Language identification of controlled systems: Modelling, control and anomaly detection

TL;DR

This paper introduces a language-based modeling framework for controlled dynamical systems, enabling effective control and early anomaly detection through context-dependent grammars and online learning algorithms.

Contribution

It presents a novel grammar-based approach for modeling, controlling, and detecting anomalies in controlled systems, with an online learning algorithm for real-time grammar generation.

Findings

Effective anomaly detection in electromechanical drives

Accurate modeling via grammatical interpolation techniques

Pattern detection capabilities for early fault identification

Abstract

Formal language techniques have been used in the past to study autonomous dynamical systems. However, for controlled systems, new features are needed to distinguish between information generated by the system and input control. We show how the modelling framework for controlled dynamical systems leads naturally to a formulation in terms of context-dependent grammars. A learning algorithm is proposed for on-line generation of the grammar productions, this formulation being then used for modelling, control and anomaly detection. Practical applications are described for electromechanical drives. Grammatical interpolation techniques yield accurate results and the pattern detection capabilities of the language-based formulation makes it a promising technique for the early detection of anomalies or faulty behaviour.