A Framework for Identification and Validation of Affine Hybrid Automata from Input-Output Traces

Abstract

Automata-based modeling of hybrid and cyber-physical systems (CPS) is an important formal abstraction amenable to algorithmic analysis of its dynamic behaviors, such as in verification, fault identification, and anomaly detection. However, for realistic systems, especially industrial ones, identifying hybrid automata is challenging, due in part to inferring hybrid interactions, which involves inference of both continuous behaviors, such as through classical system identification, as well as discrete behaviors, such as through automata (e.g., L*) learning. In this paper, we propose and evaluate a framework for inferring and validating models of deterministic hybrid systems with linear ordinary differential equations (ODEs) from input/output execution traces. The framework contains algorithms for the approximation of continuous dynamics in discrete modes, estimation of transition conditions, and the inference of automata mode merging. The algorithms are capable of clustering trace segments and estimating their dynamic parameters, and meanwhile, deriving guard conditions that are represented by multiple linear inequalities. Finally, the inferred model is automatically converted to the format of the original system for the validation. We demonstrate the utility of this framework by evaluating its performance in several case studies as implemented through a publicly available prototype software framework called HAutLearn and compare it with a membership-based algorithm.

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“…However, it results in uncontrollability in some cases where state variables have direct relations due to the physical features of the circuit, and it does not extensively model parasitic parameters of electronic components, which may have effects on other converters. Yang et al [15] proposed a framework for identifying and validating affine hybrid automata from input-output traces. One disadvantage of their method is that it requires input and output traces to learn the hybrid automata, which may not always be available or easily obtainable.…”