Force Feedback Model-Predictive Control via Online Estimation
Résumé
Nonlinear model-predictive control has recently
shown its practicability in robotics. However it remains limited
in contact interaction tasks due to its inability to leverage
sensed efforts. In this work, we propose a novel model-predictive
control approach that incorporates direct feedback from force
sensors while circumventing explicit modeling of the contact
force evolution. Our approach is based on the online estimation
of the discrepancy between the force predicted by the dynamics
model and force measurements, combined with high-frequency
nonlinear model-predictive control. We report an experimental
validation on a torque-controlled manipulator in challenging
tasks for which accurate force tracking is necessary. We show
that a simple reformulation of the optimal control problem
combined with standard estimation tools enables to achieve
state-of-the-art performance in force control while preserving
the benefits of model-predictive control, thereby outperforming
traditional force control techniques. This work paves the way
toward a more systematic integration of force sensors in model
predictive control.
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