UM
Neural network based dynamic surface second order sliding mode control for AUVs
Zhang K.3; Li T.3; Li Z.1; Philip Chen C.L.2
2017
Source PublicationCommunications in Computer and Information Science
Volume710
Pages417-424
AbstractIn this paper, a novel neural network based dynamic surface second order sliding mode control algorithm is proposed for three-dimensional trajectory tracking control of autonomous underwater vehicles (AUVs) with modeling errors under external disturbances. The controller designed is capable of strengthening robustness of the system and attenuates inherent chattering of classical sliding mode control effectively. An innovative neural network compensator is designed to counteract effects of modeling errors, furthermore, the norm of the ideal weighting vector in neural network system is regarded as the estimated parameter, such that there is only one parameter needs to be adjusted. Meanwhile, the effect of external disturbances is handled by means of hyperbolic tangent function. As a result, the Lyapunov based stability analysis is provided to guarantee semi-global uniform boundedness of all closed-loop signals. Verification of the effectiveness of the proposed algorithm is done through simulation results.
KeywordAutonomous underwater vehicle (AUV) Dynamic surface control (DSC) Hyperbolic tangent function Neural network (NN) Second order sliding mode control Trajectory tracking
DOI10.1007/978-981-10-5230-9_41
URLView the original
Language英語
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Jimei University
2.Universidade de Macau
3.Dalian Maritime University
Recommended Citation
GB/T 7714
Zhang K.,Li T.,Li Z.,et al. Neural network based dynamic surface second order sliding mode control for AUVs[C],2017:417-424.
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