Optimization of Controller for a Cruise Control System

Hee, Sung Jack (2024) Optimization of Controller for a Cruise Control System. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

Cruise control system is very common nowadays and can be seen implemented in the majority of the cars. Cruise control system plays the role of maintaining the cruising speed of the car according to the speed set by the user. It was meant to provide comfort and ease the load on the driver. When facing external disturbance such as inclinations of road and wind resistance, the speed that should be maintain by the cruise control fluctuates and become inaccurate which may affect the performance, efficiency and driving experience. This can be achieved through the implementation of controllers. This research focuses on the optimization of the controller in order to overcome this challenge. This research explores three optimization algorithms including Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). These algorithms are employed to find the optimal combination of PID gains that minimize a cost function incorporating factors such as speed error. In this research, a PID controller is proposed into the simulink model which simulates the cruise control system. The simulink model incorporates the dynamic model of the vehicle as transfer function along with the disturbance model. The output obtained from the scope in simulink shows the error difference of speed over time. The result obtained will then be optimized through optimization techniques. The optimized PID controllers are then evaluated in terms of percent overshoot and also settling time through simulations to assess their effectiveness in achieving the desired objectives. The results are compared to a result of a manual tuning PID parameter to demonstrate the potential improvements in accuracy and stability. Among the three optimization techniques, ACO obtained the most ideal result which is 88.33% improvement in overshoot and 79.51% on the settling time. Finally, the paper discusses the trade-offs between performance gains and computational complexity associated with these optimization techniques. The future work that can be done for this may be prioritizing the real-world implementation of these optimized controllers, analyzing the influence of different cost functions and exploring alternative optimization methods for PID parameter tuning.