On-line extreme learning algorithm based identification and non-linear model predictive controller for way-point tracking application of an autonomous underwater vehicle

On-line extreme learning algorithm based identification and non-linear model predictive controller for way-point tracking application of an autonomous underwater vehicle

In most of the surveillance applications of autonomous underwater vehicle (AUV), very often it is intended to follow the desired horizontal way-points, where some oceanography data need to be collected. In view of this, the motion planning algorithm using way-points is investigated in this study. Th...

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Bibliographic Details
Main Authors: Biranchi Narayan Rath, Bidyadhar Subudhi
Format: Article
Language:English
Published: Wiley 2019-06-01
Series:Cognitive Computation and Systems
Subjects:
position control
path planning
optimal control
self-adjusting systems
mobile robots
three-term control
identification
neurocontrollers
optimisation
autonomous underwater vehicles
adaptive control
nonlinear control systems
predictive control
remotely operated vehicles
regression analysis
robot dynamics
learning systems
control system synthesis
desired surveillance region
lampc
reported optimal controller
inverse optimal self-tuning pid controller
iospid controller
way-point tracking application
autonomous underwater vehicle
surveillance applications
global positioning system coordinates
underactuated auv
heading dynamics
adaptive model predictive controllers
system identification
fast convergence rate
robustness property
on-line sequential extreme learning machine
os-elm neural network modelling performance
jaya optimisation algorithm
forward-regression orthogonal least square
nonlinear model predictive controller
os-elm model
surveying application
line-of-sight path
on-line extreme learning algorithm based identification
horizontal way-points
nampc
error reduction ratio
err criteria
Online Access:https://digital-library.theiet.org/content/journals/10.1049/ccs.2018.0008

Internet

https://digital-library.theiet.org/content/journals/10.1049/ccs.2018.0008

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