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In this paper, a hybrid visual servo (HVS) controller is proposed for underwater vehicles, in which a combination of the vehicle's 3-D Cartesian pose and the 2-D image coordinates of a single feature is exploited. A dynamic inversion-based sliding mode adaptive neural network control (DI-SMANNC) method is developed for tracking the HVS reference trajectory generated from a constant target pose. A...
This paper presents a predictive optimization-based model reference adaptive control (MRAC) approach for dynamic positioning (DP) of a fully actuated underwater vehicle subject to dynamic uncertainties and actuator saturation. Compared with conventional linear reference model-based approaches, this proposed MRAC controller utilizes an optimized reference model composed of the closed-loop approximate...
With the rapid development of sensor technology on vehicles, more and more vehicles are checked from artificial detection to auto monitoring. A great number of real-time data acquired from sensors provides important foundation for PHM application on vehicles. Aiming at the special mining vehicle, it proposes the solution for real-time monitoring, fault diagnosis and health management based on the...
This paper presents a hybrid visual servo controller for underwater vehicles by exploiting a combination of measured Euclidean information and image information of a single feature. A dynamic inversion-based neural network control scheme is proposed for tracking of the reference trajectory generated from a constant target pose. A single-hidden-layer (SHL) feedforward neural network, in conjunction...
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