Tuesday, 15:15 - 15:40 h, Room: H 0112


Markus Kögel
On real-time optimization for model predictive control using multiplier methods and Nesterov’s gradient method

Coauthor: Rolf Findeisen


Model predictive control is an optimization based approach in automatic control to control systems. It allows taking constraints explicitly into account while optimizing the performance. Model predictive control requires solving in real-time optimization problem each time a new measurement becomes available.
We focus on the important special case of linear plants, quadratic cost criterions and convex constraints, in which the optimization problems are quadratic programs with a special structure. Although, multiple efficient algorithms exist by now, model predictive control is still challenging for fast, large systems or on embedded systems with limited computing power.
Therefore we present approaches using multiplier methods and Nesterov’s gradient method, which allow efficient real-time optimization. In particular, we outline how the solution can be parallelized or distributed. This enables the use of multiple processor cores or even multiples computers to decrease the solution time.
We illustrate the proposed algorithms using application examples.


Talk 1 of the invited session Tue.3.H 0112
"Real-time optimization III" [...]
Cluster 16
"Nonlinear programming" [...]


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