  We introduce a sequential learning algorithm to address a robust controller
tuning problem, which in effect, finds (with high probability) a candidate
solution satisfying the internal performance constraint to a chance-constrained
program which has black-box functions. The algorithm leverages ideas from the
areas of randomised algorithms and ordinal optimisation, and also draws
comparisons with the scenario approach; these have all been previously applied
to finding approximate solutions for difficult design problems. By exploiting
statistical correlations through black-box sampling, we formally prove that our
algorithm yields a controller meeting the prescribed probabilistic performance
specification. Additionally, we characterise the computational requirement of
the algorithm with a probabilistic lower bound on the algorithm's stopping
time. To validate our work, the algorithm is then demonstrated for tuning model
predictive controllers on a diesel engine air-path across a fleet of vehicles.
The algorithm successfully tuned a single controller to meet a desired tracking
error performance, even in the presence of the plant uncertainty inherent
across the fleet. Moreover, the algorithm was shown to exhibit a sample
complexity comparable to the scenario approach.
