2007•Unpublished venueOpen access

Subspace-based System Identification for Helicopter Dynamic Modelling

Ping Li, Ian Postlethwaite, Matthew C. Turner

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Abstract

This paper investigates the problem of helicopter dynamic modelling using time-domain system identification techniques. The paper begins with a brief introduction to the state-space form of the perturbation model for helicopters, based on which, system identification modelling is performed. Then the MOESP (Multivariable Output-Error State sPace) subspace identification method is described. Computer simulations are carried out to illustrate the operation and performance of the method using concatenated data sets. The method is then applied to real data from EH101 helicopter flight tests and some preliminary results of identifying an extended dynamic model about the cruise condition are presented.

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This paper investigates the problem of helicopter dynamic modelling using time-domain system identification techniques. The paper begins with a brief introduction to the state-space form of the perturbation model for helicopters, based on which, system identification modelling is performed. Then the MOESP (Multivariable Output-Error State sPace) subspace identification method is described. Computer simulations are carried out to illustrate the operation and performance of the method using concatenated data sets. The method is then applied to real data from EH101 helicopter flight tests and some preliminary results of identifying an extended dynamic model about the cruise condition are presented.

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Available abstract

This paper investigates the problem of helicopter dynamic modelling using time-domain system identification techniques. The paper begins with a brief introduction to the state-space form of the perturbation model for helicopters, based on which, system identification modelling is performed. Then the MOESP (Multivariable Output-Error State sPace) subspace identification method is described. Computer simulations are carried out to illustrate the operation and performance of the method using concatenated data sets. The method is then applied to real data from EH101 helicopter flight tests and some preliminary results of identifying an extended dynamic model about the cruise condition are presented.

Key concepts: Subspace topology, Multivariable calculus, System identification, Identification (biology), State-space representation, State space, Computer science, Control theory (sociology)

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