8. In closing

The tracking results of the quadrotor UAV system show the versatility of the control strategy. Once a framework is designed it can be applied to different systems with the same dynamics, i.e. can be applied to any quadrotor UAV, within certain limits, and will converge to stability, indicating robustness. It also withstands varying system parameters during operation due to changing environmental conditions or payloads, indicating adaptability.

A well-established and highly cited SIMULINK model was used for the simulations to prove the feasibility and good performance of this control strategy on a quadrotor UAV system. The system dynamics incorporated in this model include dynamically modeled thrust and drag coefficients, more reflective of a real-world scenario. Going forward, a disturbance rejection study can be done and the controller can be run on hardware to test it under real-world conditions.

Design choices for the neural network in terms of depth, width and choice of inputs were made based on real (read: simulated) data. The methodical process for this was outlined and applied to the quadrotor UAV system to justify the decisions made.

In real world systems, such as in UAVs where on-board processing is limited, processing time is a major factor and several methods for speed-ups were discussed which are computationally light. Even dynamically unstable systems such as UAVs could be stabilized using an untrained controller in-flight by devising a training regime.
