Safety-Aware Performance Boosting for Constrained Nonlinear Systems
This work addresses safety-critical control challenges in robotics or autonomous systems, offering a novel architecture that is incremental in improving upon existing predictive safety filters.
The paper tackles the problem of enhancing control performance for nonlinear constrained systems while maintaining safety, by integrating a performance-boosting controller with a scheduled predictive safety filter, resulting in a provable expansion of safe, stable trajectories and demonstration on an inverted pendulum task.
We study a control architecture for nonlinear constrained systems that integrates a performance-boosting (PB) controller with a scheduled Predictive Safety Filter (PSF). The PSF acts as a pre-stabilizing base controller that enforces state and input constraints. The PB controller, parameterized as a causal operator, influences the PSF in two ways: it proposes a performance input to be filtered, and it provides a scheduling signal to adjust the filter's Lyapunov-decrease rate. We prove two main results: (i) Stability by design: any controller adhering to this parametrization maintains closed-loop stability of the pre-stabilized system and inherits PSF safety. (ii) Trajectory-set expansion: the architecture strictly expands the set of safe, stable trajectories achievable by controllers combined with conventional PSFs, which rely on a pre-defined Lyapunov decrease rate to ensure stability. This scheduling allows the PB controller to safely execute complex behaviors, such as transient detours, that are provably unattainable by standard PSF formulations. We demonstrate this expanded capability on a constrained inverted pendulum task with a moving obstacle.