Design of Terminal Sliding Mode Controller Based on RBF Neural Network for Underwater Transportation System

Yuan Zhanhang; MA Yuxiang; LI Yunhua

doi:10.13832/j.jnpe.2025.01.0247

Volume 46 Issue 1

Feb. 2025

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Yuan Zhanhang, MA Yuxiang, LI Yunhua. Design of Terminal Sliding Mode Controller Based on RBF Neural Network for Underwater Transportation System[J]. Nuclear Power Engineering, 2025, 46(1): 247-253. doi: 10.13832/j.jnpe.2025.01.0247

Citation:

Yuan Zhanhang, MA Yuxiang, LI Yunhua. Design of Terminal Sliding Mode Controller Based on RBF Neural Network for Underwater Transportation System[J]. Nuclear Power Engineering, 2025, 46(1): 247-253. doi: 10.13832/j.jnpe.2025.01.0247

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Design of Terminal Sliding Mode Controller Based on RBF Neural Network for Underwater Transportation System

doi: 10.13832/j.jnpe.2025.01.0247

1.
Beihang University, Beijing, 100190, China
2.
China Nuclear Power Engineering Co. LTD. Beijing, 100840, China
3.
Siemens Corporate Technology China (CT CN), Beijing, 100102, China

Received Date: 2024-04-24
Rev Recd Date: 2024-06-16
Publish Date: 2025-02-15

Abstract

Abstract

The underwater transportation system will be affected by the uncertain nonlinearity of water and other external disturbance when transporting loads. Aiming at the operational control of underwater transportation system, a non-singular terminal sliding mode control method based on radial basis function (RBF) neural network is designed for the underwater transport process of nuclear power plant fuel assembly. Firstly, according to Newton's second law and Morison's equation, the kinetic differential equation of the system is established and its state-space equation is derived. Secondly, a non-singular terminal sliding mode controller is designed, and the unknown nonlinear effect is estimated by RBF neural network and compensated in the controller. The adaptive updating law of network weight is derived by Lyapunov stability theory. The Lyapunov stability theory proves that the proposed control strategy can achieve asymptotic convergence for unknown nonlinear estimation and finite-time convergence for given instruction tracking. Simulations is carried out for the two conditions of upgoing with load and downgoing without load respectively, and the results verify that the controller designed has good performance.
- Underwater transportation,
- Radial basis function (RBF) neural network,
- Terminal sliding mode,
- Morison equation

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References(25)

References

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