Real-time Safety Index Adaptation for Parameter-varying Systems via Determinant Gradient Ascend
<|reference_start|>Real-time Safety Index Adaptation for Parameter-varying Systems via Determinant Gradient Ascend: Safety Index Synthesis (SIS) is critical for deriving safe control laws. Recent works propose to synthesize a safety index (SI) via nonlinear programming and derive a safe control law such that the system 1) achieves forward invariant (FI) with some safe set and 2) guarantees finite time convergence (FTC) to that safe set. However, real-world system dynamics can vary during run-time, making the control law infeasible and invalidating the initial SI. Since the full SIS nonlinear programming is computationally expensive, it is infeasible to re-synthesize the SI each time the dynamics are perturbed. To address that, this paper proposes an efficient approach to adapting the SI to varying system dynamics and maintaining the feasibility of the safe control law. The proposed method leverages determinant gradient ascend and derives a closed-form update to safety index parameters, enabling real-time adaptation performance. A numerical study validates the effectiveness of our approach.<|reference_end|>
@article{chen2024real-time,
    title={Real-time Safety Index Adaptation for Parameter-varying Systems via
  Determinant Gradient Ascend},
    author={Rui Chen, Weiye Zhao, Ruixuan Liu, Weiyang Zhang, Changliu Liu},
    journal={arXiv preprint arXiv:2403.14968},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14968},
    primaryClass={eess.SY cs.SY}
}