Dual control and online optimal experimental design
Dual control refers to strategies that strike a balance between control and estimation. Combined with nonlinear model predictive control, dual control offers advanced feedback methods for optimal control problems under uncertainties. We present dual control from a new perspective, namely, the interp...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
July 13, 2017
|
| In: |
SIAM journal on scientific computing
Year: 2017, Volume: 39, Issue: 4, Pages: B640-B657 |
| ISSN: | 1095-7197 |
| DOI: | 10.1137/16M1069936 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1137/16M1069936 Verlag, Volltext: http://epubs.siam.org/doi/abs/10.1137/16M1069936 
|
| Author Notes: | H. La, A. Potschka, J. Schlöder, and H. Bock |
| Summary: | Dual control refers to strategies that strike a balance between control and estimation. Combined with nonlinear model predictive control, dual control offers advanced feedback methods for optimal control problems under uncertainties. We present dual control from a new perspective, namely, the interplay between the performance control task and the information gain task in connection with optimal experimental design. A new approach to dual control is proposed in which the covariance matrix of the estimates is weighted by the derivatives of the nominal objective value with respect to unknown parameters and initial states. |
|---|---|
| Item Description: | Gesehen am 29.01.2018 |
| Physical Description: | Online Resource |
| ISSN: | 1095-7197 |
| DOI: | 10.1137/16M1069936 |