Second Order Sliding Mode Control of a STATCOM with Saturated Inputs

This paper presents a robust controller for a STATCOM device with saturated inputs. As the primary assumption, the proposed design considers the presence of unknown but bounded external perturbations and parametric variations. This proposal has a cascade structure, where a saturated super twisting control algorithm closes the currents control loop, and a high-gain proportional-integral (PI) algorithm ensures the voltage regulation. Thus, the exposed scheme provides an adequate performance of the STATCOM, considering the saturation of the inputs with the anti-windup feature. Posteriorly, a proper stability analysis presents the conditions for the appropriate operation of the closed-loop system in saturation and non-saturation regimes. Numerical simulations are also included to show the performance of the proposed controller

Fault detection and fault tolerant control in wind turbines

Renewable energy is an important sustainable energy in the world. Up to now, as an essential part of low emissions energy in a lot of countries, renewable energy has been important to the national energy security, and played a significant role in reducing carbon emissions. It comes from natural resources, such as wind, solar, rain, tides, biomass, and geothermal heat. Among them, wind energy is rapidly emerging as a low carbon, resource efficient, cost effective sustainable technology in the world. Due to the demand of higher power production installations with less environmental impacts, the continuous increase in size of wind turbines and the recently developed offshore (floating) technologies have led to new challenges in the wind turbine systems.Wind turbines (WTs) are complex systems with large flexible structures that work under very turbulent and unpredictable environmental conditions for a variable electrical grid. The maximization of wind energy conversion systems, load reduction strategies, mechanical fatigue minimization problems, costs per kilowatt hour reduction strategies, reliability matters, stability problems, and availability (sustainability) aspects demand the use of advanced (multivariable and multiobjective) cooperative control systems to regulate variables such as pitch, torque, power, rotor speed, power factors of every wind turbine, etc. Meanwhile, with increasing demands for efficiency and product quality and progressing integration of automatic control systems in high-cost and safety-critical processes, the fields of fault detection and isolation (FDI) and fault tolerant control (FTC) play an important role. This thesis covers the theoretical development and also the implementation of different FDI and FTC techniques in WTs. The purpose of wind turbine FDI systems is to detect and locate degradations and failures in the operation of WT components as early as possible, so that maintenance operations can be performed in due time (e.g., during time periods with low wind speed). Therefore, the number of costly corrective maintenance actions can be reduced and consequently the loss of wind power production due to maintenance operations is minimized. The objective of FTC is to design appropriate controllers such that the resulting closed-loop system can tolerate abnormal operations of specific control components and retain overall system stability with acceptable system performance. Different FDI and FTC contributions are presented in this thesis and published in different JCR-indexed journals and international conference proceedings. These contributions embrace a wide range of realistic WTs faults as well as different WTs types (onshore, fixed offshore, and floating). In the first main contribution, the normalized gradient method is used to estimate the pitch actuator parameters to be able to detect faults in it. In this case, an onshore WT is used for the simulations. Second contribution involves not only to detect faults but also to isolate them in the pitch actuator system. To achieve this, a discrete-time domain disturbance compensator with a controller to detect and isolate pitch actuator faults is designed. Third main contribution designs a super-twisting controller by using feedback of the fore-aft and side-to-side acceleration signals of the WT tower to provide fault tolerance capabilities to the WT and improve the overall performance of the system. In this instance, a fixed-jacket offshore WT is used. Throughout the aforementioned research, it was observed that some faults induce to saturation of the control signal leading to system instability. To preclude that problem, the fourth contribution of this thesis designs a dynamic reference trajectory based on hysteresis. Finally, the fifth and last contribution is related to floating-barge WTs and the challenges that this WTs face. The performance of the proposed contributions are tested in simulations with the aero-elastic code FAST.La energía renovable es una energía sustentable importante en el mundo. Hasta ahora, como parte esencial de la energía de bajas emisiones en muchos países, la energía renovable ha sido importante para la seguridad energética nacional, y jugó un papel importante en la reducción de las emisiones de carbono. Proviene de recursos naturales, como el viento, la energía solar, la lluvia, las mareas, la biomasa y el calor geotérmico. Entre ellos, la energía eólica está emergiendo rápidamente como una tecnología sostenible de bajo carbono, eficiente en el uso de los recursos y rentable en el mundo. Debido a la demanda de instalaciones de producción de mayor potencia con menos impactos ambientales, el aumento continuo en el tamaño de las turbinas eólicas y las tecnologías offshore (flotantes) recientemente desarrolladas han llevado a nuevos desafíos en los sistemas de turbinas eólicas. Las turbinas eólicas son sistemas complejos con grandes estructuras flexibles que funcionan en condiciones ambientales muy turbulentas e impredecibles para una red eléctrica variable. La maximización de los sistemas de conversión de energía eólica, los problemas de minimización de la fatiga mecánica, los costos por kilovatios-hora de estrategias de reducción, cuestiones de confiabilidad, problemas de estabilidad y disponibilidad (sostenibilidad) exigen el uso de sistemas avanzados de control cooperativo (multivariable y multiobjetivo) para regular variables tales como paso, par, potencia, velocidad del rotor, factores de potencia de cada aerogenerador, etc. Mientras tanto, con las crecientes demandas de eficiencia y calidad del producto y la progresiva integración de los sistemas de control automático en los procesos de alto costo y de seguridad crítica, los campos de detección y aislamiento de fallos (FDI) y control tolerante a fallos (FTC) juegan un papel importante. Esta tesis cubre el desarrollo teórico y también la implementación de diferentes técnicas de FDI y FTC en turbinas eólicas. El propósito de los sistemas FDI es detectar y ubicar las degradaciones y fallos en la operación de los componentes tan pronto como sea posible, de modo que las operaciones de mantenimiento puedan realizarse a su debido tiempo (por ejemplo, durante periodos con baja velocidad del viento). Por lo tanto, se puede reducir el número de costosas acciones de mantenimiento correctivo y, en consecuencia, se reduce al mínimo la pérdida de producción de energía eólica debido a las operaciones de mantenimiento. El objetivo de la FTC es diseñar controladores apropiados de modo que el sistema de bucle cerrado resultante pueda tolerar operaciones anormales de componentes de control específicos y retener la estabilidad general del sistema con un rendimiento aceptable del sistema. Diferentes contribuciones de FDI y FTC se presentan en esta tesis y se publican en diferentes revistas indexadas a JCR y en congresos internacionales. Estas contribuciones abarcan una amplia gama de fallos WTs realistas, así como diferentes tipos de turbinas (en tierra, en alta mar ancladas al fondo del mar y flotantes). El rendimiento de las contribuciones propuestas se prueba en simulaciones con el código aeroelástico FAST.Postprint (published version

Observation and control of PDE with disturbances

In this Thesis, the problem of controlling and Observing some classes of distributed parameter systems is addressed. The particularity of this work is to consider partial differential equations (PDE) under the effect of external unknown disturbances. We consider generalized forms of two popular parabolic and hyperbolic infinite dimensional dynamics, the heat and wave equations. Sliding-mode control is used to achieve the control goals, exploiting the robustness properties of this robust control technique against persistent disturbances and parameter uncertainties

Sliding Mode Control

The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area

Observation and control of PDE with disturbances

In this Thesis, the problem of controlling and Observing some classes of distributed parameter systems is addressed. The particularity of this work is to consider partial differential equations (PDE) under the effect of external unknown disturbances. We consider generalized forms of two popular parabolic and hyperbolic infinite dimensional dynamics, the heat and wave equations. Sliding-mode control is used to achieve the control goals, exploiting the robustness properties of this robust control technique against persistent disturbances and parameter uncertainties

New Concepts in Quantum Metrology: Dynamics, Machine Learning, and Bounds on Measurement Precision

Diese kumulative Promotionsarbeit befasst sich mit theoretischer Quantenmetrologie, der Theorie von Messung und Schätzung unter Zuhilfenahme von Quantenressourcen. Viele Vorschläge für quantenverbesserte Sensoren beruhen auf der Präparation von nichtklassischen Anfangszuständen und integrabler Dynamik. Allerdings sind solche nichtklassischen Zustände schwierig zu präparieren und gegen Dekohärenz zu schützen. Alternativ schlagen wir in dieser Promotionsarbeit sogenannte quantenchaotische Sensoren vor, die auf klassischen Anfangszuständen beruhen, die einfach zu präparieren sind, wobei Quantenverbesserungen an der Dynamik vorgenommen werden. Diese Herangehensweise hat ihren Ursprung darin, dass sowohl Quantenchaos als auch Quantenmetrologie über die Empfindlichkeit für kleine Änderungen in der Dynamik charakterisiert werden. Wir erforschen unterschiedliche Arten von Dynamik am Beispiel des Modells eines gestoßenen Quantenkreisels ("kicked top"), dessen Dynamik durch nichtlineare Kontrollpulse quantenchaotisch wird. Außerdem zeigen wir, dass Quantenchaos in der Lage ist, schädlichen Dekohärenzeffekte abzuschwächen. Insbesondere präsentieren wir einen Vorschlag für ein quantenchaotisches Cäsiumdampf-Magnetometer. Mit der Hilfe von Bestärkendem Lernen verbessern wir Zeitpunkt und Stärke der nichtlinearen Pulse im Modell des gestoßenen Quantenkreisels mit Superradianzdämpfung. Für diesen Fall finden wir, dass die Kontrollstrategie als eine dynamische Form der Spin-Quetschung verstanden werden kann. Ein anderer Teil dieser Promotionsarbeit beschäftigt sich mit bayesscher Quantenschätzung und insbesondere mit dem Problem der heuristischen Gestaltung von Experimenten. Wir trainieren neuronale Netze mit einer Kombination aus überwachtem und bestärkendem Lernen, um diese zu schnellen und starken Heuristiken für die Gestaltung von Experimenten zu machen. Die Vielseitigkeit unserer Methode zeigen wir anhand von Beispielen zu Einzel- und Mehrparameterschätzung, in denen die trainierten neuronalen Netze die Leistung der modernsten Heuristiken übertreffen. Außerdem beschäftigen wir uns mit einer lange unbewiesenen Vermutung aus dem Bereich der Quantenmetrologie: Wir liefern einen Beweis für diese Vermutung und finden einen Ausdruck für die maximale Quantenfischerinformation für beliebige gemischte Zustände und beliebige unitäre Dynamik, finden Bedingungen für optimale Zustandspräparation und optimale dynamische Kontrolle, und verwenden diese Ergebnisse, um zu beweisen, dass die Heisenberg-Schranke sogar mit thermischen Zuständen beliebiger (endlicher) Temperatur erreicht werden kann.This cumulative thesis is concerned with theoretical quantum metrology, the theory of measurement and estimation using quantum resources. Possible applications of quantum-enhanced sensors include the measurement of magnetic fields, gravitational wave detection, navigation, remote sensing, or the improvement of frequency standards. Many proposals for quantum-enhanced sensors rely on the preparation of non-classical initial states and integrable dynamics. However, such non-classical states are generally difficult to prepare and to protect against decoherence. As an alternative, in this thesis, we propose so-called quantum-chaotic sensors which make use of classical initial states that are easy to prepare while quantum enhancements are applied to the dynamics. This approach is motivated by the insight that quantum chaos and quantum metrology are both characterized by the sensitivity to small changes of the dynamics. At the example of the quantum kicked top model, where nonlinear control pulses render the dynamics quantum-chaotic, we explore different dynamical regimes for quantum sensors. Further, we demonstrate that quantum chaos is able to alleviate the detrimental effects of decoherence. In particular, we present a proposal for a quantum-chaotic cesium-vapor magnetometer. With the help of reinforcement learning, we further optimize timing and strength of the nonlinear control pulses for the kicked top model with superradiant damping. In this case, the optimized control policy is identified as a dynamical form of spin squeezing. Another part of this thesis deals with Bayesian quantum estimation and, in particular, with the problem of experiment design heuristics. We train neural networks with a combination of supervised and reinforcement learning to become fast and strong experiment design heuristics. We demonstrate the versatility of this method using examples of single and multi-parameter estimation where the trained neural networks surpass the performance of well-established heuristics. Finally, this thesis deals with a long-time outstanding conjecture in quantum metrology: we prove this conjecture and find an expression for the maximal quantum Fisher information for any mixed initial state and any unitary dynamics, provide conditions for optimal state preparation and optimal control of the dynamics, and utilize these results to prove that Heisenberg scaling can be achieved even with thermal states of arbitrary (finite) temperature