Generalized iterative learning control using successive projection: algorithm, convergence and experimental verification

Chen, Yiyang, Chu, Bing and Freeman, Christopher (2020) Generalized iterative learning control using successive projection: algorithm, convergence and experimental verification. IEEE Transactions on Control Systems Technology, 28 (6), 2079-2091. (doi:10.1109/TCST.2019.2928505).

Record type: Article

Abstract

Iterative learning control (ILC) is a high performance control design method for systems working in a repetitive manner. ILC has traditionally focused on tracking a reference defined at all points over a finite time interval; recent developments have begun to exploit the design freedom unlocked by tracking only a finite number of distinct time instants driven by the needs of e.g. robotic pick-and-place tasks. This paper proposes a generalized ILC paradigm which extends and unifies the scope of existing design frameworks by amalgamating previous task descriptions and embedding system constraints on the input and output. A novel solution is then derived using a successive projection method which provides well defined convergence properties. The proposed design framework is illustrated by applying it to a spatial reference tracking problem with experimental results on a gantry robot testing platform demonstrating its effectiveness.

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Chen_IEEECST_accepted - Accepted Manuscript

Available under License University of Southampton Accepted Manuscript Licence.

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