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6iterative

# 6iterative - Iterative modeling control design Tying...

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Iterative modeling & control design

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APC04 Vancouver Joint Modeling & Control Design Tying together the threads Modern model-based control needs two things A local performance objective using the nominal model A robustness measure involving the rough model error Modeling with closed-loop data tells us about the model mismatch Performance-based control design comes with a performance expectation P ( e j ω ) - ˆ P ( e j ω , θ ) ˆ P ( e j ω , θ ) × C ( e j ω ) ˆ P ( e j ω , θ ) 1 + C ( e j ω ) ˆ P ( e j ω , θ ) < 1 H ( e j ω ) × P ( e j ω ) - ˆ P ( e j ω , θ ) ˆ P ( e j ω , θ ) × C ( e j ω ) ˆ P ( e j ω , θ ) 1 + C ( e j ω ) ˆ P ( e j ω , θ ) × 1 1 + C ( e j ω ) P ( e j ω ) < ( 1 + P ( e j ω ) C ( e j ω ) ) - 1 ( 1 + P ( e j ω ) C ( e j ω ) ) - 1 C ( e j ω ) P ( e j ω ) ( 1 + P ( e j ω ) C ( e j ω ) ) - 1 P ( e j ω ) ( 1 + P ( e j ω ) C ( e j ω ) ) - 1 C ( e j ω ) γ
APC04 Vancouver Joint Modeling & Control Design Key principles Model-based control performance depends on nominal model and model quality Nominal model and model quality depend on the controller operating when the data is collected Can we match up these two issues? Can we set up the problem so that the successive controllers cause successive model to become more appropriate? Try to link all the frequency-domain formulae to share a common objective But first some clues ...

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APC04 Vancouver Joint Modeling & Control Design First-cut controller Design experimental reference signal r(t), collect data Fit a process model with data selection and filtering to reflect objectives Design a Linear Quadratic Gaussian controller with Loop Transfer
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• Spring '10