa, active stabilizer bar output torque [−800,800] N·m and pitch control torque [−1000, 1000] N·m.

[0266] 2) Rate of change constraints: Limit the rate of change of control commands to prevent shocks and protect actuators. This includes: pressure change rate [−30, 50] kPa / s, torque rate [−200, 200] N m / s, [−3.5,3.5]×10 6 Pa / s (used to limit the generation rate of pitch torque ).

[0267] 3) Vehicle state safety constraints: Real-time monitoring and ensuring that the predicted state sequence is within the safety boundary, prioritizing vehicle stability. This mainly includes: body roll angle [−2°, 2°] (to prevent rollover), vertical acceleration [−0.3g,0.3g] (to ensure comfort threshold).

[0268] The above constraints are embedded in the quadratic programming (QP) solver of the MPC in the form of inequalities as boundary conditions for the optimization problem, ensuring that the optimal control sequence obtained can be directly and safely applied to the actuators.

[0269] 4. Actuator interface conversion module.

[0270] The actuator interface conversion module is a bridge connecting the control quantity distribution algorithm and the physical actuator. The core function thereof is to convert the physical quantity instruction output by the optimization algorithm into an electric control signal for driving the actuator to work, so as to ensure the final landing of the control instruction.

[0271] The specific signal conversion relationship is as follows:

[0272] The optimization variable output by the control quantity distribution algorithm is a physical quantity, which needs to be converted into an actuator driving signal through the following relationship:

[0273] (1) Air suspension driving signal.

[0274] The air pressure instruction of each air spring is converted into an electromagnetic valve driving voltage , and the conversion relationship is as follows:

[0275] ,

[0276] wherein, is the air pressure-voltage conversion coefficient (kPa / V), and the value thereof is determined through actuator calibration experiment. As a preferred embodiment, it is preferable that = 25 kPa / V.

[0277] (2) Active stabilizer bar driving signal.

[0278] The stabilizer bar torque instruction is obtained from the motor current in the optimization variable , which is directly realized by the motor, and the conversion relationship is based on the motor model:

[0279] ,

[0280] wherein, is the motor torque constant (N·m / A), is the transmission efficiency, is the reduction ratio. Therefore, the driving circuit directly receives the current instruction , and the required torque can be generated.

[0281] (3) Pitch hydraulic actuator driving signal.

[0282] The hydraulic actuator pressure difference instruction is converted into a servo valve driving voltage , and the conversion relationship is as follows:

[0283] ,

[0284] wherein, ​is the hydraulic-voltage conversion coefficient (Pa / V), and its value is determined through hydraulic system calibration experiments.

[0285] 5. Fault-tolerant strategy.

[0286] The fault-tolerant strategy aims to maintain the basic stability and safety of the vehicle and realize gradual degradation of performance by controlling redundancy and algorithm reconstruction when partial components of the system fail. The strategy includes two parts: fault diagnosis and redundancy control.

[0287] Fault diagnosis and monitoring: real-time monitoring is performed based on the consistency of actuator instructions and sensor feedback. Fault determination thresholds and delays are set, for example: if the deviation between the air spring pressure instruction and the measured value is greater than the threshold (10 kPa is taken) for more than (100 ms is taken), it is determined that the air circuit fails; if the motor current of the stabilizer bar suddenly increases but the torque sensor feedback is zero, it is determined that the motor or transmission mechanism is stuck.

[0288] Redundancy control logic:

[0289] (1) Single air suspension failure: if an air spring cannot build pressure (such as leakage), the pressure of the remaining three air chambers is adjusted through a control quantity distribution algorithm, the coupling relationship of the vehicle body attitude is used for compensation, and the vehicle speed is appropriately limited, and the driver is prompted.

[0290] (2) Active stabilizer bar failure: if the stabilizer bar motor fails, the weight of the left and right pressure differential control term of the air suspension in the MPC cost function is dynamically increased, the anti-roll moment is generated through the asymmetric stiffness of the suspension, and the function of the stabilizer bar is partially replaced.

[0291] (3) Key sensor failure: if the IMU and other key sensor data are abnormal, the vehicle state (such as roll angle and pitch angle) is reconstructed and estimated based on the model-based state observer (such as Kalman filter), and the degraded control mode is continued based on the estimated value.

[0292] All fault information is reported to the instrument panel through the vehicle network to prompt the driver to repair and ensure driving safety.

[0293] The control method of the present application is simulated in the MATLAB / Simulink environment. The simulation results are shown in Figures 1-4 The vehicle parameters used in this example are: spring mass is 1450 kg; roll moment of inertia is 480 kg·m.

[0294] Pitch moment of inertia: 2200 kg-m2; center of mass height: 0.45 m. Suspension system parameters: vertical stiffness: 28000 N / m; vertical damping: 1800 N-s / m; roll stiffness: 22000 N-m / rad; roll damping: 1300 N-m-s / rad; pitch stiffness: 24000 N-m / rad; pitch damping: 1500 N-m-s / rad. Excitation conditions: road excitation: composite sine wave (1.8 Hz + 3.5 Hz + 6 Hz) with amplitude 0.025 m; lateral excitation: 0.24 g; steering input (exponential growth); longitudinal excitation: -0.21 g braking input (exponential growth); simulation time from 0 seconds to 10 seconds; sampling time: 0.005 seconds.

[0295] Simulation result analysis:

[0296] 1. Vertical displacement response Figure 1 ).

[0297] Response characteristics: multi-frequency oscillation under composite road excitation.

[0298] Key data: initial fluctuation amplitude: ±40 mm; stabilization time: about 4 seconds; final fluctuation range: ±20 mm.

[0299] From Figure 1 it can be seen that the suspension system effectively absorbs most of the road impact, only a small amplitude oscillation exists, indicating that the suspension stiffness and damping are well matched.

[0300] 2. Roll angle response Figure 2 ).

[0301] Response characteristics: steering input causes rapid rise in angular velocity and then stabilizes.

[0302] Key data: peak angle: 4.7°; overshoot: about 5%; steady-state value: 4°.

[0303] From Figure 2 it can be seen that the roll control effect is good, within the safe range (usually <5°), and the steady-state error indicates that there is a continuous lateral force acting.

[0304] 3. Pitch angle response Figure 3 ).

[0305] Response characteristics: braking input causes negative change (nodding phenomenon).

[0306] Key data: maximum pitch angle: -4.0°; steady-state value: -2.5° to -3.0°; oscillation amplitude: ±0.5°.

[0307] From Figure 3It can be seen that the pitch motion is controlled reasonably, with only slight oscillation.

[0308] 4. Vertical acceleration response (Gz) Figure 4 ).

[0309] Response characteristics: high-frequency oscillation characteristics.

[0310] Key data: maximum acceleration: ±1.0 m / s²; RMS value: about 0.5 m / s² (0.05g); main frequencies: 1.8 Hz / 3.5 Hz / 6 Hz.

[0311] From Figure 4 It can be seen that the ride comfort is good (ISO 2631 evaluation: "comfortable"), and the high-frequency oscillation comes from the road excitation.

Claims

1. An air suspension and active stabilizer bar cross-coupled control method, characterized by, The method comprises the following steps: The signal input layer collects and fuses data transmission to the decision layer, and the data transmitted to the decision layer includes: road curvature, road roughness, vehicle longitudinal acceleration, lateral acceleration, vertical acceleration, roll angular velocity and pitch angular velocity; The decision layer forms a control instruction based on a model predictive control algorithm, including constructing a three-degree-of-freedom coupled dynamics model of the automobile, and iteratively solving a minimum value of a cost function in a rolling time domain to obtain the control instruction; the three-degree-of-freedom coupled dynamics model of the automobile is: ， ， ， ， ， wherein, is a vertical displacement of the vehicle body mass center, is a vertical velocity, is a vertical acceleration of the vehicle body, is a roll angle of the vehicle body, is a roll angular velocity, is a roll angular acceleration, is a pitch angle of the vehicle body, is a pitch angular velocity, is a pitch angular acceleration, is a mass-inertia matrix, is a damping matrix, is a stiffness matrix, is a control allocation matrix, is an external excitation term, the damping matrix and the stiffness matrix are non-symmetric matrices, the off-diagonal terms of the damping matrix include a vertical-roll coupling damping, a vertical-pitch coupling damping, a roll angular velocity induced vertical force coupling damping, and a pitch angular velocity induced vertical force coupling damping, the off-diagonal terms of the stiffness matrix include a vertical-pitch coupling stiffness, a vertical-roll coupling stiffness, a roll angle induced vertical force coupling stiffness, and a pitch angle induced vertical force coupling stiffness; is a control command, is an air spring pressure variation amount, is an active stabilizer bar output torque, is a pitch actuator control torque; The cost function is a combination of a vehicle body state term and a control instruction term, the vehicle body state term is a dynamic weight weighted sum of a vehicle body vertical acceleration, a roll angle, a pitch angle, a vertical velocity, a roll angular velocity and a pitch angular velocity, and the dynamic weight is determined by a vehicle working condition; The actuator layer converts the control instruction into a physical quantity directly controllable by each actuator bottom layer through an optimization objective function, and then converts the physical quantity into a driving signal of each actuator for control; The physical quantity directly controllable by each actuator bottom layer is: ， wherein, is the air pressure variation amount of the four air springs of the suspension, FL indicates the front left, FR indicates the front right, RL indicates the rear left, and RR indicates the rear right, is the active stabilizer bar motor control current, is the pitch hydraulic actuator pressure difference, The objective function is: ， wherein, is a working condition adaptive weight matrix, B is a linear mapping matrix constructed based on an actuator dynamics model, is a matrix for penalizing excessive actuator action, is a smoothing matrix, is a control instruction output by the decision layer, is a last period instruction of a directly controllable physical quantity.

2. The method of controlling an air suspension and active stabilizer bar coupled system according to claim 1, wherein The cost function is: ， in, To predict the time domain, To control the time domain, This represents the maximum vertical acceleration. This represents the maximum roll angle. This represents the maximum pitch angle. This represents the maximum vertical velocity. This represents the maximum roll rate. This represents the maximum pitch angular velocity. To dynamically adjust the weighting coefficients of each objective priority, This is the actuator energy consumption weight matrix. Representing the current moment, Represents an integer index variable. Represents the prediction of the future starting from the current moment. A specific point in time.

3. The method of controlling an air suspension and active stabilizer bar coupled system according to claim 2, wherein ， ， ， ， ， ， ， ， ， ， ， wherein is the vehicle lateral acceleration, is the vehicle speed, is the vehicle longitudinal acceleration, is the air suspension pressure rate of change, is the air suspension maximum pressure rate of change, is the stabilizer bar maximum output torque, is the pitch coordinator torque rate of change, is the pitch coordinator maximum torque rate of change, , , , , are all calibration coefficients.

4. The method of controlling an air suspension and active stabilizer bar coupled vehicle according to claim 1, wherein, When iteratively solving a minimum value of the cost function in the rolling time domain, the following constraints are set: a vehicle body roll angle constraint, a vehicle body mass center vertical acceleration constraint, an air spring pressure change amount constraint, an air spring pressure change rate constraint, an active stabilizer bar output torque constraint and an active stabilizer bar output torque change rate constraint.

5. The method of controlling an air suspension and active stabilizer bar coupled vehicle of claim 1, wherein, to control commands, said feedforward compensation comprising a road preview compensation = , a banked curve roll feedforward and a pitch feedforward for braking / acceleration wherein is a calibration coefficient, is the road unevenness at a preview future time instant, t denotes the current time instant, is the vehicle body roll moment of inertia, is the vehicle speed, is the preview road curvature, r is the tire radius, is the sprung mass, is the longitudinal acceleration, is the center of mass height.

6. The method of controlling an air suspension and active stabilizer bar coupled vehicle of claim 1, wherein, The signal input layer collects and fuses data transmission to the decision layer, collects road original curvature and vehicle speed, fuses to construct road curvature observation value, and performs Kalman filtering estimation to obtain road curvature transmitted to the decision layer, wherein the road curvature observation value is: , is the road original curvature, is the lateral acceleration, is the vehicle speed, is the dynamic weight based on the visibility adjustment.

7. The method of controlling an air suspension and active stabilizer bar coupled vehicle according to claim 1, wherein, The signal input layer collects and fuses data transmission to the decision layer, and the roll angle observation value is constructed by the collected roll angle velocity and the predicted roll angle output by the three-degree-of-freedom coupled dynamics model of the automobile, and Kalman filtering estimation is performed to obtain the roll angle transmitted to the decision layer, wherein the roll angle observation value is: , is the roll angle calculated by the collected roll angle velocity, is the predicted roll angle, is a fixed weight coefficient.

8. The method of controlling an air suspension and active stabilizer bar coupled vehicle of claim 1, wherein, , , , , is a vertical stiffness control weight, is a vertical impact frequency, is a roll moment weight, is a pitch moment weight, is a vertical base weight, is a frequency sensitivity coefficient, is a vehicle vertical resonance frequency, is a roll base weight, is a roll sensitivity coefficient, is a safe roll strength threshold, is a roll strength coefficient, is a deceleration coefficient, is a safe deceleration threshold, is a deceleration sensitivity coefficient, is a pitch base weight; , are the weight coefficients of the front and rear axle air suspension air pressure control amounts, respectively, satisfying , is the weight coefficient of the active stabilizer bar motor current control amount, is the weight coefficient of the hydraulic actuator pressure control amount; , is a smoothing weight coefficient for the air suspension air pressure variation amount, and is a monotone increasing function of the vehicle speed , is a smoothing weight coefficient for the active stabilizer bar motor current variation amount, and is a monotone increasing function of the vehicle speed , is a smoothing weight coefficient for the hydraulic actuator pressure variation amount, and is a monotone increasing function of the vehicle speed .

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