摘要：AbstractThis paper presents a target tracking technique for a quadcopter based on Model Predictive Control (MPC) tuned using machine learning. Specifically, it uses learning automata to select the weighting parameters of the objective function such that they minimize tracking error. It develops an approximate linear state-space model for the quadcopter dynamics by linearizing around a hover condition. The optimum sequence of control actions is expressed as perturbations on a stabilizing feedback law expanded over a finite prediction horizon. Simulation results demonstrate the learned weighting parameters can be used to provide optimized trajectories when implemented as receding horizon MPC. Furthermore, a comparison with previous work demonstrates improved tracking performance.
