21 research outputs found
A Bio-inspired Distributed Control Architecture: Coupled Artificial Signalling Networks
This thesis studies the applicability of computational models inspired by the structure and dynamics of signalling networks to the control of complex control problems. In particular, this thesis presents two different abstractions that aim to capture the signal processing abilities of biological cells: a stand-alone signalling network and a coupled signalling network. While the former mimics the interacting relationships amongst the components in a signalling pathway, the latter replicates the connectionism amongst signalling pathways. After initially investigating the feasibility of these models for controlling two complex numerical dynamical systems, Chirikov's standard map and the Lorenz system, this thesis explores their applicability to a difficult real world control problem, the generation of adaptive rhythmic locomotion patterns within a legged robotic system. The results highlight that the locomotive movements of a six-legged robot could be controlled in order to adapt the robot's trajectory in a range of challenging environments. In this sense, signalling networks are responsible for the robot adaptability and inter limb coordination as they self-adjust their dynamics according to the terrain's irregularities. More generally, the results of this thesis highlight the capacity of coupled signalling networks to decompose non-linear problems into smaller sub-tasks, which can then be independently solved
Engineering limit cycle systems:adaptive frequency oscillators and applications to adaptive locomotion control of compliant robots
In this thesis, we present a dynamical systems approach to adaptive controllers for locomotion control. The approach is based on a rigorous mathematical framework using nonlinear dynamical systems and is inspired by theories of self-organization. Nonlinear dynamical systems such as coupled oscillators are an interesting approach for the on-line generation of trajectories for robots with many degrees of freedom (e.g. legged locomotion). However, designing a nonlinear dynamical system to satisfy a given specification and goal is not an easy task, and, hitherto no methodology exists to approach this problem in a unified way. Nature presents us with satisfactory solutions for the coordination of many degrees of freedom. One central feature observed in biological subjects is the ability of the neural systems to exploit natural dynamics of the body to achieve efficient locomotion. In order to be able to exploit the body properties, adaptive mechanisms must be at work. Recent work has pointed out the importance of the mechanical system for efficient locomotion. Even more interestingly, such well suited mechanical systems do not need complicated control. Yet, in robotics, in most approaches, adaptive mechanisms are either missing or they are not based on a rigorous framework, i.e. they are based on heuristics and ad-hoc approaches. Over the last three decades there has been enormous progress in describing movement coordination with the help of Synergetic approaches. This has led to the formulation of a theoretical framework: the theory of dynamic patterns. This framework is mathematically rigorous and at the same time fully operational. However, it does not provide any guidelines for synthetic approaches as needed for the engineering of robots with many degrees of freedom, nor does it directly help to explain adaptive systems. We will show how we can extend the theoretical framework to build adaptive systems. For this purpose, we propose the use of multi-scale dynamical systems. The basic idea behind multi-scale dynamical systems is that a given dynamical system gets extended by additional slow dynamics of its parameters, i.e. some of the parameters become state variables. The advantages of the framework of multi-scale dynamical systems for adaptive controllers are 1) fully dynamic description, 2) no separation of learning algorithm and learning substrate, 3) no separation of learning trials or time windows, 4) mathematically rigorous, 5) low dimensional systems. However, in order to fully exploit the framework important questions have to be solved. Most importantly, methodologies for designing the feedback loops have to be found and important theoretical questions about stability and convergence properties of the devised systems have to be answered. In order to tackle this challenge, we first introduce an engineering view on designing nonlinear dynamical systems and especially oscillators. We will highlight the important differences and freedom that this engineering view introduces as opposed to a modeling one. We then apply this approach by first proposing a very simple adaptive toy-system, consisting of a dynamical system coupled to a spring-mass system. Due to its spring-mass dynamics, this system contains clear natural dynamics in the form of resonant frequencies. We propose a prototype adaptive multi-scale system, the adaptive frequency oscillator, which is able to adapt its intrinsic frequency to the resonant frequency of the body dynamics. After a small sidetrack to show that we can use adaptive frequency oscillators also for other applications than for adaptive controllers, namely for frequency analysis, we then come back to further investigation of the adaptive controller. We apply the same controller concept to a simple spring-mass hopper system. The spring-mass system consists of a body with two legs attached by rotational joints. The legs contain spring-damper elements. Finally, we present results of the implementation of the controller on a real robot, the experimental robot PUPPY II. This robot is a under-actuated robot with spring dynamics in the knee joints. It will be shown, that due to the appropriate simplification and concentration on relevant features in the toy-system the controller concepts works without a fundamental change on all systems from the toy system up to the real robot
Bio-Inspired Robotics
Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field
A Bio-inspired architecture for adaptive quadruped locomotion over irregular terrain
Tese de doutoramento
Programa Doutoral em Engenharia Electrónica e de ComputadoresThis thesis presents a tentative advancement on walking control of small quadruped and humanoid
position controlled robots, addressing the problem of walk generation by combining dynamical systems
approach to motor control, insights from neuroethology research on vertebrate motor control and
computational neuroscience.
Legged locomotion is a complex dynamical process, despite the seemingly easy and natural behavior
of the constantly present proficiency of legged animals. Research on locomotion and motor control
in vertebrate animals from the last decades has brought to the attention of roboticists, the potential of
the nature’s solutions to robot applications. Recent knowledge on the organization of complex motor
generation and on mechanics and dynamics of locomotion has been successfully exploited to pursue
agile robot locomotion.
The work presented on this manuscript is part of an effort on the pursuit in devising a general,
model free solution, for the generation of robust and adaptable walking behaviors. It strives to devise a
practical solution applicable to real robots, such as the Sony’s quadruped AIBO and Robotis’ DARwIn-
OP humanoid. The discussed solutions are inspired on the functional description of the vertebrate
neural systems, especially on the concept of Central Pattern Generators (CPGs), their structure and
organization, components and sensorimotor interactions. They use a dynamical systems approach for
the implementation of the controller, especially on the use of nonlinear oscillators and exploitation of
their properties.
The main topics of this thesis are divided into three parts.
The first part concerns quadruped locomotion, extending a previous CPG solution using nonlinear
oscillators, and discussing an organization on three hierarchical levels of abstraction, sharing the purpose
and knowledge of other works. It proposes a CPG solution which generates the walking motion
for the whole-leg, which is then organized in a network for the production of quadrupedal gaits. The
devised solution is able to produce goal-oriented locomotion and navigation as directed through highlevel
commands from local planning methods. In this part, active balance on a standing quadruped is
also addressed, proposing a method based on dynamical systems approach, exploring the integration of
parallel postural mechanisms from several sensory modalities. The solutions are all successfully tested on the quadruped AIBO robot.
In the second part, is addressed bipedal walking for humanoid robots. A CPG solution for biped
walking based on the concept of motion primitives is proposed, loosely based on the idea of synergistic
organization of vertebrate motor control. A set of motion primitives is shown to produce the basis
of simple biped walking, and generalizable to goal-oriented walking. Using the proposed CPG, the
inclusion of feedback mechanisms is investigated, for modulation and adaptation of walking, through
phase transition control according to foot load information. The proposed solution is validated on the
humanoid DARwIn-OP, and its application is evaluated within a whole-body control framework.
The third part sidesteps a little from the other two topics. It discusses the CPG as having an alternative
role to direct motor generation in locomotion, serving instead as a processor of sensory information
for a feedback based motor generation. In this work a reflex based walking controller is devised for the
compliant quadruped Oncilla robot, to serve as purely feedback based walking generation. The capabilities
of the reflex network are shown in simulations, followed by a brief discussion on its limitations,
and how they could be improved by the inclusion of a CPG.Esta tese apresenta uma tentativa de avanço no controlo de locomoção para pequenos robôs quadrúpedes
e bipedes controlados por posição, endereçando o problema de geração motora através da combinação
da abordagem de sistemas dinâmicos para o controlo motor, e perspectivas de investigação
neuroetologia no controlo motor vertebrado e neurociência computacional.
Andar é um processo dinâmico e complexo, apesar de parecer um comportamento fácil e natural
devido à presença constante de animais proficientes em locomoção terrestre. Investigação na área da locomoção
e controlo motor em animais vertebrados nas últimas decadas, trouxe à atenção dos roboticistas
o potencial das soluções encontradas pela natureza aplicadas a aplicações robóticas. Conhecimento
recente relativo à geração de comportamentos motores complexos e da mecânica da locomoção tem
sido explorada com sucesso na procura de locomoção ágil na robótica.
O trabalho apresentado neste documento é parte de um esforço no desenho de uma solução geral,
e independente de modelos, para a geração robusta e adaptável de comportamentos locomotores. O
foco é desenhar uma solução prática, aplicável a robôs reais, tal como o quadrúpede Sony AIBO e
o humanóide DARwIn-OP. As soluções discutidas são inspiradas na descrição funcional do sistema
nervoso vertebrado, especialmente no conceito de Central Pattern Generators (CPGs), a sua estrutura e
organização, componentes e interacção sensorimotora. Estas soluções são implementadas usando uma
abordagem em sistemas dinâmicos, focandos o uso de osciladores não lineares e a explorando as suas
propriedades.
Os tópicos principais desta tese estão divididos em três partes.
A primeira parte explora o tema de locomoção quadrúpede, expandindo soluções prévias de CPGs
usando osciladores não lineares, e discutindo uma organização em três níveis de abstracção, partilhando
as ideias de outros trabalhos. Propõe uma solução de CPG que gera os movimentos locomotores
para uma perna, que é depois organizado numa rede, para a produção de marcha quadrúpede. A
solução concebida é capaz de produzir locomoção e navegação, comandada através de comandos de alto
nível, produzidos por métodos de planeamento local. Nesta parte também endereçado o problema da
manutenção do equilíbrio num robô quadrúpede parado, propondo um método baseado na abordagem
em sistemas dinâmicos, explorando a integração de mecanismos posturais em paralelo, provenientes de várias modalidades sensoriais. As soluções são todas testadas com sucesso no robô quadrupede AIBO.
Na segunda parte é endereçado o problema de locomoção bípede. É proposto um CPG baseado
no conceito de motion primitives, baseadas na ideia de uma organização sinergética do controlo motor
vertebrado. Um conjunto de motion primitives é usado para produzir a base de uma locomoção bípede
simples e generalizável para navegação. Esta proposta de CPG é usada para de seguida se investigar
a inclusão de mecanismos de feedback para modulação e adaptação da marcha, através do controlo de
transições entre fases, de acordo com a informação de carga dos pés. A solução proposta é validada
no robô humanóide DARwIn-OP, e a sua aplicação no contexto do framework de whole-body control é
também avaliada.
A terceira parte desvia um pouco dos outros dois tópicos. Discute o CPG como tendo um papel
alternativo ao controlo motor directo, servindo em vez como um processador de informação sensorial
para um mecanismo de locomoção puramente em feedback. Neste trabalho é desenhado um controlador
baseado em reflexos para a geração da marcha de um quadrúpede compliant. As suas capacidades são
demonstradas em simulação, seguidas por uma breve discussão nas suas limitações, e como estas podem
ser ultrapassadas pela inclusão de um CPG.The presented work was possible thanks to the support by the Portuguese Science and Technology Foundation through the PhD grant SFRH/BD/62047/2009
Pattern Generation for Rough Terrain Locomotion with Quadrupedal Robots:Morphed Oscillators & Sensory Feedback
Animals are able to locomote on rough terrain without any apparent difficulty, but this does not mean that the locomotor system is simple. The locomotor system is actually a complex multi-input multi-output closed-loop control system. This thesis is dedicated to the design of controllers for rough terrain locomotion, for animal-like quadrupedal robots. We choose the problem of blind rough terrain locomotion as the target of experiments. Blind rough terrain locomotion requires continuous and momentary corrections of leg movements and body posture, and provides a proper testbed to observe the interaction of different mod- ules involved in locomotion control. As for the specific case of this thesis, we have to design rough terrain locomotion controllers that do not depend on the torque-control capability, have limited sensing, and have to be computationally light, all due to the properties of the robotics platform that we use. We propose that a robust locomotion controller, taking into account the aforementioned constraints, is constructed from at least three modules: 1) pattern generators providing the nominal patterns of locomotion; 2) A posture controller continuously adjusting the attitude of the body and keeping the robot upright; and 3) quick reflexes to react to unwanted momentary events like stumbling or an external force impulse. We introduce the framework of morphed oscillators to systematize the design of pattern gen- erators realized as coupled nonlinear oscillators. Morphed oscillators are nonlinear oscillators that can encode arbitrary limit cycle shapes and simultaneously have infinitely large basins of attraction. More importantly, they provide dynamical systems that can assume the role of feedforward locomotion controllers known as Central Pattern Generators (CPGs), and accept discontinuous sensory feedback without the risk of producing discontinuous output. On top of the CPG module, we add a kinematic model-based posture controller inspired by virtual model control (VMC), to control the body attitude. Virtual model control produces forces, and through the application of the Jacobian transpose method, generates torques which are added to the CPG torques. However, because our robots do not have a torque- control capability, we adapt the posture controller by producing task-space velocities instead of forces, thus generating joint-space velocity feedback signals. Since the CPG model used for locomotion generates joint velocities and accepts feedback without the fear of instability or discontinuity, the posture control feedback is easily integrated into the CPG dynamics. More- over, we introduce feedback signals for adjusting the posture by shifting the trunk positions, which directly update the limit cycle shape of the morphed oscillator nodes of the CPG. Reflexes are added, with minimal complexity, to react to momentary events. We implement simple impulse-based feedback mechanisms inspired by animals and successful rough terrain robots to 1) flex the leg if the robot is stumbling (stumbling correction reflex); 2) extend the leg if an expected contact is missing (leg extension reflex); or 3) initiate a lateral stepping sequence in response to a lateral external perturbation. CPG, posture controller, and reflexes are put together in a modular control architecture alongside additional modules that estimate inclination, control speed and direction, maintain timing of feedback signals, etc. [...
Chaotic exploration and learning of locomotor behaviours
Recent developments in the embodied approach to understanding the generation of
adaptive behaviour, suggests that the design of adaptive neural circuits for rhythmic
motor patterns should not be done in isolation from an appreciation, and indeed
exploitation, of neural-body-environment interactions. Utilising spontaneous mutual
entrainment between neural systems and physical bodies provides a useful passage
to the regions of phase space which are naturally structured by the neuralbody-
environmental interactions. A growing body of work has provided evidence
that chaotic dynamics can be useful in allowing embodied systems to spontaneously
explore potentially useful motor patterns. However, up until now there has
been no general integrated neural system that allows goal-directed, online, realtime
exploration and capture of motor patterns without recourse to external monitoring,
evaluation or training methods. For the first time, we introduce such a system
in the form of a fully dynamic neural system, exploiting intrinsic chaotic dynamics,
for the exploration and learning of the possible locomotion patterns of an articulated
robot of an arbitrary morphology in an unknown environment. The controller
is modelled as a network of neural oscillators which are coupled only through physical
embodiment, and goal directed exploration of coordinated motor patterns is
achieved by a chaotic search using adaptive bifurcation. The phase space of the
indirectly coupled neural-body-environment system contains multiple transient or
permanent self-organised dynamics each of which is a candidate for a locomotion
behaviour. The adaptive bifurcation enables the system orbit to wander through
various phase-coordinated states using its intrinsic chaotic dynamics as a driving
force and stabilises the system on to one of the states matching the given goal
criteria. In order to improve the sustainability of useful transient patterns, sensory
homeostasis has been introduced which results in an increased diversity of motor outputs,
thus achieving multi-scale exploration. A rhythmic pattern discovered by this
process is memorised and sustained by changing the wiring between initially disconnected
oscillators using an adaptive synchronisation method. The dynamical nature
of the weak coupling through physical embodiment allows this adaptive weight learning
to be easily integrated, thus forming a continuous exploration-learning system.
Our result shows that the novel neuro-robotic system is able to create and learn a
number of emergent locomotion behaviours for a wide range of body configurations
and physical environment, and can re-adapt after sustaining damage. The implications
and analyses of these results for investigating the generality and limitations of
the proposed system are discussed
Locomoção de humanoides robusta e versátil baseada em controlo analítico e física residual
Humanoid robots are made to resemble humans but their locomotion
abilities are far from ours in terms of agility and versatility. When humans
walk on complex terrains or face external disturbances, they
combine a set of strategies, unconsciously and efficiently, to regain
stability. This thesis tackles the problem of developing a robust omnidirectional
walking framework, which is able to generate versatile
and agile locomotion on complex terrains. We designed and developed
model-based and model-free walk engines and formulated the
controllers using different approaches including classical and optimal
control schemes and validated their performance through simulations
and experiments. These frameworks have hierarchical structures that
are composed of several layers. These layers are composed of several
modules that are connected together to fade the complexity and
increase the flexibility of the proposed frameworks. Additionally, they
can be easily and quickly deployed on different platforms.
Besides, we believe that using machine learning on top of analytical approaches
is a key to open doors for humanoid robots to step out of laboratories.
We proposed a tight coupling between analytical control and
deep reinforcement learning. We augmented our analytical controller
with reinforcement learning modules to learn how to regulate the walk
engine parameters (planners and controllers) adaptively and generate
residuals to adjust the robot’s target joint positions (residual physics).
The effectiveness of the proposed frameworks was demonstrated and
evaluated across a set of challenging simulation scenarios. The robot
was able to generalize what it learned in one scenario, by displaying
human-like locomotion skills in unforeseen circumstances, even in the
presence of noise and external pushes.Os robôs humanoides são feitos para se parecerem com humanos,
mas suas habilidades de locomoção estão longe das nossas em termos
de agilidade e versatilidade. Quando os humanos caminham em
terrenos complexos ou enfrentam distúrbios externos combinam diferentes
estratégias, de forma inconsciente e eficiente, para recuperar a
estabilidade. Esta tese aborda o problema de desenvolver um sistema
robusto para andar de forma omnidirecional, capaz de gerar uma locomoção
para robôs humanoides versátil e ágil em terrenos complexos.
Projetámos e desenvolvemos motores de locomoção sem modelos e
baseados em modelos. Formulámos os controladores usando diferentes
abordagens, incluindo esquemas de controlo clássicos e ideais,
e validámos o seu desempenho por meio de simulações e experiências
reais. Estes frameworks têm estruturas hierárquicas compostas por
várias camadas. Essas camadas são compostas por vários módulos
que são conectados entre si para diminuir a complexidade e aumentar
a flexibilidade dos frameworks propostos. Adicionalmente, o sistema
pode ser implementado em diferentes plataformas de forma fácil.
Acreditamos que o uso de aprendizagem automática sobre abordagens
analíticas é a chave para abrir as portas para robôs humanoides
saírem dos laboratórios. Propusemos um forte acoplamento entre controlo
analítico e aprendizagem profunda por reforço. Expandimos o
nosso controlador analítico com módulos de aprendizagem por reforço
para aprender como regular os parâmetros do motor de caminhada
(planeadores e controladores) de forma adaptativa e gerar resíduos
para ajustar as posições das juntas alvo do robô (física residual). A
eficácia das estruturas propostas foi demonstrada e avaliada em um
conjunto de cenários de simulação desafiadores. O robô foi capaz de
generalizar o que aprendeu em um cenário, exibindo habilidades de
locomoção humanas em circunstâncias imprevistas, mesmo na presença
de ruído e impulsos externos.Programa Doutoral em Informátic
Biped locomotion control through a biologically-inspired closed-loop controller
Dissertação de mestrado integrado em Engenharia BiomédicaCurrently motor disability in industrialized countries due to neural and physical impairments
is an increasingly worrying phenomenon and the percentage of patients is expected
to be increasing continuously over the coming decades due to a process of ageing the world
is undergoing. Additionally, rising retirement ages, higher demand of elderly people for an
independent, dignified life and mobility, huge cost in the provision of health care are some
other determinants that motivate the restoration of motor function as one of the main goals of
rehabilitation. Modern concepts of motor learning favor a task-specific training in which all
movements in daily life should be trained/assisted repetitively in a physically correct fashion.
Considering the functional activity of the neuronal circuits within the spinal cord, namely
the central pattern generator (CPG), as the foundation to human locomotion, motor relearning
should be based on intensive training strategies directed to the stimulation and reorganization
of such neural pathways through mechanisms addressed by neural plasticity. To this
end, neuromodelings are required to simulate the human locomotion control to overcome the
current technological challenges such as developing smaller, intelligent and cost-effective
devices for home and work rehabilitation scenarios which can enable a continuous therapy/
assistance to guide the impaired limbs in a gentle manner, avoiding abrupt perturbations
and providing as little assistance as necessary. Biomimetic models, taking neurological and
biomechanical inspiration from biological animals, have been embracing these challenges
and developing effective solutions on refining the locomotion models in terms of energy
efficiency, simplicity in the structure and robust adaptability to environment changes and
unexpected perturbations.
Thus, the aim target of this work is to study the applicability of the CPG model for
gait rehabilitation, either for assistance and/or therapy purposes. Focus is developed on the
locomotion control to increase the knowledge of the underlying principles useful for gait
restoration, exploring the brainstem-spinal-biomechanics interaction more fully. This study
has great application in the project of autonomous robots and in the rehabilitation technology,
not only in the project of prostheses and orthoses, but also in the searching of procedures that
help to recuperate motor functions of human beings.
Encouraging results were obtained which pave the way towards the simulation of more
complex behaviors and principles of human locomotion, consequently contributing for improved
automated motor rehabilitation adapted to the rehabilitation emerging needs.Actualmente a debilidade motora em países industrializados devido a deficiências neurais
e físicas é um fenómeno crescente de apreensão sendo expectável um contínuo aumento do
rácio de pacientes nas próximas décadas devido ao processo de envelhecimento. Inclusivé,
o aumento da idade de reforma, a maior procura por parte dos idosos para uma mobilidade
e vida autónoma e condigna, o elevado custo nos cuidados de saúde são incentivos para a
restauração da função motora como um dos objectivos principais da reabilitação. Conceitos
recentes de aprendizagem motora apoiam um treino de tarefas específicas no qual movimentos
no quotidiano devem ser treinados/assistidos de forma repetitiva e fisicamente correcta.
Considerando a actividade funcional dos circuitos neurais na medula, nomeadamente
o gerador de padrão central (CPG), como a base da locomoção, a reaprendizagem motora
deve-se basear em estratégias intensivas de treino visando a estimulação e reorganização
desses vias neurais através de mecanismos abordados pela plasticidade neural. Assim,
são necessários modelos neurais para simular o controlo da locomoção humana de modo
a superar desafios tecnológicos actuais tais como o desenvolvimento de dispositivos mais
compactos, inteligentes e económicos para os cenários de reabilitação domiciliar e laboral
que podem permitir uma terapia/assistência contínua na guia dos membros debilitados de
uma forma suave, evitando perturbações abruptas e fornecendo assistência na medida do
necessário. Modelos biomiméticos, inspirando-se nos princípios neurológicos e biomecânicos
dos animais, têm vindo a abraçar esses desafios e a desenvolver soluções eficazes na
refinação de modelos de locomoção em termos da eficiência de energia, da simplicidade na
estrutura e da adaptibilidade robusta face a alterações ambientais e perturbações inesperadas.
Então, o objectivo principal do trabalho é estudar a aplicabilidade do modelo de CPG para
a reabilitação da marcha, para efeitos de assistência e/ou terapia. É desenvolvido um foco no
controlo da locomoção para maior entendimento dos princípios subjacentes úteis para a recuperação
da marcha, explorando a interacção tronco cerebral-espinal medula-biomecânica de
forma mais detalhada. Este estudo tem potencial aplicação no projecto de robôs autónomos
e na tecnologia de reabilitação, não só no desenvolvimento de ortóteses e próteses, mas também
na procura de procedimentos úteis para a recuperação da função motora.
Foram obtidos resultados promissores susceptíveis de abrir caminho à simulação de comportamentos
e princípios mais complexos da marcha, contribuindo consequentemente para
uma aprimorada reabilitação motora automatizada adaptada às necessidades emergentes
18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems: Proceedings
Proceedings of the 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems, which took place in Dresden, Germany, 26 – 28 May 2010.:Welcome Address ........................ Page I
Table of Contents ........................ Page III
Symposium Committees .............. Page IV
Special Thanks ............................. Page V
Conference program (incl. page numbers of papers)
................... Page VI
Conference papers
Invited talks ................................ Page 1
Regular Papers ........................... Page 14
Wednesday, May 26th, 2010 ......... Page 15
Thursday, May 27th, 2010 .......... Page 110
Friday, May 28th, 2010 ............... Page 210
Author index ............................... Page XII