Impact-Invariant Control: Maximizing Control Authority During Impacts

Understand

When legged robots impact their environment executing dynamic motions, they undergo large changes in their velocities in a short amount of time.

• Measuring and applying feedback to these velocities is challenging, further complicated by uncertainty in the impact model and impact timing.
• This work proposes a general framework for adapting feedback control during impact by projecting the control objectives to a subspace that is invariant to the impact event.
• The resultant controller is robust to uncertainties in the impact event while maintaining maximum control authority over the impact-invariant subspace.