A Scalable Model-Based Learning Algorithm With Application to UAVs

Unmanned aerial vehicles (UAVs) play essential roles in many areas including search and rescue, monitoring, and exploration. As such, fast and accurate trajectory tracking is crucial for UAVs especially in emergency situations or cluttered environment. This letter proposes a novel learning algorithm that improves UAVs’ tracking performance through learning without human intervention. This learning algorithm, while possessing self-learning capacity, falls into the model-based learning methodology and therefore inherits the advantages of control techniques. This algorithm is particularly pursued and developed for scenarios that the reference trajectory is either too aggressive to follow or not compatible with system dynamics. Numerical study is conducted to validate the effectiveness and efficiency of the proposed learning algorithm and demonstrate the enhanced tracking and learning performance.