Anti-sway system for boom cranes based on a model predictive control approach

Arnold, Eckhard; Sawodny, Oliver; Neupert, J.; Schneider, Klaus

doi:10.1109/icma.2005.1626783

Cited by 32 publications

(18 citation statements)

References 10 publications

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“…In the transportation field, MPC has been employed to solve the path-tracking problems of terrestrial autonomous vehicles [25] and heavy-haul trains [35]. Researchers have also applied MPC to boom cranes [1] and gauntry cranes [10] for tracking and anti-swing. However, existing MPC approaches for cranes only consider minimizing tracking error and steady-state error.…”

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confidence: 99%

Model predictive control for improving operational efficiency of overhead cranes

2014

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Model predictive control (MPC) has been successfully applied to many transportation systems. For the control of overhead cranes, existing MPC approaches mainly focus on improving the regulation performance, such as tracking error or steady-state error. In this paper, energy efficiency as well as safety is newly considered in our proposed MPC approach. Based on the system model designed, the MPC approach is applied to minimize an objective function that is formulated as the integration of energy consumption and swing angle. In our approach, promising results in terms of low energy consumption and small swing angle can be found, whilst the solutions obtained can satisfy all practical constraints. Our test results indicate that the MPC approach can ensure stability and robustness of improving energy efficiency and safety.

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confidence: 99%

Model predictive control for improving operational efficiency of overhead cranes

2014

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“…Hence, the both control techniques were experimentally validated using model-estimated feedback based on the discrete-time model (1) (Fig. 4), and for mass of a payload m = 50 kg and rope length l = {1.3, 1.9} m (Fig.…”

Section: Experiments On a Laboratory Standmentioning

confidence: 99%

“…In addition, the idea of MPC, which enables to optimize prediction of process behaviour with respect to constraints of process variables, has been recently applied in different crane control approaches. The MPC-based control scheme is developed for hydraulic forestry crane [5], boom crane [1], and laboratory models of a gantry crane [13] and overhead crane [6].…”

Section: Introductionmentioning

confidence: 99%

Robust Fuzzy Model Predictive Control of an Overhead Crane

Smoczek

Szpytko

2015

Journal of KONES. Powertrain and Transport

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The method of controlling an overhead crane with respect to the variation of operating conditions and control constraints is developed using a model predictive control (MPC) and fuzzy interpolation applied in linear parameter varying (LPV) approach to crane dynamic modelling. The proposed control approach is based on the assumption that operating conditions vary within the known range of scheduling variables, and the parameters of a crane dynamic model can be interpolated by a quasi-linear fuzzy model designed through utilizing the P1-TS fuzzy theory. Hence, a crane dynamic is approximated through interpolation between a set of local linear models determined through identification experiments at the local operating points selected within the bounded intervals of scheduling variables. For the modelling assumptions, the control algorithm is developed based on a generalized predictive control (GPC) procedure taking into consideration the constraints on sway angle of a payload and control signal. Feasibility and applicability of the proposed control technique were confirmed during experiments carried out on a laboratory-scaled overhead crane. The results of experiments are presented and compared with performances of a fuzzy logic-based scheduling control scheme.

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“…In [12] MPC was used in the vertical plane in combination with feedforward control for a crane with Maryland rigging to suppress the pendulum motion. In [2] a MPC was used for a mobile harbour crane to control the combined luffing and slewing motion, where real time solution of the MPC problem was achieved by linearizing the model about the desired trajectory. This work was further developed in [16] where flatness was used to simplify the dynamics, and by including the linearized feedforward part and a stabilizing feedback part.…”

Section: Introductionmentioning

confidence: 99%

Crane load position control using Lyapunov-based pendulum damping and nonlinear MPC position control

Tysse

Egeland

2019

2019 18th European Control Conference (ECC)

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A crane control system is presented where the crane load can track a position trajectory with ultimately bounded pendulum motions of the load. This is done with an inner loop using a Lyapunov-based damping controller which is designed to damp out the pendulum motions of the load, and an outer loop where a nonlinear MPC (model predictive controller) controls the position of the load. The Lyapunov-based damping controller is designed to be exponentially stable, which means that the pendulum motion will be ultimately bounded in the presence of a bounded perturbation. Then this is achieved by constraining the control action of the nonlinear MPC so that the perturbations to the Lyapunov-based damping controller are sufficiently bounded. The suspension point of the load, which is the tip of the crane, can then be controlled to track a time-varying desired trajectory in the plane with ultimately bounded pendulum motions. The control system is validated in simulations in a simulation study.

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Anti-sway system for boom cranes based on a model predictive control approach

Cited by 32 publications

References 10 publications

Model predictive control for improving operational efficiency of overhead cranes

Model predictive control for improving operational efficiency of overhead cranes

Robust Fuzzy Model Predictive Control of an Overhead Crane

Crane load position control using Lyapunov-based pendulum damping and nonlinear MPC position control

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