A large self-propelled seafloor miner or vehicle was tested in the '70s on the seafloor at a depth of 3,000 to 5,000 m. It was a subsystem of the integrated ship-pipe-buffer-link-miner production system of manganese nodules from the seafloor at that depth. In tracking the set points of the target track, turning and varying its speed, the miner maneuver must overcome uncertainties in many unknown operational parameters, in addition to the positioning uncertainty of a long pipe. It requires a smart miner. A basic control algorithm, the successive learning track-keeping control (SLTC) algorithm, was proposed to correctly track-keep the prescribed target track of set points or miner path. Successful learning and overcoming of the uncertainties in soil friction and hydrodynamic drag by the SLTC algorithm are demonstrated with the simulation of a miner following a zigzag target track on the flat seafloor. In controlling the miner with the initial off-track disturbance, it takes the first 3 to 5 tracks of the learning process to move the miner to the zigzag tracks.


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    Titel :

    Smart seafloor mining vehicle: simulation with successive learning track-keeping control


    Beteiligte:
    Chung, Jin-S. (Autor:in) / Qi, T. (Autor:in)


    Erscheinungsdatum :

    2000


    Format / Umfang :

    5 Seiten, 6 Quellen



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch





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