Variable Gain Design in Stochastic Iterative Learning Control [electronic resource] / by Dong Shen.

Por: Shen, Dong [author.]Colaborador(es): SpringerLink (Online service)Tipo de material: TextoTextoSeries Intelligent Control and Learning Systems ; 13Editor: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: XI, 350 p. 101 illus., 100 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789819782819Tema(s): Control engineering | Stochastic processes | System theory | Control theory | Industrial engineering | Automation | Control and Systems Theory | Stochastic Systems and Control | Systems Theory, Control | Industrial AutomationFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 629.8312 | 003 Clasificación LoC:TJ212-225Recursos en línea: Libro electrónicoTexto
Contenidos:
Introduction -- Preliminary Results -- Decreasing Gain Design -- Adaptive Gain Design -- Event triggering Gain Design -- Optimal Gain Design -- Conclusions and Open Problems -- References.
En: Springer Nature eBookResumen: This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. The key concept for the gain design is to balance multiple performance indices such as high tracking precision, effective noise reduction, and fast convergence speed. These gain design techniques can be applied to various control algorithms for stochastic systems to realize a high tracking performance. This book provides a series of design and analysis techniques for the establishment of a systematic framework of gain design in SILC. The book is intended for scholars and graduate students who are interested in stochastic control, recursive algorithms design, and iterative learning control.
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Introduction -- Preliminary Results -- Decreasing Gain Design -- Adaptive Gain Design -- Event triggering Gain Design -- Optimal Gain Design -- Conclusions and Open Problems -- References.

This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. The key concept for the gain design is to balance multiple performance indices such as high tracking precision, effective noise reduction, and fast convergence speed. These gain design techniques can be applied to various control algorithms for stochastic systems to realize a high tracking performance. This book provides a series of design and analysis techniques for the establishment of a systematic framework of gain design in SILC. The book is intended for scholars and graduate students who are interested in stochastic control, recursive algorithms design, and iterative learning control.

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