CN102166982A - Vehicle light control method for adaptive front lighting system - Google Patents

Abstract

The invention discloses a car light control method of an adaptive front light system, the method is as follows: the car light steering angle of the self-adaptive front light AFS system is determined by the accelerator pedal, the brake pedal, the clutch pedal, the gear position, and the steering wheel angle And determine the height information of the front and rear axles; simulate the vehicle attitude and speed according to the characteristics of the AFS dynamic model, and combine different models and road conditions to obtain the steering angle of the headlight; combine the experience range of the headlight left and right, and pitch turning angles to create a language Control rules, and perform fuzzy reasoning based on them to construct a fuzzy control rule table; obtain fuzzy output judgments by calculating fuzzy relations, and use defuzzification to obtain the actual control amount of the lamp rotation angle, and then output the motor control value to the motor drive module.

Description

Vehicle light control method for adaptive front lighting system

technical field

The invention relates to a light control algorithm of an automobile self-adaptive headlight system based on information such as an accelerator pedal, a brake pedal, a clutch pedal, a gear position, a steering wheel angle, and heights of front and rear axles.

Background technique

At present, the headlight system of a vehicle is mainly composed of low beams, high beams, running lights and front fog lights. When the car is driving on urban roads and at the speed limit, the low beam is mainly used; when driving at high speed on country roads or expressways, the high beam is mainly used; when driving in foggy weather, the fog lights should be turned on; Running lights (European standard). But in actual use, there are many problems in the traditional headlight system. For example, the short-range lighting effect of existing low beam lights is very bad. When the traffic situation is more complicated, drivers often turn on the low beam lights, high beam lights and front fog lights; Seriously affect the driver's judgment of obstacles on the curve; when the vehicle is driving in rainy days, the water on the ground reflects the light of the headlights, resulting in reflected glare.

The existence of the above-mentioned problems makes it necessary to develop a headlight system with multiple lighting functions, and for safety reasons, the switching of these functions must be automatically realized.

The AFS adaptive headlight system is a light follow-up system that makes the light axis of the low beam rotate left and right in the horizontal direction and swing up and down in the vertical direction. The system consists of sensor groups, transmission channels, electronic control units and execution Institutional composition. The headlights are automatically adjusted and controlled based on information such as the accelerator pedal, brake pedal, clutch pedal, gear position, steering wheel angle, and height of the front and rear axles, so that the driver can obtain better visual effects. Therefore, driving at night, front intersections, and curves can significantly improve the driver's visibility of the surrounding environment, effectively reduce the driver's fatigue when driving on curves at night, and enable the driver to see clearly the actual situation at the curve. road conditions, and thus have sufficient time to deal with emergencies, thereby significantly improving driving safety.

Contents of the invention

The purpose of the present invention is to provide a fuzzy PID-based AFS car light control algorithm for the defects in the prior art, and collect information such as accelerator pedal, brake pedal, clutch pedal, gear position, steering wheel angle, and front and rear axle heights through a sensor group. The characteristics of the AFS dynamic model simulate vehicle attitude information and vehicle speed information, send this information to the fuzzy PID control unit, and modify the control parameters K _P , _KI and K _D of the motor controlling the headlights online, so as to realize the self-adaptive front of the vehicle. Light control in the lighting system.

In order to achieve the above object, the present invention adopts the following technical solutions:

The vehicle light control method of the self-adaptive headlight system of the present invention comprises the following steps:

1) The steering angle of the lights of the adaptive headlight AFS system is determined by the accelerator pedal, brake pedal, clutch pedal, gear position, steering wheel angle, and front and rear axle height information;

2) According to the characteristics of the AFS dynamic model, the vehicle attitude and speed are simulated, and the steering angle of the lights is obtained by combining different vehicle types and road conditions;

3) Combined with the experience range of left and right headlights and pitch turning angles, create language control rules, and perform fuzzy reasoning based on them to build a fuzzy control rule table;

4) Obtain the fuzzy output judgment by calculating the fuzzy relationship, and use defuzzification to obtain the actual control amount of the lamp rotation angle, and then output the motor control value to the motor drive module.

The car light control algorithm of the self-adaptive headlight system of the present invention is an intelligent PID control algorithm, which can automatically adjust PID parameters according to the actual situation on site, and combines PID control with an expert system to realize optimal control. Since various semaphores and evaluation indicators in the control process are not easy to quantitatively express, the use of fuzzy theory is an effective way to solve this problem. Fuzzy rules are used to perform fuzzy reasoning on the actual response and automatically achieve the best adjustment of PID parameters. Compared with other existing technologies, on the basis of accurate modeling of the motor, the light control algorithm of the adaptive headlight system of the present invention has good control quality, strong robustness, simple algorithm, convenient debugging, and applicability extensive features.

Description of drawings

Figure 1 is the calculation model of the lateral angle of the headlight.

Figure 2 is the calculation model of the pitch angle of the vehicle body.

Fig. 3 is the structure of the fuzzy adaptive PID control system.

Fig. 4 is a schematic diagram of the control system of the present invention.

Detailed ways

As shown in Figure 4, the AFS system mainly uses information fusion and intelligent control technology to control the lights to realize the two automatic steering functions of the lights. The invention will be described in detail below with reference to the drawings and specific embodiments.

1 Steering angle calculation

1.1 left and right follow-up steering angle

Because the light of traditional headlights is consistent with the driving direction of the vehicle, it is inevitable that there will be dark areas of illumination. The present invention calculates the rotation angle of the headlight in real time according to the angle of the steering wheel and the vehicle speed information, combined with the empirical calculation formula of the lateral angle and the experimental model, so as to ensure that the lighting direction of the headlight moves with the steering of the vehicle, thus increasing the number of vehicles. The factor of safety when driving in curves.

The left and right follow-up steering angle is calculated using the lateral steering angle calculation formula recommended by SAE (see Figure 1 for the geometric model):

This formula is an empirical formula, where H represents the installation height of the headlight, and R represents the turning radius of the vehicle. The turning radius R is a variable that is difficult to measure directly in the specific driving process. Generally, there are two methods to calculate the turning radius:

① Use the lateral acceleration sensor to calculate the turning radius;

②Use the steering wheel angle sensor to calculate the turning radius.

The latter method is used here to calculate the turning radius R. Assuming that L is the wheelbase and β is the steering angle of the outer steering wheel, then R is approximately equal to L/sinβ (Ackermann's principle). Under normal circumstances, the steering wheel angle and the steering wheel angle β are in a certain proportional relationship (generally 10:1 for cars).

However, the cornering braking characteristics of different vehicles are quite different, and even the same vehicle has different braking capabilities depending on the load, vehicle conditions, and road surface; in addition, the calculation of the turning radius also ignores the anti-deflection stiffness of the front and rear wheels and the change of the body load. And other factors. Therefore, the above calculations only provide a theoretical basis, and a more effective left-right follow-up steering angle must be obtained through the correction of a large number of experimental data.

1.2 Body pitch steering angle

The car body will change the pitch angle due to the difference of the front and rear loads, so the angle of the light emitted by the lights installed on the car body will also change, which will have an adverse effect on night driving safety. In the present invention, after the system detects the amount of change in the pitch angle of the vehicle body, the headlight axis is restored to the original horizontal state through model calculation.

Install two vehicle body height sensors at the front and rear axle terminals of the vehicle body to obtain the height change of the front and rear axles, and calculate the trim angle of the vehicle body based on the wheelbase (see Figure 2 for the geometric model). In the figure: L is the wheelbase of the vehicle; a is the pitch angle of the vehicle body; dHf is the change in front wheel height; dHr is the change in rear wheel height, and the calculation formula can be obtained:

$\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; the\; tan</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; wxya</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; f</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; )</span>$

However, since the suspension is in the process of non-stop vibration during the driving process of the vehicle, and the frequency and amplitude are large, what the actual AFS needs is the near-steady-state variation caused by the body load and acceleration and deceleration of the body, not the Transient changes caused by roughness, tire force, vehicle roll, etc. Therefore, the calculated value must also be filtered to filter out high-frequency interference, and a method such as multiple averaging is used to calculate the steady-state quantity.

1.3 Steering Angle Range Limitation

When the steering wheel angle is greater than or equal to ±10 degrees, and the vehicle speed is greater than or equal to 10Km/h, AFS is enabled; when the steering wheel angle is less than ±10 degrees, or the vehicle speed is less than 10Km/h, AFS is turned off, and the lights are in the horizontal direction back to the original position.

When the front and rear inclination angle of the vehicle body is greater than or equal to ±2 degrees, and the vehicle speed is greater than or equal to 10Km/h, AFS is enabled; when the front and rear inclination angle of the vehicle body is less than ±2 degrees, or the vehicle speed is less than 10Km/h, AFS is turned off, and the lights Return to the original position in the vertical direction.

2PID control

PID controller is a kind of linear controller, which forms a control deviation according to the given value rin(t) and the actual output value yout(t), and its discrete computer PID control rule is:

$\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; P</span>}}\mathrm{<span\; class="notranslate">\; error</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; I</span>}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; error</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}\frac{\mathrm{<span\; class="notranslate">\; error</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; error</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}$

In the formula, K _P , K _I and K _D are proportional coefficients, integral coefficients and differential coefficients respectively, T is the sampling period, k is the sampling number, error(k-1) and error(k) are the (k-1)th and the light angle deviation signal obtained at the kth moment.

Conventional PID control algorithm is widely used in process control because of its small amount of calculation, good real-time performance, and easy implementation. When the precise mathematical model of the controlled object is established, as long as the parameters K _P , _KI and K _D are set correctly, the PID controller can realize its function. But for nonlinear, time-varying and other uncertain factors, conventional PID control effect is not ideal. Therefore, the parameters K _P , K _I and K _D of the conventional PID controller should generally be different under different working conditions in order to obtain good control effects.

3 fuzzy control rules

Since the conventional PID control has the above problems, and AFS itself can be regarded as a fuzzy system, the conventional PID control and fuzzy control are combined, and the empirical parameters obtained from the experiment are used to construct fuzzy control rules to automatically correct the PID parameters online, so as to A fuzzy adaptive motor control strategy is formed.

The fuzzy self-tuning of PID parameters is to continuously detect the error e (the difference between the set value of the lamp rotation angle and the actual feedback value of the lamp rotation angle) and the error change rate ec during the operation of the system, and perform online control of the three PID control parameters according to the fuzzy control rules. Modified to meet the different requirements for control parameters in different e and ec, so that the controlled object has good dynamic and static performance.

The main function of the AFS controller is to realize the precise control of the angle of the headlights, and reduce the system overshoot and adjustment time. Therefore, the light control algorithm of the AFS adaptive headlight system adopts a fuzzy controller with two inputs and three outputs. The fuzzy adaptive PID controller takes the error e and the error change rate ec as input, and takes the control parameters K _P , K _I and K _D of the motor driver controlling the headlight as output, and uses the fuzzy control rules to realize the three-dimensional control of the PID controller. The parameters are modified online, and the structure of its control system is shown in Figure 3.

3.1 Fuzzification

Set the linguistic variable of the input error e as E, the linguistic variable of the error rate of change ec as EC, the domains of both are set as {-3, -2, -1, 0, 1, 2, 3}, and divide There are 7 levels, and the corresponding language variables are {Negative Big (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), Positive Big (PB)} ; The linguistic variable of the output proportional coefficient K _P is KP, the linguistic variable of the integral coefficient K _I is KI, and the linguistic variable of the differential coefficient K _D is KD, and the domains of the three are all set as {0, 1, 2, 3}, Divided into 4 grades, the corresponding language variables are {zero (ZO), positive small (PS), positive middle (PM), positive big (PB)}. There are many shapes of membership function curves, such as trapezoid, bell shape, triangle, etc. Considering the coverage and sensitivity of the domain of discourse, as well as the principle of stability and robustness, each fuzzy subset adopts triangle as the membership function curve.

3.2 Fuzzy reasoning

The tuning of PID parameters must take into account the role of the three parameters at different times and the relationship between them. The principle is: when the error is large, the main point is to eliminate the error as soon as possible; while when the error is small, attention should be paid to the occurrence of overshoot, and the stability of the system should be the main starting point. According to the experience summary of the manual control strategy of the lights, the fuzzy control rules of the input and output variables are listed in the following table.

Fuzzy control rule table

3.3 Defuzzification

What is obtained by fuzzy reasoning is a fuzzy quantity, and what is ultimately needed is to output a precise control quantity. Defuzzification is to realize the conversion from fuzzy quantity to precise quantity. There are many calculation methods for defuzzification, the most common ones are center of gravity method, height method and weighted average method. The present invention adopts a defuzzification method widely used in industrial control—weighted average method. This method multiplies each element x _i = 1, 2, ..., n in the domain of discourse by it as the weighting coefficient of the membership degree μ(i) of the output fuzzy set to be judged, and takes the product sum of the two. The mean x ₀ of , that is:

${\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}$

The average value x ₀ is the judgment result obtained for the fuzzy set by applying the weighted average method. Finally, the actual value of the control quantity can be obtained by multiplying the quantization factor by x ₀ , that is, the actual control parameters K _P , _KI and K _D of the lamp rotation angle.

Claims (1)

1. A car light control method of an adaptive headlamp system, characterized in that it comprises the steps: 1) The steering angle of the lights of the adaptive headlight AFS system is determined by the accelerator pedal, brake pedal, clutch pedal, gear position, steering wheel angle, and front and rear axle height information; 2) According to the characteristics of the AFS dynamic model, the vehicle attitude and speed are simulated, and the steering angle of the lights is obtained by combining different vehicle types and road conditions; 3) Combined with the experience range of left and right headlights and pitch turning angles, create language control rules, and perform fuzzy reasoning based on them to build a fuzzy control rule table; 4) Obtain the fuzzy output judgment by calculating the fuzzy relationship, and use defuzzification to obtain the actual control amount of the lamp rotation angle, and then output the motor control value to the motor drive module.

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