Designing Dynamic Positioning System Based on H∞ Robust Recurrent Cerebellar Model Articulation Controller

This research proposes a H∞ robust recurrent cerebellar model articulation controller (RRCMACS) for dynamic positioning system (DPS). In this system, the fast learning, generalization capability of the CMAC and the recurrent technique of neural network incorporated to mimic an ideal force controller...

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Bibliographic Details
Published in2018 4th International Conference on Green Technology and Sustainable Development (GTSD) pp. 652 - 657
Main Authors Ta, V.P, Dang, X.K, Dong, V.H, Do, V.D
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2018
Subjects
Aerospace electronics
Cerebellar Model Articulation Controller (CMAC)
Control systems
Disturbances
Dynamic Positioning System (DPS)
Dynamics
Force
Marine vehicles
Mathematical model
Nonlinearity
Recurrent Network
Robust Controller (RC)
Uncertainties
Uncertainty
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Summary:This research proposes a H∞ robust recurrent cerebellar model articulation controller (RRCMACS) for dynamic positioning system (DPS). In this system, the fast learning, generalization capability of the CMAC and the recurrent technique of neural network incorporated to mimic an ideal force controller. Furthermore, the H∞ robust controller efficiently vanishes the effects of external disturbances such as wave, wind and current acting on the ship to achieve the robustness of the system. The parameters of the controller are learned and adjusted in the sense of the Lyapunov-like Lemma theory. Therefore the stability of the system is guaranteed. The simulation results for the dynamic positioning system are given to prove the effectiveness of the proposed control system for the DPS and model-free nonlinear systems.
DOI:10.1109/GTSD.2018.8595553