Die Raumautomation (RA) umfasst die automatisierten Verschattungs-, Beleuchtungs- und Klimatisierungssysteme auf der Raumebene eines Gebäudes und ist ein bedeutender Einflussfaktor bei der Nachhaltigkeit von Gebäuden. Der Einfluss der RA auf die ökologischen, ökonomischen und sozialen Aspekte der Nachhaltigkeit eines Gebäudes sind Teil zahlreicher verschiedener Forschungsarbeiten. Allerdings wurde die Nachhaltigkeit der RA bei dem Entwurfsprozess der RA sowie bei der Anpassung und der Nachrüstung der RA selten bzw. nicht ausreichend erforscht. Für diese Forschungslücke wird in dieser Arbeit der Begriff funktionale Nachhaltigkeit des Engineerings und Reengineerings der RA sowie des resultierenden RA-Systems vorgestellt. Darunter werden die effiziente Einsetzbarkeit bzw. Wiederverwendbarkeit von RA-Geräten bei Szenarien des Engineerings und Reengineerings sowie die Wiederverwendbarkeit von aktuellen Engineeringdaten in verschiedenen Engineeringaufgaben im Laufe des Lebenszyklus eines Raumautomationssystems (RAS) verstanden. Ziel der vorliegenden Arbeit ist die Entwicklung eines Konzepts für eine flexible Roadmap inkl. der dazu benötigten Datenstruktur zur Sicherstellung und Verbesserung der funktionalen Nachhaltigkeit für Engineering und Reengineeringprozesse der RA sowie der daraus resultierenden RA-Systeme. Diese Roadmap kann als Teil einer kontinuierlichen Performance-Evaluation eingesetzt werden. Der Einsatz dieses Lösungsansatzes wird mit unterschiedlichen praxisrelevanten Ansatzpunkten (Szenarien) entlang des Lebenszyklus eines Raumautomationssystems geprüft. Als Baustein der Roadmap wird die Einsetzbarkeit existierender Technologien für den RA-Entwurf untersucht und die benötigten Erweiterungen bzw. die Entwicklung neuer Algorithmen für einen integralen Einsatz vorgestellt. Ein Schwerpunkt der neuen Algorithmen ist die Verbesserung der Anforderungsmodellierung und des Anforderungserfassungsprozesses der RA. Das neue Konzept der Anforderungsvarianten ermöglicht eine bessere Strukturierung der Anforderungen und die Modellierung der semantischen Bedeutung von zusammenhängenden Anforderungen sowie komplexeren Anforderungsaspekten wie Energieeeffizienzklassen (EEK). Verschiedene Algorithmen werden vorgestellt, welche die Modellierung, Ermittlung und Verwaltung von Anforderungsvarianten ermöglichen. Ein weiterer Fokuspunkt liegt in der Entwicklung von Ansätzen zur Unterstützung der Nutzer bei Reengineeringaufgaben. Dazu gehören Ansätze wie die auslöser-abhängige Ermittlung des anzupassenden Bereiches des RA-Systems bei unterschiedlichen Szenarienklassen, die Anpassung der unterschiedlichen Modellebenen bei verschiedenen Szenarien (z.B. zur Verbesserung der EEK) und ein Mechanismus zur Ermittlung des Erweiterungspotentials eines RAS. Die entwickelten Algorithmen, Verfahren und Modelle werden in Form von Unterstützungsfunktionen eines Tools zum automatischen RA-Entwurf prototypisch implementiert, in einen anpassbaren Prozessablauf eingesetzt und begleiten den Nutzer so bei verschiedenen Engineeringund Reengineeringaufgaben. / Room automation (RA) spans across important trades in a building, such as shading, lighting, heating, ventilation, and air conditioning. Although it addresses the room level, RA has a great impact on the sustainability of the building as a whole. The improvement of different aspects (environmental, economic, and social) of buildings' sustainability through the application of RA has been the objective of various research. However, there has been insufficient research regarding the sustainability of the RA itself during its planning, modification, and retrofit. A new concept regarding this research gap can be defined, namely the functional sustainability of the process of engineering and reengineering of RA as well as the resulting RA system. This new aspect of sustainability includes, on the one hand, the efficient deployment and reusability of RA devices during different engineering and reengineering use cases and on the other hand the efficient reusability of engineering data generated during the life cycle of the RA system. This work aims to develop a new concept for a flexible roadmap and the required data structures to support a functional sustainable and user-oriented engineering and reengineering of RA. The advisory component can be applied to different practical use cases during the life cycle of a room automation system. Different algorithmic components are defined as part of the component. The applicability of existing RA design technologies for the development of these algorithmic components is investigated. Based on this, necessary functional extensions of existing approaches or new approaches for the automated design and redesign of RA as part of the roadmap's components are introduced. The first part of the roadmap's algorithmic components aims to improve the quality of the requirement modeling and engineering of RA. A new concept, namely the requirement variant, is introduced. This concept improves the structuring of the requirement set and allows the modeling of semantic meaning behind interrelated requirements as well as complex forms of requirements, such as the energy efficiency class (EEC). Using this concept, various algorithms for the modeling, identification, and management of requirement variants are introduced. A variety of algorithms and courses of action to support the user with different reengineering tasks are introduced. This includes an approach to identify the part of the RA system that has to be modified, the modification of layers of the system model for different use cases (e.g. to improve the system's EEC); and a method to identify extension potential of a RA system. The introduced models, algorithms, and courses of action are implemented prototypically as user-support functions of a tool for the automated design of RA. These components of the roadmap are applied to different use cases through a flexible workflow and provide support to the user during different engineering and reengineering tasks.

<p dir="ltr">Engine downsizing with the assistance of turbomachinery and/or energy storage system has been realized to be one of the most promising and cost-effective solutions in pursuit of cleaner and more efficient engine products. Fundamental challenges however, exist in terms of control and energy management of advanced powertrain featuring turbocharged engines due to their complex dynamics, inherent coupling nature, and strict emission regulations concerning environmental preservation. For the purpose of addressing those challenges, this dissertation develops an integrated framework for modeling, robust coordinated control, and power management of advanced powertrains featuring turbocharged engines.</p><p dir="ltr">This dissertation first studies an advanced turbocharged lean-burn SI natural gas engine manufactured by Caterpillar, and develops an intuitive physics-based, control-oriented model. The obtained control-oriented model is validated against a high-fidelity truth-reference model and serves as the basis on which a robust coordinated control system is developed. The dissertation then proposes a comprehensive procedure for synthesizing a robust coordinated control system applying optimization-based H_infinity control theory. Specifically, this framework outlines a methodology of modeling uncertainties to account for system robustness, and providing valuable insights into the tuning of general coordinated control system design. For performance testing, the synthesized robust coordinated control system is implemented on the high-fidelity truth-reference model. A parallel closed-loop simulation strategy is adopted so that direct comparison between the robust coordinated control system and benchmark production control system (composed of multiple fine-tuned PID controllers) developed by Caterpillar can be carried out. Simulation results manage to demonstrate the merit of utilizing the robust coordinated control system, with better performances observed in terms of steady-state tracking, transient response, and disturbance attenuation.</p><p dir="ltr">The second part of this dissertation focuses on the development of a proposed novel hybrid electric wheel loader which features a downsized engine assisted by turbocharger and an energy storage system. Research efforts documented in this dissertation involve system configuration, controller design (both component-level and supervisory-level), simulation development (both software-in-the-loop and hardware-in-the-loop) and simulated validation for the proposed novel wheel loader. Inspired by the successful simulation results, John Deere assembled a real demo vehicle with the proposed powertrain and conducted some in-field testing, from which encouraging experimental results are observed.</p>

<p>The vehicle industry has an endless appetite to get better. Often, this appetite is justified by the need of the hour. In the agricultural space, this translates to improving agricultural productivity in the face of population growth, reduced arable land and shortage of skilled farm labor. As for torsional vibrations, which have been around ever since the wheel was invented, the problem gets redefined with new regulations demanding new powertrains with improved fuel efficiency and reduced emissions.</p><p>A solution to the agriculture problem, involves efficiently automating the harvesting process.The first section of this thesis covers the ‘Auto-Unload’ where the goal of automation is achieved. This was done by building a simulation framework that was used to develop and synthesize the ‘AutoUnload’ controller. This controller was later deployed on a combine and a successful unloading on-the-go was demonstrated with a combine, tractor, and tractor-driven grain cart.</p><p>The solution to the second problem about drivetrain vibrations involved deriving a mathematical model for simulating the powertrain of a medium-duty truck. This was done to confirm resonance seen during testing done on a chassis dynamometer. The consequent control strategy to mitigate undesired vibration was to move the torque excitation away from the natural frequency of the system. This was achieved by a ‘gear-shifting’ algorithm. Comparison between on-road tests with and without the ‘gear-shifting’ algorithm showed that such a control strategy can effectively eliminate resonance. The solution methodology developed in this work is robust and transferable to higher engine torques and harvest speeds.</p>

<p>The purpose of this research experiment was to explore and implement a cost-effective automation solution into a low volume production line for loading parts onto a coordinate measuring machine (CMM) for dimensional inspection. Quality control practices have historically been separated from production process by inspection routines being performed in a controlled lab. The system demonstrated the possibilities of an in-process automation of the quality control process that was feasible to be implemented for small and mid-sized manufacturing companies. The process involved an APSX horizontal injection mold machine dispensing parts onto the conveyor belt. The conveyor belt was controlled by a Phoenix Contact PLC and two line sensors that provided two stopping point for cooldown before inspection. A MyCobot 320-M5 robotic arm was used to select the part off the line and places it into a fixture on a Hexagon coordinate measuring machine (CMM).</p>

<p dir="ltr">This project aimed to develop an instrumentation and control system for a micro-drilling robot based on Industrial Internet of Things (IIoT) technologies. The automation system integrated IIoT technological tools to create a robust automation system capable of being used in drilling operations. The system incorporated industrial-grade sensors, which carried out direct measurements of the critical variables of the process. The indirect variables relevant to the control of the robot were calculated from the measured parameters. The system also considered the telemetry architecture necessary to reliably transmit data from the down-the-hole (DTH) robot to a receiver on the surface. Telemetry was based on wireless communication through long-range radio frequency (LoRa). The system developed had models based on Artificial Intelligence (AI) and Machine Learning (ML) for determining the mode of operation, detecting changes in the process, and changes in drilling variables in critical hydraulic components for the drilling process. Algorithms based on AI and ML models also allowed the user to make better decisions based on the variables' correlation to optimize the drilling process (e.g., dynamic change of flow, pressure, and RPMs based on automatic rock identification). A user interface (UI) was developed, and digital tools to perform data analysis were implemented. Safety assessment in all robot systems (e.g., electrical, hardware, software) was contemplated as a critical design component. The result of this research project provides innovative micro-drilling robots with the necessary technological tools to optimize the drilling process. The system made drilling more efficient, reliable, and safe, providing diagnostic and prognostic tools that allowed planning maintenance based on the actual health of the devices. The system that was developed was tested in a test bench under controlled conditions within a laboratory to characterize the system and collect data that allowed ML models' development, training, validation, and testing. The prototype of a micro-drilling robot installed on the test bench served as a case study to assess the implemented models' reliability and the proposed telemetry.</p>

<p>A thesis study investigating the development and evaluation of a computer vision (CV) system for a manufacturing assembly task is reported. The CV inference results are compared to a Manufacturing Process Plan and an automation method completes a buyoff in the software, Solumina. Research questions were created and three hypotheses were tested. A literature review was conducted recognizing little consensus of Industry 4.0 technology adoption in manufacturing industries. Furthermore, the literature review uncovered the need for additional research within the topic of CV. Specifically, literature points towards more research regarding the cognitive capabilities of CV in manufacturing. A CV system was developed and evaluated to test for 90% or greater confidence in part detection. A CV dataset was developed and the system was trained and validated with it. Dataset contextualization was leveraged and evaluated, as per literature. A CV system was trained from custom datasets, containing six classes of part. The pre-contextualization dataset and post-contextualization dataset was compared by a Two-Sample T-Test and statistical significance was noted for three classes. A python script was developed to compare as-assembled locations with as-defined positions of components, per the Manufacturing Process Plan. A comparison of yields test for CV-based True Positives (TPs) and human-based TPs was conducted with the system operating at a 2σ level. An automation method utilizing Microsoft Power Automate was developed to complete the cognitive functionality of the CV system testing, by completing a buyoff in the software, Solumina, if CV-based TPs were equal to or greater than human-based TPs.</p>