  In this work we exploit the universal approximation property of Neural
Networks (NNs) to design interconnection and damping assignment (IDA)
passivity-based control (PBC) schemes for fully-actuated mechanical systems in
the port-Hamiltonian (pH) framework. To that end, we transform the IDA-PBC
method into a supervised learning problem that solves the partial differential
matching equations, and fulfills equilibrium assignment and Lyapunov stability
conditions. A main consequence of this, is that the output of the learning
algorithm has a clear control-theoretic interpretation in terms of passivity
and Lyapunov stability. The proposed control design methodology is validated
for mechanical systems of one and two degrees-of-freedom via numerical
simulations.
