Sajeeb, R and Manohar, CS and Roy, D (2009) A conditionally linearized Monte Carlo filter in non-linear structural dynamics. In: International Journal Of Non-Linear Mechanics, 44 (7, Sp.). pp. 776-790.
pdf.pdf - Published Version
Restricted to Registered users only
Download (1631Kb) | Request a copy
State and parameter estimations of non-linear dynamical systems, based on incomplete and noisy measurements, are considered using Monte Carlo simulations. Given the measurements. the proposed method obtains the marginalized posterior distribution of an appropriately chosen (ideally small) subset of the state vector using a particle filter. Samples (particles) of the marginalized states are then used to construct a family of conditionally linearized system of equations and thus obtain the posterior distribution of the states using a bank of Kalman filters. Discrete process equations for the marginalized states are derived through truncated Ito-Taylor expansions. Increased analyticity and reduced dispersion of weights computed over a smaller sample space of marginalized states are the key features of the filter that help achieve smaller sample variance of the estimates. Numerical illustrations are provided for state/parameter estimations of a Duffing oscillator and a 3-DOF non-linear oscillator. Performance of the filter in parameter estimation is also assessed using measurements obtained through experiments on simple models in the laboratory. Despite an added computational cost, the results verify that the proposed filter generally produces estimates with lower sample variance over the standard sequential importance sampling (SIS) filter.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Elsevier Science.|
|Keywords:||State estimation;Non-linear dynamical systems;Particle filters;Ito-Taylor expansions.|
|Department/Centre:||Division of Mechanical Sciences > Civil Engineering|
|Date Deposited:||18 Aug 2009 05:11|
|Last Modified:||19 Sep 2010 05:41|
Actions (login required)