Hardware-In-The Loop setup and test of an air spring control system

In this work, a Hardware-in-the-loop test bench is designed. The bench is used to test the behaviour of an electronic control unit used in Maserati to control the dynamics of an air spring system. First the mathematical model of the plant has been defined, then the simulation enviroment and the test environment have been set up. The performed tests succesfully highlighted some bugs in the device under test.

Hardware-in-the-loop implementation of single- and dual-phase shift control for dual active bridge converters in EV applications

Isolated DC-DC converters play a significant role in fast charging and maintaining the variable output voltage for EV applications. This study aims to investigate the different Isolated DC-DC converters for onboard and offboard chargers, then, once the topology is selected, study the control techniques and, finally, achieve a real-time converter model to accomplish Hardware-In-The-Loop (HIL) results. Among the different isolated DC-DC topologies, the Dual Active Bridge (DAB) converter has the advantage of allowing bidirectional power flow, which enables operating in both Grid to Vehicle (G2V) and Vehicle to Grid (V2G) modalities. Recently, DAB has been used in the offboard chargers for high voltage applications due to SiC and GaN MOSFETs; this new technology also allows the utilization of higher switching frequencies. By empowering soft switching techniques to reduce switching losses, higher switching frequency operation is possible in DAB. There are four phase shift control techniques for the DAB converter. They are Single Phase shift, Extended Phase shift, Dual Phase shift, Triple Phase shift controls. This thesis considers two control strategies; Single-Phase, and Dual-Phase shifts, to understand the circulating currents, power losses, and output capacitor size reduction in the DAB. Hardware-In-The-Loop (HIL) experiments are carried out on both controls with high switching frequencies using the PLECS software tool and the RT box supporting the PLECS. Root Mean Square Error is also calculated for steady-state values of output voltage with different sampling frequencies in both the controls to identify the achievable sampling frequency in real-time. DSP implementation is also executed to emulate the optimized DAB converter design, and final real-time simulation results are discussed for both the Single-Phase and Dual-Phase shift controls.

Analysis, Design, and Hardware-in-the-Loop Validation of Multi-Active Bridge Converters

This master's thesis investigates different aspects of Dual-Active-Bridge (DAB) Converter and extends aspects further to Multi-Active-Bridges (MAB). The thesis starts with an overview of the applications of the DAB and MAB and their importance. The analytical part of the thesis includes the derivation of the peak and RMS currents, which is required for finding the losses present in the system. The power converters, considered in this thesis are DAB, Triple-Active Bridge (TAB) and Quad-Active Bridge (QAB). All the theoretical calculations are compared with the simulation results from PLECS software for identifying the correctness of the reviewed and developed theory. The Hardware-in-the-Loop (HIL) simulation is conducted for checking the control operation in real-time with the help of the RT box from the Plexim. Additionally, as in real systems digital signal processor (DSP), system-on-chip or field programmable gate array is employed for the control of the power electronic systems, and the execution of the control in the real-time simulation (RTS) conducted is performed by DSP.

Virtual Calibration of CO2 and Pollutant Emissions of a High-Performance PHEV using Model-in-the-Loop Methodology

The study analyses the calibration process of a newly developed high-performance plug-in hybrid electric passenger car powertrain. The complexity of modern powertrains and the more and more restrictive regulations regarding pollutant emissions are the primary challenges for the calibration of a vehicle’s powertrain. In addition, the managers of OEM need to know as earlier as possible if the vehicle under development will meet the target technical features (emission included). This leads to the necessity for advanced calibration methodologies, in order to keep the development of the powertrain robust, time and cost effective. The suggested solution is the virtual calibration, that allows the tuning of control functions of a powertrain before having it built. The aim of this study is to calibrate virtually the hybrid control unit functions in order to optimize the pollutant emissions and the fuel consumption. Starting from the model of the conventional vehicle, the powertrain is then hybridized and integrated with emissions and aftertreatments models. After its validation, the hybrid control unit strategies are optimized using the Model-in-the-Loop testing methodology. The calibration activities will proceed thanks to the implementation of a Hardware-in-the-Loop environment, that will allow to test and calibrate the Engine and Transmission control units effectively, besides in a time and cost saving manner.

Simulazioni Software-In-the-Loop per la verifica funzionale di un sistema di tracking satellitare (ALMATracker)

L'ALMATracker è un sistema di puntamento per la stazione di terra di ALMASat-1. La sua configurazione non segue la classica Azimuth-Elevazione, bensì utilizza gli assi α-β per evitare punti di singolarità nelle posizioni vicino allo zenit. Ancora in fase di progettazione, utilizzando in congiunta SolidWorks e LabVIEW si è creato un Software-in-the-loop per la sua verifica funzionale, grazie all'utilizzo del relativamente nuovo pacchetto NI Softmotion. Data la scarsa esperienza e documentazione che si hanno su questo recente tool, si è prima creato un Case Study che simulasse un sistema di coordinate cilindriche in modo da acquisire competenza. I risultati conseguiti sono poi stati sfruttati per la creazione di un SIL per la simulazione del movimento dell'ALMATracker. L'utilizzo di questa metodologia di progettazione non solo ha confermato la validità del design proposto, ma anche evidenziato i problemi e le potenzialità che caratterizzano questo pacchetto software dandone un analisi approfondita.

Neural Machine Translation in the Field of Law. Analysis and Evaluation against Human Reference of NMT Raw Outputs of an Atto Costitutivo of S.r.l. from Italian into German.

Artificial Intelligence (AI) is gaining ever more ground in every sphere of human life, to the point that it is now even used to pass sentences in courts. The use of AI in the field of Law is however deemed quite controversial, as it could provide more objectivity yet entail an abuse of power as well, given that bias in algorithms behind AI may cause lack of accuracy. As a product of AI, machine translation is being increasingly used in the field of Law too in order to translate laws, judgements, contracts, etc. between different languages and different legal systems. In the legal setting of Company Law, accuracy of the content and suitability of terminology play a crucial role within a translation task, as any addition or omission of content or mistranslation of terms could entail legal consequences for companies. The purpose of the present study is to first assess which neural machine translation system between DeepL and ModernMT produces a more suitable translation from Italian into German of the atto costitutivo of an Italian s.r.l. in terms of accuracy of the content and correctness of terminology, and then to assess which translation proves to be closer to a human reference translation. In order to achieve the above-mentioned aims, two human and automatic evaluations are carried out based on the MQM taxonomy and the BLEU metric. Results of both evaluations show an overall better performance delivered by ModernMT in terms of content accuracy, suitability of terminology, and closeness to a human translation. As emerged from the MQM-based evaluation, its accuracy and terminology errors account for just 8.43% (as opposed to DeepL’s 9.22%), while it obtains an overall BLEU score of 29.14 (against DeepL’s 27.02). The overall performances however show that machines still face barriers in overcoming semantic complexity, tackling polysemy, and choosing domain-specific terminology, which suggests that the discrepancy with human translation may still be remarkable.

Studio e implementazione di un autopilota su un UAV ad ala fissa

Si riporta inizialmente un’analisi tecnica dell’autopilota Ardupilot, utilizzato con il firmware Arduplane, che predispone la scheda all’utilizzo specifico su velivoli senza pilota ad ala fissa. La parte sostanziale della tesi riguarda invece lo studio delle leggi di controllo implementate su Arduplane e la loro modellazione, assieme ad altre parti del codice, in ambiente Matlab Simulink. Il sistema di controllo creato, chiamato Attitude Flight System, viene verificato con la tecnica del Software In the Loop in un simulatore di volo virtuale modellato anch’esso in Simulink, si utilizza la dinamica di un velivolo UAV di prova e il software FlightGear per l’ambiente grafico. Di fondamentale importanza è la verifica della compatibilità fra il firmware originale e il codice generato a partire dai modelli Simulink, verifica effettuata mediante test di tipo Hardware in the Loop. L’ultima parte della tesi descrive le prove di volo svolte per verificare le prestazioni della scheda su un aeromodello trainer.

Evoluzione di un sistema di automazione della guida di motoveicoli per test su banco a rulli

Evoluzione di un sistema Hardware in the Loop per l'automazione della guida di motoveicoli su banco a rulli. Questo sistema HIL, simulando un certo tipo di condizioni, permette di svolgere test su strategia centralina, implementate per il controllo motore.

Real-time PMU monitoring of an IEEE 5-Bus Network implemented on OPAL system

This thesis studies the state-of-the-art of phasor measurement units (PMUs) as well as their metrological requirements stated in the IEEE C37.118.1 and C37.118.2 Standards for guaranteeing correct measurement performances. Communication systems among PMUs and their possible applicability in the field of power quality (PQ) assessment are also investigated. This preliminary study is followed by an analysis of the working principle of real-time (RT) simulators and the importance of hardware-in-the-loop (HIL) implementation, examining the possible case studies specific for PMUs, including compliance tests which are one of the most important parts. The core of the thesis is focused on the implementation of a PMU model in the IEEE 5-bus network in Simulink and in the validation of the results using OPAL RT-4510 as a real-time simulator. An initial check allows one to get an idea about the goodness of the results in Simulink, comparing the PMU data with respect to the load-flow steady-state information. In this part, accuracy indices are also calculated for both voltage and current synchrophasors. The following part consists in the implementation of the same code in OPAL-RT 4510 simulator, after which an initial analysis is carried out in a qualitative way in order to get a sense of the goodness of the outcomes. Finally, the confirmation of the results is based on an examination of the attained voltage and current synchrophasors and accuracy indices coming from Simulink models and from OPAL system, using a Matlab script. This work also proposes suggestions for an upcoming operation of PMUs in a more complex system as the Digital Twin (DT) in order to improve the performances of the already-existing protection devices of the distribution system operator (DSO) for a future enhancement of power systems reliability.

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose to learn a variable selection policy for branch-and-bound in mixed-integer linear programming, by imitation learning on a diversified variant of the strong branching expert rule. We encode states as bipartite graphs and parameterize the policy as a graph convolutional neural network. Experiments on a series of synthetic problems demonstrate that our approach produces policies that can improve upon expert-designed branching rules on large problems, and generalize to instances significantly larger than seen during training.

Content and English Language Integrated Learning: The use of Moodle in Aircraft Maintenance Training Programs

The aim of this work is to develop a prototype of an e-learning environment that can foster Content and Language Integrated Learning (CLIL) for students enrolled in an aircraft maintenance training program, which allows them to obtain a license valid in all EU member states. Background research is conducted to retrace the evolution of the field of educational technology, analyzing different learning theories – behaviorism, cognitivism, and (socio-)constructivism – and reflecting on how technology and its use in educational contexts has changed over time. Particular attention is given to technologies that have been used and proved effective in Computer Assisted Language Learning (CALL). Based on the background research and on students’ learning objectives, i.e. learning highly specialized contents and aeronautical technical English, a bilingual approach is chosen, three main tools are identified – a hypertextbook, an exercise creation activity, and a discussion forum – and the learning management system Moodle is chosen as delivery medium. The hypertextbook is based on the technical textbook written in English students already use. In order to foster text comprehension, the hypertextbook is enriched by hyperlinks and tooltips. Hyperlinks redirect students to webpages containing additional information both in English and in Italian, while tooltips show Italian equivalents of English technical terms. The exercise creation activity and the discussion forum foster interaction and collaboration among students, according to socio-constructivist principles. In the exercise creation activity, students collaboratively create a workbook, which allow them to deeply analyze and master the contents of the hypertextbook and at the same time create a learning tool that can help them, as well as future students, to enhance learning. In the discussion forum students can discuss their individual issues, content-related, English-related or e-learning environment-related, helping one other and offering instructors suggestions on how to improve both the hypertextbook and the workbook based on their needs.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Assimilation of data from conventional and non conventional networks through a LETKF scheme in the COSMO model

The present work studies a km-scale data assimilation scheme based on a LETKF developed for the COSMO model. The aim is to evaluate the impact of the assimilation of two different types of data: temperature, humidity, pressure and wind data from conventional networks (SYNOP, TEMP, AIREP reports) and 3d reflectivity from radar volume. A 3-hourly continuous assimilation cycle has been implemented over an Italian domain, based on a 20 member ensemble, with boundary conditions provided from ECMWF ENS. Three different experiments have been run for evaluating the performance of the assimilation on one week in October 2014 during which Genova flood and Parma flood took place: a control run of the data assimilation cycle with assimilation of data from conventional networks only, a second run in which the SPPT scheme is activated into the COSMO model, a third run in which also reflectivity volumes from meteorological radar are assimilated. Objective evaluation of the experiments has been carried out both on case studies and on the entire week: check of the analysis increments, computing the Desroziers statistics for SYNOP, TEMP, AIREP and RADAR, over the Italian domain, verification of the analyses against data not assimilated (temperature at the lowest model level objectively verified against SYNOP data), and objective verification of the deterministic forecasts initialised with the KENDA analyses for each of the three experiments.