Optimal Trajectory Tracking: A Comparative Study of Autonomous Quadcopter Controllers

The quadcopter controller plays a significant role in autonomous vehicle trajectory tracking and has been extensively examined in the literature because it is a highly nonlinear and under-actuated system.This article contributes by methodically comparing PID, BSC, IBSC, SMC, and ISMC control algorithms under standardized conditions.The study involves implementing these algorithms and optimizing their parameters using a hybrid Flower Pollination Algorithm -Genetic Algorithm (FPA-GA).Robust testing scenarios, including mass uncertainty and time-varying disturbances, are employed to assess algorithmic performance.The research centers on trajectory tracking-control approaches for highly maneuverable Unmanned Aerial Vehicles (UAVs) equipped with four heave thrusters, emphasizing controller stability through Lyapunov functions.Theoretical exploration precedes comprehensive numerical simulations, enabling a detailed comparison of the efficacy and resilience of the proposed approaches.Results from exhaustive testing reveal the consistent superiority of the BSC controller, demonstrating notably low Integral of Time-weighted Squared Error (ITSE) values of 0.1253 and 0.5262 from two simulation tests.These findings underscore the practical advantages of the BSC controller in highly dynamic UAV trajectory tracking scenarios.