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Multi-State-Dependent parameter model identification and estimation.

Research output: Contribution in Book/Report/ProceedingsChapter

Publication date2012
Host publicationSystem Identification, Environmental Modelling and Control System Design
EditorsL. Wang, H. Garnier
Place of PublicationLondon
Number of pages20
ISBN (Print)978-0-85729-973-4
<mark>Original language</mark>English


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.