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Springer Iterative Learning Control: Analysis, Design, Integration

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Description

Iterative Learning Control (ILC) represents a distinctive approach to control systems that harnesses past control information-including tracking errors and control input signals-to optimise present control actions. This Springer publication explores the fundamental principles and practical applications of ILC methodology. The framework operates through two essential phases: long-term memory components store historical control data, whilst subsequent fusion processes ensure the system meets critical specifications such as convergence and robustness. This comprehensive resource addresses analysis, design, and integration of ILC systems, making it valuable for engineers and researchers working with adaptive control systems, repetitive processes, and advanced automation applications.

Key Features

Iterative Learning Control methodology exploits past control information including tracking errors and control input signals to enhance system performance.

Two-phase ILC framework utilises long-term memory components to store historical control data for systematic analysis and optimisation.

Springer publication covering analysis, design, and integration approaches for implementing iterative learning control in practical applications.

Addresses convergence and robustness specifications essential for reliable control system operation across industrial and research environments.

Comprehensive resource suitable for engineers designing repetitive process control systems and researchers advancing adaptive control technologies.

Product Specifications

Format
paperback
Domain
Amazon UK
Publication Date
12 October 2012
Listed Since
03 March 2013

Barcode

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