Variable Gain Design in Stochastic Iterative Learning Control by Dong Shen
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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Set Theory-Based Spacecraft Dynamics and Control
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Data-Driven Spatiotemporal Modeling and Control of Nonlinear Distributed Parameter Systems and Their Applications
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Robust Adaptive Control of Nonlinear Interconnected Systems with Input Nonlinearities and Faults
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Constrained Cooperation: Event-Triggered and Intelligent Backstepping Control for Nonlinear Multiagent Systems
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Disagreement Behavior Analysis of Signed Networks
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Strict-Feedback Nonlinear Systems
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Iterative Learning Control for Network Systems Under Constrained Information Communication
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Discrete-Time Adaptive Iterative Learning Control
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Complex-Valued Neural Networks Systems with Time Delay
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Data-Driven Fault Detection and Reasoning for Industrial Monitoring
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Distributed Fault-Tolerant Consensus Control of Leader-Following Systems
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Variance-Constrained Filtering for Stochastic Complex Systems
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Analysis and Design of Delayed Neural Networks
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Distributed Impulsive Coordination of Multi-Agent Systems
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Fault Diagnosis and Fault-Tolerant Control of Nonlinear Systems with Higher System Input Powers
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Intelligent Control of Nonlinear Systems and Nonlinear Multi-Agent Systems with Complex Constraints
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Finite-Time Control of Networked Systems
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Adaptive Collaborative Control of Multiagent Systems by Event-Triggered Mechanisms
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Fault-Tolerant Control for Time-Varying Delayed T-S Fuzzy Systems
From 2010 to 2012, Dr. Shen was a Post-Doctoral Fellow with the Institute of Automation, CAS. Since 2012, he has been an Associate Professor with College of Information Science and Technology, Beijing University of Chemical Technology (BUCT), Beijing, China. He was a visiting scholar at National University of Singapore from 2016 to 2017.
Dr. Shen's current research interests include iterative learning control, stochastic control and optimization. He has published more than 50 refereed journal and conference papers. He is the author of Stochastic Iterative Learning Control (Science Press, 2016, in Chinese), co-author of Iterative Learning Control for Multi-Agent Systems Coordination (Wiley, 2017), and co-editor of Service Science, Management and Engineering: Theory and Applications (Academic Press and Zhejiang University Press, 2012). Dr. Shen received the IEEE CSS Beijing Chapter Young Author Prize in 2014 and the Wentsun Wu Artificial Intelligence Science and Technology Progress Award in 2012.
| SKU | Unavailable |
| ISBN 13 | 9789819782802 |
| Title | Variable Gain Design in Stochastic Iterative Learning Control |
| Author | Dong Shen |
| Series | Intelligent Control And Learning Systems |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer Verlag, Singapore |
| Year published | 2025-01-03 |
| Number of pages | 350 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |