Data-driven Modeling and Adaptive Predictive Anti-swing Control of Overhead Cranes

Kim, Gyoung-Hahn; Yoon, Mahnjung; Jeon, Jae Young; Hong, Keum–Shik

doi:10.1007/s12555-022-0025-8

Cited by 10 publications

(2 citation statements)

References 32 publications

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“…For instance, an adaptive neuro-fuzzy inference system has been trained by a genetic algorithm in [309] to realize such a data-driven model. Alternatively, a neural network with online parameter tuning has been developed in [120] for this purpose. Estimating cable's dynamics (even multi-cable systems) using data-driven machine learning, seems to be largely open in the OC literature.…”

Section: Discussionmentioning

confidence: 99%

Modeling and control of overhead cranes: A tutorial overview and perspectives

Mojallizadeh

Brogliato

Prieur

2023

Annual Reviews in Control

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Section: Discussionmentioning

confidence: 99%

Modeling and control of overhead cranes: A tutorial overview and perspectives

Mojallizadeh

Brogliato

Prieur

2023

Annual Reviews in Control

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“…In [31], an overhead crane's dynamics were approximated by an adaptive neuro-fuzzy inference system (ANFIS) trained with operational data using a hairpin RNA genetic algorithm. Kim et al [32] used the NARX-NN data-driven model trained offline and online using extreme learning machines (ELMs) in the predictive control of a small-scale overhead crane with a prediction and control horizon of N p = N c = 15. The Koopman operator theory and deep learning are merged in [33] to identify a model of a gantry crane subsequently used to develop a linear quadratic controller tested on a laboratory stand.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control with Evolutionary Data-Driven Prediction Model and Particle Swarm Optimization Optimizer for an Overhead Crane

Kusznir,

Smoczek

2024

Applied Sciences

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This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regression method. Grammar-guided genetic programming combined with regularized least squares was applied to identify a nonlinear autoregressive model with an exogenous input (NARX) prediction model of the crane dynamics from input–output data. The resulting prediction model was implemented in the NMPC scheme, using a particle swarm optimization (PSO) algorithm as a solver to find an optimal sequence of the control actions satisfying multi-objective performance requirements and input constraints. The feasibility and performance of the controller were experimentally verified using a laboratory crane actuated by AC motors and compared with a discrete-time feedback controller developed using the pole placement technique. A series of experiments proved the effectiveness of the controller in terms of robustness against operating condition variation and external disturbances.

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Modeling and vibration suppression for overhead crane in planar space with nonlinear time-varying actuator faults and uncertain control directions

Wang,

Liu

2024

Nonlinear Dyn

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Data-driven Modeling and Adaptive Predictive Anti-swing Control of Overhead Cranes

Cited by 10 publications

References 32 publications

Modeling and control of overhead cranes: A tutorial overview and perspectives

Modeling and control of overhead cranes: A tutorial overview and perspectives

Nonlinear Model Predictive Control with Evolutionary Data-Driven Prediction Model and Particle Swarm Optimization Optimizer for an Overhead Crane

Modeling and vibration suppression for overhead crane in planar space with nonlinear time-varying actuator faults and uncertain control directions

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