Safe Control Synthesis with Uncertain Dynamics and Constraints
This work addresses safety-critical control for autonomous systems like robots in uncertain environments, representing an incremental advancement by extending existing CLF-CBF methods to handle uncertainty.
The paper tackles safe control synthesis for dynamical systems with uncertain dynamics and constraints by formulating probabilistic and robust control Lyapunov and barrier functions, resulting in a second-order cone program for efficient control. In simulations of an autonomous robot navigating unknown environments, it demonstrates performance improvements over a baseline quadratic programming approach.
This paper considers safe control synthesis for dynamical systems with either probabilistic or worst-case uncertainty in both the dynamics model and the safety constraints. We formulate novel probabilistic and robust (worst-case) control Lyapunov function (CLF) and control barrier function (CBF) constraints that take into account the effect of uncertainty in either case. We show that either the probabilistic or the robust (worst-case) formulation leads to a second-order cone program (SOCP), which enables efficient safe and stable control synthesis. We evaluate our approach in PyBullet simulations of an autonomous robot navigating in unknown environments and compare the performance with a baseline CLF-CBF quadratic programming approach.