A bias-correction method for indirect identification of closed-loop systems
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)
Discrete-Time Stochastic Systems: Estimation and Control
Discrete-Time Stochastic Systems: Estimation and Control
On the equivalence of time and frequency domain maximum likelihood estimation
Automatica (Journal of IFAC)
Closed-loop identification revisited
Automatica (Journal of IFAC)
Bias of indirect non-parametric transfer function estimates for plants in closed loop
Automatica (Journal of IFAC)
Technical Communique: Asymptotic variance expressions for closed-loop identification
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
Box-Jenkins identification revisited-Part I: Theory
Automatica (Journal of IFAC)
Instrumental variable methods for closed-loop system identification
Automatica (Journal of IFAC)
Hi-index | 22.23 |
Indirect methods for the identification of linear plant models on the basis of closed loop data are based on the use of (reconstructed) input signals that are uncorrelated with the noise. This generally requires exact (linear) controller knowledge. On the other hand, direct identification requires exact plant and noise modelling (system in the model set) in order to achieve accurate results, although the controller can be non-linear. In this paper, a generalized approach to closed loop identification is presented that includes both methods as special cases and which allows novel combined methods to be generated. Besides providing robustness with respect to inexact controller knowledge, the method does not rely on linearity of the controller nor on exact noise modelling. The generalization is obtained by balancing input-noise decorrelation against noise whitening in a user-chosen flexible fashion. To this end, a user-chosen virtual controller is used to parametrize the plant model, thereby generalizing the dual-Youla method to cases where knowledge of the controller is inexact. Asymptotic bias and variance results are presented for the method. Also, the benefits of the approach are demonstrated via simulation studies.