Variable Gain Design in Stochastic Iterative Learning Control
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Beschreibung
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.
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.
Provides first comprehensive investigation of multiple improved stochastic iterative learning control via gain design Proposes four mainstream gain design strategies for stochastic ILC to achieve multiple performance demands Establishes a systematic design and analysis framework for stochastic ILC with in-depth technical discussions
Autor*in
Dong Shen
Themen in »Variable Gain Design in Stochastic Iterative Learning Control«
Iterative learning control Variable gain Decreasing gain design Adaptive gain design Event-triggering gain design Optimal gain design Point-to-point tracking systems Large-scale systems Varying trial lengths Multi-agent systems Fading communication Multi-sensor systems Networked control structure
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“The methods presented in this monograph, widely based on the author’s contributions … . Each method is justified by a careful convergence analysis completed by nice simulation examples; the latter are very helpful for the reader and contribute to make the book pedagogical. The book ends with a list of more than 200 references and a (rather small) subject index.” (Henri Bourlès, zbMATH 1561.93001, 2025)
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