PSO-MPC based Trajectory Planning for RIS-assisted UAV Communication Systems

In this paper, a trajectory planning scheme for the Aerial Reconfigurable Intelligent Surface - assisted UAV (ARU) to track multiple Intelligent Vehicles (IVs) on the ground based on Particle Swarm Optimization (PSO) and Model Predictive Control (MPC) algorithm is proposed, aiming to better serve the intelligent vehicles on the ground. Through the alternating iterative approach of PSO search and MPC correction, the following steps are carried out: First, the PSO algorithm is used to track the IVs on the ground to find the position where the ARU can achieve the maximum signal transmission. Then, the MPC algorithm is employed to correct and optimize the points found by the PSO algorithm, preventing the algorithm from getting trapped in local optima. In this way, the next flight position of the ARU is gradually determined, realizing the dynamic planning and optimization of the ARU's flight trajectory. The simulation results demonstrate that the algorithm proposed can flexibly determine the trajectory for tracking the IVs according to the actual dynamic environment, and improve the sum rate of the communication system.