The challenge of high-performance maneuvers for a deep-ocean underwater vehicle is to design proper controllers and ensure the high accuracy of vehicle state measurements. Current underwater vehicle control solutions are considered to be a unique controller designed to reject the disturbance and noise. However, the controller is based on a precise hydrodynamic model. It requires multiple underwater experiments and complex theoretical analysis. In this article, a hybrid control strategy is presented for the work-class remote operated vehicle. It tries to compose several proportion–integral–differential controllers into one intelligent sequence in terms of task requirements. The proposed approach employs the iterative learning method to improve the performance of the depth holding and the way-point tracking. A remote operated vehicle system weighted about 1.5 tons is provided as a physical platform for scientific investigations using acoustic Doppler current profiler, inertial navigation system, depth sensor, and an altimeter. Results from the simulation and the experiment in the basin show that the proposed approach provides high accuracy at both conditions: tracking way-points with or without ocean currents and disturbances, which show the effectiveness of the proposed approach. In addition, the thrust vector defined for a propeller facilitates the control of underwater vehicle as the thruster configuration changes.
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