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Leveraging Data Engineering to Improve Unmanned Aerial Vehicle Control Design

The potential benefits of big data and machine learning techniques are yet to be fully realized in real-time, safety-critical applications like unmanned aerial vehicle control. This is because of challenges related to interpretation, error susceptibility, and resources requirements. Due to their robustness and reliability, traditional model-based design techniques still dominate this landscape. However, a growing body of research in adaptive control has demonstrated the potential benefits of merging these two distinct design philosophies. This article investigates the benefits of using a combination of machine learning techniques to automatically tune parameters within a strictly defined model predictive control architecture. Fast orthogonal search and finite action-set learning automata are used to tune model coefficients and objective function weights, respectively. The strategy is validated experimentally on an actual Quanser Qball2 quadcopter and through several simulations of a Parrot AR.drone. Results demonstrate that the proposed approach improves performance while reducing design effort.