Fuzzy PID Control Based on Freescale Single Chip Electronic Control Air Suspension

The electronically controlled air suspension (ECAS) uses the electronic control module as the control core to control the air suspension parameters in real time, which can automatically control the parameters such as the stiffness, damping coefficient and vehicle height of the vehicle suspension. Under the working conditions, it can realize active adjustment, active control, and add many auxiliary functions (such as fault diagnosis function); it can maximize the ride comfort and steering stability of the car, and can meet the ride comfort of modern cars. Higher requirements for driving safety. It has been widely used in large passenger cars in some European countries. China has studied air springs in the 1950s, but the industrial conversion rate of many research results is very low, which has led to many valuable researches that have not been able to continue and deepen, so that the research and application of China's automotive suspension technology and Developed countries such as Europe and the United States are significantly behind. At present, no car company in China has been able to independently design and provide the market with a relatively mature air suspension electronic control unit [1]. Therefore, it is of great practical significance to study the air suspension electronic control unit and reduce the gap with foreign applications in the electronically controlled air suspension system as soon as possible.

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This article takes the YBL6891H passenger car as the control object. The bus originally used the height of the car as the main control target. When the load changed, the height of the car remained within a certain range, and did not really relate to the improvement of the ride comfort of the passenger car. Based on the 1/4 vehicle model of the passenger car, the fuzzy PID control algorithm is used to adjust the stiffness of the air spring to reduce the vertical acceleration of the vehicle body, thus improving the ride comfort of the passenger car. The electronic control unit was designed by using Freescale's MC9S08GB60A microcontroller as the control chip.

1 system hardware design

The overall structure of the system is shown in Figure 1. The dotted line in the figure is a two-degree-of-freedom 1/4 vehicle model. The MCU of the control system uses the MC9S08GB60A of Freescale Semiconductor. The processor is highly reliable and has strong anti-interference ability, and is widely used in automotive electronic products. The overall circuit structure consists of ECU, height sensor, speed sensor, acceleration sensor and its detection circuit, keyboard (for mode selection and control in manual mode), indicator light and other circuits. The vertical acceleration signal detected by the acceleration sensor is transmitted to the single chip microcomputer, and the single chip generates a control signal, and the stiffness of the air spring is controlled by the electromagnetic valve. The adjustment of the stiffness is achieved by controlling the control valve between the main air chambers. The height sensor continuously transmits the height signal of the passenger car to the single-chip microcomputer, and the magnitude of the acceleration reflects the road condition information to a certain extent, and the single-chip microcomputer adjusts the height of the vehicle body according to the current road condition and the vehicle speed. Once the vehicle height reaches the set minimum or maximum position limit, the ECU will perform protection and automatically end the adjustment.

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1.1 Height signal acquisition and processing circuit

The height detection circuit works as follows: body height - sensor angle - inductance - pulse signal period. The body height sensor is equivalent to a variable inductance in series with a resistor. When the vehicle body vibrates up and down, the swing lever is rotated up and down, thereby moving the iron core, so that the inductance value is constantly changing. When the vehicle body rises, the swing lever rotates upward, and the inductance value becomes larger. When the vehicle body descends, the swing lever rotates downward, and the inductance value becomes small. Figure 2 shows the height detection circuit. The two terminals of the height sensor are connected to the height1i and HCOM terminals respectively. The output of the detection circuit is a series of pulse signals. The sensor detection circuit was simulated by multisim10[2]. The results are shown in Fig. 3. Fig. 3(a), Fig. 3(b) and Fig. 3(c) show that the inductance values ​​of the height sensor are 13mH, 20mH and 30mH respectively. The height of the signal. It can be seen that when the height of the vehicle changes, the inductance value also changes, and the change of the inductance value causes a change in the pulse width, so the single chip can obtain the height information of the vehicle according to the width of the pulse.

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1.2 Solenoid valve drive circuit

The driver chip uses the integrated relay driver NUD3124 from ON Semiconductor. Its integrated design significantly simplifies design and reduces cost, replacing traditional discrete component solutions such as bipolar transistors and freewheeling diodes. Each piece of NUD3124 has two drivers, which are suitable for driving inductive loads such as relays. Its drive circuit is shown in Figure 4. It is electrically isolated by the optocoupler at the input end of the signal, which enhances the reliability and anti-interference ability of the circuit. .

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2 control strategy design

2.1 1/4 vehicle model

According to Newton's second law, the dynamic equation of the 1/4 vehicle model system of the YBL6891H passenger car is:

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In the formula, the sprung mass m1=1 718kg, the unsprung mass m2=300kg, the tire stiffness is k1=9.5×105N·m-1, and the equivalent damping of the damper is c=9 358N·m·s-1. K2 is the stiffness of the air spring, x0 is the road surface excitation, x1 is the unsprung mass displacement, and x2 is the sprung mass displacement.

2.2 Fuzzy Adaptive PID Control Algorithm for Suspension

The fuzzy adaptive controller together with the conventional PID controller constitutes a fuzzy adaptive PID (FAPID) controller. The output of the Fuzzy Adaptive Controller (FAC) is the input to the PID controller. The structure of the control system is shown in Figure 5. In order to control the acceleration of the vehicle body, a fuzzy PID controller is designed. The final parameters are: KP is the proportional coefficient, KI is the integral action coefficient, and KD is the differential action coefficient. The fuzzy set theory is used to establish the relationship between the parameters KP, KI, KD and the systematic error e and the error rate de, and the fuzzy controller is used to customize the PID parameters according to different e and de. This is the core of the control system design. The calculation formulas of KP, KI, and KD are: KP=KPS+uKPX; KI=KIS+uKIX; KD=KDS+uKDX. Among them, KPS, KIS, KDS are initial tuning parameters, KPX, KIX, KDX are correction coefficients, and u is the adjustment factor. Therefore, it is only necessary to establish the relationship between the coefficient u and the error e and the error rate de [3].

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The root mean square of the vertical acceleration of the vehicle body and its rate of change are the fuzzy input linguistic variables e and de, and the coefficient u is the output linguistic variable. All three variables are fuzzyly divided into 7 fuzzy subsets {NB, NM, NS, NULL, PS, PM, PB}, and a two-dimensional fuzzy controller is constructed to synthesize the root mean square and root mean square variation of vertical acceleration of the vehicle body. In the case of road disturbance input, the basic domain defining the two input variables is (0, 0.6) and (-60, 60), the corresponding fuzzy domain is (-3, 3), and the fuzzy output domain is ( -0.4, 0.4), the membership functions of the three variables all use a triangle function.

The fuzzy control rule table of u is designed below. The principle of determining the change of control quantity is: when the error is large or small, the control quantity is selected to eliminate the error as soon as possible; and when the error is small, the control quantity should be selected to prevent overshoot, and the stability of the system is the main starting point. The error is positive and the error is negative, and the corresponding sign changes. Therefore, the fuzzy adjustment rules for designing u according to the fuzzy control principle are shown in Table 1.

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2.3 Software design and control algorithm implementation

The software of the single-chip microcomputer is written in C language, and the overall structure of the software adopts a modular approach. The general flow is shown in Figure 6. The main subroutines include high data synthesis, communication information processing, and control signal generation. The capture detection mainly detects the vehicle speed detection interrupt subroutine, the altitude detection interrupt subroutine, the acceleration detection interrupt subroutine, and the communication interruption subroutine. The auxiliary switch input detection is mainly for detecting the vehicle speed, braking, ignition and door status signals. The operation switch detection is mainly for detecting the key signal in the manual mode. The subroutine of the fuzzy PID control of stiffness is shown in Fig. 7.

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3 Simulation analysis

The control algorithm was simulated by MATLAB [4] software. The sampling time of the whole system was 0.01s. The time domain mathematical model of road excitation can be used to describe, where q(t) is the road surface excitation, a is a constant, according to the road grade, v is the vehicle speed, and w(t) is the white noise. The suspension system simulation was carried out on Class B and Class C roads, and the speed was 50km/h. The excitation of the road surface is simulated in MATLAB/SIMULINK [6], as shown in Figure 8 and Figure 9.

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Figure 10 and Figure 11 show the vertical acceleration of passive suspension, PID control and fuzzy PID control suspension under the conditions of Bkm and C road speeds of 50km/h. It can be seen that the fuzzy PID control suspension can effectively reduce the vertical acceleration of the vehicle body compared with the PID control suspension and the passive suspension. Tables 2 and 3 show the suspension performance comparisons for Class B and Class C road excitations. It can be seen from the table that the performance of the fuzzy PID control suspension is better than that of the ordinary PID control suspension and the passive suspension. In the case of the B and C grades, the root mean square value of the vertical acceleration is reduced by 23.4% and 17.3%, the dynamic stroke decreased by 1.9% and 0.5%, respectively, and the relative dynamic load of the wheel decreased by 10% and 7.9%, respectively, and the improvement was generally better than the improvement of ordinary PID control.

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This paper introduces the circuit structure of the air suspension electronic control unit for the YBL6891H passenger car, and simulates the height sensor detection circuit with MULTISIM 10. The fuzzy PID control algorithm is used to control the air suspension, and the 1/4 suspension model is simulated. The results show that the algorithm can effectively reduce the vertical acceleration of the vehicle body and improve the ride comfort and steering stability of the vehicle. On the B-class and C-class roads, the acceleration rms of the fuzzy PID control suspension is reduced by 23.4% and 17.3%, respectively, and the traverse root of the dynamic stroke and the relative dynamic load of the wheel is also improved. Practice has proved that the electronic control suspension system can meet the overall requirements of the system and achieve good control results. Controlling the roll and pitch angles of the body is the next step.

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