Identification for control: Optimal input intended to identify a minimum variance controller
Automatica (Journal of IFAC)
Robust optimal experiment design for system identification
Automatica (Journal of IFAC)
Survey paper: Optimal experimental design and some related control problems
Automatica (Journal of IFAC)
Closed loop experiment design for linear time invariant dynamical systems via LMIs
Automatica (Journal of IFAC)
On the equivalence of least costly and traditional experiment design for control
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Least costly identification experiment for control
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Least-squares estimation of a class of frequency functions: A finite sample variance expression
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
Relations between uncertainty structures in identification for robust control
Automatica (Journal of IFAC)
The cost of complexity in system identification: The Output Error case
Automatica (Journal of IFAC)
Variance error, interpolation and experiment design
Automatica (Journal of IFAC)
Hi-index | 0.04 |
Parameter identification experiments deliver an identified model together with an ellipsoidal uncertainty region in parameter space. The objective of robust controller design is thus to stabilize all plants in the identified uncertainty region. The subject of the present contribution is to design an identification experiment such that the worst-case $\nu$-gap over all plants in the resulting uncertainty region between the identified plant and plants in this region is as small as possible. The experiment design is performed via input power spectrum optimization. Two cost functions are investigated, which represent different levels of trade-off between accuracy and computational complexity. It is shown that the input optimization problem with respect to these cost functions is amenable to standard numerical algorithms used in convex analysis.