191 research outputs found
Distributed Linear Quadratic Optimal Control: Compute Locally and Act Globally
In this paper we consider the distributed linear quadratic control problem
for networks of agents with single integrator dynamics. We first establish a
general formulation of the distributed LQ problem and show that the optimal
control gain depends on global information on the network. Thus, the optimal
protocol can only be computed in a centralized fashion. In order to overcome
this drawback, we propose the design of protocols that are computed in a
decentralized way. We will write the global cost functional as a sum of local
cost functionals, each associated with one of the agents. In order to achieve
'good' performance of the controlled network, each agent then computes its own
local gain, using sampled information of its neighboring agents. This
decentralized computation will only lead to suboptimal global network behavior.
However, we will show that the resulting network will reach consensus. A
simulation example is provided to illustrate the performance of the proposed
protocol.Comment: 7 pages, 2 figure
A Suboptimality Approach to Distributed Linear Quadratic Optimal Control
This paper is concerned with the distributed linear quadratic optimal control
problem. In particular, we consider a suboptimal version of the distributed
optimal control problem for undirected multi-agent networks. Given a
multi-agent system with identical agent dynamics and an associated global
quadratic cost functional, our objective is to design suboptimal distributed
control laws that guarantee the controlled network to reach consensus and the
associated cost to be smaller than an a priori given upper bound. We first
analyze the suboptimality for a given linear system and then apply the results
to linear multiagent systems. Two design methods are then provided to compute
such suboptimal distributed controllers, involving the solution of a single
Riccati inequality of dimension equal to the dimension of the agent dynamics,
and the smallest nonzero and the largest eigenvalue of the graph Laplacian.
Furthermore, we relax the requirement of exact knowledge of the smallest
nonzero and largest eigenvalue of the graph Laplacian by using only lower and
upper bounds on these eigenvalues. Finally, a simulation example is provided to
illustrate our design method.Comment: 11 pages, 2 figure
Distributed linear quadratic optimal control:Compute locally and act globally
In this letter we consider the distributed lin- ear quadratic (LQ) control problem for networks of agents with single integrator dynamics. We first establish a general formulation of the distributed LQ problem and show that the optimal control gain depends on global information on the network. Thus, the optimal protocol can only be com- puted in a centralized fashion. In order to overcome this drawback, we propose the design of protocols that are com- puted in a decentralized way. We will write the global cost functional as a sum of local cost functionals, each asso- ciated with one of the agents. In order to achieve âgoodâ performance of the controlled network, each agent then computes its own local gain, using sampled information of its neighboring agents. This decentralized computa- tion will only lead to suboptimal global network behavior. However, we will show that the resulting network will reach consensus
A Suboptimality Approach to Distributed Linear Quadratic Optimal Control
This note is concerned with a suboptimal version of the distributed linear quadratic optimal control problem for multiagent systems. Given a multiagent system with identical agent dynamics and an associated global quadratic cost functional, our objective is to design distributed control laws that achieve consensus and whose cost is smaller than an a priori given upper bound, for all initial states of the network that are bounded in norm by a given radius. A centralized design method is provided to compute such suboptimal controllers, involving the solution of a single Riccati inequality of dimension equal to the dimension of the agent dynamics, and the smallest nonzero and the largest eigenvalue of the Laplacian matrix. Furthermore, we relax the requirement of exact knowledge of the smallest nonzero and largest eigenvalue of the Laplacian matrix by using only lower and upper bounds on these eigenvalues. Finally, a simulation example is provided to illustrate our design method
Distributed Linear Quadratic Control and Filtering:a suboptimality approach
Design of distributed protocols for multi-agent systems has received extensive attention in the past two decades. A challenging problem in this context is to develop distributed synchronizing protocols that minimize given cost criteria. Recent years have also witnessed an increasing interest in problems of distributed state estimation for large-scale systems. Two challenging problems in this context are the problems of distributed H-2 and H-infinity optimal filtering.In this dissertation, we study both distributed linear quadratic optimal control problems and distributed filtering problems. In the framework of distributed linear quadratic control, both for leaderless and leader-follower multi-agent systems we provide design methods for computing state-feedback-based distributed suboptimal synchronizing protocols. In the framework of distributed H-2 suboptimal control, both for homogeneous and heterogeneous multi-agent systems we establish design methods for computing state-feedback-based and output-feedback-based distributed suboptimal synchronizing protocols.The distributed H-2 and H-infinity optimal filtering problem are the problems of designing local filter gains such that the H-2 or H-infinity norm of the transfer matrix from the disturbance input to the output estimation error is minimized, while all local filters reconstruct the full system state asymptotically. Due to their non-convex nature, it is not clear whether optimal solutions exist. Instead of studying these optimal filtering problems, in this dissertation we therefore address suboptimality versions of these problems and provide conceptual algorithms for obtaining H-2 and H-infinity suboptimal distributed filters, respectively
Analysis of a parallelized nonlinear elliptic boundary value problem solver with application to reacting flows
A parallelized finite difference code based on the Newton method for systems of nonlinear elliptic boundary value problems in two dimensions is analyzed in terms of computational complexity and parallel efficiency. An approximate cost function depending on 15 dimensionless parameters is derived for algorithms based on stripwise and boxwise decompositions of the domain and a one-to-one assignment of the strip or box subdomains to processors. The sensitivity of the cost functions to the parameters is explored in regions of parameter space corresponding to model small-order systems with inexpensive function evaluations and also a coupled system of nineteen equations with very expensive function evaluations. The algorithm was implemented on the Intel Hypercube, and some experimental results for the model problems with stripwise decompositions are presented and compared with the theory. In the context of computational combustion problems, multiprocessors of either message-passing or shared-memory type may be employed with stripwise decompositions to realize speedup of O(n), where n is mesh resolution in one direction, for reasonable n
Synchronization in complex networks
Synchronization processes in populations of locally interacting elements are
in the focus of intense research in physical, biological, chemical,
technological and social systems. The many efforts devoted to understand
synchronization phenomena in natural systems take now advantage of the recent
theory of complex networks. In this review, we report the advances in the
comprehension of synchronization phenomena when oscillating elements are
constrained to interact in a complex network topology. We also overview the new
emergent features coming out from the interplay between the structure and the
function of the underlying pattern of connections. Extensive numerical work as
well as analytical approaches to the problem are presented. Finally, we review
several applications of synchronization in complex networks to different
disciplines: biological systems and neuroscience, engineering and computer
science, and economy and social sciences.Comment: Final version published in Physics Reports. More information
available at http://synchronets.googlepages.com
Computational Methods for Cognitive and Cooperative Robotics
In the last decades design methods in control engineering made substantial progress in
the areas of robotics and computer animation. Nowadays these methods incorporate the
newest developments in machine learning and artificial intelligence. But the problems
of flexible and online-adaptive combinations of motor behaviors remain challenging for
human-like animations and for humanoid robotics. In this context, biologically-motivated
methods for the analysis and re-synthesis of human motor programs provide new insights
in and models for the anticipatory motion synthesis.
This thesis presents the authorâs achievements in the areas of cognitive and developmental robotics, cooperative and humanoid robotics and intelligent and machine learning methods in computer graphics. The first part of the thesis in the chapter âGoal-directed Imitation for Robotsâ considers imitation learning in cognitive and developmental robotics.
The work presented here details the authorâs progress in the development of hierarchical
motion recognition and planning inspired by recent discoveries of the functions of mirror-neuron cortical circuits in primates. The overall architecture is capable of âlearning for
imitationâ and âlearning by imitationâ. The complete system includes a low-level real-time
capable path planning subsystem for obstacle avoidance during arm reaching. The learning-based path planning subsystem is universal for all types of anthropomorphic robot arms, and is capable of knowledge transfer at the level of individual motor acts.
Next, the problems of learning and synthesis of motor synergies, the spatial and spatio-temporal combinations of motor features in sequential multi-action behavior, and the
problems of task-related action transitions are considered in the second part of the thesis
âKinematic Motion Synthesis for Computer Graphics and Roboticsâ. In this part, a new
approach of modeling complex full-body human actions by mixtures of time-shift invariant
motor primitives in presented. The online-capable full-body motion generation architecture
based on dynamic movement primitives driving the time-shift invariant motor synergies
was implemented as an online-reactive adaptive motion synthesis for computer graphics
and robotics applications.
The last chapter of the thesis entitled âContraction Theory and Self-organized Scenarios
in Computer Graphics and Roboticsâ is dedicated to optimal control strategies in multi-agent scenarios of large crowds of agents expressing highly nonlinear behaviors. This last
part presents new mathematical tools for stability analysis and synthesis of multi-agent
cooperative scenarios.In den letzten Jahrzehnten hat die Forschung in den Bereichen der Steuerung und Regelung
komplexer Systeme erhebliche Fortschritte gemacht, insbesondere in den Bereichen
Robotik und Computeranimation. Die Entwicklung solcher Systeme verwendet heutzutage
neueste Methoden und Entwicklungen im Bereich des maschinellen Lernens und der
kĂŒnstlichen Intelligenz. Die flexible und echtzeitfĂ€hige Kombination von motorischen Verhaltensweisen
ist eine wesentliche Herausforderung fĂŒr die Generierung menschenĂ€hnlicher
Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch
motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme
neue Erkenntnisse und Modelle fĂŒr die antizipatorische Bewegungssynthese.
Diese Dissertation prÀsentiert die Ergebnisse der Arbeiten des Autors im Gebiet der
kognitiven und Entwicklungsrobotik, kooperativer und humanoider Robotersysteme sowie
intelligenter und maschineller Lernmethoden in der Computergrafik. Der erste Teil der
Dissertation im Kapitel âZielgerichtete Nachahmung fĂŒr Roboterâ behandelt das Imitationslernen
in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben
neue Methoden fĂŒr die hierarchische Bewegungserkennung und -planung, die durch
Erkenntnisse zur Funktion der kortikalen Spiegelneuronen-Schaltkreise bei Primaten inspiriert
wurden. Die entwickelte Architektur ist in der Lage, âdurch Imitation zu lernenâ
und âzu lernen zu imitierenâ. Das komplette entwickelte System enthĂ€lt ein echtzeitfĂ€higes
Pfadplanungssubsystem zur Hindernisvermeidung wĂ€hrend der DurchfĂŒhrung von Armbewegungen.
Das lernbasierte Pfadplanungssubsystem ist universell und fĂŒr alle Arten von
anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer
Handlungen zu ĂŒbertragen.
Im zweiten Teil der Arbeit âKinematische Bewegungssynthese fĂŒr Computergrafik und
Robotikâ werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h.
von rÀumlichen und rÀumlich-zeitlichen Kombinationen motorischer Bewegungselemente
bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs ĂŒbergĂ€ngen behandelt.
Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen
durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem
wurde ein online-fĂ€higer Synthesealgorithmus fĂŒr Ganzköperbewegungen entwickelt, der
auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten
verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde fĂŒr
verschiedene Probleme der Bewegungssynthese fĂŒr die Computergrafik- und Roboteranwendungen
implementiert.
Das letzte Kapitel der Dissertation mit dem Titel âKontraktionstheorie und selbstorganisierte
Szenarien in der Computergrafik und Robotikâ widmet sich optimalen Kontrollstrategien
in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare
Kinematik gekennzeichnet sind. Dieser letzte Teil prÀsentiert neue mathematische Werkzeuge
fĂŒr die StabilitĂ€tsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien
Stochastic timeseries analysis in electric power systems and paleo-climate data
In this thesis a data science study of elementary stochastic processes is laid, aided with the development of two numerical software programmes, applied to power-grid frequency studies and Dansgaard--Oeschger events in paleo-climate data.
Power-grid frequency is a key measure in power grid studies.
It comprises the balance of power in a power grid at any instance.
In this thesis an elementary Markovian Langevin-like stochastic process is employed, extending from existent literature, to show the basic elements of power-grid frequency dynamics can be modelled in such manner.
Through a data science study of power-grid frequency data, it is shown that fluctuations scale in an inverse square-root relation with their size, alike any other stochastic process, confirming previous theoretical results.
A simple Ornstein--Uhlenbeck is offered as a surrogate model for power-grid frequency dynamics, with a versatile input of driving deterministic functions, showing not surprisingly that driven stochastic processes with Gaussian noise do not necessarily show a Gaussian distribution.
A study of the correlations between recordings of power-grid frequency in the same power-grid system reveals they are correlated, but a theoretical understanding is yet to be developed.
A super-diffusive relaxation of amplitude synchronisation is shown to exist in space in coupled power-grid systems, whereas a linear relation is evidenced for the emergence of phase synchronisation.
Two Python software packages are designed, offering the possibility to extract conditional moments for Markovian stochastic processes of any dimension, with a particular application for Markovian jump-diffusion processes for one-dimensional timeseries.
Lastly, a study of Dansgaard--Oeschger events in recordings of paleoclimate data under the purview of bivariate Markovian jump-diffusion processes is proposed, augmented by a semi-theoretical study of bivariate stochastic processes, offering an explanation for the discontinuous transitions in these events and showing the existence of deterministic couplings between the recordings of the dust concentration and a proxy for the atmospheric temperature
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