|
|
books
| book details |
Deep Reinforcement Learning with Guaranteed Performance: A Lyapunov-Based Approach
By (author) Yinyan Zhang, By (author) Shuai Li, By (author) Xuefeng Zhou
|
| on special |
normal price: R 4 173.95
Price: R 3 756.95
|
| book description |
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.
| product details |
Normally shipped |
Publisher | Springer Nature Switzerland AG
Published date | 20 Nov 2019
Language |
Format | Hardback
Pages | 225
Dimensions | 235 x 155 x 0mm (L x W x H)
Weight | 0g
ISBN | 978-3-0303-3383-6
Readership Age |
BISAC | technology / automation
| other options |
Normally shipped |
Readership Age |
Normal Price | R 5 383.95
Price | R 4 844.95
| on special |
|
|
To view the items in your trolley please sign in.
| sign in |
|
|
|
| specials |
|
|
Mason Coile
Paperback / softback
224 pages
was: R 520.95
now: R 468.95
|
A terrifying locked-room mystery set in a remote outpost on Mars.
|
|
An epic love story with the pulse of a thriller that asks: what would you risk for a second chance at first love?
|
|
|
|
|
