Model Predictive Control of Biped Walking with Bounded Uncertainties - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2018

Model Predictive Control of Biped Walking with Bounded Uncertainties


A biped walking controller for humanoid robots has to handle together hard constraints, dynamic environments, and uncertainties. Model Predictive Control (MPC) is a suitable and widely used control method to handle the first two issues. Uncertainties on the robot imply a non-zero tracking error when trying to follow a reference motion. A standard solution for this issue is to use tighter constraints by introducing some hand tuned safety margins, for the reference motion generation to ensure that the actual robot motion will satisfy all constraints even in presence of the tracking error. In this article, we find bounds for the tracking error and we show how such safety margins can be precisely computed from the tracking error bounds. Also, a tracking control gain is proposed to reduce the restrictiveness introduced with the safety margins. MPC with these considerations ensure the correct operation of the biped robot under a given degree of uncertainties when it is implemented in open-loop. Nevertheless, the straightforward way to implement an MPC closed-loop scheme fails. We discuss the reasons for this failure and propose a robust closed-loop MPC scheme.
Fichier principal
Vignette du fichier
Villa-Wieber_2017_RCLMPC.pdf (338.43 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01649580 , version 1 (27-11-2017)


Open licence - etalab



Nahuel A Villa, Pierre-Brice Wieber. Model Predictive Control of Biped Walking with Bounded Uncertainties. HUMANOIDS 2017 - IEEE RAS International Conference on Humanoid Robots, Nov 2017, Birmingham, United Kingdom. pp.836-841, ⟨10.1109/HUMANOIDS.2017.8246969⟩. ⟨hal-01649580⟩
380 View
664 Download



Gmail Facebook X LinkedIn More