Cantitate/Preț
Produs
Total produse
Vezi coșul
Deep Reinforcement Learning with Guaranteed Performance: A Lyapunov-Based Approach de Yinyan Zhang

Deep Reinforcement Learning with Guaranteed Performance: A Lyapunov-Based Approach: Studies in Systems, Decision and Control, cartea 265

Autor Yinyan Zhang, Shuai Li, Xuefeng Zhou
en Limba Engleză Paperback – 20 noi 2020
This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances.
It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution.
Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 67836 lei  38-44 zile
  Springer International Publishing – 20 noi 2020 67836 lei  38-44 zile
Hardback (1) 76874 lei  43-57 zile
  Springer International Publishing – 20 noi 2019 76874 lei  43-57 zile

Din seria Studies in Systems, Decision and Control

Preț: 67836 lei

Preț vechi: 89258 lei
-24% Nou

Puncte Express: 1018
Preț estimativ în valută:
12982€ • 13485$ • 10784£

Carte tipărită la comandă

Livrare economică 29 ianuarie-04 februarie 25

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783030333867
ISBN-10: 3030333868
Pagini: 225
Ilustrații: XVII, 225 p. 61 illus., 50 illus. in color.
Dimensiuni: 155 x 235 mm
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Systems, Decision and Control

Locul publicării:Cham, Switzerland

Cuprins

A Survey of Near-Optimal Control of Nonlinear Systems.- Near-Optimal Control with Input Saturation.- Adaptive Near-Optimal Control with Full-State Feedback.- Adaptive Near-Optimal Control Using Sliding Mode.- Model-Free Adaptive Near-Optimal Tracking Control.- Adaptive Kinematic Control of Redundant Manipulators.- Redundancy Resolution with Periodic Input Disturbance.

Notă biografică

Yinyan Zhang received his B.E. degree from Sun Yat-sen University, China. He is currently a Ph.D. student at the Department of Computing at The Hong Kong Polytechnic University. His main research interests include nonlinear systems, dynamic neural networks, and robotics. He has authored or co-authored more than 40 scientific papers, including 17 IEEE transaction papers.
Dr Shuai Li received his B.E. degree in Precision Mechanical Engineering from Hefei University of Technology, China, in 2005, his M.E. in Automatic Control Engineering from University of Science and Technology of China in 2008, and his Ph.D. in Electrical and Computer Engineering from Stevens Institute of Technology, USA, in 2014. He is currently working at the Department of Computing at The Hong Kong Polytechnic University. He has been involved in research on and application of neural networks/dynamics for over 10 years, and has published numerous scientific works of various types, including morethan 50 IEEE transactions papers, and 60 SCI-indexed papers. He is now serving as an associate editor of the International Journal of Advanced Robotic Systems, Frontiers in Neurorobotics, Neural Processing Letters, and the Journal of Rehabilitation Robotics. He is also an editorial board member of the International Journal of Distributed Sensor Networks and Neural Computation & Applications. His main research interests include neural networks, robotics, control of networked systems, computation and optimization.
Dr Xuefeng Zhou received his master’s and Ph.D. degrees in Mechanical Engineering from the South China University of Technology, Guangzhou, China, in 2006 and 2011, respectively. He was a Postdoctoral Fellow at the School of Automation Science and Engineering, South China University of Technology, from 2012 to 2013. He is currently the Team Leader of the Robotics Group, Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science, Guangzhou. Hisresearch interests include motion planning and force control. Dr Zhou was a recipient of the Best Student Paper Award at the IEEE International Conference on Robotics and Biomimetics in 2010.

Textul de pe ultima copertă

This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances.
It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution.
Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.

Caracteristici

Focuses on adaptive near-optimal control of nonlinear systems utilizing Taylor expansion and auxiliary systems Discusses both theoretical and practical aspects of control of industrial robotics and DC motors Identifies novel methods for the redundancy resolution of redundant manipulators