Evolutionary and Sparse Regression Approach for Data-Driven Modelling of an Overhead Crane Dynamics

• Autorzy: Kusznir Tom , Smoczek Jarosław , Karwat Bolesław

Identyfikatory

DOI

10.12913/22998624/189497

• Języki publikacji: EN

Abstrakty

EN

Identification of an accurate and simple model of a complex underactuated crane dynamics for varying operational conditions is a crucial step towards designing and implementation of real-time monitoring and control systems to enhance crane safety and operational efficiency. This paper considers a non-parametric data-driven identification of an overhead crane dynamics using symbolic regression techniques to find compromise between model complexity and predicted output accuracy. A grammar-guided genetic programming (G3P) combined with l0 sparse regression is applied with two different variants of grammar to automatically construct a nonlinear autoregressive exogenous (NARX) model of different forms, termed extended and polynomial models. The proposed method is compared with a linear parameter-varying ARX (LPV-ARX) model. Identification is performed on experimental data obtained from a laboratory-scale overhead crane. The identified models are compared in terms of prediction accuracy, model’s complexity measured using number of model terms, and execution time. The regularized G3P method outperformed the LPV-ARX model in terms of model predictive output accuracy. The G3P with the extended grammar resulted in more accurate crane velocity prediction models than the models with the polynomial grammar. The payload sway prediction model with the polynomial grammar was less complex in all measured metrics while there was no statistical significance in the accuracy when compared to the models with extended grammar.

Słowa kluczowe

EN

overhead crane; nonlinear dynamics; data-driven modelling; grammar-guided genetic programming; G3P; sparse regression

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 4
• Strony: 282--295
• Opis fizyczny: Bibliogr. 29 poz., fig., tab.

Twórcy

autor

Kusznir Tom

• Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, ul. Mickiewicza 30, 30-059 Kraków, Poland

• https://orcid.org/0000-0002-6353-0311

autor

Smoczek Jarosław

• Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, ul. Mickiewicza 30, 30-059 Kraków, Poland

• https://orcid.org/0000-0002-1576-2724

autor

Karwat Bolesław

• Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, ul. Mickiewicza 30, 30-059 Kraków, Poland

• https://orcid.org/0000-0001-5675-3392

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