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

Mojallizadeh, Mohammad Rasool; Brogliato, Bernard; Prieur, Christophe

doi:10.1016/j.arcontrol.2023.03.002

Cited by 16 publications

(11 citation statements)

References 293 publications

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“…The proposed data-driven modeling and NMPC approach are studied for a planar lumped-mass model of an overhead crane. A simplified planar single-pendulum system, often used as a benchmark case to develop and evaluate different anti-sway control strategies [1][2][3], is illustrated in Figure 1, where M, m, l, α, x, v, F are the masses of the trolley and pendulum, length of the pendulum, sway angle of the pendulum, trolley displacement and velocity, and force, respectively. The force consists of the actuating force (in real applications, usually produced by AC motors supplied by frequency inverters), disturbances, and friction forces.…”

Section: Data-driven Modeling Of Crane Dynamicsmentioning

confidence: 99%

“…Since cranes are widely used in the industrial, construction, and logistic sectors for transporting heavy and oversized loads by using wire ropes, cables, or chains, an excessive transient and residual cable-suspended payload oscillation caused by actuated horizontal motion degrades positioning performance and may pose safety hazards [1]. This problem is extensively studied in the literature, and various open-loop and closed-loop control strategies are comprehensively reviewed in [2], while the more current state of the art focusing on the modeling and control of overhead cranes is provided in [3]. Recent works include input shaping [4,5], time-optimal [6], command shaping [7], and feedforward [8] anti-sway crane control strategies.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Data-driven Modeling Of Crane Dynamicsmentioning

confidence: 99%

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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“…Cranes are widely employed devices for handling heavy-weight goods during manufacturing processes [1]. These devices consist of a platform moving along a linear, planar, or spatial trajectory to transport a load which is connected to the platform through wires, ropes, or chains.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning through Model Inversion of an Underactuated Spatial Gantry Crane Moving in Structured Cluttered Environments

Bettega,

Richiedei,

Tamellin

2024

Actuators

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Handling suspended loads in cluttered environments is critical due to the oscillations arising while the load is traveling. Exploiting active control algorithms is often unviable in industrial applications, due to the necessity of installing sensors on the load side, which is expensive and often impractical due to technological limitations. In this light, this paper proposes a trajectory planning method for underactuated, non-flat, non-minimum phase spatial gantry crane moving in structured cluttered environments. The method relies on model inversion. First, the system dynamics is partitioned into actuated and unactuated coordinates and then the load displacements are described as a non-linear separable function of these. The unactuated dynamic is unstable; hence, the displacement, velocity, and acceleration references are modified through the output redefinition technique. Finally, platform trajectory is computed, and the desired displacements of the load are obtained. The effectiveness of the proposed method is assessed through numerical and experimental tests performed on a laboratory testbed composed by an Adept Quattro robot moving a pendulum. The load is moved in a cluttered environment, and collisions are avoided while simultaneously tracking the prescribed trajectory effectively.

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“…A comprehensive review of various crane dynamic modeling methods and modelbased control strategies reported in the literature is presented in [5][6][7]. A majority of the presented approaches are derived from the Euler-Lagrange equation.…”

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

Cited by 16 publications

References 293 publications

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

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

Trajectory Planning through Model Inversion of an Underactuated Spatial Gantry Crane Moving in Structured Cluttered Environments

Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming

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