196,555 research outputs found
Monte Carlo simulations of metal-poor star clusters
No full textContext. Metal-poor globular clusters (GCs) can provide a probe of the earliest epoch of star formation in the Universe, being the oldest observable stellar systems. In addition, young and intermediate-age low-metallicity GCs are present in external galaxies. Nevertheless, inferring their evolutionary status by using integrated properties may suffer from large intrinsic uncertainty caused by the discrete nature of stars in stellar systems, especially in the case of faint objects. Aims. In this paper, we evaluate the intrinsic uncertainty ( due to statistical effects) affecting the integrated colours and mass-to-light ratios as a function of the cluster's integrated visual magnitude (M(V)(tot)), which represents a directly measured quantity. We investigate the case of metal-poor, single-burst stellar populations with age from a few million years to a likely upper value for the Galactic globular cluster ages (similar to 15 Gyr). Methods. Our approach is based on Monte Carlo techniques for randomly generating stars distributed according to the cluster's mass function. Results. Integrated colours and mass-to-light ratios in different photometric bands are checked for good agreement with the observational values of low-metallicity Galactic clusters; the effect of different assumptions on the horizontal branch (HB) morphology is shown to be irrelevant, at least for the photometric bands explored here. We present integrated colours and mass-to-light ratios as a function of age for different assumptions on the cluster total V magnitude. We find that the intrinsic uncertainty cannot be neglected. In particular, in models with M(V)(tot) V = -4 the broad-band colours show an intrinsic uncertainty high enough to prevent the precise age of the cluster from being evaluated. The effects of different assumptions on the initial mass function and on the minimum mass for which carbon burning is ignited for both integrated colours and mass-to-light ratios are also analysed. Finally, the present predictions are compared with recent results available in the literature, showing non-negligible differences in some cases
Time to revisit the definition and treatment of bipolar I disorder in a multidimensional way
No full textTreatment of Agitation With Lorazepam in Clinical Practice: A Systematic Review
No full textAcute agitation is a frequent occurrence in both inpatient and outpatient psychiatric settings, and the use of medication to calm a patient may be warranted to mitigate the situation. Lorazepam is a benzodiazepine that is widely used for management of acute agitation. Despite its widespread use, there is remarkably little clinical evidence for the benefits of lorazepam in acute agitation. We performed a systematic review with focus on lorazepam, including all randomized clinical trials on lorazepam in mental and behavioral disorders, excluding studies on dementia and pediatric patients and in mixed conditions. A total of 11 studies met inclusion criteria, and all were in patients with mental and behavioral disorders. Most trials generally found improvements across a variety of outcomes related to agitation, although there was some disparity if specific outcomes were considered. In the five studies with haloperidol, the combination of lorazepam and haloperidol was superior to either agent alone, but with no differences between monotherapy with the individual agents. In the study comparing lorazepam to olanzapine, olanzapine was superior to lorazepam, and both were superior to placebo. As expected, the safety of lorazepam among the different studies was consistent with its well-characterized profile with dizziness, sedation, and somnolence being the most common adverse events. Based on this structured review, lorazepam can be considered to be a clinically effective means of treating the acutely agitated patient
Force/Torque-Sensorless Joint Stiffness Estimation in Articulated Soft Robots
No full textCurrently, the access to the knowledge of stiffness values is typically constrained to a-priori identified models or datasheet information, which either do not usually take into ac- count the full range of possible stiffness values or need extensive experiments. This work tackles the challenge of stiffness estimation in articulated soft manipulators, and it proposes an innovative solution adding value to the previous research by removing the necessity for force/torque sensors and generalizing to multi-degree- of-freedom robots. Built upon the theory of unknown input-state observers and recursive least-square algorithms, the solution is independent of the actuator model parameters and its internal control signals. The validity of the approach is proven analytically for single and multiple degree-of-freedom robots. The obtained estimators are first evaluated via simulations on articulated soft robots with different actuations and then tested in experiments with real robotic setups using antagonistic variable stiffness actuators
Logical Consensus for Distributed Network Agreement
No full textIn this paper we introduce a novel consensus mechanism where agents of a network are able to share logical values, or Booleans, representing their local opinions on e.g. the presence of an intruder or of a fire within an indoor environment. Under suitable joint conditions on agents’ visibility and communication capability, we provide an algorithm generating a logical linear consensus system that is globally stable. The solution is optimal in terms of the number of messages to be exchanged and the time needed to reach a consensus. Moreover, to cope with possible sensor failure, we propose a second design approach that produces robust logical nonlinear consensus systems tolerating a maximum number of faults. Finally, we show applicability of the agreement mechanism to a distributed Intrusion Detection System (IDS
Adaptive Control of Soft Robots Based on an Enhanced 3D Augmented Rigid Robot Matching
No full textDespite having proven successful in generating precise motions under dynamic conditions in highly deformable soft-bodied robots, model based techniques are also prone to robustness issues connected to the intrinsic uncertain nature of the dynamics of these systems. This letter aims at tackling this challenge, by extending the augmented rigid robot formulation to a stable representation of three dimensional motions of soft robots, under Piecewise Constant Curvature hypothesis. In turn, the equivalence between soft-bodied and rigid robots permits to derive effective adaptive controllers for soft-bodied robots, achieving perfect posture regulation under considerable errors in the knowledge of system parameters. The effectiveness of the proposed control design is demonstrated through extensive simulations
Lateral Wind Estimation and Backstepping Compensation for Safer Self-Driving Racecars
No full textThis paper addresses the lateral wind gust estimation and compensation problem for racecar models. A wind-sensorless solution, i.e. a solution not using direct wind measures, is proposed. More precisely, by modeling the wind disturbance as a fully unknown input signal, an input-state observer is derived using only information about the vehicle’s longitudinal speed and lateral pose relative to the road. The observer is characterized by a simple structure, explicit closed-form, direct implementability on a micro-controller, and dead-beat property, i.e. it ensures the convergence of the estimation error in a finite time. Moreover, leveraging on the reconstructed wind data, a backstepping wind-compensation controller is also proposed, allowing asymptotic tracking of a path with desired curvature and providing the end-user with a free control parameter specifying the desired tracking speed. Formal proofs of the estimation error and tracking error convergence are given. Performance evaluation of the proposed solution is obtained in simulation by closing in the loop the full nonlinear model of a real racecar, the Robocar system, with the proposed estimation and control method. Both the estimator and the controller are shown to outperform existing solutions, even in the presence of noisy measurements
Decoupled nonlinear adaptive control of position and stiffness for pneumatic soft robots
No full textThis article addresses the problem of simultaneous and robust closed-loop control of joint stiffness and position, for a class of antagonistically actuated pneumatic soft robots with rigid links and compliant joints. By introducing a first-order dynamic equation for the stiffness variable and using the additional control degree of freedom, embedded in the null space of the pneumatic actuator matrix, an innovative control approach is introduced comprising an adaptive compensator and a dynamic decoupler. The proposed solution builds upon existing adaptive control theory and provides a technique for closing the loop on joint stiffness in pneumatic variable stiffness actuators. Under a very mild assumption involving the inertia and actuator matrices, the solution is able to cope with uncertainties of the model and, when the desired stiffness is constant or slowly varying, also of the pneumatic actuator. Position and stiffness decoupling is achieved by the introduction of a first-order differential equation for an internal state variable of the controller, which takes into account the time derivative of pressure in the stiffness dynamics. A formal proof of the stability of the position and stiffness tracking errors is provided. An appealing property of the approach is that it does not require higher derivatives of position or any derivatives of stiffness. The solution is validated with respect to several use-cases, first in simulation and then via a real pneumatic soft robot with McKibben muscles. A comparison with respect to existing techniques reveals a more robust position and stiffness tracking skill
Joint Stiffness Estimation in a Single-Link Soft Robot Driven by Pneumatic McKibben Muscles
No full textThe possibility to control not only the position of a robot but also the stiffness of its joints has enabled safe human-robot collaboration and nature-like robot behaviour. However, since stiffness is not a measurable variable, one needs to perform either its extensive offline identification or apply the real-time stiffness estimators. To the best of the authors' knowledge, this paper proposes for the first time an online technique for the estimation of stiffness and elastic torque in an articulated soft robot joint driven by McKibben pneumatic artificial muscles. We address this problem in a two-phase process: first, we reconstruct the elastic torque by leveraging the theory of the delayed Unknown Input Observers, and then a Recursive Least Squares algorithm is used to determine the parameters of a stiffness approximation. Besides robot link dynamics, this approach requires information on link position and commanded pressures to the muscles. The solution is validated on the simulated single-link robot driven by a pair of McKibben muscles in an antagonistic setup
An input observer-based stiffness estimation approach for flexible robot joints
No full textThis letter addresses the stiffness estimation problem for flexible robot joints, driven by variable stiffness actuators in antagonistic setups. Due to the difficulties of achieving consistent production of these actuators and the time-varying nature of their internal flexible elements, which are subject to plastic deformation over time, it is currently a challenge to precisely determine the total flexibility torque applied to a robot's joint and the corresponding joint stiffness. Herein, by considering the flexibility torque acting on each motor as an unknown signal and building upon Unknown Input Observer theory, a solution for electrically-driven actuators is proposed, which consists of a linear estimator requiring only knowledge about the positions of the joints and the motors as well as the drive's dynamic parameters. Beyond its linearity advantage, another appealing feature of the solution is the lack of need for torque and velocity sensors. The presented approach is first verified via simulations and then successfully tested on an experimental setup, comprising bidirectional antagonistic variable stiffness actuators
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