MATLAB based cruise control system using PID controller

This project presents a MATLAB/Simulink-based simulation of a Cruise Control System for a vehicle using a PID controller.
The system is designed to automatically maintain a constant vehicle speed set by the driver (75 km/h), thereby improving driving comfort, speed regulation, and fuel efficiency.

This is a prototype and simulation-based academic mini project, developed as part of the Control Systems Engineering Laboratory.

During long-distance driving, continuously controlling the accelerator can cause driver fatigue and inefficient fuel usage.
Cruise control systems solve this problem by automatically regulating vehicle speed using feedback control techniques.
This project demonstrates how classical control theory (PID control) can be effectively applied to automotive systems.

The cruise control system operates as a closed-loop feedback system:

1. The driver sets a desired speed.
2. The actual vehicle speed is continuously measured.
3. The error between desired and actual speed is calculated.
4. A PID controller generates the required throttle input.
5. The vehicle dynamics respond and adjust the speed accordingly.

The vehicle model is derived using Newton's Second Law of Motion.

• Engine force ( F_e )
• Drag force ( F_d )
• Normal force ( N )
• Gravitational force ( mg )

Assuming motion on a flat road:

[ F_e - F_d = ma ]

Where:

• ( F_e = k_1 \times \text{throttle input} )
• ( F_d = k_2 \times v )

Substituting and simplifying:

[ \frac{dv}{dt} = -\frac{k_2}{m}v + \frac{k_1}{m}(\text{input}) ]

This equation represents the plant model used in the Simulink simulation.

The free body diagram illustrates all forces acting on the vehicle.

• Vehicle moves on a flat road (zero slope)
• Drag force is proportional to velocity (Stoke's law)
• Rolling resistance is neglected
• Vehicle mass remains constant

These assumptions simplify the analysis while maintaining realistic system behavior.

A PID controller is used to regulate vehicle speed:

• Proportional (P): Reduces present speed error
• Integral (I): Eliminates steady-state error
• Derivative (D): Improves stability and reduces overshoot

The PID controller ensures smooth and accurate speed tracking even under disturbances.

The cruise control system is implemented in MATLAB Simulink as a closed-loop system.

• Desired speed input (75 km/h)
• Error summation block
• PID controller
• Vehicle dynamics block
• Integrator (1/s)
• Disturbance input
• Feedback loop

• Vehicle mass ( m = 1000 , kg )
• Engine constant ( k_1 = 2 )
• Drag coefficient ( k_2 = 100 )

The simulation output shows the vehicle speed response over time.

• Vehicle speed reaches the desired value of 75 km/h
• Initial overshoot is minimal
• System settles quickly
• External disturbances are effectively rejected
• Steady-state error is nearly zero

This confirms that the PID controller successfully maintains constant vehicle speed.

• Automotive cruise control systems
• Adaptive Cruise Control (ACC)
• Advanced Driver Assistance Systems (ADAS)
• Autonomous vehicle speed regulation
• Highway and fleet vehicle management

• Integration with Adaptive Cruise Control (ACC)
• Use of radar and camera-based sensors
• AI-based adaptive PID tuning
• Vehicle-to-Vehicle (V2V) communication
• Real-time hardware implementation

• MATLAB
• Simulink
• Control Systems Toolbox

This project is technically inspired by the following educational video, which explains cruise control modeling and PID-based control:

YouTube:
https://youtu.be/cP7XbaOf8iA

The implementation follows standard control system principles and academic references.

Jeeva S
B.E - Electronics and Communication Engineering
Panimalar Engineering College, Chennai

• Project Report (PDF)
• Presentation (PPT)
• Mathematical model, free body diagram, Simulink model, and output plots

If you find this project useful, feel free to star the repository!