Sliding mode control of a nonlinear wave energy converter model

The most accurate wave energy converter models for heaving point absorbers include nonlinearities, which increase as resonance is achieved to maximize the energy capture. Over the power production spectrum and within the physical limits of the devices, the efficiency of wave energy converters can be enhanced by employing a control scheme that accounts for these nonlinearities. This paper proposes a sliding mode control for a heaving point absorber that includes the nonlinear effects of the dynamic and static Froude‐Krylov forces. The sliding mode controller tracks a reference velocity that matches the phase of the excitation force to ensure higher energy absorption. This control algorithm is tested in regular linear waves and is compared to a complex‐conjugate control and a nonlinear variation of the complex‐conjugate control. The results show that the sliding mode control successfully tracks the reference and keeps the device displacement bounded while absorbing more energy than the other control strategies. Furthermore, due to the robustness of the control law, it can also accommodate disturbances and uncertainties in the dynamic model of the wave energy converter

Towards real-time reinforcement learning control of a wave energy converter

The levellised cost of energy of wave energy converters (WECs) is not competitive with fossil fuel-powered stations yet. To improve the feasibility of wave energy, it is necessary to develop effective control strategies that maximise energy absorption in mild sea states, whilst limiting motions in high waves. Due to their model-based nature, state-of-the-art control schemes struggle to deal with model uncertainties, adapt to changes in the system dynamics with time, and provide real-time centralised control for large arrays of WECs. Here, an alternative solution is introduced to address these challenges, applying deep reinforcement learning (DRL) to the control of WECs for the first time. A DRL agent is initialised from data collected in multiple sea states under linear model predictive control in a linear simulation environment. The agent outperforms model predictive control for high wave heights and periods, but suffers close to the resonant period of the WEC. The computational cost at deployment time of DRL is also much lower by diverting the computational effort from deployment time to training. This provides confidence in the application of DRL to large arrays of WECs, enabling economies of scale. Additionally, model-free reinforcement learning can autonomously adapt to changes in the system dynamics, enabling fault-tolerant control

Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning

To achieve persistent systems in the future, autonomous underwater vehicles (AUVs) will need to autonomously dock onto a charging station. Here, reinforcement learning strategies were applied for the first time to control the docking of an AUV onto a fixed platform in a simulation environment. Two reinforcement learning schemes were investigated: one with continuous state and action spaces, deep deterministic policy gradient (DDPG), and one with continuous state but discrete action spaces, deep Q network (DQN). For DQN, the discrete actions were selected as step changes in the control input signals. The performance of the reinforcement learning strategies was compared with classical and optimal control techniques. The control actions selected by DDPG suffer from chattering effects due to a hyperbolic tangent layer in the actor. Conversely, DQN presents the best compromise between short docking time and low control effort, whilst meeting the docking requirements. Whereas the reinforcement learning algorithms present a very high computational cost at training time, they are five orders of magnitude faster than optimal control at deployment time, thus enabling an on-line implementation. Therefore, reinforcement learning achieves a performance similar to optimal control at a much lower computational cost at deployment, whilst also presenting a more general framework

Towards Real-Time Reinforcement Learning Control of a Wave Energy Converter

This is the final version. Available on open access from MDPI via the DOI in this recordThe levellised cost of energy of wave energy converters (WECs) is not competitive with fossil-fuel powered stations yet. To improve the feasibility of wave energy, it is necessary to develop effective control strategies that maximise energy absorption in mild sea states, whilst limiting motions in high waves. Due to their model-based nature, state-of-the-art control schemes struggle to deal with model uncertainties, adapt to changes in the system dynamics with time, and provide real-time centralised control for large arrays of WECs. Here, an alternative solution is introduced to address these challenges, applying deep reinforcement learning (DRL) to the control of WECs for the first time. A DRL agent is initialised from data collected in multiple sea states under linear model predictive control in a linear simulation environment. The agent outperforms model predictive control for high wave heights and periods, but suffers close to the resonant period of the WEC. The computational cost at deployment time of DRL is also much lower by diverting the computational effort from deployment time to training. This provides confidence in the application of DRL to large arrays of WECs, enabling economies of scale. Additionally, model-free reinforcement learning can autonomously adapt to changes in the system dynamics, enabling fault-tolerant control

Pseudogap temperature as a Widom line in doped Mott insulators

The pseudogap refers to an enigmatic state of matter with unusual physical properties found below a characteristic temperature $T^*$ in hole-doped high-temperature superconductors. Determining $T^*$ is critical for understanding this state. Here we study the simplest model of correlated electron systems, the Hubbard model, with cluster dynamical mean-field theory to find out whether the pseudogap can occur solely because of strong coupling physics and short nonlocal correlations. We find that the pseudogap characteristic temperature $T^*$ is a sharp crossover between different dynamical regimes along a line of thermodynamic anomalies that appears above a first-order phase transition, the Widom line. The Widom line emanating from the critical endpoint of a first-order transition is thus the organizing principle for the pseudogap phase diagram of the cuprates. No additional broken symmetry is necessary to explain the phenomenon. Broken symmetry states appear in the pseudogap and not the other way around.Comment: 6 pages, 4 figures and supplementary information; published versio

Purifying Selection in Deeply Conserved Human Enhancers Is More Consistent than in Coding Sequences

(c) 2014 De Silva et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

The death and the resurrection of (psy)critique: the case of neuroeducation

A rapidly emerging hegemonic neuro-culture and a booming neural subjectivity signal the entry point for an inquiry into the status of the signifier neuro as a universal passe-partout. The wager of this paper is that the various (mis)appropriations of the neurosciences in the media and in academia itself point to something essential, if not structural, in connection with both the discipline of the neurosciences and the current socio-cultural and ideological climate. Starting from the case of neuroeducation (the application of neuroscience within education), the genealogy of the neurological turn is linked to the history of psychology and its inextricable bond with processes of psychologisation. If the neurological turn risks not merely neglecting the dimension of critique, but also obviating its possibility, then revivifying a psy-critique (understanding the academified modern subject as grounded in the scientific point of view from nowhere) might be necessary in order to understand today’s neural subjectivity and its place within current biopolitics

Quantum physics in inertial and gravitational fields

Covariant generalizations of well-known wave equations predict the existence of inertial-gravitational effects for a variety of quantum systems that range from Bose-Einstein condensates to particles in accelerators. Additional effects arise in models that incorporate Born reciprocity principle and the notion of a maximal acceleration. Some specific examples are discussed in detail.Comment: 25 pages,1 figure,to appear in "Relativity in Rotating Frame

The Gaia-ESO Survey: Asymmetric expansion of the Lagoon Nebula cluster NGC 6530 from GES and Gaia DR2

The combination of precise radial velocities from multi-object spectroscopy and highly accurate proper motions from Gaia DR2 opens up the possibility for detailed 3D kinematic studies of young star-forming regions and clusters. Here, we perform such an analysis by combining Gaia-ESO Survey spectroscopy with Gaia astrometry for ∼900 members of the Lagoon Nebula cluster, NGC 6530. We measure the 3D velocity dispersion of the region to be 5.35+0.39 -0.34 km s-1, which is large enough to suggest the region is gravitationally unbound. The velocity ellipsoid is anisotropic, implying that the region is not sufficiently dynamically evolved to achieve isotropy, though the central part of NGC 6530 does exhibit velocity isotropy that suggests sufficient mixing has occurred in this denser part. We find strong evidence that the stellar population is expanding, though this is preferentially occurring in the declination direction and there is very little evidence for expansion in the right ascension direction. This argues against a simple radial expansion pattern, as predicted by models of residual gas expulsion. We discuss these findings in the context of cluster formation, evolution, and disruption theories.NJW acknowledges an STFC Ernest Rutherford Fellowship (grant number ST/M005569/1). RJP acknowledges support from the Royal Society in the form of a Dorothy Hodgkin Fellowship. AB acknowledges support from ICM (Iniciativa Científica Milenio) via the Núcleo Milenio de Formación Planetaria. EJA acknowledges support from the Spanish Government Ministerio de Ciencia, Innovacion y Universidades though grant AYA2016-75 931-C2-1 and from the State Agency for Research of the Spanish MCIU through the ‘Center of Excellence Severo Ochoa’ award for the Instituto de Astrofisica de Andalucia (SEV-2017-0709)

Building Babies - Chapter 16

In contrast to birds, male mammals rarely help to raise the offspring. Of all mammals, only among rodents, carnivores, and primates, males are sometimes intensively engaged in providing infant care (Kleiman and Malcolm 1981). Male caretaking of infants has long been recognized in nonhuman primates (Itani 1959). Given that infant care behavior can have a positive effect on the infant’s development, growth, well-being, or survival, why are male mammals not more frequently involved in “building babies”? We begin the chapter defining a few relevant terms and introducing the theory and hypotheses that have historically addressed the evolution of paternal care. We then review empirical findings on male care among primate taxa, before focusing, in the final section, on our own work on paternal care in South American owl monkeys (Aotus spp.). We conclude the chapter with some suggestions for future studies.Deutsche Forschungsgemeinschaft (HU 1746/2-1) Wenner-Gren Foundation, the L.S.B. Leakey Foundation, the National Geographic Society, the National Science Foundation (BCS-0621020), the University of Pennsylvania Research Foundation, the Zoological Society of San Dieg