This paper focuses on the problem of determining the most appropriate Two Degrees of Freedom (2DoF) control architecture, when the FeedForward (FF) action is the result of a stable model inversion procedure. The purpose is to define a control scheme with enhanced tracking performance even in the case of non minimum phase MIMO plant affected by polytopic uncertainty and with a possible non hyperbolic internal dynamics. The new proposed 2DoF architecture is given by an optimal balance of the control actions produced by FeedForward Plant Inversion (FFPI) and FeedForward Closed Loop Inversion (FFCLI). This new architecture is referred to as FeedForward Optimally Balanced Inversion (FFOBI). Robustness with respect to polytopic uncertainty is obtained using a min-max optimization approach. Numerical results show that the FFOBI improves the tracking of both FFPI and FFCLI

Enhanced trajectory tracking using optimally combined feedforward plant inversion and feedforward closed loop inversion / Jetto, Leopoldo; Orsini, Valentina. - In: EUROPEAN JOURNAL OF CONTROL. - ISSN 0947-3580. - 63:(2022), pp. 223-231. [10.1016/j.ejcon.2021.11.004]

Enhanced trajectory tracking using optimally combined feedforward plant inversion and feedforward closed loop inversion

leopoldo jetto;valentina orsini
2022-01-01

Abstract

This paper focuses on the problem of determining the most appropriate Two Degrees of Freedom (2DoF) control architecture, when the FeedForward (FF) action is the result of a stable model inversion procedure. The purpose is to define a control scheme with enhanced tracking performance even in the case of non minimum phase MIMO plant affected by polytopic uncertainty and with a possible non hyperbolic internal dynamics. The new proposed 2DoF architecture is given by an optimal balance of the control actions produced by FeedForward Plant Inversion (FFPI) and FeedForward Closed Loop Inversion (FFCLI). This new architecture is referred to as FeedForward Optimally Balanced Inversion (FFOBI). Robustness with respect to polytopic uncertainty is obtained using a min-max optimization approach. Numerical results show that the FFOBI improves the tracking of both FFPI and FFCLI
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/294535
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