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Springer - Iterative Learning Control: An Optimization Paradigm

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Description

Master the complexities of algorithm design with Iterative Learning Control: An Optimization Paradigm. Published by Springer as part of the Advances in Industrial Control series, this book provides a coherent and general theoretical approach to control systems. Readers gain access to advanced concepts including operator representations and quadratic optimization. The text explores essential ideas such as inverse model control and gradient-based design. By utilizing detailed examples from linear, discrete, and continuous-time systems, the author makes complex theories accessible. This resource covers both signal and parameter optimization. While these two approaches are mathematically related, the book presents them separately to ensure clarity regarding their specific algorithms. It is an essential technical resource for those studying industrial control and computer science architecture.

Key Features

Develops a general theoretical approach to algorithm design for iterative learning control.

Covers advanced quadratic optimization concepts and operator representations.

Includes detailed examples from linear, discrete, and continuous-time systems.

Provides instruction on both signal and parameter optimization methods.

Explores related ideas such as inverse model control and gradient-based design.

Part of the professional Advances in Industrial Control series by Springer.

Product Specifications

Format
hardcover
Domain
Amazon UK
Publication Date
10 November 2015
Listed Since
11 August 2015

Barcode

No barcode data available

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