Optimal and Predictive Control

Control theory is hidden in many technical applications and natural phenomena. It has developed to a fundamental interdisciplinary scientific discipline since the first industrial revolution and today it is widely applied in almost all technical branches. The aim of optimal control theory is to develop  methods and tools for influencing the dynamics of a process in a prescribed manner in order to enforce or maintain a desired operation. To this end,  the researchers and engineers need to be equipped with fundamental understanding of mathematical methods and tools in order to  accomplish a proper process analysis and / or synthesis.

The aim of this course is a concise introduction to optimization and control with particular emphasis on the conceptual and technical explanations of theoretical and algorithmic approaches to static optimization, dynamic optimization, model predictive control (MPC) and game theory. This course will be delivered in one week seminar-based program, covering both theory and exercises, starting from mid June. After completing this course, students should  be able to study in-depth and independently the advanced courses of optimization and control.

• Static optimization (Constrained and unconstrained optimization)
• Dynamic optimization (Pontryagin maximum principle, Dynamic programming)
• Model Predictive Control (MPC)
• Game theory
  
  

• Hassan K. Khalil:  “Nonlinear Systems”, 3rd Edition. Prentice Hall, 2002.
• Liberzon, D.: “Calculus of Variations and Optimal Control Theory: A Concise Introduction”. Princeton University Press, 2011.
• Jurdjevic, V.:  “Geometric Control Theory”. Cambridge Studies in Adv. Math., 2008.
• Boyd, S., Boyd, S.P. and Vandenberghe, L.: “Convex Optimization”. Cambridge University Press, 2004.
• Fletcher, R.: “Practical Methods of Optimization”. 2nd Edition. John Wiley & Sons, 2000.
• Nocedal, J. and Wright, S.J.: “Numerical Optimization”. 2nd Edition. New York (NY): Springer, 2006.
• Bertsekas, D.P.: “Dynamic Programming and Optimal Control”, 4th Edition. Athena Scientific, 2017.
• Maciejowski, J.M.: “Predictive Control with Constraints”. Prentice Hall, 2002.
• Bertsekas, D.P.: “Nonlinear Programming”. 2nd Edition. Belmont, Massachusetts: AthenaScientific, 1999.
• Rawlings, J.B. and Mayne, D.Q.: “Model Predictive Control: Theory and Design”. Madison, Wisconsin: Nob Hill Publishing, 2009.
• Magni, L., Raimondo, D.M., and Allgöwer, F.: “Nonlinear Model Predictive Control”. Series vol. 384, Berlin, Heidelberg: Springer, 2009.

Prof. Dr.-Ing. Naim Bajcinca
naim.bajcinca(at)mv.uni-kl.de
+49 631/205-3230
Gebäude 42, Raum 262
Sprechstunde: nach Vereinbarung

Teaching assistant

Argtim Tika
argtim.tika(at)mv.uni-kl.de
+49 631/205-5093
Gebäude 42, Raum 259
Sprechstunde: Mittwoch, 15:00-16:00 Uhr

• Seminar course (5-7 days)
• Starting 14.06.2021
• GoTo Webinar (link in OLAT)
• OLAT

• Oral exam: 30-45 Min.
• Exam time by appointment
• Credit points: 5 ECTS

• Abstraction skills
• Linear algebra
• Basic calculus
• Differential equations

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