Applying Q-learning to designing feasible morphing UAV control system

This paper develops a control methodology for morphing, which combines Q-Learning and PID Control. Sections 1 and 2 of the full paper explain our design mentioned in the tide, which we believe is feasible or effective and whose core consists of; "The morphing control function, which uses Q-Learning, is integrated with the trajectory tracking function, which uses PID Control. Optimality is addressed by cost functions representing optimal shapes corresponding to specified operating conditions, and an episodic 'reinforcement learning' simulation is developed to leam the optimal shape change policy. The methodology is demonstrated by a numerical example of a morphing air vehicle, which simultaneously tracks a specified trajectory and autonomously morphs over a set of shapes corresponding to flight conditions along the trajectory." Simulation results, presented in Figs. 4 and 5, and their analysis show preliminarily that this methodology is capable of learning the required shape and morphing into it and accurately tracking the reference trajectory, thus showing that our design is indeed feasible.