# Cruise Control: PID Controller Design

Key MATLAB commands used in this tutorial are: `tf` , `step` , `feedback`

## Contents

## System model and parameters

The transfer function model for the cruise control problem is given below. Please see the Cruise Control: System Modeling page for the derivation.

(1)

The parameters used in this example are as follows:

(m) vehicle mass 1000 kg

(b) damping coefficient 50 N.s/m

(r) reference speed 10 m/s

## Performance specifications

- Rise time < 5 s
- Overshoot < 10%
- Steady-state error < 2%

## PID overview

The block diagram of a typical unity feedback system is shown below.

Recall from the Introduction: PID Controller Design page, the transfer function of a PID controller is

(2)

We can define a PID controller in MATLAB using the transfer function directly:

```
Kp = 1;
Ki = 1;
Kd = 1;
s = tf('s');
C = Kp + Ki/s + Kd*s
```

C = s^2 + s + 1 ----------- s Continuous-time transfer function.

Alternatively, we may use MATLAB's **pid controller object** to generate an equivalent continuous time controller as follows:

C = pid(Kp,Ki,Kd)

C = 1 Kp + Ki * --- + Kd * s s with Kp = 1, Ki = 1, Kd = 1 Continuous-time PID controller in parallel form.

## Proportional control

The first thing to do in this problem is to find a closed-loop transfer function with a proportional control () added.

By reducing the unity feedback block diagram, the closed-loop transfer function with a proportional controller becomes:

(3)

Recall from the Introduction: PID Controller Design page, a proportional controller, , decreases the rise time, which is desirable in this case.

For now, use equal to 100 and a reference speed of 10 m/s. Create a new m-file and enter the following commands.

```
m = 1000;
b = 50;
r = 10;
s = tf('s');
P_cruise = 1/(m*s + b);
Kp = 100;
C = pid(Kp);
T = feedback(C*P_cruise,1)
t = 0:0.1:20;
step(r*T,t)
axis([0 20 0 10])
```

T = 100 ------------ 1000 s + 150 Continuous-time transfer function.

Note that we have used the MATLAB `feedback` command to simplify the block diagram reduction of the closed-loop system. Please verify for yourself that the result agrees
with the closed-loop transfer function, T, derived above.

Running the m-file in MATLAB should give you the step response above. As you can see from the plot, neither the steady-state error nor the rise time satisfy our design criteria.

You can increase the proportional gain, , to reduce the rise time and the steady-state error. Change the existing m-file so that equals 5000 and rerun it in the MATLAB command window. You should see the following plot.

Kp = 5000; C = pid(Kp); T = feedback(C*P_cruise,1); step(r*T,t) axis([0 20 0 10])

The steady-state error is now essentially zero, and the rise time has been reduced substantially. However, this response is unrealistic because a real cruise control system generally can not change the speed of the vehicle from 0 to 10 m/s in less than 0.5 seconds due to power limitations of the engine and drivetrain.

**Actuator limitations** are very frequently encountered in practice in control systems engineering, and consequently, the required control action
must always be considered when proposing a new controller. We will discuss this issue much more in subsequent tutorials.

The solution to this problem in this case is to choose a lower proportional gain, , that will give a reasonable rise time, and add an integral controller to eliminate the steady-state error.

## PI control

The closed-loop transfer function of this cruise control system with a PI controller () is:

(4)

Recall from the Introduction: PID Controller Design page, an addition of an integral controller to the system eliminates the steady-state error. For now, let equal 600 and equal 1 and see what happens to the response. Change your m-file to the following.

Kp = 600; Ki = 1; C = pid(Kp,Ki); T = feedback(C*P_cruise,1); step(r*T,t) axis([0 20 0 10])

Now adjust both the proportional gain, , and the integral gain, , to obtain the desired response. When you adjust the integral gain, , we suggest you to start with a small value since a large can de-stabilize the response. When equals 800 and equals 40, the step response will look like the following:

Kp = 800; Ki = 40; C = pid(Kp,Ki); T = feedback(C*P_cruise,1); step(r*T,t) axis([0 20 0 10])

## PID control

For this particular example, no implementation of a derivative controller was needed to obtain the required output. However, you might want to see how to work with a PID control for the future reference. The closed-loop transfer function for this cruise control system with a PID controller () is:

(5)

Let equal 1, equal 1, and equal 1 and enter the following commands into a new m-file.

Kp = 1; Ki = 1; Kd = 1; C = pid(Kp,Ki,Kd); T = feedback(C*P_cruise,1);

Plot the step response and adjust all of , , and until you obtain satisfactory results. We will leave this as an exercise for you to work on.

**Suggestion:** Usually choosing appropriate gains requires a trial and error process. The best way to attack this tedious process is to
adjust one variable (, , or ) at a time and observe how changing one variable influences the system output. The characteristics of , , and are summarized in the Introduction: PID Controller Design page.