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Advances in Industrial Control
  • Advances in Industrial Control
Advances in Industrial Control

Advances in Industrial Control

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Advances in Industrial Control

Lian, Bosen; Xue, Wenqian; Lewis, Frank L.; Modares, Hamidreza; Kiumarsi, Bahare

Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games develops its specific learning techniques, motivated by application to autonomous driving and microgrid systems, with breadth and depth: integral reinforcement learning (RL) achieves model-free control without system estimation compared with system identification methods and their inevitable estimation errors; novel inverse RL methods fill a gap that will help them to attract readers interested in finding data-driven model-free solutions for inverse optimization and optimal control, imitation learning and autonomous driving among other areas.

 

Graduate students will find that this book offers a thorough introduction to integral and inverse RL for feedback control related to optimal regulation and tracking, disturbance rejection, and multiplayer and multiagent systems. For researchers, it provides a combination of theoretical analysis, rigorous algorithms, and a wide-ranging selection of examples. The book equips practitioners working in various domains – aircraft, robotics, power systems, and communication networks among them – with theoretical insights valuable in tackling the real-world challenges they face.

Details

Published by: Springer

Publication Date: 2025-04-02

Format: Paperback

ISBN-13: 9783031452543

DOI: 10.1007/978-3-031-45252-9

Dimensions: 235cm x155cm

Pages: 267

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