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• W3017210588 abstract "The paper addresses the problem of effectively and robustly controlling a 3D overhead crane under the payload mass uncertainty, where the control performance is shown to be consistent. It is proposed to employ the sliding mode control technique to design the closed-loop controller due to its robustness, regardless of the uncertainties and nonlinearities of the under-actuated crane system. The radial basis function neural network has been exploited to construct an adaptive mechanism for estimating the unknown dynamics. More importantly, the adaptation methods have been derived from the Lyapunov theory to not only guarantee stability of the closed-loop control system, but also approximate the unknown and uncertain payload mass and weight matrix, which maintains the consistency of the control performance, although the cargo mass can be varied. Furthermore, the results obtained by implementing the proposed algorithm in the simulations show the effectiveness of the proposed approach and the consistency of the control performance, although the payload mass is uncertain." @default.
• W3017210588 created "2020-04-24" @default.
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• W3017210588 date "2020-04-16" @default.
• W3017210588 modified "2023-09-27" @default.
• W3017210588 title "Consistency of Control Performance in 3D Overhead Cranes under Payload Mass Uncertainty" @default.
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• W3017210588 doi "https://doi.org/10.3390/electronics9040657" @default.
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