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 and correct values during the operations, in" addition to the positioning uncertainty of a long pipe. It requires a smart miner. A basic control algorithm was proposed to correctly track-keep the prescribed target track of set points or miner path. One of the control algorithms is the successive learning track-keeping control (SLTC) algorithm. It can learn and overcome the uncertainties, such as variation of soil friction and hydrodynamic drag during such a maneuver. The effectiveness of the SLTC algorithm is successfully demonstrated with the simulation of a miner maneuvering along the set points of the target or following a, zigzag track on the flat seafloor for an extended period of mining. Without any advance information on these uncertainties, the control of the miner with the initial off-track disturbance takes the first 3 to 5 tracks of learning process to move the miner to the target zigzag tracks.


Self-propelled seafloor miners or vehicles have recently been adopted by many countries to develop their respective production system for the manganese nodules from the deep-ocean floor (Chung, 1996). A deep-ocean mining system is an integration of a seafloor miner system, miner to- buffer link system, lift pipe/buffer system, ship system and, ocean transportation system, as shown in Fig. 1 (Brink and Chung, 198,0, 1982). The miner is designed to maneuver on the seafloor with some intelligent control system, and track-keep" the prescribed target track.

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