### Optimization and Systems Theory Seminar

Friday, March 19, 2004, 11.00-12.00, Room 3721, Lindstedtsv. 25

**Moritz Diehl**

Interdisciplinary Center for Scientific Computing

University of Heidelberg

D-69120 Heidelberg

Germany

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Real-time optimization of large scale systems

Nonlinear Model Predictive Control (NMPC) is a feedback control
technique that uses a nonlinear dynamic process model for prediction
and optimization. Feedback is obtained by using the observed system
state as initial value of an optimal control problem on a prediction
horizon, solving the problem online, and implementing the first part
of the optimized control trajectory at the real process. The
optimization is repeated after a short time, sufficiently fast to
react to disturbances or to the effects of modelling errors.
A major challenge for any nontrivial NMPC application is the real-time
optimization of large scale process models of differential algebraic
(DAE) or partial differential equation (PDE) type. We present an
efficient embedding technique to initialize subsequent problems,
implemented in an online algorithm for NMPC, that has already been
applied to experimentally control a pilot plant distillation column
described by DAE. We show how the technique can be exteded to very
large scale DAE systems, arising from the discretization of
instationary PDEs by the method of lines, and apply this technique to
a periodically operated chromatographic separation process.

Calendar of seminars

*Last update: February 6, 2004 by
Anders Forsgren.
*