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Authors
Tae-Hyoung Kim; Xiaoguang Zheng; Toshiharu Sugie

Identifier
10.3182/20060329-3-AU-2901.00135

Index Terms
continuous-time systems,system identification,identificationalgorithm,learning control,measurement noise

Abstract
This paper considers the problem of noise tolerant identification of a class of continuous-time systems with unknown constant-input disturbance. To this aim, we first propose an extended formulation of identification method using iterative learning control (ILC) scheme based on sampled I/O data in the presence of measurement noise. The proposed ILC method has distinctive features as follows. Its learning law works in the prescribed finite dimensional parameter space and employs I/O data of all past trials efficiently. Also the time-derivative of tracking error is not required. Then, it is presented how the parameter estimation can be achieved by the proposed ILC method and how robust it is against measurement noise through numerical examples.

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