Advanced Process Control and Optimization

Qualification aims

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

• 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 3 courses:

Linear, nonlinear and model-predictive control

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

Contents

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

Optimization

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

• 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