Optimal experiment designs with respect to the intended model application
Automatica (Journal of IFAC)
For model-based control design, closed-loop identification gives better performance
Automatica (Journal of IFAC)
Probabilistic robustness analysis: explicit bounds for the minimum number of samples
Systems & Control Letters
Hyperstability of Control Systems
Hyperstability of Control Systems
SIAM Journal on Control and Optimization
Brief Some results on optimal experiment design
Automatica (Journal of IFAC)
Brief Robustness analysis tools for an uncertainty set obtained by prediction error identification
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
A survey of randomized algorithms for control synthesis and performance verification
Journal of Complexity
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)
Automatica (Journal of IFAC)
On the equivalence of least costly and traditional experiment design for control
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Variance-error quantification for identified poles and zeros
Automatica (Journal of IFAC)
Excitation signal design for closed-loop system identification
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Brief paper: Identification for robust H2 deconvolution filtering
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 | 22.16 |
All approaches to optimal experiment design for control have so far focused on deriving an input signal (or input signal spectrum) that minimizes some control-oriented measure of plant/model mismatch between the nominal closed-loop system and the actual closed-loop system, typically under a constraint on the total input power. In practical terms, this amounts to finding the (constrained) input signal that minimizes a measure of a control-oriented model uncertainty set. Here we address the experiment design problem from a ''dual'' point of view and in a closed-loop setting: given a maximum allowable control-oriented model uncertainty measure compatible with our robust control specifications, what is the cheapest identification experiment that will give us an uncertainty set that is within the required bounds? The identification cost can be measured by either the experiment time, the performance degradation during experimentation due to the added excitation signal, or a combination of both. Our results are presented for the situation where the control objective is disturbance rejection only.