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    Item type:Publication, Access status: with files ,
    Identification of hybrid systems with dynamics-based modeling through symbolic regression
    (Elsevier, 2026-01)
    Plambeck, Swantje
    ;
    Schmidt, Maximilian
    ;
    Subias, Audine
    ;
    Travé-massuyès Louise
    ;
    Fey, Görschwin
    Hybrid systems combine both continuous and discrete behavior. These systems serve as models in many fields, including control systems, robotics, and industrial processes. However, due to their complexity, finding an accurate model is a challenge. This paper presents a holistic approach to learning models of hybrid systems using symbolic regression. Our method leverages symbolic regression to automatically discover accurate and interpretable mathematical models in the form of hybrid systems from observed data. An advantage of our algorithm is that it detects transitions between different behavioral modes of a system based on the inherent dynamics. From learned expressions for the dynamical behavior of a system, we form a hybrid system by combining the learned expressions with a decision tree determining the current behavioral mode from data. This hybrid decision tree serves regression, prediction, and further related tasks. Our results demonstrate that symbolic regression can effectively identify the underlying dynamics of a real hybrid system and predict output signals on new input data with high accuracy.
    Publicationtype: Journal Article
      14  41
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    Item type:Publication, Access status: with files ,
    Usability of Symbolic Regression for Hybrid System Identification - System Classes and Parameters
    (2024-11-26)
    Plambeck, Swantje
    ;
    Schmidt, Maximilian
    ;
    Subias, Audine
    ;
    Travé-Massuyès, Louise
    ;
    Fey, Görschwin
    Hybrid systems, which combine both continuous and discrete behavior, are used in many fields, including robotics, biological systems, and control systems. However, due to their complexity, finding an accurate model is a challenge. This paper discusses the usage of symbolic regression to learn hybrid systems from data and specifically analyses learning parameters for a recent algorithm. Symbolic regression is a powerful tool that can automatically discover accurate and interpretable mathematical models in the form of symbolic expressions. Models generated by symbolic regression are a valuable tool for system identification and diagnosis, e.g., to predict future system behavior or detect anomalies. A major opportunity of our approach is the ability to detect transitions between different continuous behaviors of a system directly based on the dynamics. From a diagnosis perspective, this can advantageously be used to detect the system entering fault modes and identify their models. This paper presents a parameter study for a symbolic regression based identification algorithm.
    Publicationtype: Conference Paper
      13  17
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    Item type:Publication, Access status: without files ,
    Dynamics-based identification of hybrid systems using symbolic regression
    (IEEE, 2024-08)
    Plambeck, Swantje
    ;
    Schmidt, Maximilian
    ;
    Fey, Görschwin
    ;
    Subias, Audine
    ;
    Travé-Massuyès, Louise
    Symbolic regression has shown potential in the identification of physical systems. Hybrid systems, which combine both continuous and discrete behavior, are a relevant extension of purely physical systems, used in many fields, including robotics, biological systems, and control systems. However, due to their complexity, finding an accurate model is a challenge. This paper presents a novel approach to learning models of hybrid systems using symbolic regression. Our method leverages the power of genetic programming to automatically discover accurate and interpretable mathematical models in the form of hybrid systems from observed data. Symbolic regression detects transitions between different continuous behavior of a system directly based on the dynamics, instead of pure distances of observed trajectories. Furthermore, models generated by symbolic regression can be used to predict future system behavior, detect anomalies, and identify the underlying dynamics of the system while providing a human-readable representation. Our results demonstrate that symbolic regression can effectively identify the underlying dynamics of a real system represented in a hybrid model, providing a valuable tool for system identification and diagnosis.
    Publicationtype: Conference Paper
      21