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Springer Iterative Learning Control: Robustness and Monotonic Convergence

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£70.18 £36.72 £44.02 £51.32 £58.62 £65.92 £73.22 26 July 2026 16 August 2026 07 September 2026 29 September 2026 21 October 2026
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Description

This monograph examines the design of robust, monotonically-convergent iterative learning controllers for discrete-time systems. Iterative learning control (ILC) represents an efficient method that delivers significant performance improvements for systems operating in iterative or repetitive fashion, such as robot arms in manufacturing environments or batch processes in industrial settings. The work addresses fundamental ILC design principles whilst tackling two persistent research challenges: the limitations of ILC strategies that rely on nominal system knowledge, and the complexities of ensuring convergence guarantees. Published by Springer as part of the Communications and Control Engineering series, this monograph provides essential insights for engineers and researchers seeking to advance control system design for repetitive operations.

Key Features

Focuses on robust iterative learning controller design for discrete-time systems operating in repetitive cycles.

Addresses monotonic convergence challenges central to ILC implementation in manufacturing and industrial processes.

Part of Springer's Communications and Control Engineering series, offering authoritative technical depth.

Explores design strategies beyond nominal system assumptions for practical robustness.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
26 June 2007
Listed Since
19 February 2007

Barcode

No barcode data available

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