Optimal experiment designs with respect to the intended model application
Automatica (Journal of IFAC)
System identification: theory for the user
System identification: theory for the user
Qualitative and quantitative experiment design for phenomenological models—a survey
Automatica (Journal of IFAC)
Iterative weighted least-squares identification and weighted LQG control design
Automatica (Journal of IFAC)
Robust and optimal control
For model-based control design, closed-loop identification gives better performance
Automatica (Journal of IFAC)
SIAM Journal on Control and Optimization
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)
Least costly identification experiment for control
Automatica (Journal of IFAC)
Paper: Optimal experiment design for linear systems with input-output constraints
Automatica (Journal of IFAC)
Identification of processes in closed loop-identifiability and accuracy aspects
Automatica (Journal of IFAC)
Brief Some results on optimal experiment design
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
Variance error, interpolation and experiment design
Automatica (Journal of IFAC)
Input design as a tool to improve the convergence of PEM
Automatica (Journal of IFAC)
Hi-index | 22.15 |
All stationary experimental conditions corresponding to a discrete-time linear time-invariant causal internally stable closed loop with real rational system and feedback controller are characterized using the Youla-Kucera parametrization. Finite dimensional parametrizations of the input spectrum and the Youla-Kucera parameter allow a wide range of closed loop experiment design problems, based on the asymptotic (in the sample size) covariance matrix for the estimated parameters, to be recast as computationally tractable convex optimization problems such as semi-definite programs. In particular, for Box-Jenkins models, a finite dimensional parametrization is provided which is able to generate all possible asymptotic covariance matrices. As a special case, the very common situation of a fixed controller during the identification experiment can be handled and optimal reference signal spectra can be computed subject to closed loop signal constraints. Finally, a brief numerical comparison with closed loop experiment design based on a high model order variance expression is presented.