Safety-Critical Control via Recurrent Tracking Functions
This addresses safety control for nonlinear systems, but is incremental as it builds on existing CBF methods with a novel relaxation.
The paper tackles the challenge of synthesizing safety-critical controllers for high-order nonlinear systems by introducing Recurrent Tracking Functions (RTFs), which relax monotonic decay requirements to ensure safety through finite-time recurrence, validated in a proof-of-concept numerical experiment.
This paper addresses the challenge of synthesizing safety-critical controllers for high-order nonlinear systems, where constructing valid Control Barrier Functions (CBFs) remains computationally intractable. Leveraging layered control, we design CBFs in reduced-order models (RoMs) while regulating full-order models' (FoMs) dynamics at the same time. Traditional Lyapunov tracking functions are required to decrease monotonically, and systematic synthesis methods for such functions exist only for fully-actuated systems. To overcome this limitation, we introduce Recurrent Tracking Functions (RTFs), which replace the monotonic decay requirement with a weaker finite-time recurrence condition. This relaxation permits transient deviations of tracking errors while ensuring safety. By integrating CBFs for RoMs with RTFs, we construct recurrent CBFs (RCBFs) whose zero-superlevel set is control $Ï$-recurrent, and guarantee safety for all initial states in such a set when RTFs are satisfied. We establish theoretical safety guarantees and validate the approach through a proof-of-concept numerical experiment, demonstrating RTFs' effectiveness and the safety of FoMs.