Networked Control System Design and Parameter Estimation

Networked control systems (NCSs) are a kind of distributed control systems in which the data between control components are exchanged via communication networks. Because of the attractive advantages of NCSs such as reduced system wiring, low weight, and ease of system diagnosis and maintenance, the research on NCSs has received much attention in recent years. The first part (Chapter 2 - Chapter 4) of the thesis is devoted to designing new controllers for NCSs by incorporating the network-induced delays. The thesis also conducts research on filtering of multirate systems and identification of Hammerstein systems in the second part (Chapter 5 - Chapter 6). Network-induced delays exist in both sensor-to-controller (S-C) and controller-to-actuator (C-A) links. A novel two-mode-dependent control scheme is proposed, in which the to-be-designed controller depends on both S-C and C-A delays. The resulting closed-loop system is a special jump linear system. Then, the conditions for stochastic stability are obtained in terms of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be efficiently solved by a sequential LMI optimization algorithm. Further, the control synthesis problem for the NCSs is considered. The definitions of H₂ and H∞ norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed H₂/H∞ control problem is solved under the framework of LMIs. To compensate for both S-C and C-A delays modeled by Markov chains, the generalized predictive control method is modified to choose certain predicted future control signal as the current control effort on the actuator node, whenever the control signal is delayed. Further, stability criteria in terms of LMIs are provided to check the system stability. The proposed method is also tested on an experimental hydraulic position control system. Multirate systems exist in many practical applications where different sampling rates co-exist in the same system. The l₂-l∞ filtering problem for multirate systems is considered in the thesis. By using the lifting technique, the system is first transformed to a linear time-invariant one, and then the filter design is formulated as an optimization problem which can be solved by using LMI techniques. Hammerstein model consists of a static nonlinear block followed in series by a linear dynamic system, which can find many applications in different areas. New switching sequences to handle the two-segment nonlinearities are proposed in this thesis. This leads to less parameters to be estimated and thus reduces the computational cost. Further, a stochastic gradient algorithm based on the idea of replacing the unmeasurable terms with their estimates is developed to identify the Hammerstein model with two-segment nonlinearities. Finally, several open problems are listed as the future research directions

Development of a methodology for the diagnosis of internal combustion engines using non-invasive measurements based on the use of interpretable neural networks applicable to databases with multiple annotators

Pressure is one of the essential variables that give information for engine condition and monitoring. Direct recording of this signal is complex and invasive, while the angular velocity can be measured easily. Nonetheless, the challenge is to predict the cylinder pressure using the shaft kinematics accurately. On the other hand, the increasing popularity of crowdsourcing platforms, i.e., Amazon Mechanical Turk, changes how datasets for supervised learning are built. In these cases, instead of having datasets labeled by one source (which is supposed to be an expert who provided the absolute gold standard), databases holding multiple annotators are provided. However, most state-of-the-art methods devoted to learning from multiple experts assume that the labeler's behavior is homogeneous across the input feature space. Besides, independence constraints are imposed on annotators' outputs. This document presents a Regularized Chained Deep Neural Network to deal with classification tasks from multiple annotators. In this thesis, we develop 2 strategies aiming to avoid intrusive techniques that are commonly used to diagnose Internal Combustion Engines (ICE). The first consist of a time-delay neural network (TDNN), interpreted as a finite pulse response (FIR) filter to estimate the in-cylinder pressure of a single-cylinder ICE from fluctuations in shaft angular velocity. The experiments are conducted over data obtained from an ICE operating in 12 different states by changing the angular velocity and load. The TDNN's delay is adjusted to get the highest possible correlation-based score. Our methodology can predict pressure with an R2>0.9, avoiding complicated pre-processing steps. The second technique, termed RCDNN, jointly predicts the ground truth label and the annotators' performance from input space samples. In turn, RCDNN codes interdependencies among the experts by analyzing the layers' weights and includes l1, l2, and Monte-Carlo Dropout-based regularizers to deal with the overfitting issue in deep learning models. Obtained results (using both simulated and real-world annotators) demonstrate that RCDNN can deal with multi-labelers scenarios for classification tasks, defeating state-of-the-art techniques.La presión es una de las variables esenciales que dan información para el estado del motor y su monitorización. El registro directo de esta señal es complejo e invasivo, mientras que la velocidad angular puede medirse fácilmente. No obstante, el reto consiste en predecir la presión del cilindro utilizando la cinemática del eje con precisión. Por otro lado, la creciente popularidad de las plataformas de crowdsourcing, por ejemplo, Amazon Mechanical Turk, cambia la forma de construir conjuntos de datos para el aprendizaje supervisado. En estos casos, en lugar de tener conjuntos de datos etiquetados por una sola fuente (que se supone que es un experto que proporcionó el estándar de oro absoluto), se proporcionan bases de datos con múltiples anotadores. Sin embargo, la mayoría de los métodos de vanguardia dedicados al aprendizaje a partir de múltiples expertos suponen que el comportamiento del etiquetador es homogéneo en todo el espacio de características de entrada. Además, se imponen restricciones de independencia a los resultados de los anotadores. Este documento presenta una Red Neuronal Profunda Encadenada Regularizada para abordar tareas de clasificación a partir de múltiples anotadores. En esta tesis, desarrollamos dos estrategias con el objetivo de evitar las técnicas intrusivas que se utilizan habitualmente para diagnosticar motores de combustión interna (ICE). La primera consiste en una red neuronal de retardo temporal (TDNN), interpretada como un filtro de respuesta de pulso finito (FIR) para estimar la presión en el cilindro de un ICE de un solo cilindro a partir de las fluctuaciones de la velocidad angular del eje. Los experimentos se realizan sobre datos obtenidos de un ICE que opera en 12 estados diferentes cambiando la velocidad angular y la carga. El retardo de la TDNN se ajusta para obtener la mayor puntuación posible basada en la correlación. Nuestra metodología puede predecir la presión con un R2>0,9, evitando complicados pasos de preprocesamiento.MaestríaMagíster en Ingeniería EléctricaContent 1 Introduction 10 1.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.3.1 General objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.3.2 Specific objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2 TDNN-based Engine In-cylinder Pressure Estimation from Shaft Velocity Spectral Representation 18 2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.1 Time Delay Neural Network fundamentals . . . . . . . . . . . . . . . 19 2.2.2 Harmonic prediction performance based on Magnitude-Squared Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3.1 Engine Measurements, Data Acquisition, and Preprocessing . . . . . 22 2.3.2 Pressure signal estimation . . . . . . . . . . . . . . . . . . . . . . . . 26 2.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5 Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3 Master Thesis: Content 3 Regularized Chained Deep Neural Network Classifier for Multiple Annotators 37 3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.3 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3.1 Tested datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3.2 RCDNN detailed architecture and training . . . . . . . . . . . . . . . 46 3.3.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.3.4 Introducing spammers and malicious annotators . . . . . . . . . . . . 55 3.3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4 Final Remarks 58 4.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.1.1 TDNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.1.2 RCDNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.1 TDNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.2 RCDNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.3 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Design of an integrated airframe/propulsion control system architecture

The design of an integrated airframe/propulsion control system architecture is described. The design is based on a prevalidation methodology that uses both reliability and performance. A detailed account is given for the testing associated with a subset of the architecture and concludes with general observations of applying the methodology to the architecture

Open research issues on multi-models for complex technological systems

Abstract -We are going to report here about state of the art works on multi-models for complex technological systems both from the theoretical and practical point of view. A variety of algorithmic approaches (k-mean, dss, etc.) and applicative domains (wind farms, neurological diseases, etc.) are reported to illustrate the extension of the research area

Modified Predictive Control for a Class of Electro-Hydraulic Actuator

Many model predictive control (MPC) algorithms have been proposed in the literature depending on the conditionality of the system matrix and the tuning control parameters. A modified predictive control method is proposed in this paper. The modified predictive method is based on the control matrix formulation combined with optimized move suppression coefficient. Poor dynamics and high nonlinearities are parts of the difficulties in the control of the Electro-Hydraulic Actuator (EHA) functions, which make the proposed matrix an attractive solution. The developed controller is designed based on simulation model of a position control EHA to reduce the overshoot of the system and to achieve better and smoother tracking. The performance of the designed controller achieved quick response and accurate behavior of the tracking compared to the previous study

Cure kinetics of wood phenol-formaldehyde systems

This project aims to develop kinetic models for chemical and mechanical cure development and correlate chemical and mechanical degrees of cure in order to create a comprehensive cure model that encompasses both of these tasks. With these objectives, the cure processes of two commercial phenol-formaldehyde (PF) resol resins with differing molecular weights were evaluated using differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) under isothermal and linear heating regimes. For both resins, cure was characterized in the neat state with DSC, in mixtures of PF/wood flour with DSC, and as a bondline between two wood substrates with DMA. The synergy of DSC and DMA techniques picked up the phase transitions of PF curing processes and characterization were comparable between the two techniques. During a DSC temperature scan, PF resols typically exhibited two exotherms, while in wood/PF mixtures another small exotherm appeared in a lower temperature range, indicating the impact of wood/PF interactions. In contrast, DMA offered a quantitative view of the adhesion mechanics from which the glass transition of uncured resin, gelation, and vitrification points were inferred. An analytical solution was developed to estimate the in situ shear modulus of the adhesive layer during the curing process, a change estimated from 0.01 to 16MPa. The maximum storage modulus and the ratio of maximum to minimum storage modulus were recommended for direct evaluation of wood-PF system.; Model-fitting kinetics of nth order and autocatalytic models can be reasonably applied to the DSC data, while autocatalytic, Prout-Tompkins, and Avrami-Erofeev models have been successfully applied to describe cure development in the DMA. The activation energy of PF curing processes in the neat state was around 85-100 kJ/mol and decreased to 50-70 kJ/mol in the presence of wood. However, it was the model-free kinetics of the Kissinger-Akhira-Sunnose, Friedman and Vyazovkin methods that offered insight into the cure mechanisms of commercial PF resoles and predicted the cure development under isothermal and linear heating regimes for both mechanical and chemical degrees of cure. Mechanical cure development has been correlated with chemical advancement in an empirical equation analog to the Weibull cumulative function. After either mechanical or chemical cure development is characterized, the other can be estimated through connection of the correlation equation between the mechanical and chemical degrees of cure

Desarrollo de algoritmos para el tratamiento de datos GNSS : su aplicación a los escenarios GPS modernizado y Galileo

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Matemáticas, Sección Departamental de Física de la Tierra, Astronomía y Astrofísica I (Geofísica y Meteorología) (Astronomía y Geodesia), leída el 24-07-2012Nowadays, the major GNSS systems are the american GPS and the russian GLONASS, however, in a near future the european project Galileo and the chinesse system COMPASS will become part of the current GNSS scenario. These systems will transmit for the first time three different frequencies, giving place to a multi-system and multi-frequency scenario which will dramatically push the boundaries of the positioning techniques. Currently, one of the most studied positioning techniques is known as Precise Point Positioning (PPP), which is aimed at estimating precise receiver position from undifferenced GNSS code and carrier phase observations and precise satellite products. In this thesis, some new and original algorithms for static PPP have been developed, which are able to deal with the future multi-system and multifrequency GNSS observations. The new algorithms have been named MAP3. In the new approach, the least squares theory is applied twice to estimate the ionospheric delay, initial ambiguities and smoothed pseudodistances from undifferenced observations, which in turn are used to recover the receiver position and its clock offset. MAP3 provides position estimations with an accuracy of 2.5 cm after 2 hours observation and 7 mm in 1 day, being at the same level as other PPP programs and even better results are obtained with MAP3 in short observation periods. Moreover, MAP3 have provided some of the first results in positioning from GIOVE observations and GPC products. In addition, these algorithms have been applied in the analysis of the influence of ionospheric disturbances on the point positioning, concluding that the presence of a high ROT (Rate of TEC), observed at equatorial latitudes, reflects a significant degradation of the point positioning from dual-frequency observations.Actualmente, los únicos sistemas globales de navegación por satélites operativos son GPS y GLONASS, sin embargo, en un futuro cercano el proyecto europeo Galileo y el sistema chino COMPASS entrarán a formar parte del actual escenario GNSS. Estos sistemas emplearán por primera vez, tres frecuencias distintas, dando lugar a un escenario multi-frecuencia que revolucionará las técnicas de posicionamiento. Entre las técnicas actuales de posicionamiento con GNSS destaca el Posicionamiento Preciso Puntual (PPP), que consiste en determinar la posición de un receptor a partir de observaciones de código y fase no differenciadas y productos precisos. En este trabajo de tesis se han desarrollado unos nuevos y originales algoritmos para PPP estático, llamados MAP3, capaces de procesar observaciones GNSS multifrecuencia y multi-sistema del futuro escenario GNSS y determinar la posición de un receptor de forma precisa y exacta. Los algoritmos MAP3 se dividen en dos partes en las cuales se ha aplicado la teoría mínimos cuadrados y se han obtenido expresiones explícitas para estimar el retraso ionosférico, ambigüedades de fase inicial y pseudodistancias suavizadas, que se emplean para determinar la posición del receptor y el offset de su reloj. MAP3 proporciona una estimación de la posición con una exactitud de 2.5 cm tras 2 horas de observación y de 7 mm tras 24 h, resultados que mejoran los obtenidos hasta el momento con otros programas para PPP en periodos cortos de tiempo. Además, MAP3 han proporcionado los primeros resultados en el posicionamiento con observaciones GIOVE y productos del GPC. Por otro lado, estos algoritmos se han aplicado al análisis de los efectos de ciertas perturbaciones ionosféricas en el posicionamiento concluyendo que la presencia de un ROT (Rate of TEC) elevado, observado en latitudes ecuatoriales, refleja una degradación significativa del posicionamiento puntual con observaciones doble frecuencia.Unidad Deptal. de Astronomía y GeodesiaFac. de Ciencias MatemáticasTRUEunpu

Doctor of Philosophy

dissertatio

Advanced Information Processing System (AIPS)

Advanced Information Processing System (AIPS) is a computer systems philosophy, a set of validated hardware building blocks, and a set of validated services as embodied in system software. The goal of AIPS is to provide the knowledgebase which will allow achievement of validated fault-tolerant distributed computer system architectures, suitable for a broad range of applications, having failure probability requirements of 10E-9 at 10 hours. A background and description is given followed by program accomplishments, the current focus, applications, technology transfer, FY92 accomplishments, and funding