9,204 research outputs found
Coordinated Robot Navigation via Hierarchical Clustering
We introduce the use of hierarchical clustering for relaxed, deterministic
coordination and control of multiple robots. Traditionally an unsupervised
learning method, hierarchical clustering offers a formalism for identifying and
representing spatially cohesive and segregated robot groups at different
resolutions by relating the continuous space of configurations to the
combinatorial space of trees. We formalize and exploit this relation,
developing computationally effective reactive algorithms for navigating through
the combinatorial space in concert with geometric realizations for a particular
choice of hierarchical clustering method. These constructions yield
computationally effective vector field planners for both hierarchically
invariant as well as transitional navigation in the configuration space. We
apply these methods to the centralized coordination and control of
perfectly sensed and actuated Euclidean spheres in a -dimensional ambient
space (for arbitrary and ). Given a desired configuration supporting a
desired hierarchy, we construct a hybrid controller which is quadratic in
and algebraic in and prove that its execution brings all but a measure zero
set of initial configurations to the desired goal with the guarantee of no
collisions along the way.Comment: 29 pages, 13 figures, 8 tables, extended version of a paper in
preparation for submission to a journa
Secondary Frequency and Voltage Control of Islanded Microgrids via Distributed Averaging
In this work we present new distributed controllers for secondary frequency
and voltage control in islanded microgrids. Inspired by techniques from
cooperative control, the proposed controllers use localized information and
nearest-neighbor communication to collectively perform secondary control
actions. The frequency controller rapidly regulates the microgrid frequency to
its nominal value while maintaining active power sharing among the distributed
generators. Tuning of the voltage controller provides a simple and intuitive
trade-off between the conflicting goals of voltage regulation and reactive
power sharing. Our designs require no knowledge of the microgrid topology,
impedances or loads. The distributed architecture allows for flexibility and
redundancy, and eliminates the need for a central microgrid controller. We
provide a voltage stability analysis and present extensive experimental results
validating our designs, verifying robust performance under communication
failure and during plug-and-play operation.Comment: Accepted for publication in IEEE Transactions on Industrial
Electronic
A survey on modeling of microgrids - from fundamental physics to phasors and voltage sources
Microgrids have been identified as key components of modern electrical
systems to facilitate the integration of renewable distributed generation
units. Their analysis and controller design requires the development of
advanced (typically model-based) techniques naturally posing an interesting
challenge to the control community. Although there are widely accepted reduced
order models to describe the dynamic behavior of microgrids, they are typically
presented without details about the reduction procedure---hampering the
understanding of the physical phenomena behind them. Preceded by an
introduction to basic notions and definitions in power systems, the present
survey reviews key characteristics and main components of a microgrid. We
introduce the reader to the basic functionality of DC/AC inverters, as well as
to standard operating modes and control schemes of inverter-interfaced power
sources in microgrid applications. Based on this exposition and starting from
fundamental physics, we present detailed dynamical models of the main microgrid
components. Furthermore, we clearly state the underlying assumptions which lead
to the standard reduced model with inverters represented by controllable
voltage sources, as well as static network and load representations, hence,
providing a complete modular model derivation of a three-phase inverter-based
microgrid
An enhanced classifier system for autonomous robot navigation in dynamic environments
In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.Publicad
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