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Tobias Wrigstad

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Abstract

Welcome!

Discussion Leader: Dr. Martin Enqvist, Division of Automatic Control, Department of Electrical Engineering, Linköping University

The discussion will be held in English.

Abstract
System identification finds nowadays application in various areas of biomedical research as a tool of empiric mathematical modeling and model individualization. Hormone regulation is a classical example of biological feedback where control theories, in general, and system identification, in particular, are indispensable in unraveling the regulation mechanisms and explicating the complex dynamical phenomena arising in endocrine systems.

The main function of endocrine feedback regulation is to maintain the hormone levels within a particular physiological range as well as to sustain an appropriate hormone secretion pattern. Therefore, a natural operating mode of a closed-loop endocrine system is a stable periodic cycle. This property significantly reduces the repertoire of readily available identification techniques, not least due to the fact that the regulation (input) signal is immeasurable in many practical cases.

There are two approaches to blind identification of hormone dynamics presented in this thesis. The first one is based on constrained nonlinear least-squares method. Weighting functions play an important role in satisfying the biological conditions on the identified model. The second approach is derived from a novel time-delay system identification method in Laguerre domain. In the latter, the time delay appears due to a specific input signal model and is estimated along with the finite-dimensional dynamics of hormone kinetics.

Presentation av lokalalternativen att institutionen sitter kvar på Polacksbacken (efter ombyggnad) eller flyttar till nybyggnation på Ångström.

Medverkar gör Ulf Danielsson och Annika Sundås-Larsson.

In English:

Meeting on Tuesday June 5 at 15-16 in 2247 about the alternative for the department to remain in these buildnings (including redevelopment) or move to new buildnings at Ångström.

Speakers are Ulf Danielsson and Annika Sundås-Larsson. The meeting will be held in Swedish.

Inbjudan gäller all personal / Invitation for all staff

Välkommen! Welcome!

Håkan

Opponent: Professor Tomas McKelvey, Signal Processing Group, Dept. of Signals and Systems, Chalmers University of Technology, Göteborg

The discussion will be held in Swedish.

Abstract
The stringent regulations on the emissions levels of heavy duty vehicles create a demand for new methods of reducing harmful emissions from the engine. In order to be able to follow these increasingly stricter legislations, complex aftertreatment systems are used. Achievement of optimal performance of these systems requires accurate models that can be used for control design. As a result, the interest in modelling and control of aftertreatment systems has increased.

This thesis deals with the modelling of the nitrogen oxide (NOx) emissions from heavy duty vehicles using the selective catalyst as an aftertreatment system for its reduction. The process of the selective catalytic reduction (SCR) is nonlinear since the chemical reactions involved are highly depending on the operating point. The momentary operating point is defined by the driving profile of the vehicle which, for example, includes cold and hot engine starts, highway and urban driving.

The purpose of this thesis is to investigate different methods for nonlinear system identification of SCR systems with control in mind. The first two papers contain the theoretical work of this thesis. The first paper deals with improvement of an existing recursive prediction error method (RPEM) where a more accurate discretisation algorithm was used to improve the accuracy of the estimated nonlinear model. The second paper deals with analysis of the convergence properties of the algorithm. For this analysis several conditions were formulated that link the global and local convergence properties of the algorithm to stability properties of an associated differential equation. Global convergence to a stationary point was shown. In the third paper, the RPEM is used for identification of the SCR system and finally the fourth paper a Hammerstein-Wiener model for identification of the SCR system is applied. In both these cases the black-box models could predict the NOx behaviour of the SCR system quite well. The nonlinear models were shown to describe the SCR system more accurately than linear models.

Opponent: Prof. Dr. Marcus Grote, Mathematisches Institut, Universität Basel

Opponent: Dr. Dirk Duellmann, CERN, Switzerland

Opponent: Prof. Dr. Oleg Iliev, Fraunhofer ITWM

Language: English

Abstract

Recent advances in wired and wireless technology necessitate the development of theory, models and tools to cope with new challenges posed by large-scale optimization problems over networks. In this talk, we consider distributed multi-agent network systems where each agent has its own convex objective function, which can be evaluated with stochastic errors. The problem consists of minimizing the sum of the agent functions, without a central coordinator and without agents sharing the explicit form of their objectives. However, the agents are willing to cooperate with each other locally to solve the problem, by exchanging their estimates of an optimal solution. We discuss such distributed algorithms for synchronous and asynchronous implementations. We present convergence results and convergence rate estimates, and provide some numerical results.

Biography

Angelia Nedich received her B.S. degree from the University of Montenegro (1987) and M.S. degree from the University of Belgrade (1990), both in Mathematics. She received her Ph.D. degrees from Moscow State University (1994) in Mathematics and Mathematical Physics, and from Massachusetts Institute of Technology in Electrical Engineering and Computer Science (2002). She has been at the BAE Systems Advanced Information Technology from 2002-2006. In Fall 2006, as Assistant Professor, she has joined the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign, USA. She is the recipient of the NSF CAREER Award 2008 in Operations Research for her work in distributed multi-agent optimization.

Her general interest is in optimization including fundamental theory, models, algorithms, and applications. Her current research interest is focused on large-scale convex optimization, distributed multi-agent optimization, stochastic approximations in optimization and variational inequalities with applications in signal processing, machine learning, and decentralized control.

All are welcome!