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A Newton method with always feasible iterates for Nonlinear Model Predictive Control of walking in a multi-contact situation

Authors :
Pierre-Brice Wieber
Dimitar Dimitrov
Diana Serra
Alexander Sherikov
Camille Brasseur
Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione [Napoli] (DIETI)
Università degli studi di Napoli Federico II
Modelling, Simulation, Control and Optimization of Non-Smooth Dynamical Systems (BIPOP)
Inria Grenoble - Rhône-Alpes
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK )
Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
University of Naples Federico II = Università degli studi di Napoli Federico II
Source :
Humanoids, IEEE-RAS 2016-International Conference on Humanoid Robots (Humanoids), IEEE-RAS 2016-International Conference on Humanoid Robots (Humanoids), Nov 2016, Cancun, Mexico. pp.932-937, ⟨10.1109/HUMANOIDS.2016.7803384⟩
Publication Year :
2016
Publisher :
IEEE, 2016.

Abstract

International audience; In this paper, we present a Nonlinear Model Predictive Control scheme, which is able to generate walking motions in multi-contact situations. Walking up and down stairs with an additional hand support is a typical example, which we address in simulation. Computing such a nonlinear control scheme is usually done with a Newton method, a potentially time-consuming procedure involving iterative linearizations. We propose here a Newton method which is specifically designed to provide at each iteration a feasible solution, always satisfying the (nonlinear) dynamic balance constraints. This results in a significant reduction in computation time, by minimizing the number of necessary iterations to reach a feasible solution.

Details

Database :
OpenAIRE
Journal :
2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)
Accession number :
edsair.doi.dedup.....62d91126e7e4e88baae4af917a64bb49
Full Text :
https://doi.org/10.1109/humanoids.2016.7803384