Bridge-Ship Collision Avoidance Control Based on AFSMC with a FRNN Estimator

For collision avoidance and maneuvering control in bridge areas, an adaptive fractional sliding mode control with fractional recurrent neural network (FRNN-AFSMC) is proposed. The uncertainties are estimated by FRNN, and the fractional gradient is adopted to improve the recurrent neural network (RNN...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021, Vol.2021, p.1-13
Main Authors: Fang, Qionglin, Cao, Enguang
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Bridges
Calculus
Collision avoidance
Collision dynamics
Computer simulation
Marine environment
Mathematical functions
Motion control
Neural networks
Recurrent neural networks
Robust control
Ship bridge collisions
Sliding mode control
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Summary:For collision avoidance and maneuvering control in bridge areas, an adaptive fractional sliding mode control with fractional recurrent neural network (FRNN-AFSMC) is proposed. The uncertainties are estimated by FRNN, and the fractional gradient is adopted to improve the recurrent neural network (RNN). Its convergence has been proven. The influence of fractional order on algorithm performance is analyzed, and the simulation platform of ship collision avoidance control is built. Dynamic collision avoidance of multiple ships is simulated and verified. The results show the feasibility and effectiveness of dynamic autonomous collision avoidance motion control in a dynamic ocean environment.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/2026104