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Springer Iterative Learning Control: An Optimization Paradigm - Springer

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Description

This comprehensive text presents a coherent theoretical framework for iterative learning control algorithm design using operator representations and quadratic optimization methods. The book explores both signal and parameter optimisation approaches, demonstrating their formal mathematical relationships whilst treating them as distinct methodologies. Through detailed examples spanning linear systems, discrete-time and continuous-time implementations, readers gain practical access to inverse model control techniques and gradient-based design principles. Ideal for researchers and engineers working in industrial control systems, this volume from Springer's Advances in Industrial Control series bridges theoretical foundations with real-world applications, offering structured guidance for developing effective learning control algorithms across multiple system types and operational contexts.

Key Features

Iterative Learning Control develops algorithms grounded in operator theory and quadratic optimisation for industrial control applications.

The text covers signal optimisation and parameter optimisation approaches with detailed mathematical relationships and practical distinctions.

Comprehensive examples using linear, discrete-time and continuous-time systems provide concrete guidance for algorithm implementation.

Inverse model control and gradient-based design methods are integrated throughout the Springer publication for unified understanding.

Part of the Advances in Industrial Control series, offering academic rigour suitable for researchers and practising engineers.

Product Specifications

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

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