A novel robust predictive control system over imperfect networks

This paper aims to study on feedback control for a networked system with both uncertain delays, packet dropouts and disturbances. Here, a so-called robust predictive control (RPC) approach is designed as follows: 1- delays and packet dropouts are accurately detected online by a network problem detector (NPD); 2- a so-called PI-based neural network grey model (PINNGM) is developed in a general form for a capable of forecasting accurately in advance the network problems and the effects of disturbances on the system performance; 3- using the PINNGM outputs, a small adaptive buffer (SAB) is optimally generated on the remote side to deal with the large delays and/or packet dropouts and, therefore, simplify the control design; 4- based on the PINNGM and SAB, an adaptive sampling-based integral state feedback controller (ASISFC) is simply constructed to compensate the small delays and disturbances. Thus, the steady-state control performance is achieved with fast response, high adaptability and robustness. Case studies are finally provided to evaluate the effectiveness of the proposed approach

Stochastic optimal adaptive controller and communication protocol design for networked control systems

Networked Control System (NCS) is a recent topic of research wherein the feedback control loops are closed through a real-time communication network. Many design challenges surface in such systems due to network imperfections such as random delays, packet losses, quantization effects and so on. Since existing control techniques are unsuitable for such systems, in this dissertation, a suite of novel stochastic optimal adaptive design methodologies is undertaken for both linear and nonlinear NCS in presence of uncertain system dynamics and unknown network imperfections such as network-induced delays and packet losses. The design is introduced in five papers. In Paper 1, a stochastic optimal adaptive control design is developed for unknown linear NCS with uncertain system dynamics and unknown network imperfections. A value function is adjusted forward-in-time and online, and a novel update law is proposed for tuning value function estimator parameters. Additionally, by using estimated value function, optimal adaptive control law is derived based on adaptive dynamic programming technique. Subsequently, this design methodology is extended to solve stochastic optimal strategies of linear NCS zero-sum games in Paper 2. Since most systems are inherently nonlinear, a novel stochastic optimal adaptive control scheme is then developed in Paper 3 for nonlinear NCS with unknown network imperfections. On the other hand, in Paper 4, the network protocol behavior (e.g. TCP and UDP) are considered and optimal adaptive control design is revisited using output feedback for linear NCS. Finally, Paper 5 explores a co-design framework where both the controller and network scheduling protocol designs are addressed jointly so that proposed scheme can be implemented into next generation Cyber Physical Systems --Abstract, page iv

Robust aircraft trajectory optimization under meteorological uncertainty

Mención Internacional en el título de doctorThe Air Traffic Management (ATM) system in the busiest airspaces in the world is currently being overhauled to deal with multiple capacity, socioeconomic, and environmental challenges. One major pillar of this process is the shift towards a concept of operations centered on aircraft trajectories (called Trajectory-Based Operations or TBO in Europe) instead of rigid airspace structures. However, its successful implementation (and, thus, the realization of the associated improvements in ATM performance) rests on appropriate understanding and management of uncertainty. Due to its complex socio-technical structure, the design and operations of the ATM system are heavily impacted by uncertainty, proceeding from multiple sources and propagating through the interconnections between its subsystems. One major source of ATM uncertainty is weather. Due to its nonlinear and chaotic nature, a number of meteorological phenomena of interest cannot be forecasted with complete accuracy at arbitrary lead times, which leads to uncertainty or disruption in individual air and ground operations that propagates to all ATM processes. Therefore, in order to achieve the goals of SESAR and similar programs, it is necessary to deal with meteorological uncertainty at multiple scales, from the trajectory prediction and planning processes to flow and traffic management operations. This thesis addresses the problem of single-aircraft flight planning considering two important sources of meteorological uncertainty: wind prediction error and convective activity. As the actual wind field deviates from its forecast, the actual trajectory will diverge in time from the planned trajectory, generating uncertainty in arrival times, sector entry and exit times, and fuel burn. Convective activity also impacts trajectory predictability, as it leads pilots to deviate from their planned route, creating challenging situations for controllers. In this work, we aim to develop algorithms and methods for aircraft trajectory optimization that are able to integrate information about the uncertainty in these meteorological phenomena into the flight planning process at both pre-tactical (before departure) and tactical horizons (while the aircraft is airborne), in order to generate more efficient and predictable trajectories. To that end, we frame flight planning as an optimal control problem, modeling the motion of the aircraft with a point-mass model and the BADA performance model. Optimal control methods represent a flexible and general approach that has a long history of success in the aerospace field. As a numerical scheme, we use direct methods, which can deal with nonlinear systems of moderate and high-dimensional state spaces in a computationally manageable way. Nevertheless, while this framework is well-developed in the context of deterministic problems, the techniques for the solution of practical optimal control problems under uncertainty are not as mature, and the methods proposed in the literature are not applicable to the flight planning problem as it is now understood. The first contribution of this thesis addresses this challenge by introducing a framework for the solution of general nonlinear optimal control problems under parametric uncertainty. It is based on an ensemble trajectory scheme, where the trajectories of the system under multiple scenarios are considered simultaneously within the same dynamical system and the uncertain optimal control problem is turned into a large conventional optimal control problem that can be then solved by standard, well-studied direct methods in optimal control. We then employ this approach to solve the robust flight plan optimization problem at the planning horizon. In order to model uncertainty in the wind and estimating the probability of convective conditions, we employ Ensemble Prediction System (EPS) forecasts, which are composed by multiple predictions instead of a single deterministic one. The resulting method can be used to optimize flight plans for maximum expected efficiency according to the cost structure of the airline; additionally, predictability and exposure to convection can be incorporated as additional objectives. The inherent tradeoffs between these objectives can be assessed with this methodology. The second part of this thesis presents a solution for the rerouting of aircraft in uncertain convective weather scenarios at the tactical horizon. The uncertain motion of convective weather cells is represented with a stochastic model that has been developed from the output of a deterministic satellite-based nowcast product, Rapidly Developing Thunderstorms (RDT). A numerical optimal control framework, based on the pointmass model with the addition of turn dynamics, is employed for optimizing efficiency and predictability of the proposed trajectories in the presence of uncertainty about the future evolution of the storm. Finally, the optimization process is initialized by a randomized heuristic procedure that generates multiple starting points. The combined framework is able to explore and as exploit the space of solution trajectories in order to provide the pilot or the air traffic controller with a set of different suggested avoidance trajectories, as well as information about their expected cost and risk. The proposed methods are tested on example scenarios based on real data, showing how different user priorities lead to different flight plans and what tradeoffs are then present. These examples demonstrate that the solutions described in this thesis are adequate for the problems that have been formulated. In this way, the flight planning process can be enhanced to increase the efficiency and predictability of individual aircraft trajectories, which would lead to higher predictability levels of the ATM system and thus improvements in multiple performance indicators.El sistema de gestión del tráfico aéreo (Air Traffic Management, ATM) en los espacios aéreos más congestionados del mundo está siendo reformado para lidiar con múltiples desafíos socioeconómicos, medioambientales y de capacidad. Un pilar de este proceso es el gradual reemplazo de las estructuras rígidas de navegación, basadas en aerovías y waypoints, hacia las operaciones basadas en trayectorias. No obstante, la implementación exitosa de este concepto y la realización de las ganancias esperadas en rendimiento ATM requiere entender y gestionar apropiadamente la incertidumbre. Debido a su compleja estructura socio-técnica, el diseño y operaciones del sistema ATM se encuentran marcadamente influidos por la incertidumbre, que procede de múltiples fuentes y se propaga por las interacciones entre subsistemas y operadores humanos. Uno de los principales focos de incertidumbre en ATM es la meteorología. Debido a su naturaleza no-linear y caótica, muchos fenómenos de interés no pueden ser pronosticados con completa precisión en cualquier horizonte temporal, lo que crea disrupción en las operaciones en aire y tierra que se propaga a otros procesos de ATM. Por lo tanto, para lograr los objetivos de SESAR e iniciativas análogas, es imprescindible tener en cuenta la incertidumbre en múltiples escalas espaciotemporales, desde la predicción de trayectorias hasta la planificación de flujos y tráfico. Esta tesis aborda el problema de la planificación de vuelo de aeronaves individuales considerando dos fuentes importantes de incertidumbre meteorológica: el error en la predicción del viento y la actividad convectiva. Conforme la realización del viento se desvía de su previsión, la trayectoria real se desviará temporalmente de la planificada, lo que implica incertidumbre en tiempos de llegada a sectores y aeropuertos y en consumo de combustible. La actividad convectiva también tiene un impacto en la predictibilidad de las trayectorias, puesto que obliga a los pilotos a desviarse de sus planes de vuelo para evitarla, cambiado así la situación de tráfico. En este trabajo, buscamos desarrollar métodos y algoritmos para la optimización de trayectorias que puedan integrar información sobre la incertidumbre en estos fenómenos meteorológicos en el proceso de diseño de planes de vuelo en horizontes de planificación (antes del despegue) y tácticos (durante el vuelo), con el objetivo de generar trayectorias más eficientes y predecibles. Con este fin, formulamos la planificación de vuelo como un problema de control óptimo, modelando la dinámica del avión con un modelo de masa puntual y el modelo de rendimiento BADA. El control óptimo es un marco flexible y general con un largo historial de éxito en el campo de la ingeniería aeroespacial. Como método numérico, empleamos métodos directos, que son capaces de manejar sistemas dinámicos de alta dimensión con costes computacionales moderados. No obstante, si bien esta metodología es madura en contextos deterministas, la solución de problemas prácticas de control óptimo bajo incertidumbre en la literatura no está tan desarrollada, y los métodos propuestos en la literatura no son aplicables al problema de interés. La primera contribución de esta tesis hace frente a este reto mediante la introducción de un marco numérico para la resolución de problemas generales de control óptimo no-lineal bajo incertidumbre paramétrica. El núcleo de este método es un esquema de conjunto de trayectorias, en el que las trayectorias del sistema dinámico bajo múltiples escenarios son consideradas de forma simultánea, y el problema de control óptimo bajo incertidumbre es así transformado en un problema convencional que puede ser tratado mediante métodos existentes en control óptimo. A continuación, empleamos este método para resolver el problema de la planificación de vuelo robusta. La incertidumbre en el viento y la probabilidad de ocurrencia de condiciones convectivas son modeladas mediante el uso de previsiones de conjunto o ensemble, compuestas por múltiples predicciones en lugar de una única previsión determinista. Este método puede ser empleado para maximizar la eficiencia esperada de los planes de vuelo de acuerdo a la estructura de costes de la aerolínea; además, la predictibilidad de la trayectoria y la exposición a la convección pueden ser incorporadas como objetivos adicionales. El trade-off entre estos objetivos puede ser evaluado mediante la metodología propuesta. La segunda parte de la tesis presenta una solución para reconducir aviones en escenarios tormentosos en un horizonte táctico. La evolución de las células convectivas es representada con un modelo estocástico basado en las proyecciones de Rapidly Developing Thunderstorms (RDT), un sistema determinista basado en imágenes de satélite. Este modelo es empleado por un método de control óptimo numérico, basado en un modelo de masa puntual en el que se modela la dinámica de viraje, con el objetivo de maximizar la eficiencia y predictibilidad de la trayectoria en presencia de incertidumbre sobre la evolución futura de las tormentas. Finalmente, el proceso de optimizatión es inicializado por un método heurístico aleatorizado que genera múltiples puntos de inicio para las iteraciones del optimizador. Esta combinación permite explorar y explotar el espacio de trayectorias solución para proporcionar al piloto o al controlador un conjunto de trayectorias propuestas, así como información útil sobre su coste y el riesgo asociado. Los métodos propuestos son probados en escenarios de ejemplo basados en datos reales, ilustrando las diferentes opciones disponibles de acuerdo a las prioridades del planificador y demostrando que las soluciones descritas en esta tesis son adecuadas para los problemas que se han formulado. De este modo, es posible enriquecer el proceso de planificación de vuelo para incrementar la eficiencia y predictibilidad de las trayectorias individuales, lo que contribuiría a mejoras en el rendimiento del sistema ATM.These works have been financially supported by Universidad Carlos III de Madrid through a PIF scholarship; by Eurocontrol, through the HALA! Research Network grant 10-220210-C2; by the Spanish Ministry of Economy and Competitiveness (MINECO)'s R&D program, through the OptMet project (TRA2014-58413-C2-2-R); and by the European Commission's SESAR Horizon 2020 program, through the TBO-Met project (grant number 699294).Programa de Doctorado en Mecánica de Fluidos por la Universidad Carlos III de Madrid; la Universidad de Jaén; la Universidad de Zaragoza; la Universidad Nacional de Educación a Distancia; la Universidad Politécnica de Madrid y la Universidad Rovira iPresidente: Damián Rivas Rivas.- Secretario: Xavier Prats Menéndez.- Vocal: Benavar Sridha

Observer-based event-triggered and set-theoretic neuro-adaptive controls for constrained uncertain systems

In this study, several new observer-based event-triggered and set-theoretic control schemes are presented to advance the state of the art in neuro-adaptive controls. In the first part, six new event-triggered neuro-adaptive control (ETNAC) schemes are presented for uncertain linear systems. These comprehensive designs offer flexibility to choose a design depending upon system performance requirements. Stability proofs for each scheme are presented and their performance is analyzed using benchmark examples. In the second part, the scope of the ETNAC is extended to uncertain nonlinear systems. It is applied to a case of precision formation flight of the microsatellites at the Sun-Earth/Moon L1 libration point. This dynamic system is selected to evaluate the performance of the ETNAC techniques in a setting that is highly nonlinear and chaotic in nature. Moreover, factors like restricted controls, response to uncertainties and jittering makes the controller design even trickier for maintaining a tight formation precision. Lyapunov function-based stability analysis and numerical results are presented. Note that most real-world systems involve constraints due to hardware limitations, disturbances, uncertainties, nonlinearities, and cannot always be efficiently controlled by using linearized models. To address all these issues simultaneously, a barrier Lyapunov function-based control architecture called the segregated prescribed performance guaranteeing neuro-adaptive control is developed and tested for the constrained uncertain nonlinear systems, in the third part. It guarantees strict performance that can be independently prescribed for each individual state and/or error signal of the given system. Furthermore, the proposed technique can identify unknown dynamics/uncertainties online and provides a way to regulate the control input --Abstract, page iv

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive

Assessing the extent of the application of strategic thinking in Mangaung Metropolitan Municipality.

Master Master of Commerce in Leadership Studies. University of KwaZulu-Natal, Westville 2014.The meaning of the concept strategy has had many interpretations since adaption in other domains beyond its origin in the military realm. Its historical development illustrates that scientific inquiry in ‘organisational strategies’ and perspectives have been twisted to cognitivist and constructivist paradigms. As a result, two intrinsically linked concepts – strategic planning and strategic thinking have dominated the scene in the study of strategy. The purpose of this study, therefore, was to assess the extent of application of strategic thinking in the Mangaung Metropolitan Municipality. It was aimed at initiating an inquiry in the relevance of strategic thinking to local governance: its concept and its theoretical orientation in the systems approach paradigm and/or science of complexity. The practice of strategy has been defined by characteristics such as winning, provision for coherence and direction towards the realization of organizational vision are its core purposes and its formulation is predominately a managerial function. The rise and the fall of every organization depend largely on its strategic objectives. This is because strategy gives precedence to organizational vision or development as well as the deployment of its resources (human and financial) in order to survive within a particular domain. While the conventionalist approach assumes strategy as a linear, programmatic and analytical thought process, strategic thinking adopts a broader perspective articulating strategy as a thought process involving nonlinearity, creativity and divergence. Due to its reliance on the thicket of legislative prescripts (command-and-control), the Integrated Development Plan (IDP), a principal strategic planning instrument for municipalities in South Africa, resembles conventional strategic planning. The study adopted a qualitative methodology, following a deductive process as the general and established theories were considered and applied to the municipal strategy making context. Hence the study gave primacy to the key role-players in the IDP process, which was treated as equivalent to a strategy making process. The participants interviewed involved senior staff members, ward councillors and ward committee members because of their strategic positions to influence the current and future strategic decision making as well determining how to improve it. This is because of uncertainties and messy problems as defined by systems thinkers and/or complexity theorists. As a result, an holistic approach, wherein every element of a municipal system including its environmental factors (as strategic thinking advocates), was endorsed. Findings confirm conventional prescripts involving a managerialist approach or linearity remain intact in municipal governance. This is due to a demand for compliance by the many legislative prescripts, including oversight institutions. The study recommends a paradigm shift towards the incorporation of strategic thinking into municipalities in order to improve the current conventional planning practices and encourage effective participatory democracy. In this context, strategic thinking should not be embraced as rendering the IDP obsolete, but rather as complementing it. It further recommended that strategic thinking should precede strategic planning or IDP per se

Flow control for road vehicle drag reduction

This thesis covers topics that span bluff-body aerodynamics, hybrid RANS-LES CFD methods, flow control and model-order reduction. These topics arise from investigating the flow past three geometries: the bullet shaped D-body, the canonical squareback Ahmed body and the commerical Nissan NDP. The study on the D-body was aimed at transitioning the research group from the restrictive block-structured formulated StreamLES solver to the more flexible OpenFOAM code that can use unstructured meshes. Linear feedback control for base pressure increase was applied as was done in the work by Dalla Longa et al. (2017). Identification of the plant, G(s), that represents the wake's response to forcing was completed and correlated well with the results from Dalla Longa et al. (2017). The same can also be said of the sensitivity based designed feedback control law, K(s). When applied in simulation, an attenuation of the base pressure fluctuations was, as desired, achieved, although the base pressure increased by 24.5% as opposed to the 38% achieved by Dalla Longa et al. (2017). In the study on the squareback Ahmed body, wall-resolving (WRLES) and wall-modelled (WMLES) large eddy simulation were successfully applied. First, a simulation setup that is both able to resolve wake bimodality, while remaining reasonable in computational resource use, was created. Subsequently, variants of this setup were used to identify a flow feature that plays a critical role in forcing wake bimodality events. More specifically, a heavily under-resolved WMLES simulation in which both the near-wall and part of the outer-region of the turbulent boundary layer are Reynolds-averaged did not capture the front recirculation bubble near the Ahmed body nose; neither did it resolve a bimodal wake switching event. Meanwhile, the simulations with a more refined near-wall mesh did capture the front separation bubble as well as bimodal switching events of the wake. This front separation bubble sends out powerful hairpin vortices that interact with the rear wake. Specifically, these vortices go on to produce significant amounts of TKE, which, upon convection to the rear of the Ahmed body, ultimately help trigger a bimodal event. The Ahmed body study also involved the application of linear feedback control for drag reduction as was done in the D-body study. In the short term, mean blowing did lead to a base pressure increase, but as the zero-net-mass-flux (ZNMF) jet settled, it oscillated around zero making its effects indiscernible. The final geometry analyzed was the Nissan NDP. This was done by performing benchmark wall-resolving LES (WRLES). First, the benefit of appending a rear cavity to an otherwise "squareback" geometry was assessed. It was concluded that the cavity allows the wake to move more freely about the rear base. Specifically, the wake is freed from its more restricted motion that is present with the "squareback" Nissan NDP. In doing so, the drag reduction achieved with the cavity appendage is about 13.6%. Work on the Nissan NDP also involved an assessment of a moving ground in the simulation. It was concluded that, in the stationary ground simulation, flow detachment at the ground where the flow exits from the underbody has an adverse drag effect. In other words, although moving ground simulations better replicate the real-world conditions, the stationary ground variant is in this case more conservative, as it returns slightly higher drag values.Open Acces

Exploring adaptive policy management and evaluation for improved water resources management in the face of uncertainty and complexity in South Africa

Evidence-based water resources policy management is bedevilled by the challenge of uncertainty, with increased risk of policy failure and/or unintended or negative policy outcomes. Moreover, there is increased policy management complexity emerging from related systems' interdependencies particularly between the water resources policy management system with other environmental, economic, social and political systems. Such complexity imposes external interference with the performance dynamics of water resources policy management efforts. Consequently, water resources policy management strategies in furtherance of ‘water equity' as the ultimate goal of water resources management policy in South Africa, may be misplaced. As a result, the performance of water resources management policy is unlikely to follow a linear logic of change/impact. The adoption of adaptive policy management strategies to ensure policy flexibility and efficiency is warranted especially for policies managed in the face of deep uncertainty and complexity mainly driven by the interactions and interdependencies between numerous social, economic, environmental and political variables with risk for the emergence of more unpredictable policy outcomes. Successful adaptive policy management, however, must be guided through real-time credible and comprehensive evidence, which is complicated to generate in a context plagued with deep uncertainty and complexity. Using systems mapping as a systems' analysis tool, this study identified a comprehensive list of environmental, economic, social and political variables that interactively determine water resources policy management performance towards ‘water equity'. The different environmental, economic, social and political variables that interactively influence ‘Water Equity' results as identified in this study, help to determine key policy drivers and leverage points that can be monitored and evaluated in pursuit of credible and comprehensive water resources policy planning, implementing and performance evidence. The availability of credible and comprehensive evidence, however, does not imply automatic success of the adopted adaptive strategy. The study found that there are numerous other barriers on different aspects and levels of the policy that would have to be addressed to ensure the contextual success of adaptive and integrated water resources policy management in South Africa. These include, transformational changes in substantive water resources management policy design to ensure proactive intentionality to improve water resources policy management in the face of deep uncertainty; designing institutional policy governance structures that demonstrate clear appreciation of the heterogeneous water resources management needs across the country; and active commitment to fully and timely implementation policy decisions in a manner that ensures continuous learning, capitalises on policy performance opportunities, defends working policy strategies and facilitates real-time policy corrections