Data-Driven, Non-Parametric Model Reference Adaptive Control Methods for Autonomous Underwater Vehicles
This thesis details the implementation of two adaptive controllers on autonomous underwater vehicle(AUV) attitude dynamics starting from the standard six degree-of-freedom dynamic model. I apply two model reference adaptive control (MRAC) algorithms which make use of kernel functions for learning functional uncertainty present in the system dynamics. The first method extends recent results on model reference adaptive control using reproducing kernel Hilbert space (RKHS) learning techniques for some general cases of multi-input systems. The first controller design is a model reference adaptive controller (MRAC) based on a vector- valued RKHS that is induced by operator-valued kernels. This paper formulates a model reference adaptive control strategy based on a dead zone robust modification, and derives conditions for the ultimate boundedness of the tracking error in this case. The second controller is an implementation of the Gaussian Process MRAC developed by Chowdhary, et al. I discuss the method of each of these algorithms before contrasting the underlying theoretical structure of each algorithm. Finally, I provide a comparison of each algorithm's performance on the six degree-of-freedom dynamic model of the Virginia Tech 690 AUV and provide field trial results for the RKHS based MRAC implementation.