Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inference
This work addresses model discrimination for switched systems with temporal logic tasks, which is incremental as it builds on existing data-driven and LTL inference methods to handle unknown dynamics and specifications.
The paper tackles the problem of discriminating between unknown switched nonlinear systems with unknown linear temporal logic specifications using only sampled data, by proposing data-driven methods to over-approximate dynamics and infer specifications with guarantees, and developing algorithms for distinguishability analysis and model discrimination to rule out inconsistent models at runtime.
This paper addresses the problem of data-driven model discrimination for unknown switched systems with unknown linear temporal logic (LTL) specifications, representing tasks, that govern their mode sequences, where only sampled data of the unknown dynamics and tasks are available. To tackle this problem, we propose data-driven methods to over-approximate the unknown dynamics and to infer the unknown specifications such that both set-membership models of the unknown dynamics and LTL formulas are guaranteed to include the ground truth model and specification/task. Moreover, we present an optimization-based algorithm for analyzing the distinguishability of a set of learned/inferred model-task pairs as well as a model discrimination algorithm for ruling out model-task pairs from this set that are inconsistent with new observations at run time. Further, we present an approach for reducing the size of inferred specifications to increase the computational efficiency of the model discrimination algorithms.