Ding, J. orcid.org/0000-0002-2396-9688, Han, L. orcid.org/0000-0002-4023-3322, Ge, L. orcid.org/0000-0003-3754-1911 et al. (2 more authors) (2022) Robust Locomotion Exploiting Multiple Balance Strategies: An Observer-Based Cascaded Model Predictive Control Approach. IEEE/ASME Transactions on Mechatronics, 27 (4). pp. 2089-2097. ISSN 1083-4435
Abstract
Robust locomotion is a challenging task for humanoid robots, especially when considering dynamic disturbances. This article proposes a disturbance observer-based cascaded model predictive control (MPC) approach for bipedal locomotion, with the capability of exploiting ankle, stepping, hip and height variation strategies. Specifically, based on the variable-height inverted pendulum model, a nonlinear MPC that is run at a low frequency is built for 3-D locomotion (i.e., with height variation) while accounting for the footstep modulation as well. Differing from previous works, the nonlinear MPC is formulated as a convex optimization problem by semidefinite relaxation. Subsequently, assuming a flywheel at the pelvis center, a linear MPC that is run at a high frequency is proposed to regulate angular momentum (e.g., through rotating the upper body), which is solved by convex quadratic programming. To run the cascaded MPC in a closed-loop manner, a high order sliding mode observer is designed to estimate system states and dynamic disturbances simultaneously. Simulation and hardware experiments demonstrate the walking robustness in real-world scenarios, including 3-D walking with varying speeds, walking across non-coplanar terrains and push recovery.
Metadata
Item Type: | Article |
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Authors/Creators: | |
Keywords: | Legged locomotion; Trajectory; Foot; Solid modeling; Predictive control; Hip; Computational modeling; Bipedal locomotion; convex optimization; disturbance observer; model predictive control; reactive walking |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Sep 2023 13:00 |
Last Modified: | 07 Sep 2023 13:00 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/tmech.2022.3173805 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:203152 |