Search for: All records

Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders their real-world deployment. This letter constructs a decentralized RL-based controller for inverter-based real-time voltage control in distribution systems. It features two components: a transient control policy and a steady-state performance optimizer. The transient policy is parameterized as a neural network, and the steady-state optimizer represents the gradient of the long-term operating cost function. The two parts are synthesized through a safe gradient flow framework, which prevents the violation of reactive power capacity constraints. We prove that if the output of the transient controller is bounded and monotonically decreasing with respect to its input, then the closed-loop system is asymptotically stable and converges to the optimal steady-state solution. We demonstrate the effectiveness of our method by conducting experiments with IEEE 13-bus and 123-bus distribution system test feeders. 

Spider pulsars are compact binary systems composed of a millisecond pulsar and a low-mass companion. The relativistic magnetically dominated pulsar wind impacts onto the companion, ablating it and slowly consuming its atmosphere. The interaction forms an intrabinary shock, a proposed site of particle acceleration. We perform global fully kinetic particle-in-cell simulations of the intrabinary shock, assuming that the pulsar wind consists of plane-parallel stripes of alternating polarity and that the shock wraps around the companion. We find that particles are efficiently accelerated via shock-driven reconnection. We extract first-principles synchrotron spectra and light curves, which are in good agreement with X-ray observations: (1) the synchrotron spectrum is nearly flat,F_ν∝ const; (2) when the pulsar spin axis is nearly aligned with the orbital angular momentum, the light curve displays two peaks, just before and after the pulsar eclipse (pulsar superior conjunction), separated in phase by ∼0.8 rad; (3) the peak flux exceeds the one at the inferior conjunction by a factor of 10.

 

Deep-sea chemosynthesis-based ecosystems support unique biological communities, but human impacts are an increasing threat. Understanding the life-history traits of species from deep-sea chemosynthesis-based ecosystems can help to develop adequate management strategies, as these can have impacts on ecological responses to changes in the environment. Here we examined the occurrence of sexual dimorphism in the yeti crab Kiwa puravida , an endemic species from the Costa Rican Pacific margin that aggregates at active methane seeps and depends on chemosynthetic bacteria for nutrition. The two morphological features examined included the claws, suspected to be under sexual selection and used for defense, and the carpus of the second pereopod not suspected to be under sexual selection. A total of 258 specimens, 161 males, 81 females, 16 juveniles, were collected from Mound 12 at 1,000-1,040 m depth in 2017 and 2018 and analyzed. We found that males have larger and wider claws than females, while there were no differences in carpus length. These results suggest that claw weaponry is under sexual selection in K . puravida , which is probably related to the mating system of this deep-sea species. This is the first attempt to study the reproductive biology of K . puravida , and additional observations will be necessary to shed more light on this matter. 

Continental margins host methane seeps, animal falls and wood falls, with chemosynthetic communities that may share or exchange species. The goal of this study was to examine the existence and nature of linkages among chemosynthesis-based ecosystems by deploying organic fall mimics (bone and wood) alongside defaunated carbonate rocks within high and lesser levels of seepage activity for 7.4 years. We compared community composition, density, and trophic structure of invertebrates on these hard substrates at active methane seepage and transition (less seepage) sites at Mound 12 at ~1,000 m depth, a methane seep off the Pacific coast of Costa Rica. At transition sites, the community composition on wood and bone was characteristic of natural wood- and whale-fall community composition, which rely on decay of the organic substrates. However, at active sites, seepage activity modified the relationship between fauna and substrate, seepage activity had a stronger effect in defining and homogenizing these communities and they depend less on organic decay. In contrast to community structure, macrofaunal trophic niche overlap between substrates, based on standard ellipse areas, was greater at transition sites than at active sites, except between rock and wood. Our observations suggest that whale- and wood-fall substrates can function as stepping stones for seep fauna even at later successional stages, providing hard substrate for attachment and chemosynthetic food. 

This paper proposes a data-driven framework to solve time-varying optimization problems associated with unknown linear dynamical systems. Making online control decisions to steer a system to the solution trajectory of a time-varying optimization problem is a central goal in many modern engineering applications. Yet, the available methods critically rely on a precise knowledge of the system dynamics, thus requiring ad-hoc system identification and model refinement phases. In this work, we leverage tools from behavioral theory to show that the steady-state transfer function of a system can be computed from control experiments without knowledge or estimation of the system model. Such direct computation allows us to avoid the explicit model identification phase, and is significantly more tractable than the direct model-based computation. We leverage the data-driven representation to design a controller inspired from a gradient-descent method that drives the system to the solution of an unconstrained optimization problem, without any knowledge of time-varying disturbances affecting the model equation. Results are tailored to cost functions that are smooth and satisfy the Polyak-Lojasiewicz inequality. Simulation results illustrate the technical findings.