C:/Documents and Settings/butterwo/My Documents/conference_journal_papers/ACC_2011_ILC/ACC_2011_ILC/ACC_2011_ILC.dvi
نویسندگان
چکیده
In previous work, we compared the raster tracking performance of two distinct combined feedforward/feedback control architectures while using model-inverse control [1], [2]. In this paper, we extend that previous work into the application of parallel and serial iterative learning control (ILC) architectures. These ILC architectures naturally relate to the two previously studied combined feedforward/feedback control architectures, feedforward closed-loop injection (FFCLI) and feedforward plant injection (FFPI). Experimental learning results from an AFM raster scanner are provided as well as results comparing the FFPI and FFCLI architectures with those of the learned performance for parallel and series ILC. Further, a discussion of the use of ILC for raster learning and the series and parallel ILC architectures is included.
منابع مشابه
C:/Documents and Settings/butterwo/My Documents/conference_journal_papers/AJC_spcl_iss_SPM/AJC_AFM_control_arch/AJC_AFM_control_arch.dvi
We evaluate the performance of two control architectures applied to atomic force microscopes (AFM). Feedback-only control is a natural solution and has been applied widely. Expanding on that, combining feedback controllers with plant-injection feedforward filters has been shown to greatly improve tracking performance in AFMs. Alternatively, performance can also be improved by the use of a close...
متن کاملC:/Documents and Settings/butterwo/My Documents/conference_journal_papers/CMMI_2009/CMMM_09_AFM_arch.dvi
We evaluate the performance of two control architectures applied to atomic force microscopes (AFM). Feedback-only control is a natural solution and has been applied widely. Expanding on that, combining feedback controllers with plant-injection feedforward filters has been shown to greatly improve tracking performance in AFMs. Alternatively, performance can also be improved by the use of a close...
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