Variable Gain Design in Stochastic Iterative Learning Control

Dong Shen

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Springer Nature Singapore img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Elektronik, Elektrotechnik, Nachrichtentechnik

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.

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Schlagwörter

Varying trial lengths, Variable gain, Large-scale systems, Point-to-point tracking systems, Multi-sensor systems, Optimal gain design, Decreasing gain design, Event-triggering gain design, Networked control structure, Iterative learning control, Adaptive gain design, Multi-agent systems, Fading communication