Neural Network Observer-based Output Attitude Control of a Towed Underwater Vehicle

doi:10.3969/j.issn.1000-1093.2020.12.016

Abstract

Abstract: The attitude control of towed underwater vehicles (TUVs) is a challenging problem due to the strong nonlinearity and uncertainty of dynamic model, and unmeasurable external disturbances and angular velocity. An observer-based output compensation controller was designed for a TUV, in which two neural networks are employed to identify the dynamic nonlinear model on-line for the state observer and compensated sliding-mode controller, respectively. And a robust term is added to the observer to suppress additional interference. The adaptive weight updating laws and a projection modification adaptive law are designed for neural networks to ensure the stability of the system. It is proved by the Lyapunov method that the observation and control errors of the system are uniformly and ultimately bounded under certain conditions. Simulated and experimental results show that the designed compensation control system has good adaptability and robustness.

Key words: towedunderwatervehicle, neuralnetwork, observer, adaptivelaw, compensationcontrolsystem

CLC Number:

TP242.3

JING Anyan， SHE Huqing. Neural Network Observer-based Output Attitude Control of a Towed Underwater Vehicle[J]. Acta Armamentarii, 2020, 41(12): 2504-2513.

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