|Type||Lecture (2 SWS) + Exercise (2 SWS)|
|Audience||Master BIW, CIW, PSE, A&R and other|
Analysis of linear dynamic systems: Stability, controllability, observability. Stability of nonlinear systems using Lyapunov theory and summary of nonlinear control design methods. State estimation for linear and nonlinear systems: Kalman Filter, Extended Kalman Filter, Particle Filter, Moving Horizon Estimation. Advanced model predictive control: linear and nonlinear model predictive control, robust model predictive control, learning-based model predictive control. Efficient implementation of model predictive control.
The course provides indepth knowledge of state of the art techniques for advanced process control and prepares for further scientific work in this area and for industrial jobs in process control and operation depart- ments or companies. The students understand the methods listed above and are able to choose the appropriate methods for the solution of practical problems, to synthesize solutions and to critically evaluate the results.
|Exam||Written / Oral |
Active participation in 75% of computer exercises is mandatory. The students can acquire 15% additional bonus point doing a small controller design project.
|Preliminaries||Basic knowledge of dynamic systems and control as provided by the course Prozessdynamik und Regelung / Introduction to Process Dynamics and control.|
|Literature||The slides of the course and any additional materials such as literature lists and website recommendations will be published in the virtual workrooms in Moodle provided for this purpose. Details will be announced at the beginning of the course.|
Only the information found in the LSF and the most recent edition of the Modulhandbuch der Fakultät
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