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Multi-state dependent parameter model identification and estimation for nonlinear dynamic systems.

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>09/2010
<mark>Journal</mark>Electronics Letters
Issue number18
Number of pages2
Pages (from-to)1265-1266
<mark>Original language</mark>English


An important generalisation of the state dependent parameter approach to the modelling of nonlinear dynamic systems to include multi-state dependent parameter (MSDP) nonlinearities is described. The recursive estimation of the MSDP model parameters in a multivariable state space occurs along a multipath 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 its use in a multi-state dependent context, so producing efficient parameterisation for a fairly wide class of nonlinear, stochastic dynamic systems. The format of the estimated model allows its direct use in control system design.