Neural network-based nonlinear sliding-mode control for an AUV without velocity measurements

DOI10.1080/00207179.2017.1366669zbMath1414.93054OpenAlexW2742566142MaRDI QIDQ5742545

Rongxin Cui, Weisheng Yan, Xinxin Guo

Publication date: 15 May 2019

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207179.2017.1366669

zbMATH Keywords

neural networkAUVrobust exact differentiatornonlinear sliding-mode controlunknown hydrodynamics

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Nonlinear systems in control theory (93C10) Lyapunov and other classical stabilities (Lagrange, Poisson, (L^p, l^p), etc.) in control theory (93D05) Automated systems (robots, etc.) in control theory (93C85) Variable structure systems (93B12)

Related Items (4)

Neural dynamic sliding mode control of nonlinear systems with both matched and mismatched uncertainties ⋮ A homogeneous domination output feedback control method for active suspension of intelligent electric vehicle ⋮ Hybrid deep learning controller for nonlinear systems based on adaptive learning rates ⋮ Improved SSA‐RBF neural network‐based dynamic 3‐D trajectory tracking model predictive control of autonomous underwater vehicles with external disturbances

Cites Work

• Robust exact differentiation via sliding mode technique
• Second order sliding mode control scheme for an autonomous underwater vehicle with dynamic region concept
• Modelling and simulation of a robust energy efficient AUV controller
• Distributed coordinated tracking of multiple autonomous underwater vehicles
• Integral sliding mode controller for precise manoeuvring of autonomous underwater vehicle in the presence of unknown environmental disturbances
• Higher-order sliding modes, differentiation and output-feedback control

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