Simulation of adaptive cruise control using PID and model predictive controllers

Hazli, Hilal Hazman (2022) Simulation of adaptive cruise control using PID and model predictive controllers. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

The research on Autonomous vehicles was started nearly a century ago, but significant parts of significant advancements have been done over the past few decades. One of the significant advancements have been done is Advance Driver Assistance System (ADAS). The role of ADAS is to prevent death and injuries by reducing the number of accidents. Unfortunately, most favourite assistance system, traditional cruise control, was not one of ADAS. there are several disadvantages to the conventional cruise control system. For example, when the car in front is travelling less than the desired speed, the driver must engage with the brake pedal at any stage to prevent a collision. The best solution is Adaptive cruise control where this advance cruise control can avoid collision by automatically adjusts the vehicle speed to maintaining the safe distance between two vehicles. In addition, this technology is in level 1 automated vehicles 13 state by SAE International's standard J3016. In this thesis, a Model Predictive Controller (MPC) and the Proportional integrated derivative (PID) controller are compared using the Adaptive Cruise Control (ACC) systems. The goal of the control system is to maintain a safe distance between the lead vehicle and the Adaptive cruise control vehicle. The ACC system automatically adjusts the vehicle speed to maintain a safe distance from vehicles ahead. The frontal or rear-end collisions and traffic congestions can be reduced by maintaining the safe distance using the spacing control by the Adaptive Cruise Control system. Two scenarios are considered MATLAB/Simulink to analyse the performances of the system. In Scenario 1 the simulation run in low traffic density and in scenario 2 the simulation run in high traffic density. These two simulations was shown to be effective way to observe the performances between two different controller of adaptive cruise control system. Acceleration, velocity, and distance between two cars is the important result in this research. The acceleration of ACC car indicates the comfortability driving, where the result show that Adaptive Cruise Control using MPC have better acceleration response. While the velocity result shows capable of ACC car the car-following capability and regulates velocity to achieve velocity set. Each controller achieves the car-following capability however the velocity response of Adaptive Cruise Control with MPC show fast response significantly in scenario 2 by 1.2% better than Adaptive Cruise Control using PID. Then, Distance between ACC car and lead car shows the capability of ACC system to maintain the safe distance as well as avoid frontal collision. The result shows that the PID controller did not achieve the safety driving capability where the relative distance exceed the safe distance repeatedly. However, the MPC controller have excellent response when it able to maintain the relative distance above safe distance. The RMS difference of velocity and distance is also important result for the simulation. The velocity in two scenarios show different result of percentage difference between both of controller, 0.01% and 1.2% for scenario 1 and scenario 2. The percentage difference of relative distance and safe distance for adaptive cruise control using MPC are 6.12 % for scenario 1 and 2.69 % for scenario 2 compared to the percentage difference of adaptive cruise control using PID, 8.13% and 5.21%.

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