  Online approximation of the optimal station keeping strategy for a fully
actuated six degrees-of-freedom marine craft subject to an irrotational ocean
current is considered. An approximate solution to the optimal control problem
is obtained using an adaptive dynamic programming technique. The hydrodynamic
drift dynamics of the dynamic model are assumed to be unknown; therefore, a
concurrent learning-based system identifier is developed to identify the
unknown model parameters. The identified model is used to implement an adaptive
model-based reinforcement learning technique to estimate the unknown value
function. The developed policy guarantees uniformly ultimately bounded
convergence of the vehicle to the desired station and uniformly ultimately
bounded convergence of the approximated policies to the optimal polices without
the requirement of persistence of excitation. The developed strategy is
validated using an autonomous underwater vehicle, where the three
degrees-of-freedom in the horizontal plane are regulated. The experiments are
conducted in a second-magnitude spring located in central Florida.
