2008 "An overview of Methods for analysis of Identifiability and Observability in Nonlinear State and Parameter Estimation"Trial lecture, pHd-defence Steinar M. Elgsæter
Semelhante a 2008 "An overview of Methods for analysis of Identifiability and Observability in Nonlinear State and Parameter Estimation"Trial lecture, pHd-defence Steinar M. Elgsæter
Semelhante a 2008 "An overview of Methods for analysis of Identifiability and Observability in Nonlinear State and Parameter Estimation"Trial lecture, pHd-defence Steinar M. Elgsæter (20)
2008 "An overview of Methods for analysis of Identifiability and Observability in Nonlinear State and Parameter Estimation"Trial lecture, pHd-defence Steinar M. Elgsæter
1. An overview of Methods for analysis of Identifiability and Observability in Nonlinear State and Parameter Estimation Steinar M. Elgsæter Trial Lecture - October 14 2008
5. Example: An oil well Reservoir Oil,water and gas rates Choke Well Pipeline Pressures and temperature Density Choke opening Unmeasured Measured
6. Internal system dynamics un-modeled disturbances output(y) input(u) Map internal states (x) System
7. An open-loop ”ballistic” state estimator internal states (x) modeled internal states (x) Internal dynamics measured output (y) Map Modeled internal dynamics modeled output(y) input(u) Map Plant Model Fitted parameters ( θ )
8. Closing the loop improves estimates in the face of uncertainty or disturbances Plant Model State Estimator - input output state injection term fitted state Plant Model Parameter Estimator - input output parameter injection term fitted parameter Duality Feedback loop
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13. Flavors of nonlinear filters Type Constrained Solution Model considered Linearized Kalman Filter Unconstrained Explicit 1.Order local approximation Extended Kalman Filter Unconstrained Explicit 1. Order local approximation Unscented Kalman Filter Unconstrained Explicit 2.Order local approximation Moving Horizon Estimator Constrained Numerical Full model