Iterative learning control and repetitive control in hard disk drive industry—A tutorial

Chen, YangQuan; Moore, Kevin L.; Yu, Jie; Zhang, Tao

doi:10.1002/acs.1003

Cited by 138 publications

(80 citation statements)

References 38 publications

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“…A tutorial on iterative learning control and repetitive control is given in [156]. The drawback of repetitive control is that exact knowledge of the period of the external signals is required.…”

Section: ) Focus Servomentioning

confidence: 99%

Control Methods in Data-Storage Systems

Cherubini

Chung

Messner

et al. 2012

IEEE Trans. Contr. Syst. Technol.

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Abstract-The recording performance of data-storage devices, in which write/read elements move relative to a storage medium to reliably store and retrieve information, depends on the capability of servo mechanisms to provide the necessary positioning accuracy. The desired characteristics of servo mechanisms for datastorage systems include robustness against variations of environmental parameters, high resolution, accuracy, stability, and fast response. This paper presents a comprehensive overview of advanced servo-control methods for data storage. The applications are to well-established recording technologies, including magnetic tape and magnetic disk systems as well as CD/DVD/Blue-Ray optical data-storage systems. Moreover, newer holographic and nearfield optical systems and the emerging probe-storage technology are also addressed. Emphasis is given to the potential exhibited by the technologies considered for achieving ultra-high storage capacity, as required by the exploding demand in data-storage capacity for archival systems and massive multimedia data storage.Index Terms-Control systems, hard-disk drive, holographic data storage, optical-disk drive, scanning-probe data storage, servomechanism, tape drive.

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Section: ) Focus Servomentioning

confidence: 99%

Control Methods in Data-Storage Systems

Cherubini

Chung

Messner

et al. 2012

IEEE Trans. Contr. Syst. Technol.

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“…The prime idea of ILC is to refine the control input to make better operation performance of the system on the next trial by use of updated data on the previous trial. Thus, ILC is applicable to systems with repetitively operated dynamics, such as multiagent systems consensus tracking, 2 robot arms for manufacturing, 3 hard disk drive, 4 and traffic flow in consecutive sections of a freeway. 5 As a model-free control, ILC can be applied to systems with strong nonlinearity, unknown dynamics structure, or uncertain parameters.…”

Section: Introductionmentioning

confidence: 99%

A novel iterative learning path-tracking control for nonholonomic mobile robots against initial shifts

Zhao

Zhou

et al. 2017

International Journal of Advanced Robotic Systems

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In this article, we propose a novel discrete-time iterative learning control framework for robust path-tracking problem of nonholonomic mobile robots. The contributions of this iterative learning control framework are threefolds: (1) With the application of a conventional feedback-aided P-type learning algorithm, the tracking error caused by a nonzero initial shift is detected. (2) By the introduction of an initial rectifying term, a novel iterative learning control scheme is proposed to improve the tracking performance. Sufficient conditions of convergence of this approach are given. (3) The convergence of the proposed algorithm for achieving the desired trajectory over a specified interval is proven theoretically. Simulation results validate the effectiveness of the proposed scheme.

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“…These results however, only include experimental results on a simple laboratory-scale motion system, and are more suited for a different class of systems than considered in this paper. The main contribution of the present paper is the design and experimental implementation of an iterative learning controller [8,10,24], in which the varying references and offline position measurements are explicitly addressed.…”

Section: Introductionmentioning

confidence: 99%