CN117360139A - ECAS system vehicle body control method based on fuzzy control - Google Patents

Abstract

The invention discloses a fuzzy control-based ECAS system body control method, which belongs to the field of automotive electronics technology and solves the problems of traditional control systems, complex principles and low control accuracy in the existing technology, including highly adjusting the ECAS system. Design the control strategy and determine the main switching parameters, and then process the signals collected by the sensors through the vehicle to determine the driving conditions of the vehicle, and control the charging and deflation according to the driving conditions to achieve high, medium and low levels of bodywork Height adjustment, and finally use fuzzy control theory to build a controller model to simulate static and dynamic body height adjustment of the vehicle. The invention is insensitive to the influence of parameter changes and nonlinear characteristics, making fuzzy control more reliable and stable in practical applications, has strong robustness, can handle uncertainty and nonlinear problems, and has strong adaptability. The decisions of fuzzy control are derived from fuzzy rule reasoning, and the response speed is faster.

Description

A fuzzy control-based vehicle body control method for ECAS system

Technical field

The invention belongs to the field of automotive electronic technology, and specifically relates to a fuzzy control-based ECAS system body control method.

Background technique

With the rapid development of the automotive electronics industry, automotive electronic control systems have become an indispensable part of automotive China. As the information source of automobile electronic control systems, automobile sensors can accurately measure external physical quantities such as temperature, pressure, position, height, acceleration, etc. in real time. Their performance has become a key factor in measuring the level of automobile electronic control systems. The controller is the core part of the electronically controlled air spring system, and its quality is directly related to the performance of the vehicle equipped with the ECAS system. Compared with modern small passenger cars, commercial vehicles have larger loads and higher operating intensity, but the development level of their automotive electronic control systems is relatively backward. In particular, the development status of commercial vehicle suspension systems does not match existing needs to a high degree. At present, electronic-controlled air suspension systems (electronic-controlled air suspension, hereinafter referred to as ECAS systems) have gradually become a hot spot in the research and development of electronic control systems for commercial vehicles. The ECAS system actively adjusts the suspension height according to driving conditions, allowing the vehicle to select different vehicle body heights according to different road conditions. Compared with traditional air suspension controlled by a mechanical height valve, it can adapt to more driving conditions. The ECAS system is mainly composed of electronic control unit (ECU), solenoid valve, height sensor, shock absorber, guide mechanism, air spring and other components. Its basic working principle is that the height sensor is responsible for detecting changes in vehicle height, and transmits this information to the ECU. Then the ECU integrates the input information, determines the current vehicle status, and activates the solenoid valve according to its internal control logic. The solenoid valve realizes Adjust the inflation and deflation of each air spring. The ECAS system can not only improve ride comfort, but also reduce the damage caused by the wheels to the road surface. Therefore, the ECAS system has been widely used in high-end passenger vehicles such as buses and trucks in developed countries such as Europe and the United States. Major famous automobiles Manufacturing companies all have their own related ECAS products. Each heavy-duty vehicle has a large load capacity and causes great damage to the road surface. The use of the ECAS system can greatly reduce the damage to the road surface caused by the wheels. Therefore, it is of great significance to equip trucks with an ECAS system.

The existing ECAS system mainly controls the vehicle body height. The height switching control adopts a hierarchical structure and adjusts the vehicle body height according to the signals collected by the sensors. When the vehicle is in an emergency to avoid obstacles, turn, etc., it is prone to unstable conditions such as yaw. , the vehicle will deviate from the ideal trajectory, and the vehicle's handling stability and safety are not strong. Although many scholars have proposed some improvements to the ECAS system, after the car is stimulated, the ECU may still not handle the situation well. For example, when the vehicle height reaches the target value, the airbag will continue to expand until the vertical force of the air spring and its static load reach the Balance, therefore, the existing ECAS system has certain limitations, and it is necessary to select a suitable control algorithm to achieve precise vehicle height adjustment and avoid the phenomena of "overcharge" and "overdischarge".

Contents of the invention

The purpose of the present invention is to address the shortcomings of the existing technology and provide a fuzzy control-based ECAS system body control method that adopts a fuzzy control system, has simple principles, and effectively improves control accuracy.

In order to achieve the above technical objectives, the technical solution adopted by the fuzzy control-based ECAS system body control method of the present invention is:

An ECAS system car body control method based on fuzzy control, including a fuzzy controller, an ECAS system electrically connected to the fuzzy controller, and a car body control method using the ECAS system;

The design of the fuzzy controller includes the following steps:

(1) Determine the input and output variables of fuzzy control

For a two-dimensional fuzzy controller, the deviation between the actual output value of the system and the expected value and the deviation change rate are generally used as the input of the control system. For the study of vehicle height adjustment, the deviation sum of the four suspension strokes and the target lifting height is generally selected. The deviation change rate is used as input, and the switching signal to the solenoid valve is used as the output of the control system to inflate and deflate each air spring to achieve the purpose of adjusting the height of the vehicle body;

(2) Determine the fuzzy language value, quantization factor, scaling factor and membership function of fuzzy variables of fuzzy control

Fuzzification is to find the membership degree of each fuzzy language from the precise input value according to the membership function. Through fuzzification, the precise input value can be converted into different language values. According to the input value, the size of the output value and the positive and negative, it is usually used {Negative big (NB), negative middle (NM), negative small (NS), zero (ZO), positive small (PS), positive middle (PM), positive big (PB)} to describe the input and output values;

In the fuzzy control system, the error e, the error change rate ec and the actual change range of the control variable u are called the basic domain of discussion of these variables. In this application, the height adjustment range is set to ±0.04m, so the basic scope of the error e The domain of discussion can be set as [-0.1, 0.1]. At the same time, according to several simulation adjustments, the basic domain of discussion of the error change rate can be set as [-1, 1]. The domain of discussion of the fuzzy sets E and EC taken by the error can be set. is {-6 -4 -2 0 2 4 6}, then the quantization factor is k _e =6/0.1=60, k _ec =6/1=6, similarly, the basic domain of output is [-3,3 ], the fuzzy universe defining the output is also {-6 -4 -2 0 2 4 6}, then the scale factor is k _u =3/6=0.5;

Currently, there are six membership functions that are widely used in fuzzy control: Gaussian, generalized bell, S-shaped, trapezoidal, triangular, and Z-shaped. The sharper the membership function curve, the higher the resolution and the lower the control sensitivity. The higher the value, on the contrary, the smoother the membership function curve shape and the smoother the control characteristics. When selecting the membership function, in the area where the error is small, in order to prevent the error from increasing and maintain the stability of the system, a higher value is selected at this time. For resolution fuzzy sets, when the error is large, in order to eliminate the error, a lower resolution fuzzy set is selected. This application uses trapezoidal and triangular membership functions;

(3) Write fuzzy control rules and perform fuzzy reasoning

The main work of fuzzy reasoning is to formulate fuzzy rules. Fuzzy rules are a set of fuzzy conditional statements obtained by summarizing expert experience or manual control strategies. In this application, a dual-input-single-output fuzzy controller is used to formulate general fuzzy rules. It is composed of a series of IF and THEN statements. The antecedent of the conditional sentence is the input and the consequent is the control variable;

A three-dimensional diagram of its input and output variable fuzzy rules;

(4) Anti-blurring

What is obtained through fuzzy reasoning is a fuzzy quantity, but the control quantity of the actual system must be a clear quantity, and defuzzification is the process of converting fuzzy quantities into clear quantities. Anti-fuzzification calculations usually include the average maximum membership method, maximum membership method There are many methods such as the method of minimizing the degree, the method of maximizing the maximum membership degree, the median method and the area center of gravity method. This application uses the area center of gravity method. The central idea is to form a region between the membership function curve and the X-axis. The center of gravity of the area is used as the final value of the input variable, and the formula is expressed as:

Preferably, the control signal output by the fuzzy controller is a continuous analog signal, while the input signal of the high-speed solenoid valve is a digital signal of "1" or "0". A pulse width needs to be added between the solenoid valve and the controller. Pulse Width Modulation (hereinafter referred to as PWM) achieves its goal by controlling the duty cycle of the solenoid valve. The principle is to compare the control signal with a sawtooth wave with a fixed period. When the control signal is greater than the sawtooth wave , the PWM wave is 1, and the solenoid valve is fully open; when the control signal is smaller than the sawtooth wave, the PWM wave is 0, and the solenoid valve is fully closed.

Preferably, the control signal determined by the fuzzy controller is positive and represents the inflation signal, and negative represents the deflation signal.

Preferably, the fuzzy controller takes the sinusoidal signal as the input of the signal conversion model, separates the positive and negative values of the original control signal, and compares the absolute values of the positive value and the negative value of the control signal with the saw-shaped carrier wave respectively. When the positive value of the control signal is greater than the saw-shaped carrier wave, the PWM signal is 1; when it is less than the saw-shaped carrier wave, the PWM signal is 0; when the absolute value of the negative value of the control signal is greater than the saw-shaped carrier wave, the PWM signal is -1; when it is less than the saw-shaped carrier wave When , the PWM signal is 0, where the PWM signal is positive, indicating the inflation signal. At this time, the solenoid valve of the inflation circuit is fully open, the solenoid valve of the deflation circuit is fully closed, and the air spring air bag is inflated from the air storage tank; the PWM signal is When negative, it indicates the deflation signal. At this time, the solenoid valve of the inflation circuit is fully closed, the solenoid valve of the deflation circuit is fully open, and the air spring air bag deflates into the atmosphere. When the PWM signal is 0, it indicates the inflation circuit and the deflation circuit. All the solenoid valves are closed. At this time, it is neither inflated nor deflated, keeping the amount of gas in the air spring constant.

Preferably, the ECAS system can realize step-by-step adjustment of the vehicle body height, and the vehicle body height switching control is divided into three modes: low mode (the vehicle body is lowered by 40mm), middle mode (the original vehicle height) and high mode (the vehicle body is raised by 40mm). ).

Preferably, the vehicle body control method includes the following steps: S1 determines whether the vehicle is in a straight-line driving condition or a turning condition based on the lateral acceleration signal of the vehicle body collected by the sensor; S2 if the vehicle is in a straight-line driving state, determines whether the vehicle is in a straight-line driving condition based on the lateral acceleration signal collected by the sensor. The vehicle speed signal and suspension travel signal are used to adjust the vehicle body height and switch between three height modes; S3, if the vehicle is in turning condition, controlling the vehicle body height at this time will have an adverse impact on the vehicle safety performance, so When the vehicle turns, the current vehicle height is locked and no inflation or deflation is performed.

Compared with the prior art, the beneficial effects of the present invention are:

1. The present invention is insensitive to the influence of parameter changes and nonlinear characteristics, making fuzzy control more reliable and stable in practical applications, and has strong robustness;

2. The present invention can deal with uncertainty and nonlinear problems, and performs better than traditional control systems when facing problems, and has strong adaptability;

3. Compared with the traditional control system, the present invention adopts a fuzzy control system, which does not require precise mathematical models and does not require complex calculation processes, so it is easier to implement in practical applications;

4. The present invention adopts fuzzy control. Since the decisions of fuzzy control are derived from fuzzy rule reasoning, the response speed is faster, making the fuzzy control system advantageous in applications that require fast response.

Description of the drawings

Figure 1 is a fuzzy control flow chart in the present invention;

Figure 2 is a structural diagram of the gas circuit of the automobile ECAS system in the present invention;

Figure 3 is a membership function curve diagram of error e, error change rate ec, and control variable u in the present invention;

Figure 4 is a fuzzy rule table in the present invention;

Figure 5 is a three-dimensional diagram of input and output variable fuzzy rules in the present invention;

Figure 6 is the Simulink model of the fuzzy controller in the present invention;

Figure 7 is a PWM signal model of the solenoid valve in the present invention;

Figure 8 shows the PWM wave generation principle in the present invention;

Figure 9 shows the original (sinusoidal) control signal and control signal processing;

Figure 10 shows the static height adjustment response of the front and rear suspension;

Figure 11 shows the input height adjustment for Class A pavement;

Figure 12 shows the input height adjustment for Class B pavement;

Figure 13 shows the input height adjustment for Class D pavement.

Detailed ways

The invention will be further described below in conjunction with the accompanying drawings and specific implementation modes:

When performing body control, it is necessary to determine the state of the vehicle by determining the main switching parameters. First of all, there is no conclusion at home and abroad as to how much lateral acceleration the vehicle reaches before it enters the steering condition. This application refers to other documents and based on the serpentine experiment of an air suspension bus, the critical lateral acceleration a _y0 is set to 1.5 m/s ² , when the lateral acceleration of the vehicle during driving is greater than the critical lateral acceleration, the vehicle is considered to enter the steering driving condition. Secondly, when the vehicle is traveling in a straight line, the vehicle body height mode is switched based on the physical quantity of the vehicle's driving state as a basis for judgment. Combined with the actual driving conditions of the vehicle and China's regulations on passenger car speed limit, when 75km/h is selected as the low mode critical speed u _a1 , it can be judged that the vehicle is driving at high speed on a good road surface at this time, and the requirements for vehicle passability are low at this time. , lowering the vehicle body height has little impact on vehicle passability, and can also effectively improve vehicle fuel economy and handling stability. When the vehicle is driving on a bumpy road, switching to the high mode can improve the vehicle's passability and comfort. Referring to China's speed limit for large buses on roads with poor road facilities, the critical vehicle speed for entering high mode is set u _a2 =30km/h. When the vehicle speed u _a is less than the critical vehicle speed u _a1 and greater than or equal to the critical vehicle speed u _a2 for a period of time T, the vehicle will switch to the neutral mode.

When the vehicle body height of the ECAS system is adjusted, the controller sends a signal to control the switch of the solenoid valve, thereby controlling the air tank to inflate and deflate the air bag. In the simulation, the absolute air pressure in the gas storage tank is kept constant at 1.1Mpa. Using the solenoid valve of the inflation or deflation circuit as an equivalent throttling orifice, the gas mass flow rate Qc flowing through the solenoid valve when the solenoid valve is opened can be obtained. Since the high-speed solenoid valve used in the ECAS system can only be in two states: open and closed, due to the existence of damping force, when the vehicle body height reaches the target value, the airbag will continue to expand until the vertical vertical axis of the air spring reaches a balance with its static load, so In the process of filling and deflating, it is easy to cause "over-charging" and "over-discharging" phenomena. Therefore, the fuzzy control method is introduced to control the height of the vehicle body.

As shown in Figure 1-9, an ECAS system body control method based on fuzzy control includes a fuzzy controller, an ECAS system electrically connected to the fuzzy controller, and a body control method using the ECAS system;

The design of the fuzzy controller includes the following steps:

(1) Determine the input and output variables of fuzzy control

For a two-dimensional fuzzy controller, the deviation between the actual output value of the system and the expected value and the deviation change rate are generally used as the input of the control system. For the study of vehicle height adjustment, the deviation sum of the four suspension strokes and the target lifting height is generally selected. The deviation change rate is used as input, and the switching signal to the solenoid valve is used as the output of the control system to inflate and deflate each air spring to achieve the purpose of adjusting the height of the vehicle body;

(2) Determine the fuzzy language value, quantization factor, scaling factor and membership function of fuzzy variables of fuzzy control

Fuzzification is to find the membership degree of each fuzzy language from the precise input value according to the membership function. Through fuzzification, the precise input value can be converted into different language values. According to the input value, the size of the output value and the positive and negative, it is usually used {Negative big (NB), negative middle (NM), negative small (NS), zero (ZO), positive small (PS), positive middle (PM), positive big (PB)} to describe the input and output values;

In the fuzzy control system, the error e, the error change rate ec and the actual change range of the control variable u are called the basic domain of discussion of these variables. In this application, the height adjustment range is set to ±0.04m, so the basic scope of the error e The domain of discussion can be set as [-0.1, 0.1]. At the same time, according to several simulation adjustments, the basic domain of discussion of the error change rate can be set as [-1, 1]. The domain of discussion of the fuzzy sets E and EC taken by the error can be set. is {-6 -4 -2 0 2 4 6}, then the quantization factor is k _e =6/0.1=60, k _ec =6/1=6, similarly, the basic domain of output is [-3,3 ], the fuzzy universe defining the output is also {-6 -4 -2 0 2 4 6}, then the scale factor is k _u =3/6=0.5;

Currently, there are six membership functions that are widely used in fuzzy control: Gaussian, generalized bell, S-shaped, trapezoidal, triangular, and Z-shaped. The sharper the membership function curve, the higher the resolution and the lower the control sensitivity. The higher the value, on the contrary, the smoother the membership function curve shape and the smoother the control characteristics. When selecting the membership function, in the area where the error is small, in order to prevent the error from increasing and maintain the stability of the system, a higher value is selected at this time. For resolution fuzzy sets, when the error is large, in order to eliminate the error, a lower resolution fuzzy set is selected. This application uses trapezoidal and triangular membership functions;

(3) Write fuzzy control rules and perform fuzzy reasoning

The main work of fuzzy reasoning is to formulate fuzzy rules. Fuzzy rules are a set of fuzzy conditional statements obtained by summarizing expert experience or manual control strategies. In this application, a dual-input-single-output fuzzy controller is used to formulate general fuzzy rules. It is composed of a series of IF and THEN statements. The antecedent of the conditional sentence is the input and the consequent is the control variable. These rules are shown in Figure 4;

The three-dimensional diagram of its input and output variable fuzzy rules is shown in Figure 5;

(4) Anti-blurring

What is obtained through fuzzy reasoning is a fuzzy quantity, but the control quantity of the actual system must be a clear quantity, and defuzzification is the process of converting fuzzy quantities into clear quantities. Anti-fuzzification calculations usually include the average maximum membership method, maximum membership method There are many methods such as the method of minimizing the degree, the method of maximizing the maximum membership degree, the median method and the area center of gravity method. This application uses the area center of gravity method. The central idea is to form a region between the membership function curve and the X-axis. The center of gravity of the area is used as the final value of the input variable, and the formula is expressed as:

Preferably, the control signal output by the fuzzy controller is a continuous analog signal, while the input signal of the high-speed solenoid valve is a digital signal of "1" or "0". A pulse width needs to be added between the solenoid valve and the controller. Pulse Width Modulation (hereinafter referred to as PWM) achieves its goal by controlling the duty cycle of the solenoid valve. The principle is to compare the control signal with a sawtooth wave with a fixed period. When the control signal is greater than the sawtooth wave , the PWM wave is 1, and the solenoid valve is fully open; when the control signal is smaller than the sawtooth wave, the PWM wave is 0, and the solenoid valve is fully closed.

In the present invention, the positive signal in the control signal decided by the fuzzy controller represents the inflation signal, and the negative represents the deflation signal.

In the present invention, the fuzzy controller uses the sinusoidal signal as the input of the signal conversion model, separates the positive and negative values of the original control signal, and compares the positive value of the control signal and the absolute value of the negative value of the control signal with the saw-shaped carrier wave. When the positive value of the control signal is greater than the saw-shaped carrier wave, the PWM signal is 1; when it is less than the saw-shaped carrier wave, the PWM signal is 0; when the absolute value of the negative value of the control signal is greater than the saw-shaped carrier wave, the PWM signal is -1; when it is less than the saw-shaped carrier wave When , the PWM signal is 0, where the PWM signal is positive, indicating the inflation signal. At this time, the solenoid valve of the inflation circuit is fully open, the solenoid valve of the deflation circuit is fully closed, and the air spring air bag is inflated from the air storage tank; the PWM signal is When negative, it indicates the deflation signal. At this time, the solenoid valve of the inflation circuit is fully closed, the solenoid valve of the deflation circuit is fully open, and the air spring air bag deflates into the atmosphere. When the PWM signal is 0, it indicates the inflation circuit and the deflation circuit. All the solenoid valves are closed. At this time, it is neither inflated nor deflated, keeping the amount of gas in the air spring constant.

In the present invention, the ECAS system can realize step-by-step adjustment of the vehicle body height, and the vehicle body height switching control is divided into three modes: low mode (the vehicle body is lowered by 40mm), middle mode (the original vehicle height) and high mode (the vehicle body is raised). 40mm).

In the present invention, the vehicle body control method includes the following steps: S1 determines whether the vehicle is in a straight-line driving condition or a turning condition according to the lateral acceleration signal of the vehicle body collected by the sensor; S2 if the vehicle is in a straight-line driving state, determines whether the vehicle is in a straight-line driving condition according to the lateral acceleration signal collected by the sensor. The received vehicle speed signal and suspension travel signal are used to adjust the vehicle body height and switch between three height modes; S3, if the vehicle is in turning condition, controlling the vehicle body height at this time will have an adverse impact on vehicle safety performance. Therefore, when the vehicle turns, the current vehicle height is locked and no inflation or deflation is performed.

After the vehicle body height control model is built in the Matlab/Simulink environment, the height is adjusted respectively when the vehicle is stationary on the road and when the vehicle is stimulated by random road surfaces.

Example 1

Static height adjustment

When the vehicle is stationary on the road, there is no road map input from the outside world. At this time, taking the vehicle body height reduction as an example, the vehicle switches from the neutral mode to the low mode. The vehicle body height needs to be lowered by 40mm. The simulation time is 6s and the step size is 0.001s. The vehicle body height The adjustment response results are shown in Figure 10.

It can be seen from the static height adjustment simulation results that the static vehicle body height adjustment is better. The system first reaches the target height in about 2 seconds, and then there is a certain amount of overshoot, but the overshoot is controlled within 5mm and within the acceptable range. In about 4 seconds, the system remains stable at the target height and is in a steady state. The error is very small and has good control accuracy.

Example 2

Dynamic height adjustment

Dynamic height adjustment is defined as the height control performed by the vehicle when it is stimulated by different road surfaces while driving. To simulate the dynamic height adjustment of the actual road, the specific simulation working conditions are set as follows: (1) Height adjustment is performed on good road surfaces, such as Class A and Class B roads, and the vehicle is traveling at a speed of 80km/h. In order to reduce the height of the vehicle center of mass and improve the height of the vehicle To improve the handling and stability performance at high speed, switch the vehicle from normal mode (i.e., neutral mode) to high-speed mode (i.e., low mode); (2) Considering the height adjustment accuracy requirements of the vehicle when driving on bad roads, on D-level roads Under stimulation, the vehicle travels at a speed of 30km/h and switches the vehicle from the neutral mode to the high mode to improve the vehicle's passability.

Based on the settings of the simulation working conditions, for working condition 1, when the vehicle is driving on Class A and Class B roads, the vehicle height mode is switched from the neutral mode to the low mode. The control effect is shown in Figure 11 and Figure 12. For working conditions In case 2, when the vehicle is driving on a D-class road at a low speed, the simulation results of vehicle body height adjustment are shown in Figure 13.

It can be seen from the dynamic vehicle height adjustment simulation results that the built model can realize vehicle body height adjustment. As shown in Figures 11 and 12, on Class A and B roads, the vehicle can be adjusted to the target vehicle height for the first time within 2 seconds. value, and can basically maintain stability within 3 seconds, meeting the requirements for the height adjustment function. At the same time, when the vehicle is driving on Class A and Class B roads, since the external interference to the vehicle is not so strong, the controller has high control accuracy and can basically stabilize around the target height. When the vehicle is driving on a D-level road, the vehicle adjusts to the target vehicle height value for the first time in 1 second and can basically remain stable in about 3 seconds. However, due to the poor road surface, large external interference and gas compressibility, etc. As a result, the control accuracy is not very high. From the simulation results, the vehicle body height fluctuates up and down the target height. After the control is stabilized, the maximum error value is basically maintained within 0.02m, which meets the requirements for the vehicle body height adjustment function.

In summary, these are only preferred embodiments of the present invention and are not intended to limit the scope of the present invention. All equivalent changes and modifications in shape, structure, features and spirit within the scope of the claims of the present invention shall include within the scope of the claims of the present invention.

Claims (6)

1. A vehicle body control method of an ECAS system based on fuzzy control is characterized in that: the system comprises a fuzzy controller, an ECAS system electrically connected with the fuzzy controller and a vehicle body control mode adopting the ECAS system;

the design of the fuzzy controller comprises the following steps:

(1) Determining input and output variables for fuzzy control

For a two-dimensional fuzzy controller, the deviation and the deviation change rate of the actual output value and the expected value of the system are generally adopted as the input of the control system, for the study of the height adjustment of the vehicle body, the deviation and the deviation change rate of four suspension moving strokes and the target lifting height are generally selected as the input, the switch signal of the electromagnetic valve is used as the output of the control system, and the air springs are inflated and deflated to achieve the purpose of adjusting the height of the vehicle body;

(2) Determining membership functions of fuzzy linguistic values, quantization factors, scale factors, and fuzzy variables of fuzzy control

Blurring is to find membership degrees of each fuzzy language according to a membership degree function, and the precise input quantity can be converted into different language values through blurring, and the input value and the output value are generally described by { Negative Big (NB), negative Medium (NM), negative Small (NS), zero (ZO), positive Small (PS), median (PM), positive Big (PB) } according to the magnitude and the positive and negative of the input value and the output value;

in the fuzzy control system, the actual variation ranges of the error e, the error change rate ec and the control quantity u are called as basic arguments of the variables, and the range of the height adjustment is set to be +/-0.04 m in the present application, so the basic arguments of the error e can be defined as [ -0.1,0.1)]At the same time, according to several simulation adjustments, the basic theory of the error change rate can be defined as [ -1,1]The argument of the fuzzy set E, EC taken by the error is set to be { -6-4-20246}, the quantization factor is k _e ＝6/0.1＝60，k _ec =6/1=6, and similarly, the basic argument of the output is [ -3,3]The fuzzy argument of the defined output is also { -6-4-20246}, the scale factor is k _u ＝3/6＝0.5；

At present, more membership functions are applied to fuzzy control, namely 6 membership functions of Gaussian type, generalized bell type, S-shaped, trapezoid, triangle and Z-shaped, the more the membership function curve is sharp, the higher the resolution is, the higher the control sensitivity is, conversely, the more the membership function curve is smooth, the more the control characteristic is gentle, when the membership function is selected, in a smaller error area, in order to prevent error increase, the stability of the system is maintained, a fuzzy set with higher resolution is selected at the moment, and when the error is larger, a fuzzy set with lower resolution is selected for eliminating the error, and the trapezoidal and triangle membership functions are selected;

(3) Writing fuzzy control rules to perform fuzzy reasoning

The main work of fuzzy reasoning is to formulate a fuzzy rule, wherein when the fuzzy rule is obtained by summarizing expert experience or manual control strategy, a double-input-single-output fuzzy controller is adopted in the fuzzy rule, the formulation of the general fuzzy rule is composed of a series of IF and THEN sentences, the front part of the condition sentence is input, and the rear part is a control variable;

a three-dimensional graph of the input/output variable fuzzy rule;

(4) Defuzzification of

The fuzzy quantity is obtained through fuzzy reasoning, but the control quantity of an actual system is required to be a clear quantity, and the reverse fuzzy is a process of converting the fuzzy quantity into the clear quantity, and the reverse fuzzy calculation generally comprises a mean maximum membership method, a minimum membership method, a maximum membership maximum method, a median method, an area barycenter method and other methods, wherein the area barycenter method is adopted, the central thinking is that the barycenter of the area of an area surrounded by a membership function curve and an X axis is used as the final value of an input variable, and the formula is as follows:

2. the ECAS system body control method based on the fuzzy control of claim 1, wherein: the control signal output by the fuzzy controller is a continuous analog signal, the input signal of the high-speed electromagnetic valve is a digital signal of 1 or 0, a pulse width modulation control valve (Pulse Width Modulation, hereinafter referred to as PWM) is needed to be added between the electromagnetic valve and the controller, the purpose is achieved by controlling the duty ratio of the electromagnetic valve, the principle is that the control signal is compared with a periodic fixed sawtooth wave, and when the control signal is larger than the sawtooth wave, the PWM wave is 1, and the electromagnetic valve is fully opened; when the control signal is smaller than the sawtooth wave, the PWM wave is 0, and the electromagnetic valve is fully closed.

3. The ECAS system body control method based on the fuzzy control of claim 1, wherein: the control signals decided by the fuzzy controller are positive signals representing inflation signals and negative signals representing deflation signals.

4. The ECAS system body control method based on the fuzzy control of claim 1, wherein: the fuzzy controller takes a sine signal as the input of a signal conversion model, separates the original control signal from the original control signal, and compares the positive value of the control signal and the absolute value of the negative value of the control signal with the saw-shaped carrier wave respectively. When the positive value of the control signal is larger than that of the saw-shaped carrier wave, PWM is 1; when the PWM signal is smaller than the saw-shaped carrier wave, the PWM signal is 0; when the absolute value of the negative value of the control signal is larger than that of the saw-shaped carrier wave, the PWM signal is-1; when the signal is smaller than the saw-shaped carrier wave, the PWM signal is 0, wherein the PWM signal is positive and indicates an inflation signal, the electromagnetic valve of the inflation loop is fully opened at the moment, the electromagnetic valve of the deflation loop is fully closed, and the air spring air bag is inflated from the air storage tank; when the PWM signal is negative, the air release signal is represented, the electromagnetic valve of the air charging loop is fully closed at the moment, the electromagnetic valve of the air release loop is fully opened, the air spring air bag releases air to the atmosphere, and when the PWM signal is 0, the electromagnetic valves of the air charging loop and the air release loop are fully closed, and at the moment, the air is not charged or discharged, so that the air quantity in the air spring is kept constant.

5. The ECAS system body control method based on the fuzzy control of claim 1, wherein: the ECAS system can realize the gradual adjustment of the height of the vehicle body, and the switching control of the height of the vehicle body is divided into three modes: a low position mode (40 mm lowered body), a medium position mode (original body height) and a high position mode (40 mm raised body).

6. The ECAS system body control method based on the fuzzy control of claim 1, wherein: the vehicle body control mode comprises the following steps: s1, judging whether the vehicle is in a straight running condition or a turning running condition according to a lateral acceleration signal of the vehicle body acquired by a sensor; s2, if the vehicle is in a straight running state, the height of the vehicle body is adjusted according to a vehicle speed signal and a suspension moving stroke signal which are acquired by a sensor, and the vehicle is switched in three height modes; s3, if the vehicle is in a turning working condition, the vehicle body height is controlled to adversely affect the safety performance of the vehicle, so that when the vehicle turns, the current vehicle body height is locked, and inflation or deflation is not performed.