Robust Adaptive Self-Structuring Neural Network Bounded Target Tracking Control of Underactuated Surface Vessels
| Autor: | Haitao Liu, Jianfei Lin, Guoyan Yu, Jianbin Yuan |
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| Rok vydání: | 2021 |
| Předmět: |
Article Subject
General Computer Science General Mathematics General Neuroscience Computer applications to medicine. Medical informatics R858-859.7 Uncertainty Neurosciences. Biological psychiatry. Neuropsychiatry General Medicine Nonlinear Dynamics Computer Simulation Neural Networks Computer RC321-571 Research Article |
| Zdroj: | Computational Intelligence and Neuroscience Computational Intelligence and Neuroscience, Vol 2021 (2021) |
| ISSN: | 1687-5273 1687-5265 |
| DOI: | 10.1155/2021/2010493 |
| Popis: | This paper studies the target-tracking problem of underactuated surface vessels with model uncertainties and external unknown disturbances. A composite robust adaptive self-structuring neural-network-bounded controller is proposed to improve system performance and avoid input saturation. An extended state observer is proposed to estimate the uncertain nonlinear term, including the unknown velocity of the tracking target, when only the measurement values of the line-of-sight range and angle can be obtained. An adaptive self-structuring neural network is developed to approximate model uncertainties and external unknown disturbances, which can effectively optimize the structure of the neural network to reduce the computational burden by adjusting the number of neurons online. The input-to-state stability of the total closed-loop system is analyzed by the cascade stability theorem. The simulation results verify the effectiveness of the proposed method. |
| Databáze: | OpenAIRE |
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