Systematic robustness for layered control architectures

Develop a systematic methodology to incorporate model uncertainty and process noise into the synthesis and analysis of layered control architectures, providing robustness guarantees across layers within the optimal-control-decomposition framework used to derive decision-making, trajectory-planning, and real-time feedback subproblems.

Background

In setting up the overall synthesis problem, the paper adopts nominal dynamics without uncertainty to simplify exposition, while acknowledging that real systems invariably face modeling errors and process noise.

The authors discuss promising directions such as Lyapunov-based tracking certificates and assume–guarantee contracts but emphasize that a principled approach starting from a robust formulation and propagating guarantees through the architectural decomposition is missing.

References

Of course, real systems are subject to both, and how to systematically account for such uncertainty in LCAs remains an important open problem (see Robust LCAs).

— Towards a Theory of Control Architecture: A quantitative framework for layered multi-rate control  (2401.15185 - Matni et al., 2024) in Part 1.1: The overall synthesis problem (following Eq. (1))

Future work will extend the framework to be wind-aware while retaining its aerodynamic prior-free design.

— Aerodynamic Prior-Free Coordinated Trajectory Generation and Tracking Control for a Tail-Sitter UAV  (2609.11698 - Rong et al., 10 Sep 2026) in Conclusion and Future Work, Section VI, paragraph 2

The principal open questions are robustness to diagnostic noise and reconstruction errors, systematic characterization of surrogate gradient accuracy, generalization of the surrogate beyond the training distribution, real-time implementation on PCS hardware with deterministic timing guarantees, and design of a fallback hierarchy for safe degradation.

— On the feasibility of model-based feedback control of vertical instability growth rate using out-vessel coils in ARC-like scenarios  (2608.22677 - Kumar et al., 24 Aug 2026) in Section 6, Conclusions