A flatness-based nonlinear predictive approach for crane control
Authors: Th. Devos, J. Lévine, IFAC Workshop on Nonlinear Model Predictive Control for Fast Systems, October 2006, Grenoble
We study in this paper a flatness-based nonlinear predictive control law for a reduced size model of a crane studied in (Kiss, 2001; Kiss et al., 1999; Kiss et al., 2000a; Kiss et al., 2000b). The controller is composed of two parts: the first one is a traditional PD output feedback to track the reference trajectory and reject small perturbations, the second one consists of updating the reference trajectory from the current estimated state of the crane to the desired equilibrium point on a receding horizon each time the pursuit error exceeds a given threshold.
Simulations are presented to illustrate its performances.
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BibTeX:
@Proceedings{,
author = {Th. Devos, J. Lévine},
editor = {},
title = {A flatness-based nonlinear predictive approach for crane control},
booktitle = {IFAC Workshop on Nonlinear Model Predictive Control for Fast Systems},
volume = {},
publisher = {},
address = {Grenoble},
pages = {1-6},
year = {2006},
abstract = {We study in this paper a flatness-based nonlinear predictive control law for a reduced size model of a crane studied in (Kiss, 2001; Kiss et al., 1999; Kiss et al., 2000a; Kiss et al., 2000b). The controller is composed of two parts: the first one is a traditional PD output feedback to track the reference trajectory and reject small perturbations, the second one consists of updating the reference trajectory from the current estimated state of the crane to the desired equilibrium point on a receding horizon each time the pursuit error exceeds a given threshold.
Simulations are presented to illustrate its performances.},
keywords = {nonlinear predictive control, differential flatness, motion planning, crane control}}
We study in this paper a flatness-based nonlinear predictive control law for a reduced size model of a crane studied in (Kiss, 2001; Kiss et al., 1999; Kiss et al., 2000a; Kiss et al., 2000b). The controller is composed of two parts: the first one is a traditional PD output feedback to track the reference trajectory and reject small perturbations, the second one consists of updating the reference trajectory from the current estimated state of the crane to the desired equilibrium point on a receding horizon each time the pursuit error exceeds a given threshold.
Simulations are presented to illustrate its performances.
Download PDF
BibTeX:
@Proceedings{,
author = {Th. Devos, J. Lévine},
editor = {},
title = {A flatness-based nonlinear predictive approach for crane control},
booktitle = {IFAC Workshop on Nonlinear Model Predictive Control for Fast Systems},
volume = {},
publisher = {},
address = {Grenoble},
pages = {1-6},
year = {2006},
abstract = {We study in this paper a flatness-based nonlinear predictive control law for a reduced size model of a crane studied in (Kiss, 2001; Kiss et al., 1999; Kiss et al., 2000a; Kiss et al., 2000b). The controller is composed of two parts: the first one is a traditional PD output feedback to track the reference trajectory and reject small perturbations, the second one consists of updating the reference trajectory from the current estimated state of the crane to the desired equilibrium point on a receding horizon each time the pursuit error exceeds a given threshold.
Simulations are presented to illustrate its performances.},
keywords = {nonlinear predictive control, differential flatness, motion planning, crane control}}