Learning-enabled Parameter Synthesis for Nonlinear Systems from Signal Temporal Logic

Abstract:Signal Temporal Logic (STL) is increasingly used to describe interpretable objectives and constraints for optimal control and learning methods, especially when no target time series data is available. In this work, we propose to synthesize parameters for nonlinear systems that robustly satisfy continuous-time STL specifications for uncertain initial conditions. To this end, we use gradient-based optimization along with set-based reachability verification to efficiently learn in high-dimensional parameter spaces while providing provable satisfaction guarantees for the optimized parameters. We demonstrate the effectiveness and scalability of our method on three systems with up to 18 parameter dimensions.

Systems and Control (eess.SY); Machine Learning (cs.LG)

arXiv:2607.08899 [eess.SY]

(or arXiv:2607.08899v2 [eess.SY] for this version)

https://doi.org/10.48550/arXiv.2607.08899 Focus to learn more arXiv-issued DOI via DataCite

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