Cruise control system based on simulink

Table of contents

1. Working principle:

2. Design process:

2.1 The basic theory of PID control:

2.3 Schematic diagram of PID control system

2.4 Vehicle dynamics model establishment:

2.5 simulink modeling:

3. Results Analysis and Discussion:

4. Summary:

1. Working principle:

The cruise control component reads the pulse signal sent by the vehicle speed sensor and compares it with the set speed, so as to issue an instruction to adjust the increase or decrease of the throttle opening mechanically by the servo, so that the vehicle always maintains the set speed. speed. The electronic multifunctional cruise control system eliminates the mechanical control part of the cable-type cruise control, and completely adopts precise electronic control to make the control more precise and avoid the risk of mechanical failure. The working principle of the pull-wire cruise control system is simply that the cruise control component reads the pulse signal sent by the vehicle speed sensor and compares it with the set speed to adjust the increase or decrease of the throttle opening to Keep the vehicle at the set speed at all times.

2. Design process:

Topic: It is known that the vehicle is driving on the road, and the force is shown in Figure 2.2. Please analyze the force of the vehicle in combination with the force and establish a longitudinal dynamics mathematical model, and use the PID control algorithm or other control algorithms to design the controller. Vehicle cruise control. The vehicle parameters are shown in Figure 2.1.

Figure 2.1 Basic vehicle parameters

Figure 2.2 Vehicle force

2.1 The basic theory of PID control:

PID controller is a proportional, integral, differential parallel controller. It is the most widely used type of controller. In the PID controller, its mathematical model consists of three parts: proportional, integral and differential.

The functions of each link of the PID controller are as follows:

(1) Proportional link: It reflects the deviation signal e(t) of the control system in proportion. Once the deviation occurs, the controller immediately has a control effect, making the control amount change in the direction of reducing the deviation. The strength of the control action depends on the proportion The larger the coefficient $K_{p}$ , $K_{p}$ the shorter the transition process, and the smaller the steady-state error of the control result; but $K_{p}$ the larger the value, the larger the overshoot, the easier it is to generate full vibration, which will lead to worse dynamic performance, and even make the closed-loop system unstable. Stablize. Therefore, the proportional coefficient $K_{p}$ must be properly selected in order to achieve the effect of less transition time, small steady-state error and stability.

(2) Integral link: It is mainly used to eliminate the deviation. As long as there is a deviation, its control effect will continue to accumulate, and the control value will be output to eliminate the deviation. It can be seen that the function of the integral part can eliminate the deviation of the system. However, the integral action has hysteresis characteristics. If the integral control action is too strong, the overshoot of the system will increase, the dynamic performance of the control will deteriorate, and even the closed-loop system will be unstable. The integration time $T_{i}$ has a great influence on the action of the integral part. When $T_{i}$ it is larger, the integral action is weaker. At this time, it is beneficial to reduce the overshoot of the system, and the transition process is not easy to generate oscillation. But it takes a long time to eliminate the error. $T_{i}$ When it is smaller, the integral effect is stronger. At this time, oscillation may occur during the transition of the system, and the time required to eliminate the error is relatively short.

(3) Differentiation link: reflect the variation trend of the deviation signal, and the variation trend of the deviation can be obtained by the differential control. Increasing the differential control function can speed up the system response, reduce the overshoot, overcome the oscillation, and improve the stability of the system, but the system Reduced ability to suppress interference. The strength of the differential part is $T_{d}$ determined by the differential time. $T_{d}$ The larger it is, $e(t)$ the stronger its effect of inhibiting change is, and the smaller it is, the weaker $T_{d}$ its effect of resisting change. $e(t)$ It has a great impact on the stability of the system.

2.2 Basic formula:

PID control is a kind of linear control, which is the control deviation composed of the given value and the actual value:

$e(t)=r(t)-c(t)$

The proportion (P), integral (I) and differential (D) of the linear combination form the control quantity, and the controlled object is controlled. Its simulation expression is:

$u(t)=K_{p}\left [ e(t)+\frac{1}{T_{i}}\int_{0}^{t}e(t)dt+\frac{de(t)}{dt} \right ]$

in:

$u(t)$ A controller output.

$e(t)$ A controller input, which is the difference between the given value and the output value of the controlled object, called the deviation signal.

$K_{p}$ A scaling factor for the controller.

$T_{i}$ Integral time of a controller.

$T_{d}$ Derivative time of a controller.

The form of the transfer function is:

$G(s)=\frac{U(s)}{E(s)}=K_{p}\left [ 1+\frac{1}{T_{i}s}+T_{d}s \right ]$

In the above formula, $K_{p}$ is the proportional coefficient, $T_{i}$ is the integral time constant, and $T_{d}$ is the differential time constant.

2.3 Schematic diagram of PID control system

Figure 2.3 Schematic diagram of conventional PID control system

2.4 Vehicle dynamics model establishment :

The longitudinal dynamics model of the vehicle is as follows:

According to Newton's law of motion, the equation of motion of the car is as follows:

$\dot{S_{i}}=\dot{V_{i}}$

$\dot{V_{i}}=\frac{1}{m^{_{i}}}\left ( F_{I}-F_{a}-F_{r}-F_{g} \right )$

In the formula, $m_{i}$ , $s_{i}$ , $v_{i}$ are the mass, position and speed of the current vehicle respectively, $F_{i}$ are the traction or braking force of the vehicle, $F_{a}$ are the air resistance during the running of the vehicle, $F_{r}$ are the rolling resistance, and $F_{g}$ are the slope resistance. Among them, the air resistance is determined by the speed of the vehicle, the drag coefficient of the air $C_{D}$ , the air density ρ and the windward area A.

The air resistance is: $F_{a}=\tfrac{1}{2}C_{D}\rho Av_{i}^{2}$

The rolling resistance is: $Fr=\mu m_{i}gcos\theta$

In the formula, μ is the coefficient of rolling resistance, g is the acceleration due to gravity, and θ is the road gradient.

The ramp resistance of the vehicle is: $F_{g}=m_{i}sin\theta$

$\dot{V_{i}}=\frac{1}{m_{i}}\left [ F_{i}-\left (\frac{1}{2}C_{D}\rho Av_{i}^{2} +\mu m_{i}gcos\theta +m_{i}gsin\theta \right ) \right ]$

2.5 simulink modeling:

Use simulink in matlab to build a vehicle power simulation model as shown in Figure 2.5-1, 2.5-2

Figure 2.5-1 Vehicle power simulation model:

Figure 2.5-2 Vehicle power simulation model:

Build the vehicle power simulation model into a subsystem and make the final simulation connection with the PID control simulation model to obtain the overall simulation model as shown in Figure 2.5-3, 2.5-4

Figure 2.5-3 The overall simulation model of the system:

Figure 2.5-4 The total simulation model of the system:

3. Results Analysis and Discussion:

By adjusting the preset cruising speed at the far left, a better cruising speed adjustment within a certain range can be achieved. When the cruising speed changes greatly, the relevant parameters of the above simulation model can be adjusted again to achieve the best effect. We set the cruising speed as 80km/h, 100km/h, and 120km/h respectively. After running the entire simulation model, the simulation result curve is shown in Figure 3.1.

When the cruising speed is set to 80km/h, the simulation situation is as follows:

Figure 3.1 Simulation results of cruising speed 80km/h:

From the simulation results in the above figure, it can be seen that when the cruising speed is 80km/h, the adjustment time is about 2s, and the car can move forward at a constant speed of 80km/h.

4. Summary:

1. On the basis of searching, consulting and researching the research results of automobile cruise control technology made by predecessors, and extracting the automobile force model and PID control model from a large number of documents.

2. According to the force model of the car, carefully analyzed the driving force and resistance during the driving process of the car, and established a simulation model of the car cruise control system; and basically determined the appropriate range of parameters for each module in the test one by one .

3. The simulation model of the whole system is established with the Simulink software in MATLAB. Through continuous adjustment, the simulation results of the cruising speed of 80km/h are finally made, and the adjustment effect is relatively ideal.

In general, the vehicle cruise control system based on positional and incremental PID control algorithms has great advantages over other control systems with a single PID algorithm, and can achieve higher requirements for cruise control conditions.