1,125 research outputs found
Nonlinear Systems
The editors of this book have incorporated contributions from a diverse group of leading researchers in the field of nonlinear systems. To enrich the scope of the content, this book contains a valuable selection of works on fractional differential equations.The book aims to provide an overview of the current knowledge on nonlinear systems and some aspects of fractional calculus. The main subject areas are divided into two theoretical and applied sections. Nonlinear systems are useful for researchers in mathematics, applied mathematics, and physics, as well as graduate students who are studying these systems with reference to their theory and application. This book is also an ideal complement to the specific literature on engineering, biology, health science, and other applied science areas. The opportunity given by IntechOpen to offer this book under the open access system contributes to disseminating the field of nonlinear systems to a wide range of researchers
Detection, Prediction and Control of Epileptic Seizures
abstract: From time immemorial, epilepsy has persisted to be one of the greatest impediments to human life for those stricken by it. As the fourth most common neurological disorder, epilepsy causes paroxysmal electrical discharges in the brain that manifest as seizures. Seizures have the effect of debilitating patients on a physical and psychological level. Although not lethal by themselves, they can bring about total disruption in consciousness which can, in hazardous conditions, lead to fatality. Roughly 1\% of the world population suffer from epilepsy and another 30 to 50 new cases per 100,000 increase the number of affected annually. Controlling seizures in epileptic patients has therefore become a great medical and, in recent years, engineering challenge.
In this study, the conditions of human seizures are recreated in an animal model of temporal lobe epilepsy. The rodents used in this study are chemically induced to become chronically epileptic. Their Electroencephalogram (EEG) data is then recorded and analyzed to detect and predict seizures; with the ultimate goal being the control and complete suppression of seizures.
Two methods, the maximum Lyapunov exponent and the Generalized Partial Directed Coherence (GPDC), are applied on EEG data to extract meaningful information. Their effectiveness have been reported in the literature for the purpose of prediction of seizures and seizure focus localization. This study integrates these measures, through some modifications, to robustly detect seizures and separately find precursors to them and in consequence provide stimulation to the epileptic brain of rats in order to suppress seizures. Additionally open-loop stimulation with biphasic currents of various pairs of sites in differing lengths of time have helped us create control efficacy maps. While GPDC tells us about the possible location of the focus, control efficacy maps tells us how effective stimulating a certain pair of sites will be.
The results from computations performed on the data are presented and the feasibility of the control problem is discussed. The results show a new reliable means of seizure detection even in the presence of artifacts in the data. The seizure precursors provide a means of prediction, in the order of tens of minutes, prior to seizures. Closed loop stimulation experiments based on these precursors and control efficacy maps on the epileptic animals show a maximum reduction of seizure frequency by 24.26\% in one animal and reduction of length of seizures by 51.77\% in another. Thus, through this study it was shown that the implementation of the methods can ameliorate seizures in an epileptic patient. It is expected that the new knowledge and experimental techniques will provide a guide for future research in an effort to ultimately eliminate seizures in epileptic patients.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Benelux meeting on systems and control, 23rd, March 17-19, 2004, Helvoirt, The Netherlands
Book of abstract
Data-driven deconvolution for large eddy simulations of Kraichnan turbulence
In this article, we demonstrate the use of artificial neural networks as
optimal maps which are utilized for convolution and deconvolution of
coarse-grained fields to account for sub-grid scale turbulence effects. We
demonstrate that an effective eddy-viscosity is predicted by our purely
data-driven large eddy simulation framework without explicit utilization of
phenomenological arguments. In addition, our data-driven framework precludes
the knowledge of true sub-grid stress information during the training phase due
to its focus on estimating an effective filter and its inverse so that
grid-resolved variables may be related to direct numerical simulation data
statistically. The proposed predictive framework is also combined with a
statistical truncation mechanism for ensuring numerical realizability in an
explicit formulation. Through this we seek to unite structural and functional
modeling strategies for modeling non-linear partial differential equations
using reduced degrees of freedom. Both a priori and a posteriori results are
shown for a two-dimensional decaying turbulence case in addition to a detailed
description of validation and testing. A hyperparameter sensitivity study also
shows that the proposed dual network framework simplifies learning complexity
and is viable with exceedingly simple network architectures. Our findings
indicate that the proposed framework approximates a robust and stable sub-grid
closure which compares favorably to the Smagorinsky and Leith hypotheses for
capturing the theoretical scaling in Kraichnan turbulence
Dynamic Walking: Toward Agile and Efficient Bipedal Robots
Dynamic walking on bipedal robots has evolved from an idea in science fiction to a practical reality. This is due to continued progress in three key areas: a mathematical understanding of locomotion, the computational ability to encode this mathematics through optimization, and the hardware capable of realizing this understanding in practice. In this context, this review article outlines the end-to-end process of methods which have proven effective in the literature for achieving dynamic walking on bipedal robots. We begin by introducing mathematical models of locomotion, from reduced order models that capture essential walking behaviors to hybrid dynamical systems that encode the full order continuous dynamics along with discrete footstrike dynamics. These models form the basis for gait generation via (nonlinear) optimization problems. Finally, models and their generated gaits merge in the context of real-time control, wherein walking behaviors are translated to hardware. The concepts presented are illustrated throughout in simulation, and experimental instantiation on multiple walking platforms are highlighted to demonstrate the ability to realize dynamic walking on bipedal robots that is agile and efficient
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
Stability analysis and control of DC-DC converters using nonlinear methodologies
PhD ThesisSwitched mode DC-DC converters exhibit a variety of complex behaviours in power
electronics systems, such as sudden changes in operating region, bifurcation and
chaotic operation. These unexpected random-like behaviours lead the converter to
function outside of the normal periodic operation, increasing the potential to generate
electromagnetic interference degrading conversion efficiency and in the worst-case
scenario a loss of control leading to catastrophic failure.
The rapidly growing market for switched mode power DC-DC converters demands
more functionality at lower cost. In order to achieve this, DC-DC converters must
operate reliably at all load conditions including boundary conditions. Over the last
decade researchers have focused on these boundary conditions as well as nonlinear
phenomena in power switching converters, leading to different theoretical and
analytical approaches. However, the most interesting results are based on abstract
mathematical forms, which cannot be directly applied to the design of practical
systems for industrial applications.
In this thesis, an analytic methodology for DC-DC converters is used to fully
determine the inherent nonlinear dynamics. System stability can be indicated by the
derived Monodromy matrix which includes comprehensive information concerning
converter parameters and the control loop. This methodology can be applied in
further stability analysis, such as of the influence of parasitic parameters or the effect
of constant power load, and can furthermore be extended to interleaved operating
converters to study the interaction effect of switching operations. From this analysis,
advanced control algorithms are also developed to guarantee the satisfactory
performance of the converter, avoiding nonlinear behaviours such as fast- and slowscale
bifurcations. The numerical and analytical results validate the theoretical
analysis, and experimental results with an interleaved boost converter verify the
effectiveness of the proposed approach.Engineering and Physical Sciences
Research Council (EPSRC), China Scholarship Council (CSC), and school of
Electrical and Electronic Engineerin
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