Derivative-Agnostic Inference of Nonlinear Hybrid Systems
This addresses the challenge of modeling complex hybrid systems with nonlinear dynamics for applications in control and verification, representing an incremental improvement by removing derivative dependencies and thresholds.
The paper tackles the problem of inferring hybrid automata from input-output traces of hybrid systems with discrete mode switching and nonlinear dynamics, presenting Dainarx, a derivative-agnostic method that uses NARX models for threshold-free detection and clustering. Experimental results show it yields significantly more accurate approximations than state-of-the-art techniques on benchmarks.
This paper addresses the problem of inferring a hybrid automaton from a set of input-output traces of a hybrid system exhibiting discrete mode switching between continuously evolving dynamics. Existing approaches mainly adopt a derivative-based method where (i) the occurrence of mode switching is determined by a drastic variation in derivatives and (ii) the clustering of trace segments relies on signal similarity -- both subject to user-supplied thresholds. We present a derivative-agnostic approach, named Dainarx, to infer nonlinear hybrid systems where the dynamics are captured by nonlinear autoregressive exogenous (NARX) models. Dainarx employs NARX models as a unified, threshold-free representation through the detection of mode switching and trace-segment clustering. We show that Dainarx suffices to learn models that closely approximate a general class of hybrid systems featuring high-order nonlinear dynamics with exogenous inputs, nonlinear guard conditions, and linear resets. Experimental results on a collection of benchmarks indicate that our approach can effectively and efficiently infer nontrivial hybrid automata with high-order dynamics yielding significantly more accurate approximations than state-of-the-art techniques.