The paper addresses the development of an artificial intelligence algorithm implemented for maximum power point tracking control of a unmanned underwater vehicle. It is shown that this algorithm tracks the optimum operation point and provides fast response even in the presence of faults. The strategy implements the tracking algorithm by using real—time measurements, while providing maximum power to the grid without using online data training. The solution is simulated in the Matlab and Simulink to verify the effectiveness of the proposed approach when fault–free and faulty conditions are considered. The simulation results highlight efficient, intrinsic and passive fault tolerant performances of the algorithm for general unmanned underwater vehicles with low inertia. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG

Artificial Intelligence Tools for Actuator Fault Diagnosis of an Unmanned Underwater Vehicle

Farsoni, Saverio
Secondo
;
Simani, Silvio
Ultimo
2022

Abstract

The paper addresses the development of an artificial intelligence algorithm implemented for maximum power point tracking control of a unmanned underwater vehicle. It is shown that this algorithm tracks the optimum operation point and provides fast response even in the presence of faults. The strategy implements the tracking algorithm by using real—time measurements, while providing maximum power to the grid without using online data training. The solution is simulated in the Matlab and Simulink to verify the effectiveness of the proposed approach when fault–free and faulty conditions are considered. The simulation results highlight efficient, intrinsic and passive fault tolerant performances of the algorithm for general unmanned underwater vehicles with low inertia. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG
978-3-031-10463-3
978-3-031-10464-0
Fault diagnosis, neural networks, actuator faults, high-fidelity simulator, underwater autonomous vehicles
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2491675
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