Bridging robustness and performance guarantees in control under uncertainty

Develop a unified control framework that reconciles robustness to latent parametric uncertainty with strong closed-loop performance guarantees for nonlinear dynamical systems, thereby bridging the gap between conservative robust control designs and high-performance control under uncertainty.

Background

The paper contrasts classical robust control—often formulated via worst-case distributionally robust optimization—with stochastic and risk-sensitive control that optimizes expected performance given a prescribed distribution. The authors argue that worst-case formulations are overly conservative and can degrade task performance, while task-agnostic stochastic approaches may fail to capture task-relevant uncertainty.

Motivated by this tension, the paper proposes a Stein variational inference-based approach to adapt uncertainty representations toward task-sensitive parameters. The explicit open problem highlights the need for a principled framework that simultaneously ensures robustness and strong performance guarantees, rather than prioritizing one at the expense of the other.

References

Bridging this gap between robustness and control performance guarantees remains a fundamental open problem.

— Stein Variational Uncertainty-Adaptive Model Predictive Control  (2604.01034 - Sathyanarayan et al., 1 Apr 2026) in Introduction

Several of these simplifications point directly to open work. Foremost among them, a rigorous, system-specific derivation of the stability and performance guarantees sketched in Table~\ref{tab:cross}, verifying the exact hypotheses of the cited theorems against the CWH dynamics, constraint sets, and terminal ingredients used in each operation, is left as future work.

— Mission-Centric Requirements Analysis of Model Predictive Control in Spacecraft Rendezvous and Proximity Operations  (2610.02782 - Maier et al., 2 Oct 2026) in Section Conclusion, Section 6

On full-scale vehicles, Dallas et al. filter steering and drive commands during drifting using a closed-form safe set with a fixed friction coefficient, identifying adaptation to model uncertainty as an open problem.

— Context-Conditioned Hamilton-Jacobi Reachability for Adaptive Safety Filtering  (2610.08115 - Sahin et al., 6 Oct 2026) in Section 2, Related Work, subsection “Vehicle safety at the handling limit”