Liang Tang

TL;DR: This paper presents a class of risk measures to be used as damage indicators within particle filtering (PF)-based real-time prognosis algorithms, with application to the case of state-of-charge prediction in lithium-ion batteries.

TL;DR: Parts of unmanned aerial vehicle autonomy as suggested by the Autonomous Control Logic chart of the U.S. DoD UAV autonomy roadmap are reviewed; levels of vehicle autonomy addressed through intelligent control practices and a hierarchical/intelligent control architecture are presented for UAVs.

TL;DR: In this article, a bank of adaptive nonlinear fault detection and isolation estimators were developed for flight control actuator faults; a real-time system identification method was developed for assessing the dynamics and performance limitation of impaired aircraft; online learning neural networks were used to approximate selected aircraft dynamics which were then inverted to estimate command margins.

TL;DR: In this article, the authors present a rigorous set of algorithms for uncertainty management that are generic and capable of addressing a variety of uncertainty sources, including model parameter uncertainty, measurement and estimation uncertainties, future load uncertainty, among other factors, all potentially contribute to prognostic uncertainty.

TL;DR: This paper explores the advantages and disadvantages of a Risk-Sensitive PF (RSPF) prognosis framework that complements the benefits of the classic approach, by representing the probability of rare events and highly non-monotonic phenomena within the formulation of the nonlinear dynamic equation that describes the evolution of the fault condition in time.