Fuzzy modeling and design for a 3D Crane

Petrehus, Paul; Lendek, Zsófia; Raica, Paula

doi:10.3182/20130902-3-cn-3020.00058

Cited by 13 publications

(6 citation statements)

References 9 publications

(12 reference statements)

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“…However, a certain motion profile must be employed such that the control actions leading to the acceleration and deceleration of the payload ensure secure and sway-free transportation [63]. The characteristics of the system allow the application of various control strategies [62], [64], [65]. This makes it very appealing as an educational tool in the control systems laboratory.…”

Section: Case Study: Digital Twin Based Implementation Of a 3d Crane ...mentioning

confidence: 99%

ReImagine Lab: Bridging the Gap Between Hands-On, Virtual and Remote Control Engineering Laboratories Using Digital Twins and Extended Reality

et al. 2022

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Practical work is one of the most important instructional tools in control engineering. To address concerns linked to the cost and space requirements of traditional hands-on laboratories, technology-enabled laboratory modes, such as virtual, remote, and take-home laboratory modes are proposed. Each of these alliterative laboratory modes has its own set of benefits and emphasizes a distinct learning goal. Furthermore, due to lockdown and physical proximity restrictions imposed by policies in response to the COVID-19 pandemic, the employment of these alliterative laboratory modes has been quickly increasing. The laboratories' development, operation, and maintenance become more fragmented as a result of these many possibilities. In this study, we propose "ReImagine Lab" as a framework for leveraging digital twins and extended reality technologies to streamline the development and operation of hands-on, virtual, and distant laboratories. By increasing the level of interaction, immersion, and collaboration in technologyenabled laboratory forms, this framework intends to boost student engagement. The benefits of this framework are demonstrated by examining several use cases, and a 37-person "system usability study" is conducted to assess the usability of virtual laboratories employing desktop computers and immersive virtual reality.

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Section: Case Study: Digital Twin Based Implementation Of a 3d Crane ...mentioning

confidence: 99%

ReImagine Lab: Bridging the Gap Between Hands-On, Virtual and Remote Control Engineering Laboratories Using Digital Twins and Extended Reality

et al. 2022

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“…where m is the load mass and g is the gravitational acceleration; D x and D y are the viscous damping coefficients associated with the xand z-axis respectively; finally, M x and M l are the traveling and hoisting components of the crane mass, respectively, i.e., M x = m + m c and M l = m, where m c is the cart mass. The parameter values considered in this paper are close to a real laboratory crane system, e.g., [37]. The state-space derivation is obtained solving (1) forq, such as:…”

Section: Mathematical Modelingmentioning

confidence: 99%

Robust qLPV Tracking Fault-Tolerant Control of a 3 DOF Mechanical Crane

López‐Estrada

Santos-Estudillo

Valencia‐Palomo

et al. 2020

MCA

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The main aim of this paper is to propose a robust fault-tolerant control for a three degree of freedom (DOF) mechanical crane by using a convex quasi-Linear Parameter Varying (qLPV) approach for modeling the crane and a passive fault-tolerant scheme. The control objective is to minimize the load oscillations while the desired path is tracked. The convex qLPV model is obtained by considering the nonlinear sector approach, which can represent exactly the nonlinear system under the bounded nonlinear terms. To improve the system safety, tolerance to partial actuator faults is considered. Performance requirements of the tracking control system are specified in an H∞ criteria that guarantees robustness against measurement noise, and partial faults. As a result, a set of Linear Matrix Inequalities is derived to compute the controller gains. Numerical experiments on a realistic 3 DOF crane model confirm the applicability of the control scheme.

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“…A flat output identification algorithm is proposed in [13] to identify a rotary crane based on data measured on a laboratory stand. 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].…”

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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Fuzzy modeling and design for a 3D Crane

Cited by 13 publications

References 9 publications

ReImagine Lab: Bridging the Gap Between Hands-On, Virtual and Remote Control Engineering Laboratories Using Digital Twins and Extended Reality

ReImagine Lab: Bridging the Gap Between Hands-On, Virtual and Remote Control Engineering Laboratories Using Digital Twins and Extended Reality

Robust qLPV Tracking Fault-Tolerant Control of a 3 DOF Mechanical Crane

Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming

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