An adaptive cruise control system for autonomous vehicles using PRDC–CoROA-based hybrid optimization

An adaptive cruise control (ACC) system has a key role to play in enhancing the safety and stability of autonomous vehicles, along with ensuring improved ride quality through proper management of vehicle velocity and distance between vehicles. However, the conventional design of an ACC system, such as Proportional-Integral-Derivative (PID), Model Predictive Control (MPC), fuzzy logic, and neural network-based controllers, has certain difficulties due to nonlinearity, uncertainty, and real-time parameter optimization. In order to overcome these shortcomings, this study proposes a hybrid optimization-based approach to ACC based on a Probabilistic Robust Damping Controller (PRDC), integrated with the Composed Remora Optimization Algorithm (CoROA). This novel approach seeks to optimize the performance of the ACC through improvements in vehicular stability, distance control, and velocity tracking. The controller uses data about the speed, acceleration, distance between the vehicles, and the velocity error to provide proper control signals while keeping the vehicle’s operation safe and smooth. The algorithm has been developed and verified in the MATLAB environment, and its performance has been compared to other ACC techniques. The simulation outcomes show that the proposed method reduces the errors of velocity and distance by less than 10% and facilitates smoother regenerative braking operation up to 43.65% and vehicle driving stability. The outcomes achieved show that the proposed PRDC-CoROA method improves the performance of adaptive cruise control.