Course info

Course Contents
Optimal control approaches, like model predictive control, are one of the most versatile, flexible and most often used modern control approaches by now. Fields of applications span from robotics, autonomous driving, aerospace systems, energy systems, chemical processes, biotechnology, up to biomedicine. The lecture provides an introduction to fundamentals of optimal control, focusing on the method and theoretical base. It furthermore provides an outreach towards efficient numerical solution strategies and model predictive control.

The following topics are covered during the lecture:
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[*]Application examples from various fields such mechatronics, robotics, electrical systems, chemical processes, economics, as well as aeronautics
[*]Review of nonlinear programming
[*]Dynamic programming, the principle of optimality, Hamilton-Jacobi-Bellman equation
[*]Pontryagin maximum principle
[*]Infinite and finite-horizon optimal control, LQ optimal control
[*]Numerical solution approaches for optimal control problems
[*]Introduction to model predictive control (MPC)
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Literature
Lecture notes and slides will be provided in the elearning system

Further recommended literature:
Optimal Control
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[*]R. Bellman. Dynamic Programming. Princeton University Press, Princeton, New Jersey, 1957.
[*]L.D. Berkovitz. Optimal Control Theory. Springer-Verlag, New York, 1974.
[*]D.P. Bertsekas. Dynamic Programming and Optimal Control. Athena Scientific Press. 2nd edition, 2000.
[*]L.M. Hocking. Optimal Control. An Introduction to the Theory with Applications. Oxford Applied Mathematics and Computing Science Series. Oxford University Press, Oxford, 1991.
[*]J.L. Troutmann. Variational Calculus and Optimal Control. Undergraduate Texts in Mathematics. Springer, 1991.
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Optimization
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[*]S. Boyd, L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
[*]J. Nocedal, S. Wright. Numerical Optimization. Springer, 2006.
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Model Predictive Control
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[*]J.B. Rawlings, D.Q. Mayne, M. Diehl. Model Predictive Control: Theory and Design, 2009.
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Preconditions
Fundamental knowledge in control and systems theory, with a focus on state space formulations.