NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun

Implementation of gaussian process models for non-linear system identification

This thesis is concerned with investigating the use of Gaussian Process (GP) models for the identification of nonlinear dynamic systems. The Gaussian Process model is a non-parametric approach to system identification where the model of the underlying system is to be identified through the application of Bayesian analysis to empirical data. The GP modelling approach has been proposed as an alternative to more conventional methods of system identification due to a number of attractive features. In particular, the Bayesian probabilistic framework employed by the GP model has been shown to have potential in tackling the problems found in the optimisation of complex nonlinear models such as those based on multiple model or neural network structures. Furthermore, due to this probabilistic framework, the predictions made by the GP model are probability distributions composed of mean and variance components. This is in contrast to more conventional methods where a predictive point estimate is typically the output of the model. This additional variance component of the model output has been shown to be of potential use in model-predictive or adaptive control implementations. A further property that is of potential interest to those working on system identification problems is that the GP model has been shown to be particularly effective in identifying models from sparse datasets. Therefore, the GP model has been proposed for the identification of models in off-equilibrium regions of operating space, where more established methods might struggle due to a lack of data. The majority of the existing research into modelling with GPs has concentrated on detailing the mathematical methodology and theoretical possibilities of the approach. Furthermore, much of this research has focused on the application of the method toward statistics and machine learning problems. This thesis investigates the use of the GP model for identifying nonlinear dynamic systems from an engineering perspective. In particular, it is the implementation aspects of the GP model that are the main focus of this work. Due to its non-parametric nature, the GP model may also be considered a ‘black-box’ method as the identification process relies almost exclusively on empirical data, and not on prior knowledge of the system. As a result, the methods used to collect and process this data are of great importance, and the experimental design and data pre-processing aspects of the system identification procedure are investigated in detail. Therefore, in the research presented here the inclusion of prior system knowledge into the overall modelling procedure is shown to be an invaluable asset in improving the overall performance of the GP model. In previous research, the computational implementation of the GP modelling approach has been shown to become problematic for applications where the size of training dataset is large (i.e. one thousand or more points). This is due to the requirement in the GP modelling approach for repeated inversion of a covariance matrix whose size is dictated by the number of points included in the training dataset. Therefore, in order to maintain the computational viability of the approach, a number of different strategies have been proposed to lessen the computational burden. Many of these methods seek to make the covariance matrix sparse through the selection of a subset of existing training data. However, instead of operating on an existing training dataset, in this thesis an alternative approach is proposed where the training dataset is specifically designed to be as small as possible whilst still containing as much information. In order to achieve this goal of improving the ‘efficiency’ of the training dataset, the basis of the experimental design involves adopting a more deterministic approach to exciting the system, rather than the more common random excitation approach used for the identification of black-box models. This strategy is made possible through the active use of prior knowledge of the system. The implementation of the GP modelling approach has been demonstrated on a range of simulated and real-world examples. The simulated examples investigated include both static and dynamic systems. The GP model is then applied to two laboratory-scale nonlinear systems: a Coupled Tanks system where the volume of liquid in the second tank must be predicted, and a Heat Transfer system where the temperature of the airflow along a tube must be predicted. Further extensions to the GP model are also investigated including the propagation of uncertainty from one prediction to the next, the application of sparse matrix methods, and also the use of derivative observations. A feature of the application of GP modelling approach to nonlinear system identification problems is the reliance on the squared exponential covariance function. In this thesis the benefits and limitations of this particular covariance function are made clear, and the use of alternative covariance functions and ‘mixed-model’ implementations is also discussed

Integrated design of chemical waste water treatment systems.

Imperial Users onl

Health-aware predictive control schemes based on industrial processes

Aplicat embargament des de la data de defensa fins el dia 30 de desembre de 2021The research is motivated by real applications, such as pasteurization plant, water networks and autonomous system, which each of them require a specific control system to provide proper management able to take into account their particular features and operating limits in presence of uncertainties related to their operation and failures from component breakdowns. According to that most of the real systems have nonlinear behaviors, it can be approximated them by polytopic linear uncertain models such as Linear Parameter Varying (LPV) and Takagi-Sugeno (TS) models. Therefore, a new economic Model Predictive Control (MPC) approach based on LPV/TS models is proposed and the stability of the proposed approach is certified by using a region constraint on the terminal state. Besides, the MPC-LPV strategy is extended based on the system with varying delays affecting states and inputs. The control approach allows the controller to accommodate the scheduling parameters and delay change. By computing the prediction of the state variables and delay along a prediction time horizon, the system model can be modified according to the evaluation of the estimated state and delay at each time instant. To increase the system reliability, anticipate the appearance of faults and reduce the operational costs, actuator health monitoring should be considered. Regarding several types of system failures, different strategies are studied for obtaining system failures. First, the damage is assessed with the rainflow-counting algorithm that allows estimating the component’s fatigue and control objective is modified by adding an extra criterion that takes into account the accumulated damage. Besides, two different health-aware economic predictive control strategies that aim to minimize the damage of components are presented. Then, economic health-aware MPC controller is developed to compute the components and system reliability in the MPC model using an LPV modeling approach and maximizes the availability of the system by estimating system reliability. Additionally, another improvement considers chance-constraint programming to compute an optimal list replenishment policy based on a desired risk acceptability level, managing to dynamically designate safety stocks in flowbased networks to satisfy non-stationary flow demands. Finally, an innovative health-aware control approach for autonomous racing vehicles to simultaneously control it to the driving limits and to follow the desired path based on maximization of the battery RUL. The proposed approach is formulated as an optimal on-line robust LMI based MPC driven from Lyapunov stability and controller gain synthesis solved by LPV-LQR problem in LMI formulation with integral action for tracking the trajectory.Esta tesis pretende proporcionar contribuciones teóricas y prácticas sobre seguridad y control de sistemas industriales, especialmente en la forma maten ática de sistemas inciertos. La investigación está motivada por aplicaciones reales, como la planta de pasteurización, las redes de agua y el sistema autónomo, cada uno de los cuales requiere un sistema de control específico para proporcionar una gestión adecuada capaz de tener en cuenta sus características particulares y limites o de operación en presencia de incertidumbres relacionadas con su operación y fallas de averías de componentes. De acuerdo con que la mayoría de los sistemas reales tienen comportamientos no lineales, puede aproximarse a ellos mediante modelos inciertos lineales politopicos como los modelos de Lineal Variación de Parámetros (LPV) y Takagi-Sugeno (TS). Por lo tanto, se propone un nuevo enfoque de Control Predictivo del Modelo (MPC) económico basado en modelos LPV/TS y la estabilidad del enfoque propuesto se certifica mediante el uso de una restricción de región en el estado terminal. Además, la estrategia MPC-LPV se extiende en función del sistema con diferentes demoras que afectan los estados y las entradas. El enfoque de control permite al controlador acomodar los parámetros de programación y retrasar el cambio. Al calcular la predicción de las variables de estado y el retraso a lo largo de un horizonte de tiempo de predicción, el modelo del sistema se puede modificar de acuerdo con la evaluación del estado estimado y el retraso en cada instante de tiempo. Para aumentar la confiabilidad del sistema, anticipar la aparición de fallas y reducir los costos operativos, se debe considerar el monitoreo del estado del actuador. Con respecto a varios tipos de fallas del sistema, se estudian diferentes estrategias para obtener fallas del sistema. Primero, el daño se evalúa con el algoritmo de conteo de flujo de lluvia que permite estimar la fatiga del componente y el objetivo de control se modifica agregando un criterio adicional que tiene en cuenta el daño acumulado. Además, se presentan dos estrategias diferentes de control predictivo económico que tienen en cuenta la salud y tienen como objetivo minimizar el daño de los componentes. Luego, se desarrolla un controlador MPC económico con conciencia de salud para calcular los componentes y la confiabilidad del sistema en el modelo MPC utilizando un enfoque de modelado LPV y maximiza la disponibilidad del sistema mediante la estimación de la confiabilidad del sistema. Además, otra mejora considera la programación de restricción de posibilidades para calcular una política ´optima de reposición de listas basada en un nivel de aceptabilidad de riesgo deseado, logrando designar dinámicamente existencias de seguridad en redes basadas en flujo para satisfacer demandas de flujo no estacionarias. Finalmente, un enfoque innovador de control consciente de la salud para vehículos de carreras autónomos para controlarlo simultáneamente hasta los límites de conducción y seguir el camino deseado basado en la maximización de la bacteria RUL. El diseño del control se divide en dos capas con diferentes escalas de tiempo, planificador de ruta y controlador. El enfoque propuesto está formulado como un MPC robusto en línea optimo basado en LMI impulsado por la estabilidad de Lyapunov y la síntesis de ganancia del controlador resuelta por el problema LPV-LQR en la formulación de LMI con acción integral para el seguimiento de la trayectoria.Postprint (published version

Marshall Space Flight Center Research and Technology Report 2019

Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Centerwe make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This years report features 10 of the Agencys 16 Technology Areas, and I am proud of Marshalls role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Centers capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come

Kinetic Intersections as a Source of Information on Rate Coefficients

C

Experimental data-driven reduced-order modeling of nonlinear vertical sloshing for aeroelastic analyses

This thesis focuses specifically on the study of nonlinear sloshing effects caused by large tank motions in a direction perpendicular to the free liquid surface with emphasis on aeronautical applications. Sloshing is a phenomenon that typically occurs in aircraft tanks as they are subjected to loads caused by gusts, turbulence and landing impacts. This type of sloshing leads to a noticeable increase in overall structural damping, yet it is generally not modeled in the design phase of modern aircraft. The identification and study of such dissipative effects may enable the development of less conservative aircraft configurations in the future, allowing for increasingly lighter structures and reduced environmental impact. The present thesis proposes a combined experimental and numerical approach aimed at obtaining reduced-order models for vertical sloshing, to be subsequently integrated into aeroelastic modeling and applications for the assessment of their effects on overall performance. An experimental campaign is first carried out to characterise the nonlinear dissipative behaviour of vertical sloshing for different filling levels. Specifically, a controlled electrodynamic shaker is employed to provide vertical displacement by means of sine-sweep excitation. By exploiting vertical harmonic motion, it is shown how the frequency and amplitude of the imposed excitation significantly influence the dissipative capabilities of the sloshing liquid. The same experiment is used to create a database - with an acquisition phase that considers vertical sloshing as an isolated system - to build a neural-network-based reduced-order model. The dynamics to be modeled is considered as a black box process, leading to the identification of a surrogate model driven only by input/output signals, regardless the knowledge of the internal dynamics. In order to assess the capability of the identified reduced order model for sloshing, the same tank used to generate the training data is mounted at the free end of a cantilever beam to create a new experimental setup in which a fluid-structure interaction scenario is expected. Indeed, this experiment provides experimental data for the validation of the identified dynamic model by comparison with numerical data. The comparison is carried out using a dynamic virtual simulation model corresponding to the experiment, in which the numerical model of the beam interacts with the reduced-order model simulating the sloshing dynamics. Finally, the experimentally validated reduced-order model is used in two different aeroelastic applications - wing prototype and flying wing model - to finally predict the dissipative effects induced by vertical sloshing on the aeroelastic response. Aeroelastic response analyses under pre- and post-critical conditions showed how the vertical sloshing dynamics helps to alleviate the dynamic loads due to severe gusts while providing limit cycle oscillation beyond the flutter margin

Spark Ignition Energy Measurements in Jet A

Experiments have been carried out to measure the spark ignition energy of Jet A vapor in air. A range of ignition energies from 1 mJ to 100 J was examined in these tests. The test method was validated by first measuring ignition energies for lean mixtures of the fuels hexane (C6H6) and propane (C3H8) in air at normal temperature (295 K) and pressure (1 atm). These results agree with existing data and provide new results for compositions between the lean flame limit and stoichiometric mixtures. Jet A (from LAX, flashpoint 45–48 [degress] C) vapor mixtures with air have been tested at temperatures between 30 and 60 [degrees] C at two fuel mass loadings, 3 and 200 kg/m3, in an explosion test vessel with a volume of 1.8 liter. Tests at 40, 50, and 60 [degrees] C have been performed at a mass loading of 3 kg/m3 in an 1180-liter vessel. Experiments with Jet A have been carried out with initial conditions of 0.585 bar pressure to simulate altitude conditions appropriate to the TWA 800 explosion. Ignition energies and peak pressures vary strongly as a function of initial temperature, but are a weak function of mass loading. The minimum ignition energy varies from less than 1 mJ at 60 [degrees] C to over 100 J at 30 [degrees] C. At temperatures less than 30 [degrees] C, ignition was not possible with 100 J or even a neon sign transformer (continuous discharge). The peak pressure between 40 and 55 [degrees] C was approximately 4 bar. Peak pressures in the 1180-liter vessel were slightly lower and the ignition energy was higher than in the 1.8-liter vessel. The following conclusions were reached relative to the TWA 800 crash: (a) spark ignition sources with energies between 5 mJ and 1 J are sufficient to ignite Jet A vapor, resulting in a propagating flame; (b) the peak pressure rise was between 1.5 and 4 bar (20 and 60 psi). (c) a thermal ignition source consisting of a hot filament created by discharging electrical energy into a metal wire is also sufficient to ignite Jet A vapor, resulting in a propagating flame; (d) laminar burning speeds are between 15 and 45 cm/s; and (e) the limited amount of fuel available in the CWT (about 50 gal) did not significantly increase the flammability limit. The rapid decrease in spark ignition energy with increasing temperature demonstrates that hot fuel tanks are significantly more hazardous than cool ones with respect to spark ignition sources. A systematic effort is now needed in order to utilize these results and apply spark ignition energy measurements to future analyses of fuel tank flammability. Some key issues that need to be addressed in future testing are: (a) effect of flashpoint on the ignition energy-temperature relationship; (b) ignition energy vs. temperature as a function of altitude; (c) effect of fuel weathering on ignition energy; and (d) the effect of ignition source type on ignition limits