2024/10/10 by Johannes Autenrieb, Hyo‐Sang Shin, Autenrieb, Johannes +1 · 1 citation
Decision Sciences · Engineering · #Advanced Control Systems Optimization #FOS: Electrical engineering #Fault Detection and Control Systems #Simulation Techniques and Applications #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.08096
openalex publication_date 2024/10/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The existing control barrier function literature generally relies on precise mathematical models to guarantee system safety, limiting their applicability in scenarios with parametric uncertainties. While incremental control techniques have shown promise in addressing model uncertainties in flight control applications, translating these approaches to safety-critical control presents significant challenges. This paper bridges this gap by introducing measurement-robust incremental control barrier functions (MRICBFs), which leverage sensor-based reduced-order models to provide formal safety guarantees for uncertain systems. By carefully addressing the challenges of sensor accuracy and approximation errors in the incremental formulation, our approach enables substituting specific model components with real-time sensor measurements while maintaining rigorous safety guarantees. This formulation overcomes the limitations of traditional adaptive control methods that adjust system parameters over time, enabling immediate and reliable safety measures for a class of model uncertainties. The efficacy of MRICBFs is demonstrated in two simulation case studies: a simple first-order system with time-varying sensor biases and a more complex overactuated hypersonic glide vehicle with multiple state constraints.