Layered Control of Partially Observed Stochastic Systems
For control engineers dealing with large-scale stochastic systems under partial observations, this work provides theoretical foundations and practical construction for layered control, addressing a previously underexplored gap.
This paper proposes a layered control framework for partially observed stochastic systems, ensuring bounded expected output distance between layers via novel stochastic simulation functions. Demonstrated on aerial robots, the approach enables principled hierarchical control under uncertainty.
Layered control is essential for managing complexity in large-scale systems, employing progressively coarser models at higher layers. While significant advances have been made for fully observable systems, the theoretical foundations of layered control under partial observations and stochastic noise remain underexplored. To address this gap, we propose a principled layered control framework for such settings. Given a state estimator at each layer, our approach ensures that the expected output distance between systems at successive layers remains within a priori computable bounds. This is achieved by introducing a novel notion of stochastic simulation functions for partially observed systems. For the class of linear systems with Kalman estimators, we provide a systematic construction of these functions along with the corresponding control design. We demonstrate our framework on two aerial robotic scenarios: an unmanned aerial vehicle and a hexacopter with a camera payload.