In this paper, we present an approach for designing correct-by-design
controllers for cyber-physical systems composed of multiple dynamically
interconnected uncertain systems. We consider networked discrete-time uncertain
nonlinear systems with additive stochastic noise and model parametric
uncertainty. Such settings arise when multiple systems interact in an uncertain
environment and only observational data is available. We address two
limitations of existing approaches for formal synthesis of controllers for
networks of uncertain systems satisfying complex temporal specifications.
Firstly, whilst existing approaches rely on the stochasticity to be Gaussian,
the heterogeneous nature of composed systems typically yields a more complex
stochastic behavior. Secondly, exact models of the systems involved are
generally not available or difficult to acquire. To address these challenges,
we show how abstraction-based control synthesis for uncertain systems based on
sub-probability couplings can be extended to networked systems. We design
controllers based on parameter uncertainty sets identified from observational
data and approximate possibly arbitrary noise distributions using Gaussian
mixture models whilst quantifying the incurred stochastic coupling. Finally, we
demonstrate the effectiveness of our approach on a nonlinear package delivery
case study with a complex specification, and a platoon of cars.Comment: 9 pages, 4 figures, accepted to CDC 202