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Soft computing techniques in the design of a navigation, guidance and control system for an autonomous underwater vehicle
Author(s) -
Loebis D.,
Naeem W.,
Sutton R.,
Chudley J.,
Tetlow S.
Publication year - 2006
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.929
Subject(s) - inertial navigation system , kalman filter , extended kalman filter , control engineering , control theory (sociology) , quadratic programming , fuzzy logic , computer science , global positioning system , controller (irrigation) , noise (video) , navigation system , soft computing , gps/ins , model predictive control , engineering , real time computing , control (management) , inertial frame of reference , mathematical optimization , artificial intelligence , assisted gps , mathematics , telecommunications , agronomy , physics , quantum mechanics , image (mathematics) , biology
This paper discusses the navigation, guidance and control (NGC) of the Hammerhead autonomous underwater vehicle (AUV). The navigation system is based on the integrated use of the global positioning system (GPS) and several inertial navigation system (INS) sensors. A simple Kalman filter (SKF) and an extended Kalman filter (EKF) are proposed to be used subsequently to fuse the data from the INS sensors and to integrate them with that of the GPS. This paper highlights the use of soft computing techniques, with an emphasis on fuzzy logic and genetic algorithms (GAs), both in single‐ and multiobjective modes to the adaptation of the initial statistical assumption of both the SKF and EKF caused by possible changes in sensor noise characteristics. It will be shown how the adaptation made by the proposed techniques is able to enhance the accuracy of the navigation system and hence it is considered as a major contribution of this particular study in relation to AUV technology. The guidance and control system is based on a model predictive controller (MPC). The conventional MPC assumes a quadratic cost function and an optimization method such as quadratic programming (QP) to determine the optimum input to the process. For vehicle implementation, two modifications are proposed to the standard MPC problem. The first involves the replacement of the conventional optimizer with a GA in single objective mode whilst the quadratic cost function is replaced by a fuzzy performance index. The advantages of both schemes are outlined and simulation results are presented to evaluate the performance of the proposed techniques. Copyright © 2006 John Wiley & Sons, Ltd.