An Efficient Adaptive Hierarchical Sliding Mode Control Strategy Using Neural Networks for 3D Overhead Cranes

In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Par...

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Bibliographic Details
Published in:International journal of automation and computing 2019-10, Vol.16 (5), p.614-627
Main Authors: Le, Viet-Anh, Le, Hai-Xuan, Nguyen, Linh, Phan, Minh-Xuan
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
CAE) and Design
Computer Applications
Computer-Aided Engineering (CAD
Control
Control systems design
Controllers
Cranes
Dynamic models
Engineering
Liapunov functions
Materials handling
Mechatronics
Neural networks
Parameter estimation
Parameter uncertainty
Radial basis function
Research Article
Robotics
Sliding mode control
Structural hierarchy
Subsystems
Surface stability
Three dimensional models
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Summary:In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are highly difficult to determine. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and real-life systems, where the results obtained by our method are highly promising.
ISSN:1476-8186
1751-8520
DOI:10.1007/s11633-019-1174-y