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
System identification
An indirect prediction error method for system identification
Automatica (Journal of IFAC)
An indirect method for transfer function estimation from closed loop data
Automatica (Journal of IFAC)
Iterative weighted least-squares identification and weighted LQG control design
Automatica (Journal of IFAC)
On some key issues in the Windsurfer approach to adaptive robust control
Automatica (Journal of IFAC)
Identification and control—closed-loop issues
Automatica (Journal of IFAC) - Special issue on trends in system identification
For model-based control design, closed-loop identification gives better performance
Automatica (Journal of IFAC)
Identification of processes in closed loop-identifiability and accuracy aspects
Automatica (Journal of IFAC)
Identifiability of linear stochastic systems operating under linear feedback
Automatica (Journal of IFAC)
Decentralized closed-loop parameter identification for multivariable processes from step responses
Mathematics and Computers in Simulation
Brief paper: An approach to closed-loop subspace identification by orthogonal decomposition
Automatica (Journal of IFAC)
Survey paper: Optimal experimental design and some related control problems
Automatica (Journal of IFAC)
Third-order cumulants based methods for continuous-time errors-in-variables model identification
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Adaptive feedback cancellation for audio applications
Signal Processing
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Variance-error quantification for identified poles and zeros
Automatica (Journal of IFAC)
AsiaMS '07 Proceedings of the IASTED Asian Conference on Modelling and Simulation
A virtual closed loop method for closed loop identification
Automatica (Journal of IFAC)
Control relevant estimation of plant and disturbance dynamics
Automatica (Journal of IFAC)
Brief paper: Effects of feedback on active fault detection
Automatica (Journal of IFAC)
Brief Closed-loop identification with an unstable or nonminimum phase controller
Automatica (Journal of IFAC)
The variation of non-parametric estimates in closed-loop
Automatica (Journal of IFAC)
Bias of indirect non-parametric transfer function estimates for plants in closed loop
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Technical Communique: Asymptotic variance expressions for closed-loop identification
Automatica (Journal of IFAC)
Closed-loop model set validation under a stochastic framework
Automatica (Journal of IFAC)
Brief Subspace identification of closed loop systems by the orthogonal decomposition method
Automatica (Journal of IFAC)
Brief The nature of data pre-filters in MPC relevant identification-open- and closed-loop issues
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
On the frequency domain accuracy of closed-loop estimates
Automatica (Journal of IFAC)
Analysis of the variability of joint input-output estimation methods
Automatica (Journal of IFAC)
Closed-loop subspace identification using the parity space
Automatica (Journal of IFAC)
Instrumental variable methods for closed-loop system identification
Automatica (Journal of IFAC)
A Bayesian approach to sparse dynamic network identification
Automatica (Journal of IFAC)
Instrumental variable scheme for closed-loop LPV model identification
Automatica (Journal of IFAC)
International Journal of Systems, Control and Communications
Hi-index | 22.19 |
Identification of systems operating in closed loop has long been of prime interest in industrial applications. The problem offers many possibilities, and also some fallacies, and a wide variety of approaches have been suggested, many quite recently. The purpose of the current contribution is to place most of these approaches in a coherent framework, thereby showing their connections and display similarities and differences in the asymptotic properties of the resulting estimates. The common framework is created by the basic prediction error method, and it is shown that most of the common methods correspond to different parameterizations of the dynamics and noise models. The so-called indirect methods, e.g., are indeed ''direct'' methods employing noise models that contain the regulator. The asymptotic properties of the estimates then follow from the general theory and take different forms as they are translated to the particular parameterizations. We also study a new projection approach to closed-loop identification with the advantage of allowing approximation of the open-loop dynamics in a given, and user-chosen frequency domain norm, even in the case of an unknown, nonlinear regulator.