SN 2013ai: A Link between Hydrogen-rich and Hydrogen-poor Core-collapse Supernovae

We present a study of the optical and near-infrared (NIR) spectra of SN 2013ai along with its light curves. These data range from discovery until 380 days after explosion. SN 2013ai is a fast declining Type II supernova (SN II) with an unusually long rise time, 18.9 ± 2.7 days in the V-band, and a bright V-band peak absolute magnitude of −18.7 ± 0.06 mag. The spectra are dominated by hydrogen features in the optical and NIR. The spectral features of SN 2013ai are unique in their expansion velocities, which, when compared to large samples of SNe II, are more than 1,000 km s−1 faster at 50 days past explosion. In addition, the long rise time of the light curve more closely resembles SNe IIb rather than SNe II. If SN 2013ai is coeval with a nearby compact cluster, we infer a progenitor zero-age main-sequence mass of ~17 M⊙. After performing light-curve modeling, we find that SN 2013ai could be the result of the explosion of a star with little hydrogen mass, a large amount of synthesized 56Ni, 0.3–0.4 M⊙, and an explosion energy of 2.5–3.0 × 1051 erg. The density structure and expansion velocities of SN 2013ai are similar to those of the prototypical SN IIb, SN 1993J. However, SN 2013ai shows no strong helium features in the optical, likely due to the presence of a dense core that prevents the majority of γ-rays from escaping to excite helium. Our analysis suggests that SN 2013ai could be a link between SNe II and stripped-envelope SNe

Nonlinear adaptive control of feedback passive systems

An adaptive controller that solves the problem of rendering a system passive is presented for a special class of systems with parametric uncertainty. The proposed control uses techniques of speed-gradient methodology from the Russian literature. Stability results can be given that do not require commonly used assumptions such as linearity in the parameters. Algorithms that render the system strictly passive clarify some already known in the control of mechanical systems.link_to_subscribed_fulltex

Ultimate Bounds and Robust Invariant Sets for Linear Systems with State-Dependent Disturbances

International audienceThe objective of this chapter is to present a methodology for computing robust positively invariant sets for linear, discrete time-invariant systems that are affected by additive disturbances, with the particularity that these disturbances are subject to state-dependent bounds. The proposed methodology requires less restrictive assumptions compared to similar established techniques, while it provides the framework for determining the state-dependent (parameterized) ultimate bounds for several classes of disturbances. The added value of the proposed approach is illustrated by an optimization-based problem for detecting the mode of functioning of a switching system

Implications of Inverse Parametric Optimization in Model Predictive Control

International audienceRecently, inverse parametric linear/quadratic programming problem was shown to be solvable via convex liftings approach [13]. This technique turns out to be relevant in explicit model predictive control (MPC) design in terms of reducing the prediction horizon to at most two steps. In view of practical applications, typically leading to problems that are not directly invertible, we show how to adapt the inverse optimality to specific, possibly convexly non-liftable partitions. Case study results moreover indicate that such an extension leads to controllers of lower complexity without loss of optimality. Numerical data are also presented for illustration