Master of Eng. in Automation & IT
[ger]Deutsch
Englisch [eng]Englisch

Course 
People 

Automation & IT   Course   Modules   APC and Optimization

Advanced Process Control and Optimization


Qualification aims

Students will learn, understand and be able to apply to technical processes

  • digital signal processing,
  • linear, non-linear and model-predictive control,
  • automation of discrete event systems and
  • practical optimization using precise mathematical and stochastic procedures.

In particular, they will use "state of the art" control and optimization software in order to

  • record and analyse new tasks and problems,
  • choose suitable solution methods,
  • ascertain and evaluate correct solutions.

Courses

The module consists of four courses:

Fundamentals of digital signal processing

Tutor

Prof. Kampmann

Credit points

4 CP

Term

Fall

Contents

  • Conversion of signals (D/A, D/A), discrete level and time, impact of analogue environment
  • Transfer functions in S plane
  • Sampling theory fundamentals, Z-transformation, mapping S-Z-plane
  • Fundamentals of digitals filters, transposed systems, DFT/FFT
  • Multi-rate signal processing, sample rate conversion
  • Filter structures and DFT programming for microcomputers, e.g. in control loop applications


Linear, nonlinear and model-predictive control

Tutor

Prof. Scheuring

Credit points

6 CP

Term

Fall

Contents

  • Control matrix algebra
  • Matrix norms
  • State space approach
  • Interconnected systems and feedback
  • Stability, Ljapunow stability and I/O stability
  • Reachability, Observability and Controllability
  • State feedback and output feedback
  • Observers
  • Multivariable poles and zeros
  • Structural characteristics of non-linear systems
  • Nonlinearity measures
  • Input-output linearization
  • Model-based predictive control systems
  • Internal model control and Smith predictor
  • Linear model predictive control (MPC)
  • Nonlinear model predictive control (NMPC)
  • Implementation concepts of major manufacturers


Automation of discrete event systems

Tutor

Prof. Scheuring

Credit points

2 CP

Term

Spring

Contents

  • Analysis of discrete event systems
  • Design of discrete event systems
  • Safety oriented discrete event systems
  • Automation of hybrid dynamic systems


Optimization

Tutor

Prof. Bartz-Beielstein + Prof. Böhm-Rietig

Credit points

6 CP

Term

Spring

Contents

  • Optimization criteria
  • Optimization basics (calculus of variation, Euler formula, Hamilton formula, maximum principle, etc.)
  • Linear Programming (LP)
  • Nonlinear Programming (NLP)
  • Quadratic Programming (QP)
  • Integer Programming (IP)
  • Direct (extrapolation-free) searching procedures (pattern search)
  • Stochastic procedures (simulated annealing, evolutionary algorithms)
  • Application of optimization procedures to practical problems


Bibliography

  • Proakis, J.G.: Digital Signal Processing - Principles, Algorithms, and Applications. Prentice Hall, New Jersey, 1995
  • Khalil, H.K.: Nonlinear Systems. Prentice Hall, New Jersey, 2002
  • Dittmar, R., Pfeiffer, B-M.: Modellbasierte prädiktive Regelung. Oldenbourg Verlag, München, 2004
  • Lunze, J.: Ereignisdiskrete Systeme. Oldenbourg Verlag, München, 2006
  • Gill, P.E., Murray, W., Wright, M.: Practical Optimization. Academic Press, London, 1989
  • Neumann, K. und Morlock, M: Operations Research. 2. Aufl. Hanser, München 2002
  • Bartz-Beielstein, T.: Experimental Research in Evolutionary Computation. 1.Aufl., Springer, Berlin 2006
  • Markon, S., Kita, H., Kise, H., Bartz-Beielstein, T.: Modern Supervisory and Optimal Control with Applications in the Control of Passenger Traffic Systems in Buildings. Springer, Berlin, Heidelberg, New York, 2006