Modeling and Identification of Dynamic Systems

Year:
1st year/2nd year
Semester:
S2
Programme main editor:
(I2CAT)
Onsite in:
Remote:
ECTS range:
6 ETCS

Professors

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Soenke Rhein
UULM

Prerequisites:

Pedagogical objectives:

Appropriate descriptions of systems are the base of many methods in control theory and are required for model-based monitoring. Model-based techniques open extensive paths to optimize existing control systems in industrial applications. The main requirement are proper mathematical models which on the one hand represent all important dynamic effects as precisely as possible and stay feasible regarding computational complexity on the other hand. It is further important to be able to determine parameters which are not observable.

Students are able to describe and apply methods of mathematical modeling of technical processes based on physical principles. They are further able to describe technical systems of various physical domains using mathematical formalisms. They are especially able to derive suitable models for controller design and to parametrize the models with identification procedures, e.g. with using black box models. Students can design optimal state estimaters and state controllers. Thereby, they can apply according methods of identification, estimation, and control.

Evaluation modalities:

Oral exam

Description:

Topics include:

  • Modelling mechanical, electrical, and hydraulic systems
  • Parameter-based and non-parameter-based identification approaches
  • Optimal estimation procedures and filter (e.g. Kalman filter)

Required teaching material

Literature: • P.E. Wellstead: Physical Systems Modelling, Academic Press, 1979 • R. Isermann: Mechatronische Systeme: Grundlagen, Springer, 2002 • R. Isermann: Identifikation dynamischer Systeme 1 und 2, Springer, 1992 • D.G. Luenberger: Optimization by Vector Space Methods, John Wiley & Sons, 1969 • A. Gelb: Applied Optimal Estimation, M.I.T. Press, 1974 • A.E. Bryson, Y.-C. Ho: Applied Optimal Control, Hemisphere Publishing Corporation, 1975 Moodle Course at https://elearning.saps.uni-ulm.de/ - Account needed at SAPS of UUlm

Teaching volume:
lessons:
10 hours
Exercices:
10 hours
Supervised lab:
40 hours
Project:

Devices:

  • Laboratory-Based Course Structure
  • Open-Source Software Requirements