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.
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).
Future work will extend the framework to be wind-aware while retaining its aerodynamic prior-free design.
A quantitative robustness analysis and the extension to fully unknown $(\boldsymbol{A},\boldsymbol{B})$ are left for future work.
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.
The open problem is a general replacement for separation: conditions through temporal ISS gains, small-gain inequalities, residual bounds, or relative horizons under which an auxiliary subsystem with a weaker property preserves the guarantee of the regulated state, covering heterogeneous components, a finite-time observer with a predefined-time controller, or a practical filter with an exact nominal design, and ending in a certificate for the augmented state that names the property proved for each auxiliary error.