Robust Safety-Critical Control for Dynamic Robotics
This work addresses safety-critical control for dynamic robotics, particularly in uncertain environments, but appears incremental as it builds on existing control Lyapunov and barrier function frameworks.
The authors tackled the problem of ensuring stability and safety for nonlinear robotic systems under model uncertainty by developing a robust control method using quadratic programs, achieving successful dynamic walking and precise foot placement on a bipedal robot and experimental validation on a spring-cart system.
We present a novel method of optimal robust control through quadratic programs that offers tracking stability while subject to input and state-based constraints as well as safety-critical constraints for nonlinear dynamical robotic systems in the presence of model uncertainty. The proposed method formulates robust control Lyapunov and barrier functions to provide guarantees of stability and safety in the presence of model uncertainty. We evaluate our proposed control design on dynamic walking of a five-link planar bipedal robot subject to contact force constraints as well as safety-critical precise foot placements on stepping stones, all while subject to model uncertainty. We conduct preliminary experimental validation of the proposed controller on a rectilinear spring-cart system under different types of model uncertainty and perturbations.