Proceedings of the 44th IEEE Conference on Decision and Control
DOI: 10.1109/cdc.2005.1582851
|View full text |Cite
|
Sign up to set email alerts
|

Nonlinear Discrete-Time Control Approaches for Underactuated Manipulators

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
4
0

Publication Types

Select...
7
2

Relationship

0
9

Authors

Journals

citations
Cited by 9 publications
(5 citation statements)
references
References 10 publications
0
4
0
Order By: Relevance
“…In the proposed algorithm only one iteration is performed per time step. A similar approach using several iteration steps is described in Weidemann et al (2004).…”
Section: Nonlinear Model Predictive Controlmentioning
confidence: 99%
“…In the proposed algorithm only one iteration is performed per time step. A similar approach using several iteration steps is described in Weidemann et al (2004).…”
Section: Nonlinear Model Predictive Controlmentioning
confidence: 99%
“…In the proposed algorithm only one iteration is performed per time step. A similar approach using several iteration steps is described in Weidemann et al (2004). An improvement of the trajectory tracking behaviour can be achieved if an input vector resulting from an inverse system model is used as initial vector for the subsequent optimization step instead of the last input vector.…”
Section: Nonlinear Model Predictive Controlmentioning
confidence: 99%
“…Consequently, a system-specific tradeoff has to be made for the choice of M and t s . This paper follows the moving horizon approach with a constant prediction horizon and, hence, a constant dimension M of the corresponding optimization problem in contrast to the shrinking horizon approach, see Weidemann et al (2004), Weidemann et al (2005).…”
Section: Choice Of the Nmpc Design Parametersmentioning
confidence: 99%
“…Consequently, a system-specific trade-off has to be made for the choice of M and t s . This paper follows the moving horizon approach with a constant prediction horizon and, hence, a constant dimension m • M of the corresponding optimization problem in constrast to the shrinking horizon approach (Weidemann et al, 2004).…”
Section: Choice Of the Nmpc Design Parametersmentioning
confidence: 99%