CSR: Small: Uncertainty Management in Real-Time Embedded Control Systems

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

This multi-disciplinary research is at the intersection of control engineering and computer science. Specifically, methods from control and dynamical systems theory are being used to address challenges in real-time embedded systems. The central objective of the research is to develop next generation verification algorithms for real-time embedded systems. The modeling and analysis framework for determining robustness of embedded systems is based on the stochastic robustness framework, where the binary notion of robustness is discarded and the notion of a risk-adjusted robustness margin tradeoff is adopted. Key elements in this verification-based research include modeling of parametric uncertainty in embedded systems and development of computational tools for accurate prediction of uncertainty in real-time systems; robust performance analysis of various scheduling algorithms; and impact of various models of computations (anytime, imprecise, interlaced) on system level robustness. Uncertainty propagation tools will use methods based on Monte-Carlo techniques, polynomial chaos, and transfer operators (such as Perron-Frobenius and Koopman operators). Tools developed in the area of multi-rate robust control will be used to analyze the effect of various models of computation on robustness of the system. It is expected that successful completion of this project will enable accurate assessment of reliability of embedded systems across a wide array of engineering disciplines. This research project will also train a new generation of researchers with a mixed background in dynamical systems, control theory, and computer science. This is aligned with current and future research and industrial needs.