Combined neural network-based adaptive input shaping and feedback control for effective automation of tower cranes under payload hoisting
Talapiden K. Alhassan A.B. Mohamed Z. Shehu M.A. Do T.D.
2026SAGE Publications Inc.
JVC/Journal of Vibration and Control
2026
This paper proposes a hybrid control that combines an adaptive neural network (NN)-based input shaping control (ISC) with an intelligent fuzzy logic control (FLC) for control of a tower crane. The design has an advantage as the adaptive shaper can handle the payload sway control under parameter uncertainly while the FLC provides accurate trolley and jib positioning. The most challenging operation of the crane, involving simultaneous trolley displacement, jib rotation, and payload hoisting, is investigated using a laboratory tower crane with nominal cylindrical and distributed rectangular payloads. The performance of the NN is compared with gain-scheduling lookup tables (LT) while the FLC is compared with PID. Experimental results show that the proposed NNZVD+FLC provides the highest performance with satisfactory position tracking and maintains the residual sway within ±3°. The work demonstrates that the adaptive ISC can be successfully combined with intelligent feedback controllers for effective automation of crane systems.
fuzzy logic control , input shaping , neural network , sway control , tower crane
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Department of Robotics, School of Engineering and Digital Sciences (SEDS), Nazarbayev University, Astana, Kazakhstan
Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
Department of Robotics
Faculty of Electrical Engineering
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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026