1,540 research outputs found

    Learning and Control of Dynamical Systems

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    Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise. In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems. We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p

    Runway Safety Improvements Through a Data Driven Approach for Risk Flight Prediction and Simulation

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    Runway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors. To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach. A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk. The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.Ph.D

    A Changing Landscape:On Safety &amp; Open Source in Automated and Connected Driving

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    Manufacture, Refinement and Low-Speed Flight Testing of a Small-Scale, High-Speed Uncrewed Aerial Vehicle

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    This thesis details the mechanical design, manufacture, and flight testing of a sub-2.5kg UAV prototype. While the aircraft configuration is designed for supersonic flight, the goal of this work is to evaluate the low-speed flight characteristics and to prove positive stability and control at low speeds. Low-speed testing will evaluate the applicability of established design techniques---developed for full-scale, crewed aircraft---to small-scale, high-speed UAVs. Review of literature and preliminary results led to a focus on the prediction of lateral stability characteristics, especially vertical tail volume coefficient (VTVC). Flight testing of the prototype did not yield sustained flight; however, data collected during takeoff attempts provide valuable information about the behaviour of the design. These data indicate that the aircraft was laterally unstable, contradicting the vertical tail design determined from VTVC sizing methods. Analysis of VTVC for existing tailless delta-winged aircraft and comparison with the MUFASA aircraft showed that VTVC is insufficient as an early design parameter for small-scale, high-speed designs. A novel parameter, the fuselage-normalized tail volume coefficient, is proposed for use in conjunction with the conventional VTVC. Taken together, these two parameters provide a more complete prediction of lateral stability for small UAVs with supersonic design configurations. Future development work on this project could benefit from a detailed lateral stability study, thorough engine intake design, and improvements to the launch rail used to accelerate the aircraft

    EVTOL concept design

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    This master thesis consists of a research and development project¿s documentation about Electrical Vertical Take off and Landing (EVTOL) technology. The main target is providing an investigation about this technology, reviewing its history since its origins to the future lines, understanding how it works by revising all the technical aspects such as the mechanical part, hardware components, software systems, structural stress design¿ In addition, a market study is carried out around this technology to come up with a first prototype. Based on a research for the applications and utilities that it can offer regarding the future problems that humanity is facing. Furthermore, this thesis documents the analog and digital methodologies that are being used throughout the entire creative process combining design and engineering workflows in order to achieve the proposed objectives. The project¿s value resides on the creative design aspect, therefore all the content is based from the pre-production design perspective. As the most technical part involving the product production such as the stress design aspect to select the right components, or quality validation process would be carried out on further stages by the engineers.Objectius de Desenvolupament Sostenible::13 - Acció per al ClimaObjectius de Desenvolupament Sostenible::15 - Vida d'Ecosistemes Terrestre

    Analysis, Design, and Implementation of a training center for variable-speed drive assembly production : Case ABB Oy

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    In manufacturing constant developments in production, processes, and layouts are required to respond towards increased production volume, quality, and customer requirements while meeting production targets and objectives. The case company of this thesis is ABB Ltd Drives Manufacturing Unit, which specializes in variable-speed drive production. ABB has recognized the need for re-designing a new and effective training center that supports One-piece flow assembly production since the old model is based on a cell production method. The training center is used for the training and integration of the company's new and experienced assemblers. The aim of the research is to analyze the current training concept, design a new technical solution, and create a detailed implementation plan. Thus, the following research questions were developed: RQ1: How to develop and re-design a training center that supports the assembler for One-piece flow method production of variable-speed drives? RQ2: How to design and create the best possible layout and solution to guarantee safety, flexibility, ergonomics, clear flow, and the maximum utilization of space? RQ3: How to implement a training center that does not disrupt the main production lines and makes that way operations more efficient? To achieve the objectives, the waste, bottlenecks, and issues of the current design were first identified by observing the training process and organizing focus groups and workshops with the production line and logistics (customer), and with the project team. Work-time studies were also conducted to solve the flow, outputs, cycle time, and waste time of the current process. These data collection methods aided in identifying potential improvement opportunities for the new design. The layout design process was committed by utilizing Lean principles and the Systematic layout planning procedure. AutoCAD was used to create and map various layout structures, options, and alternatives. The design process required the tendering of two layout location options, which were solved using the quantitative multiple attribute decision-making method, Weighted decision matrix (WDM), with voting based on the scoring of various criteria and features. The result was a Flexible 6-phase U-model one-piece flow training center that allows assemblers to be trained in both one-piece flow and cell production methods. The new design's scope of work was delivered to the supplier, numerous negotiations were held to achieve the best final solution, and the new training center was ordered. In the end, a detailed implementation plan with an estimated schedule was created and a future action list was established. The new design fulfils the objectives and eliminates all issues, waste, and bottlenecks while also ensuring safety, ergonomics, flexibility, a clear flow, and a high-quality training process. With the new design, the efficiency, quality, and output of training and production operations will improve.Teollisuuden alalla tuotantojärjestelmiä, prosesseja ja layouteja on jatkuvasti kehitettävä sekä modifioitava reagoidakseen kasvaneisiin tuotantomääriin sekä laatu- ja asiakasvaatimuksiin ja saavuttaakseen asetetut tuotantotavoitteet ja päämäärät. Tämän opinnäytetyön toimeksiantaja on ABB Oy Drives Manufacturing -yksikkö, joka on erikoistunut taajuusmuuttajatuotantoon. Toimeksiantaja on tunnistanut tarpeen uuden ja tehokkaamman koulutuslinjan suunnitteluun One-piece flow malliseen taajuusmuuttajien kokoonpanotuotantoon, sillä vanha tuotantomalli perustuu solutuotantomenetelmään. Koulutuslinjaa käytetään niin uusien kuten jo talossa olevien vanhojen kokoonpanoasentajien koulutukseen ja integrointiin. Tutkimuksen tavoitteena on analysoida nykyinen koulutuskonsepti, suunnitella uusi tekninen ratkaisu ja laatia yksityiskohtainen implementointisuunnitelma. Tavoitteiden saavuttamista varten on kehitetty seuraavat kolme tutkimuskysymystä: RQ1: Kuinka kehittää ja suunnitella koulutuslinja, joka tukee asentajia One-piece flow malliseen kokoonpanotuotantoon? RQ2: Miten suunnitella ja luoda paras mahdollinen layout ja ratkaisu, joka takaa turvallisuuden, joustavuuden, ergonomian, selkeän virtauksen ja maksimaalisen tilankäytön? RQ3: Kuinka implementoida koulutuslinja, joka ei häiritse päätuotantolinjoja ja tehostaa siten operaatioiden tehokkuutta? Saavuttaakseen tavoitteet, nykyisen koulutuskonseptin aiheuttamat pullonkaulat, ongelmat ja hukka tunnistettiin ensin havainnoimalla koulutusprosessia ja järjestämällä haastatteluja sekä työpajoja tuotantolinjan ja logistiikan (asiakkaan) sekä projektiryhmän kanssa. Nykyisen prosessin virtauksen, ulostulon, tahti -ja hukka-ajan selvittämiseksi suoritettiin myös työaikatutkimuksia. Nämä tiedonkeruumenetelmät auttoivat kehitysmahdollisuuksien tunnistamisessa uutta ratkaisua varten. Layout suunnitteluprosessi toteutettiin Lean-periaatteita ja systemaattista layout suunnittelua käyttäen. AutoCAD layout suunnittelusovellusta käytettiin erilaisien asettelurakenteiden ja vaihtoehtojen luomiseen sekä kartoittamiseen. Suunnitteluprosessi edellytti kahden layout-sijaintivaihtoehdon kilpailuttamista. Lopputulos ratkaistiin äänestämällä kvantitatiivisen päätöksentekomatriisin (WDM) avulla, joka perustui eri kriteerien ja ominaisuuksien pisteytykseen. Tulokseksi saatiin joustava 6-vaiheinen U-mallinen One-piece flow koulutuslinja, jonka avulla asentajia voidaan kouluttaa sekä One-piece flow että solutuotantomallisesti. Uuden koulutuslinjan työn laajuus -dokumentti toimitettiin toimittajalle sekä lukuisia neuvotteluja käytiin parhaan loppuratkaisun saavuttamiseksi, jonka jälkeen uusi koulutuslinja tilattiin. Lopuksi koostettiin yksityiskohtainen implementointisuunnitelma arvioituineen aikatauluineen ja laadittiin toimenpidelista tulevaisuutta varten. Uusi ratkaisu täyttää asetetut tavoitteet ja eliminoi kaikki ongelmat, hukat ja pullonkaulat sekä takaa turvallisuuden, ergonomian, joustavuuden, selkeän virtauksen ja laadukkaan koulutusprosessin. Uuden ratkaisun myötä koulutuksen ja operaatioiden tehokkuus, laatu ja tuottavuus paranevat

    7° Jornadas ITEE 2023

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    En esta publicación se reúnen los trabajos y resúmenes extendidos presentados en las VII Jornadas de Investigación, Transferencia, Extensión y Enseñanza (ITEE), de la Facultad de Ingeniería de la Universidad Nacional de La Plata, organizadas por la Secretaría de Investigación y Transferencia de dicha facultad, que tuvieron lugar entre el 25 y el 27 de abril de 2023.Facultad de Ingenierí

    Spring 2023 Full Issue

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