AI & ML
Stability Analysis in Multi-Constraint Safety Filters for Linear Systems
arXiv eess.SY0 views
arXiv:2606.15942v2 Announce Type: replace Abstract: Multi-constraint safety filters based on control barrier functions for linear systems with affine state constraints yield continuous piecewise-affine closed-loop dynamics and may introduce boundary equilibria and unstable active-set modes. Although these filters guarantee forward invariance, they can change nominal stability, and it remains unclear when unstable modes cause divergence versus bounded, convergent behavior. This paper develops a geometric framework to analyze these cases: leveraging explicit active-set realizations, we show that equilibria associated with nonempty active sets lie on the corresponding constraint faces and that any unstable directions are tangent to those faces due to exponential enforcement of the active constraints. We characterize mode stability via a minimum-phase test, provide a sufficient recession-cone certificate for divergence under a fixed active set, and derive tractable linear-matrix-inequality conditions for global exponential stability or boundedness using Lyapunov and LaSalle arguments.
Source:https://arxiv.org/abs/2606.15942PreviousGeneralized Model Predictive Path Integral Control as Expectation--MaximizationNextReinforcement Learning-Based Traffic Signal Control for IoT-Enabled Intersections
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