Explicit Control Barrier Function-based Safety Filters and their Resource-Aware Computation
This work addresses the problem of computational efficiency in safety-critical control systems for applications requiring high-frequency updates, representing an incremental improvement over existing CBF-based methods.
The paper tackles the challenge of efficiently implementing safety filters based on control barrier functions (CBFs) for real-time control at high frequencies by introducing a closed-form expression for controllers, which partitions the state-space into regions with different solutions, and demonstrates its applicability in examples like aerospace control and safe reinforcement learning.
This paper studies the efficient implementation of safety filters that are designed using control barrier functions (CBFs), which minimally modify a nominal controller to render it safe with respect to a prescribed set of states. Although CBF-based safety filters are often implemented by solving a quadratic program (QP) in real time, the use of off-the-shelf solvers for such optimization problems poses a challenge in applications where control actions need to be computed efficiently at very high frequencies. In this paper, we introduce a closed-form expression for controllers obtained through CBF-based safety filters. This expression is obtained by partitioning the state-space into different regions, with a different closed-form solution in each region. We leverage this formula to introduce a resource-aware implementation of CBF-based safety filters that detects changes in the partition region and uses the closed-form expression between changes. We showcase the applicability of our approach in examples ranging from aerospace control to safe reinforcement learning.