Method, device and system for controlling vertical illumination angle of vehicle headlamp

Abstract

The invention relates to a method, a device and a system for controlling a vertical illumination angle of a vehicle headlamp. The control method for the vertical illumination angle comprises the steps that vehicle state information is obtained; carrying out pitch angle estimation on the vehicle state information by adopting an unknown input observer to obtain a vehicle pitch angle estimation value, and inputting the vehicle pitch angle estimation value into an autoregressive exogenous input model to obtain a vehicle pitch angle in a future time domain; performing rolling optimization on the vertical illumination target function according to the vehicle pitch angle in the future time domain to obtain a vertical illumination control quantity in the future time domain; and performing signal conversion on the vertical illumination control quantity at the first moment, so that the stepping motor correspondingly adjusts the vertical angle of the headlamp according to the control quantity after signal conversion, and illumination angle optimization is completed. According to the control method for the vertical illumination angle, the headlamp can more quickly and accurately adapt to the dynamic change of the vehicle under the complex terrain condition, and the safety and comfort of night driving are improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of vehicle dynamics and predictive control headlights, and particularly to a control method, device and system for the vertical illumination angle of vehicle headlights. Background Art

[0002] With the development of intelligent driving technology, when an automobile travels in low-light environments such as at night, in tunnels, and in underground garages, the intelligent lighting system usually combines autonomous driving and intelligent networking technologies to enable the headlights to adapt to changes in the road environment, thereby enhancing the visual perception ability of the driver or the autonomous driving system and improving the driving safety in low-light environments.

[0003] In traditional technologies, generally, the distance between the vehicle and the obstacle ahead is measured by a distance sensor, and based on a preset linear model, the illumination angle of the headlights is adjusted according to the distance input by the sensor to adapt to different road conditions. However, the simple linear model adjustment method is difficult to cope with complex road conditions. Especially when the type of obstacle, the body attitude or the road slope changes, this strategy often fails to be accurately adapted, resulting in a lag or failure in the adjustment of the illumination angle, thereby affecting the vision of the driver or the autonomous driving system and bringing potential safety risks.

[0004] Based on this, existing technologies usually adopt image detection technology for relative distance measurement. For example, through image recognition combined with calibration data, the pixel coordinates are converted into the world coordinate system to measure the distance of the obstacle, so as to more flexibly adapt to the distribution of different obstacles. However, existing technologies are difficult to perceive changes in vehicle attitude. Especially when the vertical movement of the vehicle body causes fluctuations in the vertical illumination angle, it may lead to unstable vertical illumination, thereby causing a glare effect or suddenly reducing the visual perception ability of the driver and the autonomous driving system. Accordingly, existing technologies have technical problems such as poor adaptability to terrain undulations, untimely control response, and low control accuracy in the control of the vertical illumination angle. Summary of the Invention

[0005] Based on this, the object of the present invention is to provide a control method for the vertical illumination angle of vehicle headlights.

[0006] A control method for the vertical illumination angle of vehicle headlights includes the following steps: S1. Obtain the vehicle state information at the current moment; wherein, the vehicle state information includes vehicle speed, vehicle acceleration, and body height change; S2. Estimate the pitch angle of the vehicle state information at the current moment to obtain the estimated value of the vehicle pitch angle at the current moment; S3. Perform regression prediction on the estimated value of the vehicle pitch angle and the vehicle state information to obtain the vehicle pitch angle within the future time domain; S4. Optimize a vertical lighting objective function based on the vehicle pitch angle in the future time domain to obtain the vertical lighting control quantity in the future time domain; S5. Perform signal conversion on the vertical lighting control quantity at the first moment to obtain the actual vertical control signal of the headlamp, and send the actual vertical control signal of the headlamp to the stepping motor, so that the stepping motor adjusts the vertical angle of the headlamp accordingly according to the actual vertical control signal, completing the optimization of the optimal vertical lighting angle.

[0007] For the control method of the vertical lighting angle of the vehicle headlamp described in the present invention, compared with the prior art, by introducing an unknown input observer, accurate observation of the vehicle pitch angle is achieved in the case where the pitching moment caused by the vehicle acceleration and other vibration disturbances cannot be directly observed.

[0008] At the same time, based on the estimated value of the vehicle pitch angle and the current vehicle state information, a multiple-step prediction is performed using an autoregressive exogenous input model to estimate the change trend of the vehicle pitch angle in the future time domain, so that the subsequent MPC (Model Predictive Control) can calculate the optimal control strategy of the stepping motor in advance based on the evolution of the vehicle pitch angle in the future time domain, realizing the forward-looking optimization control of the vertical angle of the headlamp.

[0009] In addition, by only increasing the execution frequency of the control cycle, the response speed of the headlamp adjustment can be dynamically improved, effectively avoiding the problem of violent swing of the lighting angle caused by the rapid change of the vehicle pitch angle, thereby improving the visual stability of the driver.

[0010] Therefore, through accurate vehicle pitch angle prediction and adaptive lighting optimization control, the present invention significantly improves the dynamic response ability and adaptability of the headlamp to complex road environments, and enhances the lighting safety of the vehicle under special working conditions such as slopes, bumpy roads, and high-speed driving.

[0011] Furthermore, step S2 uses an unknown input observer for pitch angle estimation, which is specifically expressed as follows:

[0012] In the formula, represents the estimated value of the vehicle pitch angle at the current moment , represents the estimation coefficient of the pitch angle, and specifically it is ; is the state estimation value, that is, , is the vertical displacement of the vehicle center of mass, is the first derivative of the vertical displacement of the vehicle center of mass, is the vehicle pitch angle, is the first derivative of the vehicle pitch angle, and its state estimate value is obtained through state estimation by the unknown input observer. The specific representation of the unknown input observer is as follows:

[0013] where is the internal state of the unknown input observer; represents the relevant input vector of the vehicle body height change, and its , and respectively represent the vertical displacements of the front and rear wheels of the vehicle; is the gravitational acceleration; is the observable output vector, that is , represents the state vector of the vehicle, is the observation matrix, specifically ; is the measurement feedback gain matrix; is the error compensation matrix; is the error dynamic main matrix; is the known input correction matrix; The matrix specifically follows the following constraints to ensure the convergence of the state error:

[0014]

[0015]

[0016]

[0017]

[0018] where represents the identity matrix; is the main feedback gain; is the error compensation term correction gain; is the state weight matrix, specifically represented as follows:

[0019] where and respectively represent the elastic stiffness of the front and rear suspensions; and respectively represent the damping ratio coefficients of the front and rear suspensions; and respectively represent the distances from the vehicle center of mass to the front and rear wheels; is the mass of the vehicle; represents the moment of inertia of the vehicle center; is the influence matrix of the road surface height change, and its specific representation is as follows:

[0020] is the influence coefficient matrix of gravity, and is specifically represented as ; is the influence coefficient matrix of the pitching moment, and is specifically represented as .

[0021] Accordingly, based on the half-vehicle model, the present invention constructs a vehicle vertical dynamics equation, and on this basis, defines state variables and performs discretization processing to form a state space equation to accurately describe the vertical attitude change of the vehicle.

[0022] Next, in view of the fact that it is difficult for traditional methods to directly observe the pitching moment and other interferences caused by vehicle acceleration and deceleration, an unknown input observer is constructed according to the state space equation to effectively separate the influence of unknown inputs through the unknown input observer error compensation strategy, so as to improve the estimation accuracy of the pitch angle, enabling the system to still maintain stable and accurate vehicle pitch angle calculation under complex terrains (such as slopes, bumpy roads or vehicle acceleration and deceleration conditions), thereby realizing the state observation of the vehicle pitch angle.

[0023] Furthermore, an autoregressive exogenous input model is used for The specific representation of the vehicle pitch angle predicted by regression at time

[0024] wherein, is the vehicle pitch angle at the th moment; represents the historical regression coefficient matrix of the th historical vehicle pitch angle estimated value; is the exogenous input vector, that is, the vehicle state information at the th moment; represents the exogenous input regression coefficient matrix of the th exogenous input vector; is the constant term, represents the deviation term; is the time lag parameter.

[0025] The present invention realizes the dynamic prediction of the vehicle pitch angle change within a plurality of future time steps by introducing an autoregressive exogenous input model (ARX), and further fusing the historical time series and the vehicle state information at the current moment with the corresponding vehicle pitch angle estimation value on the basis of the high-precision estimation of the vehicle pitch angle by using the unknown input observer, so as to obtain the vehicle pitch angle prediction sequence in the future time domain.

[0026] Accordingly, in the follow-up, only according to the actual application scenario or control strategy, by designing the corresponding mapping function, the vehicle pitch angle prediction value is mapped to the target vertical illumination angle, so as to improve the dynamic response ability and light adaptability of the vehicle lighting system in the complex road environment.

[0027] Furthermore, the time lag parameter is calculated and obtained by the following method:

[0028] In the formula, The optimal time lag parameter; is the BIC value of the Bayesian information criterion corresponding to the

[0029] In the formula, represents the variance of the autoregressive exogenous input model of the th time lag parameter combination; represents the total number of the historical data set; is the model complexity penalty term.

[0030] Accordingly, the present invention calculates the most suitable time lag parameter by using the Bayesian information criterion (BIC), so that the ARX prediction model achieves the best balance between accuracy and computational complexity, thereby improving the accuracy of vehicle pitch angle prediction.

[0031] Furthermore, the specific expression of the vertical illumination objective function is as follows:

[0032] In the formula, represents the vertical illumination control quantity in the future time domain, represents the prediction time domain, and the specific expression of its vertical illumination control quantity is as follows:

[0033] In the formula, represents the electromagnetic torque at the future th moment predicted forward from the current moment ; Indicates at the current moment The future The vertical illumination angle driven by the stepper motor predicted for the moment is obtained by calculating the state equation of the stepper motor rotation angle, and its specific expression is as follows:

[0034] In the formula, Indicates the vertical illumination angle driven by the stepper motor at the future moment, Indicates the current moment The control input of the stepper motor, that is, the electromagnetic torque; Is the state equation of the stepper motor rotation angle, and its state equation of the stepper motor rotation angle is determined by the stepper motor model. The simple mathematical expression of the stepper motor model is as follows:

[0035] In the formula, Indicates the moment of inertia of the stepper motor; Indicates the load torque; Indicates the ideal vertical illumination angle at the future moment, which is used to ensure that the headlamp can dynamically compensate for the body pitch change, and its specific calculation is expressed as follows:

[0036] In the formula, Is the ideal angle mapping function, which is used to map the headlamp pitch angle to the ideal vertical illumination angle; Among them, the vertical illumination objective function includes the following constraint conditions:

[0037] In the formula, Indicates the initial condition constraint; Indicates the state space equation constraint; Indicates the control quantity constraint, and its and Are the minimum and maximum values of the electromagnetic torque respectively.

[0038] Accordingly, the present invention minimizes the vertical illumination objective function and minimizes the error between the currently predicted headlight angle and the desired illumination angle, so that the optimized illumination angle can accurately track the vehicle attitude change, avoid beam jitter or glare problems caused by adjustment delay or overcorrection, and further enable the headlight to achieve smoother and more accurate vertical illumination adjustment under different road conditions (such as slopes, undulating terrains, and rapid acceleration and deceleration), improve the driver's visual stability, and enhance the safety and comfort of night driving.

[0039] A control device for the vertical illumination angle of a vehicle headlight, comprising a vehicle state information acquisition unit, a vehicle pitch angle state estimation unit, a vehicle pitch angle prediction unit, a model predictive control unit, and a control signal conversion unit; The vehicle state information acquisition unit is used to acquire the vehicle state information at the current moment; wherein, the vehicle state information includes vehicle speed, vehicle acceleration, and body height change; The vehicle pitch angle state estimation unit is used to estimate the pitch angle of the vehicle state information at the current moment to obtain the vehicle pitch angle estimation value at the current moment; The vehicle pitch angle prediction unit is used to perform regression prediction on the vehicle pitch angle estimation value and the vehicle state information to obtain the vehicle pitch angle within the future time domain; The model predictive control unit is used to perform rolling optimization on a vertical illumination objective function according to the vehicle pitch angle within the future time domain to obtain the vertical illumination control quantity within the future time domain; The control signal conversion unit is used to perform signal conversion on the vertical illumination control quantity at the first moment to obtain the actual vertical control signal of the headlight, and send the actual vertical control signal of the headlight to the stepping motor, so that the stepping motor makes corresponding adjustments to the vertical angle of the headlight according to the actual vertical control signal, and completes the optimization of the optimal vertical illumination angle.

[0040] Further, the vehicle pitch angle state estimation uses an unknown input observer for pitch angle estimation, which is specifically expressed as follows:

[0041] In the formula, represents the vehicle pitch angle estimation value at the current moment , represents the estimation coefficient of the pitch angle, which is specifically ; is the state estimation value, that is, , is the vertical displacement of the vehicle center of mass, is the first derivative of the vertical displacement of the vehicle center of mass, is the vehicle pitch angle, is the first derivative of the vehicle pitch angle, and its state estimate value is obtained through state estimation by the unknown input observer. The specific representation of the unknown input observer is as follows:

[0042] where is the internal state of the unknown input observer; represents the relevant input vector of the vehicle body height change, and its , and respectively represent the vertical displacements of the front and rear wheels of the vehicle; gravitational acceleration; is the observable output vector, that is , represents the state vector of the vehicle, is the observation matrix, specifically ; is the measurement feedback gain matrix; is the error compensation matrix; is the error dynamic main matrix; is the known input correction matrix; The matrix specifically follows the following constraints to ensure the convergence of the state error:

[0043]

[0044]

[0045]

[0046]

[0047] where represents the identity matrix; is the main feedback gain; is the error compensation term correction gain; is the state weight matrix, specifically represented as follows:

[0048] where and respectively represent the elastic stiffness of the front and rear suspensions; and respectively represent the damping ratio coefficients of the front and rear suspensions; and respectively represent the distances from the vehicle center of mass to the front and rear wheels; is the mass of the vehicle; represents the moment of inertia of the vehicle center; is the influence matrix of the road surface height change, and its specific representation is as follows:

[0049] is the influence coefficient matrix of gravity, and is specifically represented as ; is the influence coefficient matrix of the pitching moment, and is specifically represented as .

[0050] Further, the vehicle pitching angle prediction unit uses an autoregressive exogenous input model for regression prediction, and the specific representation is as follows:

[0051] In the formula, is the vehicle pitching angle at the th moment; represents the historical regression coefficient matrix of the th historical vehicle pitching angle estimate value; is the exogenous input vector, that is, the vehicle state information at the th moment; represents the exogenous input regression coefficient matrix of the th exogenous input vector; is the constant term, represents the bias term; is the time lag parameter, and the time lag parameter is obtained by the following method:

[0052] In the formula, is the optimal time lag parameter; is the BIC value of the Bayesian information criterion corresponding to the th time lag parameter combination, and its specific calculation representation is as follows:

[0053] In the formula, represents the variance of the autoregressive exogenous input model of the th time lag parameter combination; represents the total number of the historical data set; is the model complexity penalty term.

[0054] Further, the specific representation of the vertical lighting objective function is as follows:

[0055] In the formula, represents the vertical lighting control amount in the future time domain, represents the prediction time domain, and the specific representation of its vertical lighting control amount is as follows:

[0056] In the formula, represents at the current moment the electromagnetic torque predicted forward for the future th moment; represents at the current moment the vertical lighting angle driven by the stepping motor predicted forward for the future th moment, which is obtained by calculating the state equation of the stepping motor rotation angle, and its specific representation is as follows:

[0057] In the formula, represents the vertical lighting angle driven by the stepping motor at the future th moment, represents the stepping motor control input at the current moment , that is, the electromagnetic torque; is the state equation of the stepping motor rotation angle, and the state equation of the stepping motor rotation angle is determined by the stepping motor model. The simple mathematical expression of the stepping motor model is as follows:

[0058] In the formula, represents the moment of inertia of the stepping motor; represents the load torque; represents the ideal vertical lighting angle at the future th moment, which is used to ensure that the headlamp can dynamically compensate for the body pitch change, and its specific calculation representation is as follows:

[0059] In the formula, is the ideal angle mapping function, which is used to map the headlamp pitch angle to the ideal vertical lighting angle; wherein, the vertical lighting objective function includes the following constraint conditions:

[0060] In the formula, represents the initial condition constraint; represents the state space equation constraint; represents the control amount constraint, and its and They are the minimum and maximum values of the electromagnetic torque respectively.

[0061] A control system for a vehicle headlamp includes a wheel speed sensor, a plurality of vehicle body height sensors, a control device for the vertical illumination angle, and a stepper motor. The wheel speed sensor is installed on the wheel bearing, the wheel speed assembly or the transmission output shaft, monitors the motion state of the vehicle and obtains speed and acceleration data. The vehicle body height sensors are respectively installed at the center of the front axle, the center of the rear axle and the center of mass position of the vehicle, monitor the change of the vertical state of the vehicle to obtain the corresponding vehicle body height change information. The control device for the vertical illumination angle receives the speed and acceleration data from the wheel speed sensor and the vehicle body height change information from the vehicle body height sensor, then calculates the optimal illumination angle and obtains the actual vertical control signal of the headlamp. The stepper motor adjusts the vertical angle of the vehicle headlamp according to the actual vertical control signal of the headlamp to complete the optimization of the optimal vertical illumination angle.

[0062] For better understanding and implementation, the present invention will be described in detail below with reference to the accompanying drawings. Description of the Drawings

[0063] Figure 1 It is a schematic diagram of the simple structure of the control system for the vehicle headlamp described in the present invention. Figure 2 It is a schematic diagram of the simple structure of the control device for the vertical illumination angle described in the present invention. Figure 3 It is a schematic diagram of the simple process of the control method for the vertical illumination angle of the vehicle headlamp described in the present invention. Detailed Embodiment

[0064] In order to solve the problems of poor adaptability to terrain undulation, untimely control response and low control accuracy in the vertical illumination angle control of the existing technology, the present invention obtains the vehicle state information at the current moment, and uses an unknown input observer to perform state estimation on the vehicle state information to obtain the estimated value of the vehicle pitch angle at the current moment; then, inputs the estimated value of the vehicle pitch angle and the corresponding vehicle state information into an autoregressive exogenous input model to generate the vehicle pitch angle in the future time domain; then, makes the Model Predictive Control (MPC) perform rolling optimization on a vertical illumination objective function according to the vehicle pitch angle in the future time domain to obtain the vertical illumination control quantity in the future time domain; finally, performs signal conversion on the vertical illumination control quantity at the first moment and sends the converted control signal to the stepper motor to complete the dynamic adjustment of the vertical angle of the headlamp.

[0065] Accordingly, the present invention accurately estimates the current vehicle pitch angle by using an unknown input observer, and predicts the pitch angle change trend in multiple future time steps by using an ARX model, enabling the MPC to perceive vehicle attitude changes in advance, and then realizing the predictive control of the lighting system, effectively improving the adaptability of the vertical lighting angle to terrain undulations.

[0066] Meanwhile, by combining the rolling optimization strategy of the MPC, it is ensured that the optimal control quantity output by the MPC can be timely converted into the control signal of the stepping motor, thereby improving the control response speed and control accuracy of the vertical lighting angle of the headlamp.

[0067] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the simple structure of the control system of the vehicle headlamp according to the present invention.

[0068] A control system of a vehicle headlamp includes a wheel speed sensor 101, a plurality of vehicle body height sensors 102, a control device 103 for the vertical lighting angle, and a stepping motor 104.

[0069] The wheel speed sensor 101 is installed on the wheel bearing, the wheel speed assembly or the transmission output shaft, and monitors the motion state of the vehicle in real time and obtains speed and acceleration data.

[0070] By using the speed and acceleration to analyze the acceleration and deceleration changes of the vehicle under different working conditions, the pitch trend of the vehicle under the action of the longitudinal inertia force can be indirectly reflected, thereby providing auxiliary observation information for the change of the vehicle vertical attitude (especially the pitch angle).

[0071] The vehicle body height sensors 102 are respectively installed at the front axle center, the rear axle center and the center of mass position of the vehicle, and monitor the change of the vehicle vertical state in real time to obtain the corresponding vehicle body height change information.

[0072] Accordingly, through the vertical displacement measurement of the three points of the front axle, the rear axle and the center of mass, the vertical dynamic responses of the front part, the rear part and the overall center of mass of the vehicle can be respectively extracted, effectively depicting the pitch trend of the vehicle during longitudinal driving, and then providing more comprehensive and accurate observation support for the change of the vehicle vertical attitude.

[0073] The control device 103 for the vertical lighting angle receives the speed and acceleration data from the wheel speed sensor and the vehicle body height change information from the vehicle body height sensor, and then calculates the optimal lighting angle to obtain the actual vertical control signal of the headlamp.

[0074] In addition, in order to improve the recognition accuracy and environmental adaptability of the vehicle's dynamic state, the vertical lighting angle control device 103 can further combine Vehicle-to-Infrastructure (V2I) communication technology to obtain the position information and basic environmental information of the current road section, such as, but not limited to, ramp angle, road surface adhesion coefficient, climate conditions, etc., for normalizing or weighted correction of vehicle speed, acceleration, and vehicle body height change data.

[0075] Meanwhile, the vertical lighting angle control device 103 can also collect information such as the speed, acceleration, and relative position of other vehicles in the current road environment through Vehicle-to-Vehicle (V2V) communication technology, realize multi-vehicle data collaborative perception, and thus further improve the judgment accuracy of the vehicle's vertical dynamic changes, providing a higher-precision input basis for headlamp control.

[0076] The stepper motor 104 adjusts the vertical angle of the headlamp according to the actual vertical control signal of the headlamp to complete the optimization of the optimal vertical lighting angle.

[0077] Among them, the stepper motor receives the actual vertical control signal of the headlamp, drives the internal stator coil to generate a corresponding electromagnetic torque, and gradually rotates the vertical angle of the vehicle headlamp in a fixed step manner, thereby realizing high-precision control of the vertical angle of the headlamp.

[0078] Since the stepper motor can perform high-precision control on the rotation angle of the headlamp according to each received pulse signal, the optimal control quantity output by model predictive control (MPC) rolling optimization can be directly converted into the control signal of the stepper motor to realize continuous, stable, and fine angle adjustment operations of the headlamp, thereby effectively avoiding the lighting angle deviation problem caused by control hysteresis or insufficient accuracy.

[0079] Based on the above design, the present invention proposes a control method for the vertical lighting angle of a vehicle headlamp, and based on this method, a control device for the vertical lighting angle of a vehicle headlamp is proposed to serve as the vertical lighting angle control device 103.

[0080] Please also refer to Figure 2 and Figure 3 , Figure 2 which is a schematic diagram of the simple structure of the vertical lighting angle control device described in the present invention, Figure 3 and which is a schematic diagram of the simple process of the control method for the vertical lighting angle of the vehicle headlamp described in the present invention.

[0081] The control device 103 for the vertical illumination angle of the vehicle headlamp includes a vehicle state information acquisition unit 1, a vehicle pitch angle state estimation unit 2, a vehicle pitch angle prediction unit 3, a model predictive control unit 4, and a control signal conversion unit 5.

[0082] The vehicle state information acquisition unit 1 is configured to perform step S1: acquire the vehicle state information at the current moment.

[0083] Specifically, speed and acceleration data, and body height change information are acquired, and timestamp synchronization is performed on them to obtain the vehicle state information at the current moment.

[0084] Accordingly, by performing timestamp synchronization processing on different data sources, the reference benchmark of each sensor data on the time axis is unified to ensure that the extracted information corresponds to the same sampling moment, thereby constituting consistent vehicle state information.

[0085] It should be noted that in addition to timestamp synchronization, various timing registration algorithms such as interpolation alignment and inertial navigation fusion can also be combined to process data from different sources to adapt to the deployment differences of different vehicle types or sensor systems. Therefore, the integration method of vehicle state information is not specifically limited in the present invention.

[0086] The vehicle pitch angle state estimation unit 2 is configured to perform step S2: estimate the pitch angle of the vehicle state information at the current moment to obtain the estimated value of the vehicle pitch angle at the current moment.

[0087] Generally speaking, an Unknown Input Observer (UIO) is an algorithm that can still achieve system state estimation in the case of unmeasurable disturbances or unobservable partial inputs in the system. Its basic principle is that on the basis of the system state space model, by introducing a gain matrix and a compensation matrix, an observer structure that can eliminate the influence of unknown inputs is constructed, so that even if there are unknown input terms, the system state estimation error can asymptotically converge to zero, thereby ensuring the stability and accuracy of the estimation process.

[0088] In one embodiment, the vehicle pitch angle state estimation unit 2 uses an unknown input observer for pitch angle estimation, which is specifically expressed as follows:

[0089] In the formula, represents the estimated value of the vehicle pitch angle at the current moment , represents the estimation coefficient of the pitch angle, which is used to obtain the vehicle pitch angle in the estimation vector, and specifically it is ; is the state estimation value, which is used to represent the estimated value of the state vector of the vehicle obtained by state estimation through an unknown input observer at the current moment The unknown input observer is specifically expressed as follows:

[0090] In the formula, is the internal state of the unknown input observer; The known input term is obtained through discretization derivation of the state space equation based on the vertical dynamics equation; The relevant input vector representing the change in vehicle body height, that is, the relevant data in the vehicle body height change information; is the gravitational acceleration; is the measurement feedback correction term, is the observable output vector, that is , represents the state vector of the vehicle, is the observation matrix, specifically ; is the measurement feedback gain matrix, which is used to correct the response deviation of the observer to the measurement output and improve the estimation stability; is the error compensation matrix, which is used to cancel the conduction effect of the unknown input on the estimation error. The unknown input is set as the pitching moment caused by the vehicle acceleration in the state space equation based on the vertical dynamics equation , since this moment cannot be directly measured by the sensor, it is modeled as an unknown input term in the state space equation, so as to construct an unknown input observer for error elimination; is the error dynamic main matrix, which is used to define the dynamic evolution behavior of the state estimation error in the observer and needs to meet the constraint conditions to achieve error convergence; is the known input correction matrix, which is used to ensure that the known inputs and remain unchanged in direction in the observer structure, avoiding estimation deviation caused by the interference of the error compensation term.

[0091] Among them, The matrix specifically follows the following constraints to ensure the convergence of the state error:

[0092]

[0093]

[0094]

[0095]

[0096] In the formula, represents the identity matrix, which is used to keep the matrix dimensions consistent; is the main feedback gain, which is used to ensure the closed-loop stability of the convergence of the system state estimation; is the error compensation term correction gain, which is used to compensate for the deviation caused by .

[0097] Among them, through the above constraints, it can be ensured that when there are unknown inputs in the state space equation, the estimated value of the corresponding vehicle state can still be correctly estimated according to the known inputs , and its constraints are specifically obtained through the derivation of state error convergence; At the same time, in the present invention, the pitching force of the vehicle is modeled as a vertical dynamics equation, and a system state space model is constructed according to the vertical dynamics equation, so as to derive the key matrix for observer calculation , and the state space equation based on the vertical dynamics equation is constructed by deriving from the vertical dynamics equation, and the vertical dynamics equation is specifically expressed as:

[0098]

[0099] In the formula, is the vertical displacement of the vehicle's center of mass, which is used to represent the displacement of the vehicle's center of mass in the vertical direction, that is, the up and down movement of the vehicle body when the road surface is uneven or the vehicle is moving, represents the second derivative of the vertical displacement, that is, the vertical acceleration, which is used to represent the magnitude of the acceleration of the vehicle's center of mass in the vertical direction; is the mass of the vehicle, which is used to represent the inertial parameter of the whole vehicle and generally includes the load of the current vehicle; is the gravitational acceleration, which is a known constant; is used to represent the balance relationship of the vehicle in the vertical direction, which is supported by the elastic forces of the front and rear suspensions and affected by gravity; is the pitching angle of the vehicle, which is used to represent the angular change of the vehicle's front and rear pitching, represents the second derivative of the vehicle's pitching angle, that is, the pitching angular acceleration, which is used to describe the magnitude of the acceleration of the vehicle body rotating around the transverse axis; represents the inertial moment of the vehicle center, which is used to represent the inertia of the vehicle when rotating around the pitching axis; is used to represent that the pitching rotation of the vehicle around the transverse axis is affected by the moments of the front and rear suspensions and the acceleration moment of the vehicle; and respectively represent the vertical forces exerted on the vehicle body by the front suspension and the rear suspension. The suspension is generally used to support the vehicle weight and absorb the impacts from the road surface. The specific calculation of the vertical force exerted by the suspension on the vehicle body is as follows:

[0100]

[0101] In the formula, and are respectively the first derivative of the vertical displacement of the vehicle center of mass and the first derivative of the vehicle pitch angle, representing the vertical velocity of the vehicle center of mass and the vehicle pitch angular velocity respectively; and respectively represent the elastic stiffness of the front suspension and the rear suspension, which is used to represent the rigidity of the suspension. The elastic stiffness determines the supporting ability of the suspension for the vehicle. The greater the elastic stiffness, the smaller the vertical displacement of the vehicle body; and respectively represent the damping ratio coefficients of the front suspension and the rear suspension, which are used to represent the buffering ability of the suspension. The buffering ability determines the degree of vibration absorption of the vehicle; and respectively represent the distances from the vehicle center of mass to the front wheels and to the rear wheels; and respectively represent the vertical displacements of the front wheels and the rear wheels of the vehicle, which are used to describe the height differences generated at the contact points of the front and rear wheels due to terrain changes under non-flat roads. They are measured by the vehicle body height sensors installed at the centers of the front axle and the rear axle, and are used to compensate for the additional effects on the suspension under road surface undulations (such as when the vehicle enters or gradually leaves a slope from a horizontal plane, there is a height difference between the front and rear of the vehicle body), so as to improve the accuracy of the vehicle pitch angle estimation; and respectively represent the pitch moments of the front suspension and the rear suspension on the vehicle body, which are used to describe the moments generated by the vertical forces of the suspension on the vehicle center of mass. The specific calculation is as follows:

[0102]

[0103] represents the pitch moment caused by the vehicle acceleration. When the vehicle accelerates or brakes, the forward and backward movement of the vehicle center of gravity will cause additional pitch moments to affect the vehicle body angle.

[0104] Accordingly, after simplifying and arranging the above vertical dynamic equations, the state space equation of the system can be further constructed to describe the dynamic evolution process of the vehicle in the vertical direction. The differential form of its state space equation is as follows:

[0105] In the formula, represents the state vector of the vehicle pitch angle at the current moment and is the first derivative of ; represents the relevant input vector of the vehicle body height change, and ; represents the observation matrix, which is used to determine the observable variables of the system, and the default value is ; represents the state weight matrix, which is specifically expressed as follows:

[0106] represents the influence matrix of the road surface height change, and its specific expression is as follows:

[0107] represents the influence coefficient matrix of gravity, and its specific expression is as follows:

[0108] represents the influence coefficient matrix of the pitching moment, and its specific expression is as follows:

[0109] Accordingly, the above matrix parameters define the transformation of the vehicle pitch dynamic model into a linear state space equation, providing vehicle state observation support for the subsequent observer design. In particular, the above matrix parameters fully consider the pitch motion characteristics caused by ground undulation, slope disturbance, and vehicle longitudinal acceleration and deceleration, effectively improving the accuracy of state estimation.

[0110] In addition, due to the frequent acceleration and deceleration of the vehicle during driving, the vehicle will continuously generate a pitching moment , and it is difficult to directly observe through sensors or even completely unobservable, which will cause serious interference to the estimation of the vehicle pitch angle.

[0111] Therefore, the present invention models the pitching moment as an unknown input term in the state space equation, and constructs an unknown input observer on this basis to still achieve a high-precision estimation of the vehicle state under the premise of lacking disturbance observation.

[0112] In order to eliminate the influence of the unknown input on the state estimation, the gain matrix in the designed unknown input observer structure needs to satisfy certain constraint conditions. Among them, The derivation process of the constraint conditions is shown as follows: T1. Let the original unknown input observer be expressed as follows:

[0113] where, is the internal state of the unknown input observer and is used as the intermediate variable for the estimated state , represents the influence coefficient matrix.

[0114] T2. Combine the original unknown input observer with the state space equation to obtain the combined unknown input observer, which is specifically expressed as follows:

[0115] T3. Define the state estimation error, that is:

[0116] T4. Take the derivative of its state estimation error and substitute it into the combined unknown input observer for simplification to obtain the unconverged state estimation error, which is specifically expressed as follows:

[0117] T5. Perform convergence fitting on the unconverged state estimation error to obtain the constraint of , complete the derivation, and the specific expression of its constraint of is as follows:

[0118]

[0119]

[0120]

[0121]

[0122] where, by setting the constraint of , the influence of the unknown input on the error can be eliminated , the influence of the known input on the offset of the error , the influence of the error generated by the measurement feedback and , so as to obtain a stable system state, that is:

[0123] Accordingly, as long as the matrix All the real parts of the eigenvalues are less than zero, that is, the system error is dynamically stable, and the asymptotic convergence of the system error can be achieved, so as to obtain the current unknown input observer, which is specifically expressed as:

[0124] Therefore, even when the vehicle pitching moment at all times is unobservable, the current unknown input observer can still achieve high-precision estimation of the vehicle pitch angle, thus providing a stable and robust data basis for subsequent vehicle pitch angle prediction and headlight vertical lighting control.

[0125] The vehicle pitch angle prediction unit 3 is used to execute step S3: perform regression prediction on the vehicle pitch angle estimation value and vehicle state information to obtain the vehicle pitch angle in the future time domain.

[0126] Generally speaking, the Auto-Regressive with Exogenous Inputs (ARX) model is a data-driven modeling method, which is often used for time series prediction of systems with dynamic response characteristics. It mainly constructs a regression relationship between the historical values of output variables and external input quantities to describe the change trend of system behavior; the ARX model is often used in financial time series prediction, biomedical signal analysis, energy consumption prediction, etc.

[0127] In one embodiment, the vehicle pitch angle prediction unit 3 uses the auto-regressive exogenous input model for regression prediction, which is specifically expressed as follows:

[0128] In the formula, is the vehicle pitch angle at the th moment, which is the vehicle pitch angle predicted by the auto-regressive exogenous input model; is the historical pitch angle term, which is the regression prediction based on the vehicle pitch angle estimation values at the past moments; represents the historical regression coefficient matrix of the th historical vehicle pitch angle, which is used to control the influence degree of the past moment on the current moment . This is because the change of the vehicle pitch angle usually has time series correlation. Therefore, by regressing historical data to capture the time evolution characteristics of the pitch angle can improve the prediction accuracy; Denotes an exogenous input term, which is used to represent the influence of the vehicle state information at the current and past moments on the prediction of the current vehicle pitch angle. Since the vehicle pitch angle is affected not only by itself, i.e., the historical value of the vehicle pitch angle, but also by the vehicle state information, i.e., external factors, introducing the exogenous input term can further improve the accuracy of the regression prediction, thereby enhancing the robustness of the model; is the exogenous input vector, that is, the vehicle state information at the th moment; Denotes the exogenous input regression coefficient matrix of the th exogenous input vector, which is used to represent the influence degree of different input variables on the pitch angle; is the constant term, which is used to compensate for the offset of the vehicle pitch angle in the system (the pitch angle when the vehicle is static), and is a known constant. Denotes the deviation term, which is generally used to represent the error of the predicted value of the vehicle pitch angle at the sampling moment , that is, the unpredictable factors of the model, and is usually assumed to be Gaussian white noise to quantify the uncertainty of the model.

[0129] Among them, is the time lag parameter, is the lag order of the historical pitch angle, that is, how many moments of the vehicle pitch angle the model traces back for regression; is the lag order of the exogenous input vector, that is, how many historical moments of the vehicle state information the model considers for modeling, which is used to determine the memory depth of the model.

[0130] Among them, by default, the number of time lag parameters needs to be as large as possible to obtain a more accurate model; however, as the combination of time lag parameters increases, the computational complexity will increase. Therefore, the present invention further uses the Bayesian information criterion method for calculation to obtain the corresponding time lag parameters, so as to prevent overfitting and underfitting of the model and ensure that the autoregressive exogenous input model (ARX) achieves the best balance between complexity and prediction accuracy. Its specific optimization objective is expressed as follows:

[0131] In the formula, The optimal time lag parameter, that is, among several different combinations of time lag parameters, select the combination with the smallest BIC value ; is the BIC value of the Bayesian information criterion corresponding to the th combination of time lag parameters. The smaller its value, the lower the prediction error of the ARX model of this combination. Its specific calculation is expressed as follows:

[0132] In the formula, denotes the variance of the autoregressive exogenous input model representing the th combination of time lag parameters, which is used to measure the magnitude of the prediction error of the model; denotes the total number of the historical data set, which is a fixed value; is the model complexity penalty term, which is used to prevent the model from overfitting.

[0133] Among them, for the historical regression coefficient matrix and the exogenous input regression coefficient matrix of the autoregressive exogenous input model, the present invention combines the historical vehicle state information at several moments, the vehicle state information at the current moment, and the estimated / observed values of the vehicle pitch angle corresponding to the vehicle state information at all moments as the historical data set; and uses the principal component analysis method to reduce the dimension of the historical data set, extract the main features, and use the least squares method to input the main features into the untrained autoregressive exogenous input model for regression solution to obtain the historical regression coefficient matrix with the minimum error and the exogenous input regression coefficient matrix ; finally, the Lagrange multiplier is used to perform regularization calculation on the current historical regression coefficient matrix and the exogenous input regression coefficient matrix to obtain the final historical regression coefficient matrix and the exogenous input regression coefficient matrix .

[0134] It should be noted that since the exogenous input vectors in the historical data set of the present invention, that is, the vehicle state information, may have the problem of multicollinearity, resulting in potential phenomena of calculation instability or overfitting, the present invention uses principal component analysis for dimensionality reduction and further uses the Lagrange multiplier for regularization adjustment after solving by the least squares method; and in the implementation process by the user, the quality / quantity of the actually collected historical data set may be different from that of the present invention, and the user can select different training methods according to the specific quality of the historical data set. Therefore, the present invention does not specifically limit the training method of the regression coefficient matrix herein.

[0135] Based on this, the present invention selects the optimal time lag parameter based on the Bayesian information criterion (BIC) to ensure that the ARX model is neither overfitted nor underfitted; at the same time, the principal component analysis (PCA) is used to remove the redundant information in the exogenous input vectors in the historical data set, and the Lagrange multiplier is used to regularize the regression coefficients solved by the least squares method to avoid the influence of the collinearity problem between the input vectors on the regression coefficients, and finally improve the accuracy of the regression prediction of the ARX model.

[0136] The model predictive control unit 4 is configured to perform step S4: perform rolling optimization on a vertical lighting objective function according to the vehicle pitch angle within a future time domain, and obtain a vertical lighting control amount within the future time domain.

[0137] Specifically, the vertical lighting objective function is used to characterize the deviation between the current vertical lighting angle achieved by the stepper motor drive and the desired vertical lighting angle. By minimizing this deviation, precise control of the vertical lighting angle of the headlamp is completed, and it is specifically expressed as follows:

[0138] In the formula, represents the vertical lighting control amount within the future time domain, represents the prediction time domain, which is a user-defined constant, and its , that is, the control amount at each sampling moment within the future time domain obtained by online solving the open-loop optimization through MPC, is specifically expressed as follows:

[0139] In the formula, represents the electromagnetic torque at the th moment predicted forward from the current moment , which is used to enable the stepper motor to control the lighting angle according to this electromagnetic torque; represents the predicted vertical lighting angle driven by the stepper motor at the th moment predicted forward from the current moment , which is obtained by calculating through the state equation of the stepper motor rotation angle, and is specifically expressed as follows:

[0140] In the formula, represents the vertical lighting angle driven by the stepper motor at the th moment, represents the stepper motor control input at the current moment , that is, the electromagnetic torque; is the state equation of the stepper motor rotation angle, which is used to describe how the vertical lighting angle driven by the stepper motor at the next moment is affected by the stepper motor control input, that is, the electromagnetic torque affects it. The state equation of the stepper motor rotation angle is generally determined by the stepper motor model. The stepper motor model is used to describe the relationship between the electromagnetic torque and the load torque, and the simple mathematical expression of the stepper motor model is as follows:

[0141] In the formula, It represents the moment of inertia of the stepper motor, which is used to characterize the "inertial resistance" of the stepper motor to generate angular velocity changes when subjected to external torques. Its value is determined by the structure of the selected motor body and the load characteristics and is usually a constant; It represents the load torque, that is, the mechanical resistance of the headlamp mechanism or other external disturbance torques.

[0142] Accordingly, by discretizing this mathematical expression through numerical integration, the state equation of the stepper motor rotation angle can be approximately obtained .

[0143] It represents the ideal vertical illumination angle at the th future moment, which is used to ensure that the headlamp can dynamically compensate for the body pitch change. Its specific calculation is as follows:

[0144] In the formula, is the ideal angle mapping function, which is used to map the headlamp pitch angle to the ideal vertical illumination angle. Different ideal angle mapping functions can be designed according to different vehicle models, vehicle types, headlamp models or headlamp types. The present invention does not specifically limit the selection of its ideal angle mapping function, and its corresponding example is as follows:

[0145] In the formula, represents the adjustment coefficient, which is used to adjust the compensation intensity and is usually 0.95; is used to offset the influence of the vehicle pitch angle; is used to ensure that the adjustment is based on the vertical illumination angle driven by the stepper motor, thereby reducing the accumulation of adjustment errors.

[0146] Among them, the vertical illumination objective function includes the following constraint conditions:

[0147] In the formula, represents the initial condition constraint, which is used to ensure that the MPC makes predictions based on the vertical illumination angle driven by the stepper motor rather than on the assumed state; represents the state space equation constraint, which is used to ensure that the MPC correctly simulates the change trend of the headlamp angle in the future time domain; represents the control variable constraint, which is used to constrain the physical limit of the electromagnetic torque of the stepper motor. Its and are respectively the minimum and maximum values of the electromagnetic torque, and their specific values are determined according to the specifications of the stepper motor. The present invention does not specifically limit this here.

[0148] The control signal conversion unit 5 is configured to execute step S5: perform signal conversion on the vertical lighting control amount at the first moment to obtain the actual vertical control signal of the headlamp.

[0149] Specifically, extract the vertical lighting control amount within the future time domain wherein the vertical lighting control amount at the first moment .

[0150] Convert the vertical lighting control amount at the first moment into the actual drive signal of the stepper motor to obtain the actual vertical control signal of the headlamp; wherein, converting into the actual drive signal of the stepper motor can adopt PWM signal or current signal, which is used to ensure that the control signal can correctly drive the action of the stepper motor, and the specific conversion method can be selected according to the working mode, drive characteristics and model modulation method of the stepper motor. The present invention does not specifically limit this here.

[0151] Next, send the actual vertical control signal of the headlamp to the stepper motor, so that the stepper motor makes corresponding adjustments to the vertical angle of the headlamp according to the actual vertical control signal, and complete the optimization of the optimal vertical lighting angle.

[0152] Finally, after completing the adjustment of the vertical lighting angle of the headlamp, re - execute step S1 to update the vehicle state information.

[0153] Wherein, the essence of the rolling optimization is that within each control cycle, only the optimal control amount at the current moment is executed, and the optimal solution is recalculated based on the latest state information in the next control cycle.

[0154] Based on this, in order to improve the response speed of the vertical lighting angle of the headlamp when the vehicle is driving on complex terrains (such as slopes, undulating roads), it only needs to increase the frequency of the control cycle (that is, reduce the control interval time), so as to more quickly respond to the dynamic changes of the vehicle attitude, ensure that the headlamp can track the pitch angle adjustment more smoothly, avoid the sharp swing or adjustment delay of the lighting angle caused by sudden attitude changes, and thus improve the stability and safety of night driving.

[0155] Compared with the prior art, the present invention realizes high - precision estimation of the pitch angle of the vehicle under the condition of unobservable disturbances (such as pitch moment caused by acceleration / braking) by introducing an unknown input observer, and combines with the autoregressive exogenous input model (ARX) to dynamically predict the change trend of the vehicle pitch angle within the future time domain, so that the model predictive control (MPC) can optimize the calculation of the control amount in advance based on the future body attitude changes, thereby significantly improving the adaptability of the headlamp system to unstructured road conditions such as terrain undulations and ramp changes, and effectively reducing the safety hazards brought by the lag of the lighting angle adjustment response when the vehicle is driving on uphill, downhill or undulating roads and other scenarios.

[0156] Among them, the autoregressive exogenous input model adopted in the present invention adopts the vehicle body height change information of the front axle, rear axle and center of mass position of the vehicle. Therefore, the prediction of the vehicle pitch angle can reflect the dynamic response of the vehicle to the undulations of the terrain ahead, such as the beginning or end of a ramp under the current working conditions, so that the vehicle can be informed in advance of the upcoming vehicle pitch angle change before entering the ramp section or when the downhill section is about to end, and then the MPC maps the predicted vehicle pitch angle change trend into the desired vertical lighting angle, realizing the forward-looking adjustment of the lighting angle, thereby improving the adaptability to the dynamic road environment and the initiative of lighting control.

[0157] During the control execution stage, the present invention converts the first control quantity among the optimal lighting control quantities in the future time domain obtained by MPC optimization into a driving signal of the stepper motor in real time. Since the stepper motor has the characteristics of pulse-angle response, it can adjust the angle of the lighting component with high precision and high sensitivity, ensuring the continuity and stability of the lighting control response and avoiding frequent lighting jumps or delayed adjustments caused by control lag.

[0158] In summary, the present invention significantly improves the driving safety and lighting comfort of the vehicle when driving at night or on slopes by adopting an unknown input observer to observe the dynamic changes of the vehicle's pitch angle, using the ARX model to predict the vehicle's pitch angle as the core input mapped to the desired vertical lighting angle, and using MPC to make the headlamp lighting angle adjusted by the stepper motor accurately track the vertical lighting angle.

[0159] Based on the same inventive concept, the present application also provides an electronic device, which may be a terminal device such as a server, a desktop computing device or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.). The device includes one or more processors and a memory, wherein the processor is used to execute a program to implement the control method of the vertical lighting angle of the vehicle headlight of the embodiment of the present invention; and the memory is used to store a computer program executable by the processor.

[0160] Based on the same inventive concept, the present application also provides a computer-readable storage medium, corresponding to the embodiment of the aforementioned method for controlling the vertical lighting angle of a vehicle headlight, wherein the computer-readable storage medium stores a computer program thereon, and when the program is executed by a processor, the steps of the method for controlling the vertical lighting angle of a vehicle headlight recorded in any of the aforementioned embodiments are implemented.

[0161] This application may take the form of a computer program product implemented on one or more storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain program code. Computer-usable storage media include both permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0162] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and the present invention is also intended to include these modifications and improvements.

Claims

1. A control method for the vertical illumination angle of a vehicle headlamp, characterized in that, Including the following steps: S1. Obtain the vehicle state information at the current moment; wherein, the vehicle state information includes vehicle speed, vehicle acceleration, and body height change; S2. Estimate the pitch angle of the vehicle state information at the current moment to obtain the estimated value of the vehicle pitch angle at the current moment; S3. Perform regression prediction on the estimated value of the vehicle pitch angle and the vehicle state information to obtain the vehicle pitch angle within the future time domain; S4. Perform rolling optimization on a vertical lighting objective function according to the vehicle pitch angle within the future time domain to obtain the vertical lighting control amount within the future time domain; S5. Perform signal conversion on the vertical lighting control amount at the first moment to obtain the actual vertical control signal of the headlamp, and send the actual vertical control signal of the headlamp to the stepping motor, so that the stepping motor makes corresponding adjustments to the vertical angle of the headlamp according to the actual vertical control signal, and complete the optimization of the optimal vertical lighting angle.

2. The control method for the vertical illumination angle according to claim 1, wherein Step S2 uses an unknown input observer for pitch angle estimation, which is specifically expressed as follows: Wherein, represents the estimated value of the vehicle pitch angle at the current moment , represents the estimation coefficient of the pitch angle, specifically ; is the state estimation value, that is , is the vertical displacement of the vehicle's center of mass is the first derivative of the vertical displacement of the vehicle's center of mass is the vehicle pitch angle is the first derivative of the vehicle pitch angle, and its state estimation value is obtained by state estimation through the unknown input observer. The specific representation of the unknown input observer is as follows: wherein, is the internal state of the unknown input observer; represents the relevant input vector of the vehicle body height change, and its , and respectively represent the vertical displacements of the front and rear wheels of the vehicle; is the gravitational acceleration; is the observable output vector, that is , represents the state vector of the vehicle, is the observation matrix, specifically ; is the measurement feedback gain matrix; is the error compensation matrix; is the error dynamic main matrix; is the known input correction matrix; The matrix specifically follows the following constraints to ensure the convergence of the state error: In the formula, represents the identity matrix; is the main feedback gain; is the correction gain of the error compensation term; is the state weight matrix, which is specifically represented as follows: Wherein, and respectively represent the elastic stiffness of the front suspension and the rear suspension; and respectively represent the damping ratio coefficients of the front suspension and the rear suspension; and respectively represent the distances from the vehicle's center of mass to the front wheels and to the rear wheels; is the mass of the vehicle; represents the moment of inertia of the vehicle center; is the influence matrix of the road surface height change, and its specific representation is as follows: is the influence coefficient matrix of gravity, specifically expressed as ; is the influence coefficient matrix of the pitching moment, specifically expressed as .

3. The control method for the vertical illumination angle according to claim 2, characterized in that, Use an autoregressive exogenous input model to perform regression prediction on the estimated value of the vehicle pitch angle and the vehicle state information at the current moment, which is specifically expressed as follows: In the formula, is the vehicle pitch angle at the th moment; represents the historical regression coefficient matrix of the th historical vehicle pitch angle estimate; is the exogenous input vector, that is, the vehicle state information at the th moment; represents the exogenous input regression coefficient matrix of the th exogenous input vector; is the constant term, represents the bias term; is the time lag parameter.

4. The control method for the vertical illumination angle according to claim 3, characterized in that The time lag parameter is calculated and obtained through the following method: In the formula, Optimal time lag parameter; is the BIC value of the Bayesian information criterion corresponding to the th combination of time lag parameters, and its specific calculation is as follows: In the formula, represents the variance of the autoregressive exogenous input model for the th combination of time lag parameters; represents the total number of the historical data sets; is the model complexity penalty term.

5. The control method for the vertical illumination angle according to claim 4, wherein The specific expression of the vertical lighting objective function is as follows: In the formula, represents the vertical lighting control amount in the future time domain, represents the prediction time domain, and the specific representation of its vertical lighting control amount is as follows: In the formula, represents the electromagnetic torque at the th moment predicted forward from the current moment; represents the vertical illumination angle predicted forward from the current moment at the th moment, which is obtained by calculating the state equation of the stepping motor rotation angle, and is specifically expressed as follows:​ In the formula, represents the vertical illumination angle driven by the stepper motor at the th future moment, represents the stepper motor control input at the current moment , that is, the electromagnetic torque; is the state equation of the stepper motor rotation angle. The state equation of the stepper motor rotation angle is determined by the stepper motor model. The simple mathematical expression of the stepper motor model is as follows: In the formula, represents the moment of inertia of the stepper motor; represents the load torque; Indicates the ideal vertical illumination angle at the next moment, which is used to ensure that the headlamp can dynamically compensate for the body pitch change. Its specific calculation is as follows: In the formula, is the ideal angle mapping function, which is used to map the headlight pitch angle to the ideal vertical illumination angle; Wherein, the vertical lighting objective function includes the following constraint conditions: In the formula, represents the initial condition constraint; Indicates the state space equation constraint; represents the control quantity constraint, which and are the minimum and maximum values of the electromagnetic torque respectively.

6. A control device for the vertical illumination angle of a vehicle headlamp, characterized in that, Including a vehicle state information acquisition unit, a vehicle pitch angle state estimation unit, a vehicle pitch angle prediction unit, a model predictive control unit, and a control signal conversion unit; The vehicle state information acquisition unit is used to obtain the vehicle state information at the current moment; wherein, the vehicle state information includes vehicle speed, vehicle acceleration, and body height change; The vehicle pitch angle state estimation unit is used to estimate the pitch angle of the vehicle state information at the current moment to obtain the estimated value of the vehicle pitch angle at the current moment; The vehicle pitch angle prediction unit is used to perform regression prediction on the estimated value of the vehicle pitch angle and the vehicle state information to obtain the vehicle pitch angle within the future time domain; The model predictive control unit is used to perform rolling optimization on a vertical lighting objective function according to the vehicle pitch angle within the future time domain to obtain the vertical lighting control amount within the future time domain; The control signal conversion unit is used to perform signal conversion on the vertical lighting control amount at the first moment to obtain the actual vertical control signal of the headlamp, and send the actual vertical control signal of the headlamp to the stepping motor, so that the stepping motor makes corresponding adjustments to the vertical angle of the headlamp according to the actual vertical control signal, and complete the optimization of the optimal vertical lighting angle.

7. The control device for the vertical illumination angle according to claim 6, characterized in that The vehicle pitch angle state estimation unit uses an unknown input observer for pitch angle estimation, and its specific expression of pitch angle estimation is as follows: wherein, represents the estimated value of the vehicle pitch angle at the current moment ; represents the estimation coefficient of the pitch angle, specifically ; is the state estimation value, that is , is the vertical displacement of the vehicle's center of mass is the first derivative of the vertical displacement of the vehicle's center of mass is the vehicle pitch angle is the first derivative of the vehicle pitch angle, and its state estimation value is obtained through state estimation by the unknown input observer, and the specific representation of the unknown input observer is as follows: wherein, is the internal state of the unknown input observer; represents the relevant input vector of the vehicle body height change, where , and respectively represent the vertical displacements of the front and rear wheels of the vehicle; is the gravitational acceleration; is the observable output vector, that is , represents the state vector of the vehicle, is the observation matrix, specifically ; is the measurement feedback gain matrix; is the error compensation matrix; is the error dynamic main matrix; is the known input correction matrix; The matrix specifically follows the following constraints to ensure the convergence of the state error: wherein, represents the identity matrix; is the main feedback gain; is the correction gain of the error compensation term; is the state weight matrix, which is specifically expressed as follows: Wherein, and respectively represent the elastic stiffness of the front suspension and the rear suspension; and respectively represent the damping ratio coefficients of the front suspension and the rear suspension; and respectively represent the distances from the vehicle's center of mass to the front wheels and to the rear wheels; is the mass of the vehicle; represents the moment of inertia of the vehicle center; is the influence matrix for the change in road surface height, and its specific representation is as follows: is the influence coefficient matrix of gravity, specifically expressed as ; is the influence coefficient matrix of pitching moment, specifically expressed as .

8. The control device for the vertical illumination angle according to claim 7, characterized in that The vehicle pitch angle prediction unit uses an autoregressive exogenous input model for regression prediction, which is specifically expressed as follows: In the formula, is the vehicle pitch angle at the th moment; represents the historical regression coefficient matrix of the th historical vehicle pitch angle estimate; is the exogenous input vector, that is, the vehicle state information at the th moment; represents the exogenous input regression coefficient matrix of the th exogenous input vector; is the constant term, representing the bias term; is the time lag parameter, and the time lag parameter is obtained by the following method: In the formula, Optimal time lag parameter; is the BIC value of the Bayesian information criterion corresponding to the th combination of time lag parameters, and its specific calculation is as follows: In the formula, represents the variance of the autoregressive exogenous input model for the th combination of time lag parameters; represents the total number of the historical data sets; is the model complexity penalty term.

9. The control device for the vertical illumination angle according to claim 8, characterized in that, The specific expression of the vertical lighting objective function is as follows: In the formula, represents the vertical lighting control amount in the future time domain, represents the prediction time domain, and the specific representation of its vertical lighting control amount is as follows: wherein, represents the electromagnetic torque at the future th moment predicted forward from the current moment; represents the vertical illumination angle driven by the stepping motor predicted at the future th moment predicted forward from the current moment, which is obtained by calculating according to the state equation of the stepping motor rotation angle, and is specifically represented as follows: In the formula, represents the vertical illumination angle driven by the stepper motor at the th future moment, represents the stepper motor control input at the current moment , that is, the electromagnetic torque; is the state equation of the stepper motor rotation angle. The state equation of the stepper motor rotation angle is determined by the stepper motor model. The simple mathematical expression of the stepper motor model is as follows: In the formula, represents the moment of inertia of the stepper motor; represents the load torque; Indicates the ideal vertical illumination angle at the next moment, which is used to ensure that the headlamp can dynamically compensate for the body pitch change. Its specific calculation is as follows: In the formula, is the ideal angle mapping function, which is used to map the headlight pitch angle to the ideal vertical illumination angle; Wherein, the vertical lighting objective function includes the following constraint conditions: In the formula, represents the initial condition constraint; Represents the state-space equation constraint; represents the control quantity constraint, which and are the minimum and maximum values of the electromagnetic torque, respectively.

10. A control system for a vehicle headlamp, characterized in that, Including a wheel speed sensor, several body height sensors, a control device for the vertical lighting angle, and a stepping motor; The wheel speed sensor is installed on the wheel bearing, wheel speed assembly or transmission output shaft to monitor the motion state of the vehicle and obtain speed and acceleration data; The body height sensors are respectively installed at the center of the front axle, the center of the rear axle and the centroid position of the vehicle to monitor the vertical state change of the vehicle so as to obtain the corresponding body height change information; The control device for the vertical illumination angle receives the speed and acceleration data from the wheel speed sensor and the body height change information from the body height sensor, and then calculates the optimal illumination angle to obtain the actual vertical control signal of the headlamp; The stepper motor adjusts the vertical angle of the vehicle's headlamp according to the actual vertical control signal of the headlamp to complete the optimization of the optimal vertical illumination angle.

Images