Trajectory Free Linear Model Predictive Control for Stable Walking in the Presence of Strong Perturbations

By Pierre-Brice Wieber. IEEE-RAS International Conference on Humanoid Robots, 2006

594 citations recorded by the source corpus.

Subfields: Automotive Engineering, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/d735b0ba3a716704d07f00e7b1b00c3d7cdcf0cf

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In Automotive Engineering

In Biomedical Engineering

Citations in the corpus

Deep Haptic Model Predictive Control for Robot-Assisted Dressing (2017, background): “Some examples include aerial control vehicles [11], [12] and robot locomotion [13], [14].”
Control of Instantaneously Coupled Systems applied to humanoid walking (2010, methodology): “Model Predictive Control (MPC) and receding horizon control offer methods of generating trajectories online that continuously start from the current robot state [5].”
Online Walking Motion Generation with Automatic Footstep Placement (2010, background): “In order to overcome this limitation, it has been proposed to introduce these constraints explicitly into the regulator, turning the LQR scheme into a more general linear model predictive control (LMPC) scheme, which led to a significant improvement in its capacity to deal with these difficult cases”
Three-Dimensional Bipedal Walking Control Based on Divergent Component of Motion (2015, background): “Wieber [9] proposes a trajectory free linear MPC scheme, allowing for compensation of strong perturbations.”
Viability and predictive control for safe locomotion (2008, unknown): “And interestingly enough, it appears that with such an MPC scheme, considering short prediction horizons, no more than 1 s, what implies looking only one step ahead, is already enough in preserving viability and generating safe locomotion even in the presence of strong perturbations [19].”
Bipedal walking control based on Capture Point dynamics (2011, methodology): “This approach was extended into a linear model predictive controller by Wieber [6] allowing for explicit integration of constraints on the ZMP as well as footstep adaptation.”