Subspace identification of Bilinear and LPV systems for open- and closed-loop data

Wingerden, Jan‐Willem van; Verhaegen, Michel

doi:10.1016/j.automatica.2008.08.015

Cited by 218 publications

(176 citation statements)

References 33 publications

Supporting

Mentioning

175

Contrasting

Unclassified

Order By: Relevance

“…The dotted lines show the ν-gap for the nominal model (which is approximately 0.08 for all frozen θ), indicating that a significant improvement is achieved with a reasonably small number of samples. The second method examined is PBSIDopt, which is presented in an LPV version in [16]. In this approach, a subspace method is used to construct the state estimates, and consequently requires a lot of computational power.…”

Section: Simulation Examplementioning

confidence: 99%

See 1 more Smart Citation

Incremental closed-loop identification of linear parameter varying systems

Bendtsen

Trangbaek

2011

Proceedings of the 2011 American Control Conference

View full text Add to dashboard Cite

Abstract-This paper deals with system identification for control of linear parameter varying systems. In practical applications, it is often important to be able to identify small plant changes in an incremental manner without shutting down the system and/or disconnecting the controller; unfortunately, closed-loop system identification is more difficult than openloop identification. In this paper we prove that the so-called Hansen Scheme, a technique known from linear time-invariant systems theory for transforming closed-loop system identification problems into open-loop-like problems, can be extended to accommodate linear parameter varying systems as well.

show abstract

Section: Simulation Examplementioning

confidence: 99%

“…[14] refines the instrumental variables method for Box-Jenkins-type models. [15], [16], [17] and [18] propose various subspace-based approaches to identification of LPV systems. Finally, [19] examines how to choose optimal orthonormal basis functions for LPV system identification.…”

Section: Introductionmentioning

confidence: 99%

Incremental closed-loop identification of linear parameter varying systems

Bendtsen

Trangbaek

2011

Proceedings of the 2011 American Control Conference

View full text Add to dashboard Cite

show abstract

“…As a consequence, only preliminary closed-loop approaches has been proposed in the literature without being able to exploit the existing tools and knowledge available in the LTI case. In van Wingerden and Verhaegen (2009), an approximation based LPV extension of a predictor subspace approach (PSBID) has been proposed which is also applicable in a closed-loop setting, while in Boonto and Werner (2008) also an approximation based LPV extension of the CLOE algorithm (see Landau and Karimi (1997)) has been investigated w.r.t. LPV outputerror (OE) type of models.…”

Section: Introductionmentioning

confidence: 99%

On the closed loop identification of LPV models using instrumental variables

Tóth

Laurain

Gilson

et al. 2011

IFAC Proceedings Volumes

View full text Add to dashboard Cite

“…Thus, LPV models preserve the advantageous properties of LTI models, while being able to represent a large class of nonlinear systems [10]. LPV model-based control has been applied in many application areas (e.g., aerospace engineering, automotive applications, high-tech systems) as it benefits from the extension of successful LTI control design strategies, such as proportional-integral-derivative (PID) control [11], model predictive control (MPC) [12], optimal control [13], and robust control [14] (see [15][16][17][18][19][20][21][22][23][24][25]10] and the citations therein).…”

Section: Introductionmentioning

confidence: 99%

A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study

Bachnas

Tóth

Ludlage

et al. 2014

Journal of Process Control

110

View full text Add to dashboard Cite

a b s t r a c tModel-based control strategies are widely used for optimal operation of chemical processes to respond to the increasing performance demands in the chemical industry. Yet, obtaining accurate models to describe the inherently nonlinear, time-varying dynamics of chemical processes remains a challenge in most model-based control applications. This paper reviews data-driven, Linear Parameter-Varying (LPV) modeling approaches for process systems by exploring and comparing various identification methods on a high-purity distillation column case study. Several LPV identification methods that utilize input-output and series expansion model structures are explored. Two LPV identification perspectives are adopted: (i) the local approach, which corresponds to the interpolation of Linear Time-Invariant (LTI) models identified at different steady-state operating points of the system and (ii) the global approach, where a parametrized LPV model structure is identified directly using a global data set with varying operating points. For the local approach, various model interpolation schemes are studied under an Output Error (OE) noise setting, whereas in the global case, a polynomial parametrization based OE prediction error minimization approach, an Orthonormal Basis Functions (OBFs) based model estimator and a LeastSquare Support Vector Machine (LS-SVM) based non-parametric approach are investigated. Through extensive simulation studies, the aforementioned LPV identification approaches are analyzed in terms of the attainable model accuracy and local frequency response behavior of the obtained models. Recommendations are provided to achieve adequate choice between the methods for a particular process system at hand.

show abstract

Subspace identification of Bilinear and LPV systems for open- and closed-loop data

Cited by 218 publications

References 33 publications

Incremental closed-loop identification of linear parameter varying systems

Incremental closed-loop identification of linear parameter varying systems

On the closed loop identification of LPV models using instrumental variables

A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study

Contact Info

Product

Resources

About