  In this paper, we introduce a sequential learning algorithm to address a
probabilistically robust controller tuning problem. The algorithm leverages
ideas from the areas of randomised algorithms and ordinal optimisation, which
have both been proposed to find approximate solutions for difficult design
problems in control. We formally prove that our algorithm yields a controller
which meets a specified probabilisitic performance specification, assuming a
Gaussian or near-Gaussian copula model for the controller performances.
Additionally, we are able to characterise the computational requirement of the
algorithm by using a lower bound on the distribution function of the
algorithm's stopping time. To validate our work, the algorithm is then
demonstrated for the purpose of tuning model predictive controllers on a diesel
engine air-path. It is shown that the algorithm is able to successfully tune a
single controller to meet a desired performance threshold, even in the presence
of uncertainty in the diesel engine model, that is inherent when a single
representation is used across a fleet of vehicles.
