Ausgewählte Lehrveranstaltungen: Sommersemester 2011

Colloquium Materials Modelling

Prof. Dr. Siegfried Schmauder

Colloquium Materials Modelling

Distributed Model Predictive Control and Moving Horizon Estimation

Prof. James B. Rawlings (Department of Chemical and Biological Engineering University of Wisconsin, Madison, WI)

This short course is intended to introduce those interested in model predictive control (MPC) to the following two research areas:

(i) distributed MPC and

(ii) moving horizon estimation.

After a short introduction overviewing fundamental concepts and establishing notation, the following material is presented.

Distributed MPC. The topics to be covered include:

1. distributed MPC and its relationship to suboptimal MPC

2. stability properties of distributed MPC

3. inherent robustness of distributed MPC

4. proposal for nonlinear, distributed MPC

Moving horizon estimation. The topics to be covered include:

1. state estimation as an optimization problem: full information estimation

2. duality between estimation and regulation

3. stability properties of full information estimation

4. moving horizon estimation with zero prior weighting

5. moving horizon estimation with nonzero prior weighting

Nichtlineare finite Elemente für Tragwerksberechnungen

Prof. Manfred Bischoff

* Nichtlineare Strukturmechanik: Überblick, Phänomene und Begriffe

* Geometrische Nichtlinearität: große Deformationen, Stabilität; Methoden der nichtlinearen Strukturanalyse; Iterationsverfahren und Pfadverfolgung; Stabilität, Beulanalyse

* Materielle Nichtlinearität: Plastizität, Schädigungsmodelle

Tube Model Predictive Control

Dr. Sasa V. Rakovic (Oxford University and Otto-von-Guericke University Magdeburg)

This short course deals with a recently developed Model Predictive Control (MPC) method for uncertain systems. In the presence of uncertainties, it necessary to replace the predicted state and control sequences by the sets of possible states and controls due to the spread of trajectories caused by the uncertainty. This results in state and control "tubes" which represent either the exact or outer-bounding sequences of the sets of possible states and associated controls. The tubes allow for generalized but natural predictions based on which it is possible to guarantee the robust state and control constraint satisfaction for the controlled uncertain state sequences and the associated sequences of control actions.

The tube MPC resembles the classical MPC in that the prediction paradigm remains the same but with a major difference that the role of the state and control sequences is played by the state and control tubes. The adequate state-control tube and the associated control policy parameterizations lead to a computationally highly attractive tube MPC synthesis, which under mild and natural conditions, induces strong system theoretic properties of the controlled, constrained, uncertain dynamics.

Topics covered in the short course include:

- Control Synthesis Under Constraints and Uncertainty: Basic Notions and Facts.

- Tube Model Predictive Control: State Feedback Case.

- TubeModel Predictive Control: Output Feedback and Nonlinear Cases.