8,282 research outputs found
Hierarchical and Safe Motion Control for Cooperative Locomotion of Robotic Guide Dogs and Humans: A Hybrid Systems Approach
This paper presents a hierarchical control strategy based on hybrid systems
theory, nonlinear control, and safety-critical systems to enable cooperative
locomotion of robotic guide dogs and visually impaired people. We address
high-dimensional and complex hybrid dynamical models that represent
collaborative locomotion. At the high level of the control scheme, local and
nonlinear baseline controllers, based on the virtual constraints approach, are
designed to induce exponentially stable dynamic gaits. The baseline controller
for the leash is assumed to be a nonlinear controller that keeps the human in a
safe distance from the dog while following it. At the lower level, a real-time
quadratic programming (QP) is solved for modifying the baseline controllers of
the robot as well as the leash to avoid obstacles. In particular, the QP
framework is set up based on control barrier functions (CBFs) to compute
optimal control inputs that guarantee safety while being close to the baseline
controllers. The stability of the complex periodic gaits is investigated
through the Poincare return map. To demonstrate the power of the analytical
foundation, the control algorithms are transferred into an extensive numerical
simulation of a complex model that represents cooperative locomotion of a
quadrupedal robot, referred to as Vision 60, and a human model. The complex
model has 16 continuous-time domains with 60 state variables and 20 control
inputs
Long-lived states of oscillator chain with dynamical traps
A simple model of oscillator chain with dynamical traps and additive white
noise is considered. Its dynamics was studied numerically. As demonstrated,
when the trap effect is pronounced nonequilibrium phase transitions of a new
type arise. Locally they manifest themselves via distortion of the particle
arrangement symmetry. Depending on the system parameters the particle
arrangement is characterized by the corresponding distributions taking either a
bimodal form, or twoscale one, or unimodal onescale form which, however,
deviates substantially from the Gaussian distribution. The individual particle
velocities exhibit also a number of anomalies, in particular, their
distribution can be extremely wide or take a quasi-cusp form. A large number of
different cooperative structures and superstructures made of these formations
are found in the visualized time patterns. Their evolution is, in some sense,
independent of the individual particle dynamics, enabling us to regard them as
dynamical phases.Comment: 8 pages, 5 figurs, TeX style of European Physical Journa
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The role of smart sensor networks for voltage monitoring in smart grids
The large-scale deployment of the Smart Grid paradigm will support the evolution of conventional electrical power systems toward active, flexible and self-healing web energy networks composed of distributed and cooperative energy resources. In a Smart Grid platform, distributed voltage monitoring is one of the main issues to address. In this field, the application of traditional hierarchical monitoring paradigms has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of Distribution Generation Systems (DGS) require more scalable, more flexible control and regulation paradigms. To try to overcome these challenges, this paper proposes the concept of a decentralized non-hierarchal voltage monitoring architecture based on intelligent and cooperative smart entities. These devices employ traditional sensors to acquire local bus variables and mutually coupled oscillators to assess the main variables describing the global grid state
Predictability of catastrophic events: material rupture, earthquakes, turbulence, financial crashes and human birth
We propose that catastrophic events are "outliers" with statistically
different properties than the rest of the population and result from mechanisms
involving amplifying critical cascades. Applications and the potential for
prediction are discussed in relation to the rupture of composite materials,
great earthquakes, turbulence and abrupt changes of weather regimes, financial
crashes and human parturition (birth).Comment: Latex document of 22 pages including 6 ps figures, in press in PNA
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Editorial Comment on the Special Issue of "Information in Dynamical Systems and Complex Systems"
This special issue collects contributions from the participants of the
"Information in Dynamical Systems and Complex Systems" workshop, which cover a
wide range of important problems and new approaches that lie in the
intersection of information theory and dynamical systems. The contributions
include theoretical characterization and understanding of the different types
of information flow and causality in general stochastic processes, inference
and identification of coupling structure and parameters of system dynamics,
rigorous coarse-grain modeling of network dynamical systems, and exact
statistical testing of fundamental information-theoretic quantities such as the
mutual information. The collective efforts reported herein reflect a modern
perspective of the intimate connection between dynamical systems and
information flow, leading to the promise of better understanding and modeling
of natural complex systems and better/optimal design of engineering systems
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