5preparation

# 5preparation - Data preparation for System IdentiFcation...

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Unformatted text preview: Data preparation for System IdentiFcation Carpentry APC04 Vancouver Joint Modeling & Control Design Preparation and prejudice The sugar mill is available for experiment We need to get some prejudicial decision made before we can start delving into System IdentiFcation via matlab toolbox Almost all our work goes into this section of modeling In this way System IdentiFcation is more like painting Variables needing speciFcation Model structure - parametrization of and Experiment design - reference input selection Data selection - not all data is suited to linear modeling Data Fltering - to re¡ect our control objective ˆ H ( z, θ ) ˆ P ( z, θ ) APC04 Vancouver Joint Modeling & Control Design Formulations P(z) H(z) + C(z) + r(t) reference u(t) input v(t) y(t) output white noise n(t) disturbance controller plant disturbance model lim N →∞ 1 N N k =1 L ( z )[ y k- ˆ y k | k- 1 ] 2 = 1 2 π π- π P ( e j ω )- ˆ P ( e j ω , θ ) 2 | 1 + P ( e j ω ) C ( e j ω ) | 2 C ( e j ω ) 2 Φ r ( ω ) + 1 + ˆ P ( e j ω , θ ) C ( e j ω ) 2 | 1 + P ( e j ω ) C ( e j ω ) | 2 H ( e j ω ) 2 L ( e j ω ) 2 ˆ H ( e j ω , θ ) 2 d ω APC04 Vancouver Joint Modeling & Control Design Model structure selection We really insist on as simple a model as is possible Parsimony plus - we need to use the model for control design...
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## This note was uploaded on 05/27/2010 for the course MAE MAE143B taught by Professor Prof.bitmead during the Spring '10 term at UCSD.

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5preparation - Data preparation for System IdentiFcation...

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