  Capturing uncertainty in models of complex dynamical systems is crucial to
designing safe controllers. Stochastic noise causes aleatoric uncertainty,
whereas imprecise knowledge of model parameters leads to epistemic uncertainty.
Several approaches use formal abstractions to synthesize policies that satisfy
temporal specifications related to safety and reachability. However, the
underlying models exclusively capture aleatoric but not epistemic uncertainty,
and thus require that model parameters are known precisely. Our contribution to
overcoming this restriction is a novel abstraction-based controller synthesis
method for continuous-state models with stochastic noise and uncertain
parameters. By sampling techniques and robust analysis, we capture both
aleatoric and epistemic uncertainty, with a user-specified confidence level, in
the transition probability intervals of a so-called interval Markov decision
process (iMDP). We synthesize an optimal policy on this iMDP, which translates
(with the specified confidence level) to a feedback controller for the
continuous model with the same performance guarantees. Our experimental
benchmarks confirm that accounting for epistemic uncertainty leads to
controllers that are more robust against variations in parameter values.
