Design of model predictive control via learning automata for a single UAV load transportation

摘要

In recent years, autonomous aerial robots have been successfully used to perform the construction of structures composed by parts that have similar dimensions and inertial moments. However, these proposed control systems are not able to accurately control the UAVs during the handling and transporting loads with various weights and balance features. In this paper, we investigate a robust and innovative control strategy for UAV load transportation system that can deal with the load characteristics and disturbances such as ground effect and control noise. Taking into account the nonlinear and under-actuated features of the quadrotor, a Learning Automata (LA) methodology is applied to tune the Nonlinear Model Predictive Controllers (NMPCs) in the various contexts of operation. Specifically, it applies LA to select the weighting parameters of the objective function in order to minimize tracking error provided by the plant. Simulation results demonstrate the learned weighting parameters can be efficiently employed to obtain NMPC controllers for tracking optimized trajectories to deal with different load conditions.