Improvement of The Driver Simulator Control and Comparison Between Driver-Road-Vehicle Interaction in Real and Simulated Environment

The present doctoral thesis discusses the ways to improve the performance of driving simulator, provide objective measures for the road safety evaluation methodology based on driver’s behavior and response and investigates the drivers' adaptation to the driving assistant systems. The activities are divided into two macro areas; the driving simulation studies and on-road experiments. During the driving simulation experimentation, the classical motion cueing algorithm with logarithmic scale was implemented in the 2DOF motion cueing simulator and the motion cues were found desirable by the participants. In addition, it found out that motion stimuli could change the behaviour of the drivers in terms of depth/distance perception. During the on-road experimentations, The driver gaze behaviour was investigated to find the objective measures on the visibility of the road signs and reaction time of the drivers. The sensor infusion and the vehicle monitoring instruments were found useful for an objective assessment of the pavement condition and the drivers’ performance. In the last chapter of the thesis, the safety assessment during the use of level 1 automated driving “ACC” is discussed with the simulator and on-road experiment. The drivers’ visual behaviour was investigated in both studies with innovative classification method to find the epochs of the distraction of the drivers. The behavioural adaptation to ACC showed that drivers may divert their attention away from the driving task to engage in secondary, non-driving-related tasks.

Intelligent Sensor Systems for Structural Health Monitoring Applications

The convergence between the recent developments in sensing technologies, data science, signal processing and advanced modelling has fostered a new paradigm to the Structural Health Monitoring (SHM) of engineered structures, which is the one based on intelligent sensors, i.e., embedded devices capable of stream processing data and/or performing structural inference in a self-contained and near-sensor manner. To efficiently exploit these intelligent sensor units for full-scale structural assessment, a joint effort is required to deal with instrumental aspects related to signal acquisition, conditioning and digitalization, and those pertaining to data management, data analytics and information sharing. In this framework, the main goal of this Thesis is to tackle the multi-faceted nature of the monitoring process, via a full-scale optimization of the hardware and software resources involved by the {SHM} system. The pursuit of this objective has required the investigation of both: i) transversal aspects common to multiple application domains at different abstraction levels (such as knowledge distillation, networking solutions, microsystem {HW} architectures), and ii) the specificities of the monitoring methodologies (vibrations, guided waves, acoustic emission monitoring). The key tools adopted in the proposed monitoring frameworks belong to the embedded signal processing field: namely, graph signal processing, compressed sensing, ARMA System Identification, digital data communication and TinyML.

Optimal bidding strategies of an electric vehicle aggregator in an electricity market

In recent years Electric Vehicles (EVs) are getting more importance as future transport systems, due to the increase of the concerns relevant to the greenhouse gases emission and the use fossil fuel. The management of the charging and discharging process of EVs could provide new business model for participating in the electricity markets. Moreover, vehicle to grid systems have the potential of increasing utility system flexibility. This thesis develops some models for the optimal integration of the EVs in the electricity market. In particular, the thesis focuses on the optimal bidding strategy of an EV aggregator participating to both the day ahead market and the secondary reserve market. The aggregator profit is maximized taking into account the energy balance equation, as well as the technical constraints of energy settlement, power supply and state of charge of the EVs. The results obtained by using the GAMS (General Algebraic Modelling System) environment are presented and discussed.

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

Robust Adaptive Control with Applications to Multi-Agent Systems and Mobile Robotics

This thesis deals with robust adaptive control and its applications, and it is divided into three main parts. The first part is about the design of robust estimation algorithms based on recursive least squares. First, we present an estimator for the frequencies of biased multi-harmonic signals, and then an algorithm for distributed estimation of an unknown parameter over a network of adaptive agents. In the second part of this thesis, we consider a cooperative control problem over uncertain networks of linear systems and Kuramoto systems, in which the agents have to track the reference generated by a leader exosystem. Since the reference signal is not available to each network node, novel distributed observers are designed so as to reconstruct the reference signal locally for each agent, and therefore decentralizing the problem. In the third and final part of this thesis, we consider robust estimation tasks for mobile robotics applications. In particular, we first consider the problem of slip estimation for agricultural tracked vehicles. Then, we consider a search and rescue application in which we need to drive an unmanned aerial vehicle as close as possible to the unknown (and to be estimated) position of a victim, who is buried under the snow after an avalanche event. In this thesis, robustness is intended as an input-to-state stability property of the proposed identifiers (sometimes referred to as adaptive laws), with respect to additive disturbances, and relative to a steady-state trajectory that is associated with a correct estimation of the unknown parameter to be found.

Active Control of the Acoustic Field in a Vehicle Cabin

In this thesis, a thorough investigation on acoustic noise control systems for realistic automotive scenarios is presented. The thesis is organized in two parts dealing with the main topics treated: Active Noise Control (ANC) systems and Virtual Microphone Technique (VMT), respectively. The technology of ANC allows to increase the driver's/passenger's comfort and safety exploiting the principle of mitigating the disturbing acoustic noise by the superposition of a secondary sound wave of equal amplitude but opposite phase. Performance analyses of both FeedForwrd (FF) and FeedBack (FB) ANC systems, in experimental scenarios, are presented. Since, environmental vibration noises within a car cabin are time-varying, most of the ANC solutions are adaptive. However, in this work, an effective fixed FB ANC system is proposed. Various ANC schemes are considered and compared with each other. In order to find the best possible ANC configuration which optimizes the performance in terms of disturbing noise attenuation, a thorough research of \gls{KPI}, system parameters and experimental setups design, is carried out. In the second part of this thesis, VMT, based on the estimation of specific acoustic channels, is investigated with the aim of generating a quiet acoustic zone around a confined area, e.g., the driver's ears. Performance analysis and comparison of various estimation approaches is presented. Several measurement campaigns were performed in order to acquire a sufficient duration and number of microphone signals in a significant variety of driving scenarios and employed cars. To do this, different experimental setups were designed and their performance compared. Design guidelines are given to obtain good trade-off between accuracy performance and equipment costs. Finally, a preliminary analysis with an innovative approach based on Neural Networks (NNs) to improve the current state of the art in microphone virtualization is proposed.

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies.

Sensor networks optimization and software development for Structural Health Monitoring based on ultrasonic guided waves

The Structural Health Monitoring (SHM) research area is increasingly investigated due to its high potential in reducing the maintenance costs and in ensuring the systems safety in several industrial application fields. A growing demand of new SHM systems, permanently embedded into the structures, for savings in weight and cabling, comes from the aeronautical and aerospace application fields. As consequence, the embedded electronic devices are to be wirelessly connected and battery powered. As result, a low power consumption is requested. At the same time, high performance in defects or impacts detection and localization are to be ensured to assess the structural integrity. To achieve these goals, the design paradigms can be changed together with the associate signal processing. The present thesis proposes design strategies and unconventional solutions, suitable both for real-time monitoring and periodic inspections, relying on piezo-transducers and Ultrasonic Guided Waves. In the first context, arrays of closely located sensors were designed, according to appropriate optimality criteria, by exploiting sensors re-shaping and optimal positioning, to achieve improved damages/impacts localisation performance in noisy environments. An additional sensor re-shaping procedure was developed to tackle another well-known issue which arises in realistic scenario, namely the reverberation. A novel sensor, able to filter undesired mechanical boundaries reflections, was validated via simulations based on the Green's functions formalism and FEM. In the active SHM context, a novel design methodology was used to develop a single transducer, called Spectrum-Scanning Acoustic Transducer, to actively inspect a structure. It can estimate the number of defects and their distances with an accuracy of 2[cm]. It can also estimate the damage angular coordinate with an equivalent mainlobe aperture of 8[deg], when a 24[cm] radial gap between two defects is ensured. A suitable signal processing was developed in order to limit the computational cost, allowing its use with embedded electronic devices.

Development and testing of methodologies for the control and diagnosis of innovative combustion systems

In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.

Design and Characterization of an Active Three-Phase EMI Noise Separator for Three-Phase Power Electronics Systems

One of the major issues for power converters that are connected to the electric grid are the measurement of three phase Conduced Emissions (CE), which are regulated by international and regional standards. CE are composed of two components which are Common Mode (CM) noise and Differential Mode (DM) noise. To achieve compliance with these regulations the Equipment Under Test (EUT) includes filtering and other electromagnetic emission control strategies. The separation of differential mode and common mode noise in Electromagnetic Interference (EMI) analysis is a well-known procedure which is useful especially for the optimization of the EMI filter, to improve the CM or DM attenuation depending on which component of the conducted emissions is predominant, and for the analysis and the understanding of interference phenomena of switched mode power converters. However, separating both components is rarely done during measurements. Therefore, in this thesis an active device for the separation of the CM and DM EMI noise in three phase power electronic systems has been designed and experimentally analysed.

Traffic Signal Optimization of Urban Arterial Network by Vehicle-to-Infrastructure

Urbanization has occasionally been linked to negative consequences. Traffic light system in urban arterial networks plays an essential role to the operation of transport systems. The availability of new Intelligent Transportation System innovations paved the way for connecting vehicles and road infrastructure. GLOSA, or the Green Light Optimal Speed Advisory, is a recent integration of vehicle-to-everything (v2x) technology. This thesis emphasized GLOSA system's potential as a tool for addressing traffic signal optimization. GLOSA serves as an advisory to drivers, informing them of the speed they must maintain to reduce waiting time. The considered study area in this thesis is the Via Aurelio Saffi – Via Emilia Ponente corridor in the Metropolitan City of Bologna which has several signalized intersections. Several simulation runs were performed in SUMOPy software on each peak-hour period (morning and afternoon) using recent actual traffic count data. GLOSA devices were placed on a 300m GLOSA distance. Considering the morning peak-hour, GLOSA outperformed the actuated traffic signal control, which is the baseline scenario, in terms of average waiting time, average speed, average fuel consumption per vehicle and average CO2 emissions. A remarkable 97% reduction on both fuel consumption and CO2 emissions were obtained. The average speed of vehicles running through the simulation was increased as well by 7% and a time saved of 25%. Same results were obtained for the afternoon peak hour with a decrease of 98% on both fuel consumption and CO2 emissions, 20% decrease on average waiting time, and an increase of 2% in average speed. In addition to previously mentioned benefits of GLOSA, a 15% and 13% decrease in time loss were obtained during morning and afternoon peak-hour, respectively. Towards the goal of sustainability, GLOSA shows a promising result of significantly lowering fuel consumption and CO2 emissions per vehicle.

A Microleakage Study Of Gutta-percha/ah Plus And Resilon/real Self-etch Systems After Different Irrigation Protocols.

The development and maintenance of the sealing of the root canal system is the key to the success of root canal treatment. The resin-based adhesive material has the potential to reduce the microleakage of the root canal because of its adhesive properties and penetration into dentinal walls. Moreover, the irrigation protocols may have an influence on the adhesiveness of resin-based sealers to root dentin. The objective of the present study was to evaluate the effect of different irrigant protocols on coronal bacterial microleakage of gutta-percha/AH Plus and Resilon/Real Seal Self-etch systems. One hundred ninety pre-molars were used. The teeth were divided into 18 experimental groups according to the irrigation protocols and filling materials used. The protocols used were: distilled water; sodium hypochlorite (NaOCl)+eDTA; NaOCl+H3PO4; NaOCl+eDTA+chlorhexidine (CHX); NaOCl+H3PO4+CHX; CHX+eDTA; CHX+ H3PO4; CHX+eDTA+CHX and CHX+H3PO4+CHX. Gutta-percha/AH Plus or Resilon/Real Seal Se were used as root-filling materials. The coronal microleakage was evaluated for 90 days against Enterococcus faecalis. Data were statistically analyzed using Kaplan-Meier survival test, Kruskal-Wallis and Mann-Whitney tests. No significant difference was verified in the groups using chlorhexidine or sodium hypochlorite during the chemo-mechanical preparation followed by eDTA or phosphoric acid for smear layer removal. The same results were found for filling materials. However, the statistical analyses revealed that a final flush with 2% chlorhexidine reduced significantly the coronal microleakage. A final flush with 2% chlorhexidine after smear layer removal reduces coronal microleakage of teeth filled with gutta-percha/AH Plus or Resilon/Real Seal SE.

Does The Reciproc File Remove Root Canal Bacteria And Endotoxins As Effectively As Multifile Rotary Systems?

To evaluate the effectiveness of Reciproc for the removal of cultivable bacteria and endotoxins from root canals in comparison with multifile rotary systems. The root canals of forty human single-rooted mandibular pre-molars were contaminated with an Escherichia coli suspension for 21 days and randomly assigned to four groups according to the instrumentation system: GI - Reciproc (VDW); GII - Mtwo (VDW); GIII - ProTaper Universal (Dentsply Maillefer); and GIV -FKG Race(™) (FKG Dentaire) (n = 10 per group). Bacterial and endotoxin samples were taken with a sterile/apyrogenic paper point before (s1) and after instrumentation (s2). Culture techniques determined the colony-forming units (CFU) and the Limulus Amebocyte Lysate assay was used for endotoxin quantification. Results were submitted to paired t-test and anova. At s1, bacteria and endotoxins were recovered in 100% of the root canals investigated (40/40). After instrumentation, all systems were associated with a highly significant reduction of the bacterial load and endotoxin levels, respectively: GI - Reciproc (99.34% and 91.69%); GII - Mtwo (99.86% and 83.11%); GIII - ProTaper (99.93% and 78.56%) and GIV - FKG Race(™) (99.99% and 82.52%) (P < 0.001). No statistical difference were found amongst the instrumentation systems regarding bacteria and endotoxin removal (P > 0.01). The reciprocating single file, Reciproc, was as effective as the multifile rotary systems for the removal of bacteria and endotoxins from root canals.

Photoelastic Analysis Of Fixed Partial Prosthesis Crown Height And Implant Length On Distribution Of Stress In Two Dental Implant Systems.

The aim of this study was to evaluate by photoelastic analysis stress distribution on short and long implants of two dental implant systems with 2-unit implant-supported fixed partial prostheses of 8 mm and 13 mm heights. Sixteen photoelastic models were divided into 4 groups: I: long implant (5 × 11 mm) (Neodent), II: long implant (5 × 11 mm) (Bicon), III: short implant (5 × 6 mm) (Neodent), and IV: short implants (5 × 6 mm) (Bicon). The models were positioned in a circular polariscope associated with a cell load and static axial (0.5 Kgf) and nonaxial load (15°, 0.5 Kgf) were applied to each group for both prosthetic crown heights. Three-way ANOVA was used to compare the factors implant length, crown height, and implant system (α = 0.05). The results showed that implant length was a statistically significant factor for both axial and nonaxial loading. The 13 mm prosthetic crown did not result in statistically significant differences in stress distribution between the implant systems and implant lengths studied, regardless of load type (P > 0.05). It can be concluded that short implants showed higher stress levels than long implants. Implant system and length was not relevant factors when prosthetic crown height were increased.

Dentine Bond Strength And Antimicrobial Activity Evaluation Of Adhesive Systems.

This study evaluated the dentine bond strength (BS) and the antibacterial activity (AA) of six adhesives against strict anaerobic and facultative bacteria. Three adhesives containing antibacterial components (Gluma 2Bond (glutaraldehyde)/G2B, Clearfil SE Protect (MDPB)/CSP and Peak Universal Bond (PUB)/chlorhexidine) and the same adhesive versions without antibacterial agents (Gluma Comfort Bond/GCB, Clearfil SE Bond/CSB and Peak LC Bond/PLB) were tested. The AA of adhesives and control groups was evaluated by direct contact method against four strict anaerobic and four facultative bacteria. After incubation, according to the appropriate periods of time for each microorganism, the time to kill microorganisms was measured. For BS, the adhesives were applied according to manufacturers' recommendations and teeth restored with composite. Teeth (n=10) were sectioned to obtain bonded beams specimens, which were tested after artificial saliva storage for one week and one year. BS data were analyzed using two-way ANOVA and Tukey test. Saliva storage for one year reduces the BS only for GCB. In general G2B and GCB required at least 24h for killing microorganisms. PUB and PLB killed only strict anaerobic microorganisms after 24h. For CSP the average time to eliminate the Streptococcus mutans and strict anaerobic oral pathogens was 30min. CSB showed no AA against facultative bacteria, but had AA against some strict anaerobic microorganisms. Storage time had no effect on the BS for most of the adhesives. The time required to kill bacteria depended on the type of adhesive and never was less than 10min. Most of the adhesives showed stable bond strength after one year and the Clearfil SE Protect may be a good alternative in restorative procedures performed on dentine, considering its adequate bond strength and better antibacterial activity.