Neuroflight: Next Generation Flight Control Firmware

Little innovation has been made to low-level attitude flight control used by uncrewed aerial vehicles (UAVs), which still predominantly uses the classical PID controller. In this work we introduce Neuroflight, the first open source neuro-flight controller firmware. We present our toolchain for training a neural network in simulation and compiling it to run on embedded hardware. Challenges faced jumping from simulation to reality are discussed along with our solutions. Our evaluation shows the neural network can execute at over 2.67kHz on an Arm Cortex-M7 processor and flight tests demonstrate a quadcopter running Neuroflight can achieve stable flight and execute aerobatic maneuvers

Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications

Flight controller synthesis via deep reinforcement learning

Traditional control methods are inadequate in many deployment settings involving autonomous control of Cyber-Physical Systems (CPS). In such settings, CPS controllers must operate and respond to unpredictable interactions, conditions, or failure modes. Dealing with such unpredictability requires the use of executive and cognitive control functions that allow for planning and reasoning. Motivated by the sport of drone racing, this dissertation addresses these concerns for state-of-the-art flight control by investigating the use of deep artificial neural networks to bring essential elements of higher-level cognition to bear on the design, implementation, deployment, and evaluation of low level (attitude) flight controllers. First, this thesis presents a feasibility analyses and results which confirm that neural networks, trained via reinforcement learning, are more accurate than traditional control methods used by commercial uncrewed aerial vehicles (UAVs) for attitude control. Second, armed with these results, this thesis reports on the development and release of an open source, full solution stack for building neuro-flight controllers. This stack consists of a tuning framework for implementing training environments (GymFC) and firmware for the world’s first neural network supported flight controller (Neuroflight). GymFC’s novel approach fuses together the digital twinning paradigm with flight control training to provide seamless transfer to hardware. Third, to transfer models synthesized by GymFC to hardware, this thesis reports on the toolchain that has been released for compiling neural networks into Neuroflight, which can be flashed to off-the-shelf microcontrollers. This toolchain includes detailed procedures for constructing a multicopter digital twin to allow the research and development community to synthesize flight controllers unique to their own aircraft. Finally, this thesis examines alternative reward system functions as well as changes to the software environment to bridge the gap between simulation and real world deployment environments. The design, evaluation, and experimental work summarized in this thesis demonstrates that deep reinforcement learning is able to be leveraged for the design and implementation of neural network controllers capable not only of maintaining stable flight, but also precision aerobatic maneuvers in real world settings. As such, this work provides a foundation for developing the next generation of flight control systems

MPC: Relevant Identification and Control in the Latent Variable Space

Control predictivo basado en modelos (MPC) es una metodología de control ampliamente utilizada en la industria por su habilidad para controlar procesos multivariable con restricciones en sus entradas y sus salidas. Se distinguen dos fases en la implementación de MPC: identificación y control. El propósito de esta tesis es doble: realizar contribuciones en la identificación para MPC y proponer una nueva metodología de control MPC. La respuesta en bucle cerrado de una implementación de MPC depende, en gran medida, de la capacidad de predicción del modelo; luego la identificación del modelo es un punto crucial en MPC y la parte que a menudo exige la mayor parte del tiempo del proyecto. El primer objetivo que cubre la tesis es la identificación para MPC. Puesto que un modelo es una aproximación del comportamiento de un proceso, dicha aproximación se puede hacer teniendo en cuenta el fin que se le va a dar al modelo. En MPC, el modelo se utiliza para realizar predicciones dentro de una ventana futura, luego la identificación para MPC (MRI) tiene en cuenta dicho uso del modelo y considera los errores de predicción dentro de dicha ventana para el ajuste de los parámetros del modelo. En esta tesis, se cubren tres temas dentro de MRI. Primero se define MRI y las distintas formas de abordarlo. Luego se compara en términos de MRI el ajuste de un modelo con múltiples entradas y múltiples salidas con el ajuste de varios modelos con múltiples entradas y una salida concluyendo que el ajuste de un único modelo con múltiples entradas y múltiples salidas proporciona mejores resultados en términos de MRI para horizontes de predicción lo suficientemente grandes. Por último, se propone el algoritmo PLS-PH para implementar MRI con modelos paramétricos en el caso de correlación en los datos de identificación. PLS-PH es un método de optimización numérica por búsqueda lineal basado en PLS (mínimos cuadrados parciales). Se muestra en un ejemplo como PLS-PH es capaz de proporcionar mejores modelos que las técnicas convencionales de MRI en modelos paramétricos en el caso de correlación en los datos de identi ficación. Una vez obtenido el modelo se puede formular el controlador predictivo. En esta tesis se propone LV-MPC, un controlador predictivo para procesos continuos que implementa la optimización en el espacio de las componentes principales.Laurí Pla, D. (2012). MPC: Relevant Identification and Control in the Latent Variable Space [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15178Palanci

FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing

Integrating unmanned aerial vehicles (UAVs) into vehicular networks have shown high potentials in affording intensive computing tasks. In this paper, we study the digital twin driven vehicular edge computing networks for adaptively computing resource management where an unmanned aerial vehicle (UAV) named FlexEdge acts as a flying server. In particular, we first formulate an energy consumption minimization problem by jointly optimizing UAV trajectory and computation resource under the practical constraints. To address such a challenging problem, we then build the computation offloading process as a Markov decision process and propose a deep reinforcement learning-based proximal policy optimization algorithm to dynamically learn the computation offloading strategy and trajectory design policy. Numerical results indicate that our proposed algorithm can achieve quick convergence rate and significantly reduce the system energy consumption.Comment: 6 pages, 6 figure

On power system automation: a Digital Twin-centric framework for the next generation of energy management systems

The ubiquitous digital transformation also influences power system operation. Emerging real-time applications in information (IT) and operational technology (OT) provide new opportunities to address the increasingly demanding power system operation imposed by the progressing energy transition. This IT/OT convergence is epitomised by the novel Digital Twin (DT) concept. By integrating sensor data into analytical models and aligning the model states with the observed system, a power system DT can be created. As a result, a validated high-fidelity model is derived, which can be applied within the next generation of energy management systems (EMS) to support power system operation. By providing a consistent and maintainable data model, the modular DT-centric EMS proposed in this work addresses several key requirements of modern EMS architectures. It increases the situation awareness in the control room, enables the implementation of model maintenance routines, and facilitates automation approaches, while raising the confidence into operational decisions deduced from the validated model. This gain in trust contributes to the digital transformation and enables a higher degree of power system automation. By considering operational planning and power system operation processes, a direct link to practice is ensured. The feasibility of the concept is examined by numerical case studies.The electrical power system is in the process of an extensive transformation. Driven by the energy transition towards renewable energy resources, many conventional power plants in Germany have already been decommissioned or will be decommissioned within the next decade. Among other things, these changes lead to an increased utilisation of power transmission equipment, and an increasing number of complex dynamic phenomena. The resulting system operation closer to physical boundaries leads to an increased susceptibility to disturbances, and to a reduced time span to react to critical contingencies and perturbations. In consequence, the task to operate the power system will become increasingly demanding. As some reactions to disturbances may be required within timeframes that exceed human capabilities, these developments are intrinsic drivers to enable a higher degree of automation in power system operation. This thesis proposes a framework to create a modular Digital Twin-centric energy management system. It enables the provision of validated and trustworthy models built from knowledge about the power system derived from physical laws, and process data. As the interaction of information and operational technologies is combined in the concept of the Digital Twin, it can serve as a framework for future energy management systems including novel applications for power system monitoring and control, which consider power system dynamics. To provide a validated high-fidelity dynamic power system model, time-synchronised phasor measurements of high-resolution are applied for validation and parameter estimation. This increases the trust into the underlying power system model as well as the confidence into operational decisions derived from advanced analytic applications such as online dynamic security assessment. By providing an appropriate, consistent, and maintainable data model, the framework addresses several key requirements of modern energy management system architectures, while enabling the implementation of advanced automation routines and control approaches. Future energy management systems can provide an increased observability based on the proposed architecture, whereby the situational awareness of human operators in the control room can be improved. In further development stages, cognitive systems can be applied that are able to learn from the data provided, e.g., machine learning based analytical functions. Thus, the framework enables a higher degree of power system automation, as well as the deployment of assistance and decision support functions for power system operation pointing towards a higher degree of automation in power system operation. The framework represents a contribution to the digital transformation of power system operation and facilitates a successful energy transition. The feasibility of the concept is examined by case studies in form of numerical simulations to provide a proof of concept.Das elektrische Energiesystem befindet sich in einem umfangreichen Transformations-prozess. Durch die voranschreitende Energiewende und den zunehmenden Einsatz erneuerbarer Energieträger sind in Deutschland viele konventionelle Kraftwerke bereits stillgelegt worden oder werden in den nächsten Jahren stillgelegt. Diese Veränderungen führen unter anderem zu einer erhöhten Betriebsmittelauslastung sowie zu einer verringerten Systemträgheit und somit zu einer zunehmenden Anzahl komplexer dynamischer Phänomene im elektrischen Energiesystem. Der Betrieb des Systems näher an den physikalischen Grenzen führt des Weiteren zu einer erhöhten Störanfälligkeit und zu einer verkürzten Zeitspanne, um auf kritische Ereignisse und Störungen zu reagieren. Infolgedessen wird die Aufgabe, das Stromnetz zu betreiben anspruchsvoller. Insbesondere dort wo Reaktionszeiten erforderlich sind, welche die menschlichen Fähigkeiten übersteigen sind die zuvor genannten Veränderungen intrinsische Treiber hin zu einem höheren Automatisierungsgrad in der Netzbetriebs- und Systemführung. Aufkommende Echtzeitanwendungen in den Informations- und Betriebstechnologien und eine zunehmende Menge an hochauflösenden Sensordaten ermöglichen neue Ansätze für den Entwurf und den Betrieb von cyber-physikalischen Systemen. Ein vielversprechender Ansatz, der in jüngster Zeit in diesem Zusammenhang diskutiert wurde, ist das Konzept des so genannten Digitalen Zwillings. Da das Zusammenspiel von Informations- und Betriebstechnologien im Konzept des Digitalen Zwillings vereint wird, kann es als Grundlage für eine zukünftige Leitsystemarchitektur und neuartige Anwendungen der Leittechnik herangezogen werden. In der vorliegenden Arbeit wird ein Framework entwickelt, welches einen Digitalen Zwilling in einer neuartigen modularen Leitsystemarchitektur für die Aufgabe der Überwachung und Steuerung zukünftiger Energiesysteme zweckdienlich einsetzbar macht. In Ergänzung zu den bereits vorhandenen Funktionen moderner Netzführungssysteme unterstützt das Konzept die Abbildung der Netzdynamik auf Basis eines dynamischen Netzmodells. Um eine realitätsgetreue Abbildung der Netzdynamik zu ermöglichen, werden zeitsynchrone Raumzeigermessungen für die Modellvalidierung und Modellparameterschätzung herangezogen. Dies erhöht die Aussagekraft von Sicherheitsanalysen, sowie das Vertrauen in die Modelle mit denen operative Entscheidungen generiert werden. Durch die Bereitstellung eines validierten, konsistenten und wartbaren Datenmodells auf der Grundlage von physikalischen Gesetzmäßigkeiten und während des Betriebs gewonnener Prozessdaten, adressiert der vorgestellte Architekturentwurf mehrere Schlüsselan-forderungen an moderne Netzleitsysteme. So ermöglicht das Framework einen höheren Automatisierungsgrad des Stromnetzbetriebs sowie den Einsatz von Entscheidungs-unterstützungsfunktionen bis hin zu vertrauenswürdigen Assistenzsystemen auf Basis kognitiver Systeme. Diese Funktionen können die Betriebssicherheit erhöhen und stellen einen wichtigen Beitrag zur Umsetzung der digitalen Transformation des Stromnetzbetriebs, sowie zur erfolgreichen Umsetzung der Energiewende dar. Das vorgestellte Konzept wird auf der Grundlage numerischer Simulationen untersucht, wobei die grundsätzliche Machbarkeit anhand von Fallstudien nachgewiesen wird

Aeronautical engineering: A continuing bibliography with indexes (supplement 211)

A continuing bibliography (NASA SP-7037) lists 519 reports, journal articles and other documents originally announced in February 1987 in Scientific and Technical Aerospace Reports (STAR) or in the International Aerospace Abstracts (IAA). The coverage includes documents on the engineering and theoretical aspect of design, construction, evaluation, testing, operation, and performance of aircraft (including aircraft engines) and associated components, equipment, and systems. It also includes research and development in aerodynamics, aeronautics, and ground support equipment for aeronautical vehicles. Each entry in the bibliography consists of a standard bibliographic citation accompanied in most cases by an abstract. The listing of the entries is arranged by the first nine STAR specific categories and the remaining STAR major categories. The arrangement offers the user the most advantageous breakdown for individual objectives. The citations include the original accession numbers from the respective announcement journals. The IAA items will precede the STAR items within each category. Seven indexes entitled subject, personal author, corporate source, foreign technology, contract number, report number, and accession number are included