Evolving Fuzzy and Tensor Product-based Models for Tower Crane Systems

Precup, Radu; Hedrea, Elena‐Lorena; Roman, Raul‐Cristian; Petriu, Emil M.; Bojan-Dragoş, Claudia-Adina; Szedlak-Stînean, Alexandra-Iulia; Hedrea, Ciprian

doi:10.1109/iecon49645.2022.9968958

Cited by 2 publications

(3 citation statements)

References 30 publications

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“…The informational structure including the connection between the ΤC system and the personal computer through an interface is illustrated in Figure 1. (Roman et al, 2019a;Roman et al, 2019b;Roman et al, 2020;Hedrea et al, 2021;Precup et al, 2021b;Roman et al, 2021;Precup et al, 2022a;Precup et al, 2022b;Precup et al, 2022c;Precup et al, 2022d;Roman et al, 2022a) The mathematical model of the ΤCS is fully detailed in (Precup et al, 2021b;Roman et al, 2021;Precup et al, 2022a). If the time argument is omitted in order to simplify things, the state-space model of the ΤC system is derived from Inteco (2006) and expressed as (Precup et al, 2021b;Roman et al, 2021;Precup et al, 2022a)…”

Section: The Tower Crane Systemmentioning

confidence: 99%

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Fictitious Reference Iterative Tuning of Discrete-Time Model-Free Control for Tower Crane Systems

ROMAN¹,

PRECUP²,

PETRIU³

et al. 2023

SIC

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The purpose of this paper is to propose a novel controller that is based on a combination of two data-driven algorithms, namely the Fictitious Reference Iterative Tuning (FRIT) algorithm and the Model-Free Adaptive Control (MFC) algorithm while considering a particular form of MFC, that is the intelligent proportional-integral-derivative (iPID) controller. The main advantage of this combination is that the FRIT algorithm optimally tunes the parameters of the iPID controller by solving an optimization problem based on a metaheuristic African Vultures Optimization Algorithm (AVOA). The novel controller, referred to as the FRIT-iPID controller, is validated experimentally on a three-degree-of-freedom tower crane system laboratory equipment in the context of controlling the cart position, the arm angular position and the payload position for this system.

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Section: The Tower Crane Systemmentioning

confidence: 99%

“…Figure1. The informational structure of ΤCS laboratory equipment(Roman et al, 2019a;Roman et al, 2019b;Roman et al, 2020;Hedrea et al, 2021;Precup et al, 2021b;Roman et al, 2021;Precup et al, 2022a;Precup et al, 2022b;Precup et al, 2022c;Precup et al, 2022d;Roman et al, 2022a) …”

mentioning

confidence: 99%

Fictitious Reference Iterative Tuning of Discrete-Time Model-Free Control for Tower Crane Systems

ROMAN¹,

PRECUP²,

PETRIU³

et al. 2023

SIC

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“…The idea to approximate the dynamics of a nonlinear underactuated crane system through local linear models is implemented in several papers using Takagi-Sugeno fuzzy (TSF) modeling [14][15][16][17]. An incremental online identification algorithm is proposed to evolve the structure of a TSF model for a laboratoryscale overhead crane [18] and a tower crane [19]. Also, artificial neural network (ANN) models as universal approximators are used to generate black-box nonlinear structures of crane dynamic models and controllers.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming

Kusznir,

Smoczek,

Karwat

2024

Applied Sciences

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The meaningful problem of improving crane safety, reliability, and efficiency is extensively studied in the literature and targeted via various model-based control approaches. In recent years, crane data-driven modeling has attracted much attention compared to physics-based models, particularly due to its potential in real-time crane control applications, specifically in model predictive control. This paper proposes grammar-guided genetic programming with sparse regression (G3P-SR) to identify the nonlinear dynamics of an underactuated crane system. G3P-SR uses grammars to bias the search space and produces a fixed number of candidate model terms, while a local search method based on an l0-regularized regression results in a sparse solution, thereby also reducing model complexity as well as reducing the probability of overfitting. Identification is performed on experimental data obtained from a laboratory-scale overhead crane. The proposed method is compared with multi-gene genetic programming (MGGP), NARX neural network, and Takagi-Sugeno fuzzy (TSF) ARX models in terms of model complexity, prediction accuracy, and sensitivity. The G3P-SR algorithm evolved a model with a maximum mean square error (MSE) of crane velocity and sway prediction of 1.1860 × 10−4 and 4.8531 × 10−4, respectively, in simulations for different testing data sets, showing better accuracy than the TSF ARX and MGGP models. Only the NARX neural network model with velocity and sway maximum MSEs of 1.4595 × 10−4 and 4.8571 × 10−4 achieves a similar accuracy or an even better one in some testing scenarios, but at the cost of increasing the total number of parameters to be estimated by over 300% and the number of output lags compared to the G3P-SR model. Moreover, the G3P-SR model is proven to be notably less sensitive, exhibiting the least deviation from the nominal trajectory for deviations in the payload mass by approximately a factor of 10.

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Evolving Fuzzy and Tensor Product-based Models for Tower Crane Systems

Cited by 2 publications

References 30 publications

Fictitious Reference Iterative Tuning of Discrete-Time Model-Free Control for Tower Crane Systems

Fictitious Reference Iterative Tuning of Discrete-Time Model-Free Control for Tower Crane Systems

Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming

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