株式会社極東書店トップ ＞ 商品一覧 ＞ Robotic Manipulators and Vehicles: Control, Estimation and Filtering. 2018 ed.
商品詳細
Robotic Manipulators and Vehicles: Control, Estimation and Filtering. 2018 ed.
・ISBN 978-3-319-77850-1 hard EUR 199.99
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お気に入り
★★★
| 著者・編者 | Rigatos, Gerasimos / Busawon, Krishna, |
|---|---|
| シリーズ | (Studies in Systems, Decision and Control) |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2018 |
| ページ数 | 734 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A02-81019＞ |
解説
This monograph addresses problems of:
* nonlinear control, estimation and filtering for robotic manipulators (multi-degree-of freedom rigid-link robots, flexible-link robots, underactuated, redundant and cooperating manipulators and closed-chain robotic mechanisms); and* nonlinear control, estimation and filtering for autonomous robotic vehicles operating on the ground, in the air, and on and under water, independently and in cooperating groups.
The book is a thorough treatment of the entire range of applications of robotic manipulators and autonomous vehicles. The nonlinear control and estimation methods it develops can be used generically, being suitable for a wide range of robotic systems. Such methods can improve robustness, precision and fault-tolerance in robotic manipulators and vehicles at the same time as enabling the reliable functioning of these systems under variable conditions, model uncertainty and external perturbations.
* nonlinear control, estimation and filtering for robotic manipulators (multi-degree-of freedom rigid-link robots, flexible-link robots, underactuated, redundant and cooperating manipulators and closed-chain robotic mechanisms); and* nonlinear control, estimation and filtering for autonomous robotic vehicles operating on the ground, in the air, and on and under water, independently and in cooperating groups.
The book is a thorough treatment of the entire range of applications of robotic manipulators and autonomous vehicles. The nonlinear control and estimation methods it develops can be used generically, being suitable for a wide range of robotic systems. Such methods can improve robustness, precision and fault-tolerance in robotic manipulators and vehicles at the same time as enabling the reliable functioning of these systems under variable conditions, model uncertainty and external perturbations.