Thispaperdevelopsacontrolmethodologyformorphing,whichcombinesMachineLearning and Adaptive Dynamic Inversion Control. The morphing control function, which uses Reinforcement Learning, is integrated with the trajectory tracking function, which uses StructuredAdaptiveModelInversionControl.Optimalityisaddressedbycostfunctionsrepresenting optimal shapes corresponding to specified operating conditions, and an episodic Reinforcement Learning simulation is developed to learn the optimal shape change policy. The methodology is demonstrated by a numerical example of a 3-D morphing air vehicle, which simultaneously tracks a specified trajectory and autonomously morphs over a set of shapes corresponding toflight conditions along the trajectory. Results presented in the paper show that this methodology is capable of learning the required shape and morphing into it, and accurately tracking the reference trajectory in the presence of parametric uncertainties and initial error conditions.
['John Valasek', 'Monish D. Tandale', 'Jie Rong']
A Reinforcement Learning - Adaptive Control Architecture for Morphing