Neural Network-based Robust Anti-sway Control of an Industrial Crane Subjected to Hoisting Dynamics and Uncertain Hydrod
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ISSN:1598-6446 eISSN:2005-4092 http://www.springer.com/12555
Neural Network-based Robust Anti-sway Control of an Industrial Crane Subjected to Hoisting Dynamics and Uncertain Hydrodynamic Forces Gyoung-Hahn Kim, Phuong-Tung Pham, Quang Hieu Ngo, and Quoc Chi Nguyen* Abstract: In this paper, a neural network-based robust anti-sway control is proposed for a crane system transporting an underwater object. A dynamic model of the crane system is developed by incorporating hoisting dynamics, hydrodynamic forces, and external disturbances. Considering the various uncertain factors that interfere with accurate payload positioning in water, neural networks are designed to compensate for unknown parameters and unmodeled dynamics in the formulated problem. The neural network-based estimators are embedded in the anti-sway control algorithm, which improves the control performance against uncertainties. A sliding mode control with an exponential reaching law is developed to suppress the sway motions during underwater transportation. The asymptotic stability of the sliding manifold is proved via Lyapunov analysis. The embedded estimator prevents the conservative gain selection of the sliding mode control, thus reducing the chattering phenomena. Simulation results are provided to verify the effectiveness and robustness of the proposed control method. Keywords: Anti-sway control, crane control, neural network estimator, sliding mode control, underwater transference.
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INTRODUCTION
Over the past several decades, industrial cranes have been utilized to accomplish various jobs at a variety of locations, including container terminals, warehouses, and construction sites. Recently, beyond such demands confined to land, the necessity to transport underwater objects has emerged due to advanced engineering facilities such as transporting fuel rods in nuclear power plants [1–3,13], investigating underwater caves, transporting objects from the seabed, etc. However, as the task environment becomes harsh, the control problem of efficient transportation inevitably becomes more challenging because of the existence of more diverse and uncertain factors that interfere with accurate payload positioning. Hence, to maximize the productivity of the crane system operating for objects in water, a novel methodology is required to effectively suppress unexpected payload swing under unfavorable effects such as hydrodynamic forces and unknown flow disturbances. For crane systems transporting a payload in air, several
relevant studies analyzing various industrial cranes, such as gantry cranes, boom cranes, tower cranes, and container cranes, have been conducted. In particular, the control problem of crane systems has primarily focused on methods that effectively suppress unwanted payload swing during transportation. To address this issue, several effective control strategies have been developed for different structures and dynamics. These strategies can be categorized into open-loop control methods [1–7] and closed-loop control methods [8–26]. In partic
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