Journal ArticleDOI
Subspace Identification of Unstable Transfer Function Models
C. Sankar Rao,M. Chidambaram +1 more
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The unstable transfer function models are simulated and identified by the present method for both deterministic and stochastic cases and the model is validated through the residual analysis.Abstract:
This paper deals with the subspace identification of unstable transfer function models with numerator dynamics and time delays. The unstable transfer function models are simulated and identified by the present method for both deterministic and stochastic cases. Two case studies are considered for evaluating the performance of the present method. The quality of the identified model is tested by mean relative square error and mean-related variance indices. The problem associated with the identification of unstable systems corresponding to stable systems in terms of sampling time, need for improved filter, achievable performance evaluation and validation is brought out. The simulation results are compared by means of Bode plot and estimated poles with the real pole of the true plant. The model is validated through the residual analysis. A computer programme (in MATLAB) is also provided.read more
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Control of unstable systems
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TL;DR: The aim of this chapter is to point out some problems occurring in unstable systems control and review some of the basic options for stabilisation of such systems.
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Adsorption of cadmium ions by cucumber peel in continuous mode
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Virtual refrigerant charge sensor for booster refrigeration systems
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References
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Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Book
Subspace Identification for Linear Systems: Theory - Implementation - Applications
Peter Van Overschee,Bart De Moor +1 more
TL;DR: This book focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finitedimensional dynamical systems, which allow for a fast, straightforward and accurate determination of linear multivariable models from measured inputoutput data.
Journal ArticleDOI
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
TL;DR: Two new N4SID algorithms to identify mixed deterministic-stochastic systems are derived and these new algorithms are compared with existing subspace algorithms in theory and in practice.
Journal ArticleDOI
Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
TL;DR: Two algorithms to identify a linear, time-invariant, finite dimensional state space model from input-output data and a special case of the recently developed Multivariable Output-Error State Space (MOESP) class of algorithms based on instrumental variables are described.