Optimization-based Framework for Stability and Robustness of Bipedal Walking Robots

As robots become more sophisticated and move out of the laboratory, they need to be able to reliably traverse difficult and rugged environments. Legged robots -- as inspired by nature -- are most suitable for navigating through terrain too rough or irregular for wheels. However, control design and stability analysis is inherently difficult since their dynamics are highly nonlinear, hybrid (mixing continuous dynamics with discrete impact events), and the target motion is a limit cycle (or more complex trajectory), rather than an equilibrium. For such walkers, stability and robustness analysis of even stable walking on flat ground is difficult. This thesis proposes new theoretical methods to analyse the stability and robustness of periodic walking motions. The methods are implemented as a series of pointwise linear matrix inequalities (LMI), enabling the use of convex optimization tools such as sum-of-squares programming in verifying the stability and robustness of the walker. To ensure computational tractability of the resulting optimization program, construction of a novel reduced coordinate system is proposed and implemented. To validate theoretic and algorithmic developments in this thesis, a custom-built “Compass gait” walking robot is used to demonstrate the efficacy of the proposed methods. The hardware setup, system identification and walking controller are discussed. Using the proposed analysis tools, the stability property of the hardware walker was successfully verified, which corroborated with the computational results

Distributed aop middleware for large-scale scenarios

En aquesta tesi doctoral presentem una proposta de middleware distribuït pel desenvolupament d'aplicacions de gran escala. La nostra motivació principal és permetre que les responsabilitats distribuïdes d'aquestes aplicacions, com per exemple la replicació, puguin integrar-se de forma transparent i independent. El nostre enfoc es basa en la implementació d'aquestes responsabilitats mitjançant el paradigma d'aspectes distribuïts i es beneficia dels substrats de les xarxes peer-to-peer (P2P) i de la programació orientada a aspectes (AOP) per realitzar-ho de forma descentralitzada, desacoblada, eficient i transparent. La nostra arquitectura middleware es divideix en dues capes: un model de composició i una plataforma escalable de desplegament d'aspectes distribuïts. Per últim, es demostra la viabilitat i aplicabilitat del nostre model mitjançant la implementació i experimentació de prototipus en xarxes de gran escala reals.In this PhD dissertation we present a distributed middleware proposal for large-scale application development. Our main aim is to separate the distributed concerns of these applications, like replication, which can be integrated independently and transparently. Our approach is based on the implementation of these concerns using the paradigm of distributed aspects. In addition, our proposal benefits from the peer-to-peer (P2P) networks and aspect-oriented programming (AOP) substrates to provide these concerns in a decentralized, decoupled, efficient, and transparent way. Our middleware architecture is divided into two layers: a composition model and a scalable deployment platform for distributed aspects. Finally, we demonstrate the viability and applicability of our model via implementation and experimentation of prototypes in real large-scale networks

Parameter sensitivity analysis for biochemical reaction networks

Funding: This research was funded by the BBSRC Grant BB/K003097/1 (Systems Biology Analysis of Biological Timers and Inflammation) and the EPSRC Grant EP/P019811/1 (Mathematical Foundations of Information and Decisions in Dynamic Cell Signalling). DAR was also supported by funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 305564.Biochemical reaction networks describe the chemical interactions occurring between molecular populations inside the living cell. These networks can be very noisy and complex and they often involve many variables and even more parameters. Parameter sensitivity analysis that studies the effects of parameter changes to the behaviour of biochemical networks can be a powerful tool in unravelling their key parameters and interactions. It can also be very useful in designing experiments that study these networks and in addressing parameter identifiability issues. This article develops a general methodology for analysing the sensitivity of probability distributions of stochastic processes describing the time-evolution of biochemical reaction networks to changes in their parameter values. We derive the coefficients that efficiently summarise the sensitivity of the probability distribution of the network to each parameter and discuss their properties. The methodology is scalable to large and complex stochastic reaction networks involving many parameters and can be applied to oscillatory networks. We use the two-dimensional Brusselator system as an illustrative example and apply our approach to the analysis of the Drosophila circadian clock. We investigate the impact of using stochastic over deterministic models and provide an analysis that can support key decisions for experimental design, such as the choice of variables and time-points to be observed.Publisher PDFPeer reviewe

Machine learning approach to the safety assessment of a prestressed concrete railway bridge

Early structural anomalies identification allows to hold maintenance activities that avoid loss of both economic resources and human life. This is extremely important for crucial infrastructures like railway bridges. This paper illustrates the structural health monitoring approach applied to a simply supported prestressed concrete railway bridge. In the framework of long-term monitoring, both static quantities (displacements, strains, and rotations) and environmental measurements (temperatures) have been recorded. Machine learning techniques, Extreme Gradient boosting machine and Multi-Layer Perceptron, have been exploited to build regression correlation models associated with the undamaged structural condition after adequate pre-processing operations. In this way, alarm thresholds based on the expected residuals between the predicted structural quantities and the measured ones, have been defined. The thresholds turned out to be able to catch early-stage anomalies not pointed out by traditional damage thresholds based on the design values. The proposed damage index is chosen as the moving median of the residuals, allowing a significant reduction of false alarms. The used correlation models and the obtained results represent a starting point for the generalization of this approach to the bridges belonging to the same static typology

Austrian High-Performance-Computing meeting (AHPC2020)

This booklet is a collection of abstracts presented at the AHPC conference

Investigation of Concurrent Energy Harvesting from Ambient Vibrations and Wind

In recent years, many new concepts for micro-power generation have been introduced to harness wasted energy from the environment and maintain low-power electronics including wireless sensors, data transmitters, controllers, and medical implants. Generally, such systems aim to provide a cheap and compact alternative energy source for applications where battery charging or replacement is expensive, time consuming, and/or cumbersome. Within the vast field of micro-power generation, utilizing the piezoelectric effect to generate an electric potential in response to mechanical stimuli has recently flourished as a major thrust area. Based on the nature of the ambient excitation, piezoelectric energy harvesters are divided into two major categories: the first deals with harvesting energy from ambient vibrations; while the second focuses on harvesting energy from aerodynamic flow fields such as wind or other moving fluids. This Dissertation aims to investigate the potential of integrating both sources of excitation into a single energy harvester. To that end, the Dissertation presents reduced-order models that can be used to capture the nonlinear response of piezoelectric energy harvesters under the combination of external base and aerodynamic excitations; and provides approximate analytical solutions of these models using perturbation theory. The analytical solutions are used, subsequently, to identify the important parameters affecting the response under the combined loading and to develop an understanding of the conditions under which the combined loading can be used to enhance efficacy and performance. As a platform to achieve these goals, the Dissertation considers two energy harvesters; the first consisting of a piezoelectric cantilever beam rigidly attached to a bluff body at the free end to permit galloping-type responses, while the second consists of a piezoelectric cantilever beam augmented with an airfoil at its tip. The airfoil is allowed to plunge and pitch around an elastic axis to enable flutter-type responses. Theoretical and experimental studies are presented with the goal of comparing the performance of a single integrated harvester to two separate devices harvesting energy independently from the two available energy sources. It is demonstrated that, under some clearly identified conditions, using a single piezoelectric harvester for energy harvesting under the combined loading can improve its transduction capability and the overall power density. Even when the wind velocity is below the cut-in wind speed of the harvester, i.e. galloping or flutter speed, using the integrated harvester amplifies the influence of the base excitation which enhances the output power as compared to using one aeroelastic and one vibratory energy harvesters. When the wind speed is above the cut-in wind speed, the performance of the integrated harvester becomes dependent on the excitation\u27s frequency and its magnitude with maximum improvements occurring near resonance and for large base excitation levels