In many problems in optimal control, one seeks to minimise an objective function subject to constraints on the velocity of the system. Imposing these constraints directly -- the ``hard-constrained'' approach -- is often analytically and computationally challenging. A natural alternative is to penalise violations of the constraints, solving a sequence of ``soft-constrained'' problems indexed by a penalty parameter $q$, and hoping that solutions converge to solutions of the hard-constrained problem as $q \to \infty$. We show that this approach is justified when applied to a broad class of geometric control problems on a Riemannian manifold $(M,g)$. We first consider the case where there are no autonomous dynamics, and so the control problem reduces to the problem of finding a curve of minimal length or energy between two points, subject to a nonholonomic velocity constraint/penalty determined by the choice of a bracket-generating subbundle $D$ of $TM$. We show that any sequence of solutions to the soft-constrained problem has an accumulation point which is a solution to the hard-constrained problem. Subsequently, we show how to transform a broad class of optimal control problems to the problem of finding a geodesic, by trivialising the inherent dynamics of the system using a change of coordinates inspired by the interaction picture transformation in quantum mechanics.