Abstract
In 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.
This work is supported in part by the National Natural Science Foundation of China (Nos. 51179019, 61374114), the Fundamental Research Program for Key Laboratory of the Education Department of Liaoning Province (LZ2015006), the Fundamental Research Funds for the Central Universities under Grant 3132016313 and 3132016311, the Hong Kong Research Grants Council under Project no.: CityU113212, Fujian Provincial Department of education Projection (JAJ09148), and The Pan Jinlong project of Jimei University (ZC2012019).
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Zhang, K., Li, T., Li, Z., Philip Chen, C.L. (2017). Neural Network Based Dynamic Surface Second Order Sliding Mode Control for AUVs. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_41
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DOI: https://doi.org/10.1007/978-981-10-5230-9_41
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