Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201

Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market

With the availability of high frequency data and new techniques for the management of noise in signals, we revisit the question, can we predict financial asset prices? The present work proposes an algorithm for next-step log-return prediction. Data in frequencies from 1 to 15 minutes, for 25 high capitalization assets in the Mexican market were used. The model applied consists on a wavelet followed by a Long Short-Term Memory neural network (LSTM). Application of either wavelets or neural networks in finance are common, the novelty comes from the application of the particular architecture proposed. The results show that, on average, the proposed LSTM neuro-wavelet model outperforms both an ARIMA model and a benchmark dense neural network model. We conclude that, although further research (in other stock markets, at higher frequencies, etc.) is in order, given the ever increasing technical capacity of market participants, the inclusion of the LSTM neuro-wavelet model is a valuable addition to the market participant toolkit, and might pose an advantage to traditional predictive tools.Modelo de neuro-onda para predicción de precios en datos de alta frecuencia en el Mercado Bursátil MexicanoCon la disponibilidad de datos de alta frecuencia y nuevas técnicas para la filtración de señales, es pertinente preguntarse una vez más ¿podemos predecir los precios de los activos financieros? El presente trabajo propone un algoritmo para la predicción de retorno logarítmico del siguiente periodo. Se usan datos en frecuencias de 1 a 15 minutos, para 25 activos de alta capitalización en el mercado accionario mexicano. El modelo consiste en la aplicación de una wavelet seguida de una red neuronal de tipo Long Short-Term Memory (LSTM). En la literatura comúnmente se encuentra el uso de wavelets o de redes neuronales en aplicaciones financieras, la novedad de nuestro trabajo radica en la arquitectura particular que proponemos. Los resultados muestran que, en promedio, el modelo de neuro-wavelet propuesto supera tanto a un modelo ARIMA como a un modelo de red neuronal densa de referencia. Podemos concluir que, aunque más investigación es necesaria, dada la creciente capacidad técnica actual de los participantes del mercado, la inclusión del modelo LSTM neuro – wavelet al abanico de herramientas disponibles es de mucho valor, pues podría representar una ventaja sobre las herramientas predictivas tradicionales

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

Dynamic evolving neural fuzzy inference system equalization scheme in mode division multiplexing for optical fiber transmission

The performance of optical mode division multiplexing (MDM) is affected by intersymbol interference (ISI) from nonlinear channel impairments arising from higherorder mode coupling and modal dispersion in multimode fiber. However, the existing MDM equalization algorithms can only mitigate the linear distortion, but they cannot address nonlinear distortion in the signal accurately. Therefore, there is a need to explore how ISI can be mitigated to recover the transmitted signal. This research aims to control the broadening of the MDM signal and minimize the undesirable distortion among channels in MMF by signal reshaping at the receiver. A dynamic evolving neural fuzzy inference system (DENFIS) equalization scheme has been used to achieve this objective. This research was conducted through a few steps commencing with modelling the MDM system in Optsim and collecting the data. Then, the signal reshaping parameters were determined. After that, DENFIS equalization, least mean square (LMS) and recursive least squares (RLS) equalizations were implemented and evaluated. Results illustrated that nonlinear DENFIS equalization scheme can improve MDM signal at a higher accuracy than previous linear equalization schemes. DENFIS equalization demonstrates better signal reshaping accuracy with an average root mean square error (RMSE) of 0.0338 and outperformed linear LMS and RLS equalization schemes with high average RMSE values of 0.101 and 0.1914 respectively. The reduced RMSE implies that DENFIS equalization scheme mitigates ISI more effectively in a nonlinear channel. This effect can hasten data transmission rates in MDM. Moreover, the successful offline implementation of DENFIS equalization in MDM encourages future online implementation of DENFIS equalization in embedded optical systems

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