Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks

Traditional control and monitoring of water quality in drinking water distribution networks (WDN) rely on mostly model- or toolbox-driven approaches, where the network topology and parameters are assumed to be known. In contrast, system identification (SysID) algorithms for generic dynamic system models seek to approximate such models using only input-output data without relying on network parameters. The objective of this paper is to investigate SysID algorithms for water quality model approximation. This research problem is challenging due to (i) complex water quality and reaction dynamics and (ii) the mismatch between the requirements of SysID algorithms and the properties of water quality dynamics. In this paper, we present the first attempt to identify water quality models in WDNs using only input-output experimental data and classical SysID methods without knowing any WDN parameters. Properties of water quality models are introduced, the ensuing challenges caused by these properties when identifying water quality models are discussed, and remedial solutions are given. Through case studies, we demonstrate the applicability of SysID algorithms, show the corresponding performance in terms of accuracy and computational time, and explore the possible factors impacting water quality model identification

Subspace-based Identification of a Parallel Kinematic Manipulator Dynamics

This thesis deals with the identification of the dynamics of a Parallel Kinematic Manipulator, namely the Gantry-Tau patented by ABB located in the Robotics lav at LTH, Lund. The approach considered for modelling is subspace-based identification of linear models, where measurements from the robot motion are used to estimate the unknown parameters in the models. Rigid body dynamics and flexible body dynamics are taken into account and a description of the system in terms of a network with spring-damper pairs at the edges, representing the clusters, and masses at the nodes representing the end-effector and the carts, is proposed

Robust stabilization for discrete-time Takagi-Sugeno fuzzy system based on N4SID models

Nonlinear systems identification from experimental data without any prior knowledge of the system parameters is a challenge in control and process diagnostic. It determines mathematical model pa-rameters that are able to reproduce the dynamic behavior of a system. This paper combines two fun-damental research areas: MIMO state space system identification and nonlinear control system. This combination produces a technique that leads to robust stabilization of a nonlinear Takagi-Sugeno fuzzy system (T-S). Design/methodology/approach The first part of this paper describes the identification based on the Numerical algorithm for Subspace State Space System IDentification (N4SID). The second part, from the identified models of first part, explains how we use the interpolation of Linear Time Invariants (LTI) models to build a nonlinear multiple model system, T-S model. For demonstration purposes, conditions on stability and stabiliza-tion of discrete time, Takagi-Sugeno (T-S) model were discussed. Findings Stability analysis based on the quadratic Lyapunov function to simplify implementation was ex-plained in this paper. The LMIs (Linear Matrix Inequalities) technique obtained from the linearization of the BMIs (Bilinear Matrix Inequalities) was computed. The suggested N4SID2 algorithm had the smallest error value compared to other algorithms for all estimated system matrices. Originality The stabilization of the closed-loop discrete time T-S system, using the improved PDC control law (Parallel Distributed Compensation), was discussed to reconstruct the state from nonlinear Luen-berger observers

Control of amplifier flows using subspace identification techniques

International audienceA realistic, efficient and robust technique for the control of amplifier flows has been investigated. Since this type of fluid system is extremely sensitive to upstream environmental noise, an accurate model capturing the influence of these perturbations is needed. A subspace identification algorithm is not only a convenient and effective way of constructing this model, it is also re a l is tic in the sense that it is based on input and output data measurements only and does not require other information from the detailed dynamics of the fluid system. This data-based control design has been tested on an amplifier model derived from the Ginzburg-Landau equation, and no significant loss of efficiency has been observed when using the identified instead of the exact model. Even though system identification leads to a realistic control design, other issues such as state estimation, have to be addressed to achieve full control efficiency. In particular, placing a sensor too far downstream is detrimental, since it does not provide an estimate of incoming perturbations. This has been made clear and quantitative by considering the relative estimation error and, more appropriately, the concept of a visibility length, a measure of how far upstream a sensor is able to accurately estimate the flow state. It has been demonstrated that a strongly convective system is characterized by a correspondingly small visibility length. In fact, in the latter case the optimal sensor placement has been found upstream of the actuators, and only this configuration was found to yield an efficient control performance. This upstream sensor placement suggests the use of a feed-forward approach for fluid systems with strong convection. Furthermore, treating upstream sensors as inputs in the identification procedure results in a very efficient and robust control. When validated on the Ginzburg-Landau model this technique is effective, and it is comparable to the optimal upper bound, given by full-state control, when the amplifier behaviour becomes convection-dominated. These concepts and findings have been extended and verified for flow over a backward-facing step at a Reynolds number Re = 350. Environmental noise has been introduced by three independent, localized sources. A very satisfactory control of the Kelvin-Helmholtz instability has been obtained with a one-order-of-magnitude reduction in the averaged perturbation norm. The above observations have been further confirmed by examining a low-order model problem that mimics a convection-dominated flow but allows the explicit computation of control-relevant measures such as observability. This study casts doubts on the usefulness of the asymptotic notion of observability for convection-dominated flows, since such flows are governed by transient effects. Finally, it is shown that the feed-forward approach is equivalent to an optimal linear-quadratic-Gaussian control for spy sensors placed sufficiently far upstream or for sufficiently convective flows. The control design procedure presented in this paper, consisting of data-based subspace identification and feed-forward control, was found to be effective and robust. Its implementation in a real physical experiment may confidently be carried out

Recursive model-based virtual in-cylinder pressure sensing for internal combustion engines

Das Drucksignal im Zylinder ist ein sehr nützlicher Indikator für moderne Hochleistungs-Verbrennungsmotoren. Allerdings sind direkte Messungen des Zylinderdrucks unpraktisch, da die Bedingungen in den Zylindern von Verbrennungsmotoren ungünstig sind sowie die Installation von Zylinderdrucksensoren schwierig ist. Zahlreiche Methoden (z. B. virtuelle Messmethoden) wurden untersucht, um den Druck im Zylinder aus extern gemessenen Signalen zu rekonstruieren, z. B. aus dem Schwingungssignal des Motorblocks und der Winkelgeschwindigkeit der Kurbelwelle. Viele der vorgeschlagenen Methoden haben vielversprechende Ergebnisse erbracht. Allerdings gibt es immer noch einige Nachteile wie z.B. eine schlecht konditionierte Inversion oder die Notwendigkeit einer großen Datenmenge, um ein inverses Modell durch künstliche neuronale Netze abzuleiten. In dieser Arbeit werden unter Berücksichtigung der aktuellen Zylinderdruck-Rekonstruktionsprobleme lineare modellbasierte, nichtlineare modellbasierte und inverse modellbasierte ZylinderdruckRekonstruktionsmethoden vorgeschlagen, die eine Alternative zu den bestehenden ZylinderdruckRekonstruktionsmethoden darstellen. Alle vorgeschlagenen Methoden basieren auf der rekursiven Zustandsrekonstruktion unter Verwendung des Kalman-Filters oder eines Beobachters, so dass eine direkte Inversion vermieden werden kann. Darüber hinaus werden alle vorgeschlagenen Methoden rekursiv im Zeitbereich durchgeführt, so dass sie für Echtzeit-Implementierungen geeignet sind und auch keine Probleme im Frequenzbereich, wie z. B. Leckeffekte, aufweisen. Darüber hinaus handelt es sich bei allen vorgeschlagenen Methoden um modellbasierte Methoden, und die Modelle werden mit Hilfe von Systemidentifikationstechniken unter Ausschluss künstlicher neuronaler Netze identifiziert, so dass keine großen Datenmengen erforderlich sind. Für die Systemidentifikation und die Validierung der vorgeschlagenen Methoden wurden Datensätze eines Vierzylinder-Dieselmotors unter verschiedenen Motorbetriebsbedingungen erfasst. Die erfassten Daten reichen von der Betriebsbedingung 1200 U/min, 60 Nm bis zur Betriebsbedingung 3000 U/min, 180 Nm. Die rekonstruierten Zylinderdruckkurven und die beiden Verbrennungsmetriken Zylinderdruckspitze und Spitzenort wurden zur Validierung der vorgeschlagenen Zylinderdruckrekonstruktionsmethoden verwendet. Die Ergebnisse der Rekonstruktion des Zylinderdrucks, die mit den in dieser Arbeit vorgeschlagenen Methoden erzielt wurden, zeigen, dass alle vorgeschlagenen Methoden sowohl unter stationären als auch unter nicht-stationären Betriebsbedingungen verwendet werden können und dass die Ergebnisse der Rekonstruktion des Zylinderdrucks mit den Ergebnissen der bestehenden Methoden zur Rekonstruktion des Zylinderdrucks vergleichbar sind. Darüber hinaus kann festgestellt werden, dass es mehrere Faktoren gibt, die die Genauigkeit der Druckrekonstruktion beeinflussen, wie z.B. die Qualität der identifizierten Modelle, des Verzögerungsblocks und der momentanen Motordrehzahl.The in-cylinder pressure signal is a very useful indicator for modern high-performance internal combustion engines. Unfortunately, direct measurements of the in-cylinder pressure are impractical because installing cylinder pressure sensors is difficult and conditions in internal combustion engine cylinders are adverse. Numerous methods (such as virtual sensing methods) have been investigated to reconstruct the incylinder pressure from externally measured signals, such as the engine block structural vibration signal and the engine crank angular speed. Many of the proposed methodologies have shown promising results. However, there still exist some drawbacks, such as ill-conditioned inversion and the need of large number of data to derive an inverse model by artificial neural networks. In this thesis, considering current in-cylinder pressure reconstruction problems, linear model-based, nonlinear model-based, and inverse model-based in-cylinder pressure reconstruction methods, which are alternative to existing cylinder pressure reconstruction methods, are proposed. All the proposed methods are based on the recursive state reconstruction by using the Kalman filter or observer such that a direct inversion can be avoided. Moreover, all the proposed methods are recursively conducted in time domain, so they are suitable for real-time implementations and they also do not have frequency-domain problems such as spectral leakage. Additionally, all the proposed methods are model-based methods, and the models are identified by using system identification techniques excluding artificial neural networks, so the need of a large number of data is not necessary. For system identification and the validation of the proposed methods, the datasets under different engine operating conditions were acquired from a four-cylinder diesel engine. Data acquired is from the operating condition 1200 rpm, 60 Nm to the operating condition 3000 rpm, 180 Nm. The reconstructed cylinder pressure curves and two combustion metrics cylinder pressure peak and peak location were used for validating the proposed cylinder pressure reconstruction methods. According to the cylinder pressure reconstruction results obtained based on using the proposed methods in this thesis, it can be found that all the proposed methods can be used under both stationary and non-stationary operating conditions, and the reconstructed cylinder pressure results are comparable among existing cylinder pressure reconstruction methods. Furthermore, it can also be found that there exist several factors affecting the pressure reconstruction accuracy, such as the quality of the identified models, delay block and instantaneous engine cycle frequency

Modeling and Prediction in Diabetes Physiology

Diabetes is a group of metabolic diseases characterized by the inability of the organism to autonomously regulate the blood glucose levels. It requires continuing medical care to prevent acute complications and to reduce the risk of long-term complications. Inadequate glucose control is associated with damage, dysfunction and failure of various organs. The management of the disease is non trivial and demanding. With today’s standards of current diabetes care, good glucose regulation needs constant attention and decision-making by the individuals with diabetes. Empowering the patients with a decision support system would, therefore, improve their quality of life without additional burdens nor replacing human expertise. This thesis investigates the use of data-driven techniques to the purpose of glucose metabolism modeling and short-term blood-glucose predictions in Type I Diabetes Mellitus (T1DM). The goal was to use models and predictors in an advisory tool able to produce personalized short-term blood glucose predictions and on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise, to help diabetic subjects maintaining glycemia as close to normal as possible. The approaches taken to describe the glucose metabolism were discrete-time and continuous-time models on input-output form and statespace form, while the blood glucose short-term predictors, i.e., up to 120 minutes ahead, used ARX-, ARMAX- and subspace-based prediction