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Fuzzy Waypoint Guidance Controller for Underactuated Catamaran Wave Adaptive Modular Vessel

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Intelligent Systems and Applications (IntelliSys 2016)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 751))

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Abstract

The development of a GPS based position control system for Wave Adaptive Modular Vessel (WAM-V) able to navigate between waypoints is discussed in this chapter. A fuzzy reasoned double loop controller is proposed for navigation path planning of WAM-V. For outer loop fuzzy controller is used to feed the desired heading to the inner loop. In the inner loop, a PID feedback controller is used to correct the desired course generated by the fuzzy reasoned algorithm. The control system provides the required feedback signals to track the desired heading which is obtained from the fuzzy algorithm. After PID generates the appropriate command, the thrust isallocated to the port side and starboard side thrusters along with the command from lookup table. Using the proposed controller, several experiments are conducted at Osaka University free running pond facility. The WAM-V is equipped without rudder, thus it is driven by a combination of different thrusts to control both speed and heading. Several experimental results with different sets of waypoint validate the proposed algorithm. The obtained results affirmed that the proposed fuzzy waypoint guidance control algorithm is powerful to realize the navigation path planning. The waypoint navigation experimental results show that the fuzzy guided waypoint controller scheme is simple, intelligent and robust. The goal of this research is to present a solution to the waypoint control problem for the underactuated catamaran vessel (WAM-V), which is achieved successfully.

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Correspondence to Jyotsna Pandey .

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Pandey, J., Hasegawa, K. (2018). Fuzzy Waypoint Guidance Controller for Underactuated Catamaran Wave Adaptive Modular Vessel. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2016. Studies in Computational Intelligence, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-69266-1_19

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  • DOI: https://doi.org/10.1007/978-3-319-69266-1_19

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