A hybrid metaheuristic control algorithm design for improved reliability of Adaptive Cruise Control System in Autonomous Vehicle

The modern era vehicles generally have an Cruise Control (ACC) system installed in them. Intended to assist drivers, some researchers have worked on multi-objectives like road safety improvement and driving process enhancement; hence, approaches like Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) are primarily used in the literature. As indicated in various literature, ACC equipped with MPC can be designed with multi-objectives and constraints for improved and reliable performance of automated vehicles. Therefore, ACC system based on MPC with an optimization known as Coati Optimization Algorithm (COA), i.e., MPC-COA, is introduced in this manuscript. The proposed COA for ACC provides necessary stability and maintains a safer distance between the ACC equipped host vehicle and the former target vehicle. The proposed MPC-COA technique is simulated on the MATLAB platform, and the results based on performance metrics like desired acceleration input, velocity error, distance error, and acceleration of ACC equipped host vehicle are compared with those of the conventional LQR and MPC controller. By this introduced strategy, the acceleration, the velocity, and the inter-vehicle distance error are regulated optimally. Consequently, the convergence speed is enhanced, and the optimal solution is maintained without deviation in the desired performances. The controller is further evaluated for different prediction horizons(N).