Tych, Wlodek and Sadeghi, J. and Smith, Paul and Chotai, Arun and Taylor, C. J. (2012) Multi-State-Dependent parameter model identification and estimation. In: System Identification, Environmental Modelling and Control System Design. Springer, London, pp. 191-210. ISBN 978-0-85729-973-4Full text not available from this repository.
This chapter describes the generalisation of the State Dependent Parameter (SDP) approach to the modelling of nonlinear dynamic systems, to now include Multi-State Dependent Parameter (MSDP) nonlinearities. The recursive estimation of the MSDP model parameters in a multivariable state space occurs along a multi-path trajectory, employing the Kalman Filter and Fixed Interval Smoothing algorithms. The novelty of the method lies in redefining the concepts of sequence (predecessor, successor), allowing for their use in a multi-state dependent context, so producing efficient parameterisation for a fairly wide class of non-linear, stochastic dynamic systems. The format of the estimated model allows its direct use in control system design. Two worked examples in Matlab are considered.
|Item Type:||Contribution in Book/Report/Proceedings|
|Subjects:||G Geography. Anthropology. Recreation > GE Environmental Sciences|
|Departments:||Faculty of Science and Technology > Lancaster Environment Centre|
Faculty of Science and Technology > Engineering
|Deposited By:||Mr Richard Ingham|
|Deposited On:||03 May 2011 14:03|
|Last Modified:||18 Jun 2016 01:56|
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