A Feedback Optimal Control Algorithm with Optimal Measurement Time Points
AbstractNonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning) as well as minimizing a given objective (performing). We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling) independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment) or the need for fast feedback. The algorithm shows promising results with a
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Jost, F.; Sager, S.; Le, T.T.-T. A Feedback Optimal Control Algorithm with Optimal Measurement Time Points. Processes 2017, 5, 10.
Jost F, Sager S, Le TT-T. A Feedback Optimal Control Algorithm with Optimal Measurement Time Points. Processes. 2017; 5(1):10.Chicago/Turabian Style
Jost, Felix; Sager, Sebastian; Le, Thuy T.-T. 2017. "A Feedback Optimal Control Algorithm with Optimal Measurement Time Points." Processes 5, no. 1: 10.
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