identification of an autonomous underwater vehicle dynamic using extended kalman filter with arma noise model

Authors

saeed ebrahimi

yazd university mohammad bozorg

yazd university mehdi zare ernani

kavian petrochemical company

abstract

in the procedure of designing an underwater vehicle or robot, its maneuverability and controllability must be simulated and tested, before the product is finalized for manufacturing. since the hydrodynamic forces and moments highly affect the dynamic and maneuverability of the system, they must be estimated with a reasonable accuracy. in this study, hydrodynamic coefficients of an autonomous underwater vehicle (auv) are identified using velocity and displacement measurements, and implementing an extended kalman filter (ekf) estimator. the hydrodynamic coefficients are included in the augmented state vector of a six dof nonlinear model. the accuracy and the speed of the convergence of the algorithm are improved by selecting a proper covariance matrix using the arma process model. this algorithm is used to estimate the hydrodynamic coefficients of two different sample auvs: nps auv ii and isimi. the comparison of the outputs of the identified models and the outputs of the real simulated models confirms the accuracy of the identification algorithm. this identification method can be used as an efficient tool for evaluating the hydrodynamic coefficients of underwater vehicles (robots), using the experimental data obtained from the test runs.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Identification of an Autonomous Underwater Vehicle Dynamic Using Extended Kalman Filter with ARMA Noise Model

In the procedure of designing an underwater vehicle or robot, its maneuverability and controllability must be simulated and tested, before the product is finalized for manufacturing. Since the hydrodynamic forces and moments highly affect the dynamic and maneuverability of the system, they must be estimated with a reasonable accuracy. In this study, hydrodynamic coefficients of an autonomous un...

full text

Identification of an Autonomous Underwater Vehicle Dynamic Using Extended Kalman Filter with ARMA Noise Model

In the procedure of designing an underwater vehicle or robot, its maneuverability and controllability must be simulated and tested, before the product is finalized for manufacturing. Since the hydrodynamic forces and moments highly affect the dynamic and maneuverability of the system, they must be estimated with a reasonable accuracy. In this study, hydrodynamic coefficients of an autonomous un...

full text

Mobile Robot Navigation Error Handling Using an Extended Kalman Filter

Obviously navigation is one of the most complicated issues in mobile robots. Intelligent algorithms are often used for error handling in robot navigation. This Paper deals with the problem of Inertial Measurement Unit (IMU) error handling by using Extended Kalman Filter (EKF) as an Expert Algorithms. Our focus is put on the field of mobile robot navigation in the 2D environments. The main chall...

full text

OPTIMIZED FUZZY CONTROL DESIGN OF AN AUTONOMOUS UNDERWATER VEHICLE

In this study, the roll, yaw and depth fuzzy control of an Au- tonomous Underwater Vehicle (AUV) are addressed. Yaw and roll angles are regulated only using their errors and rates, but due to the complexity of depth dynamic channel, additional pitch rate quantity is used to improve the depth loop performance. The discussed AUV has four aps at the rear of the vehicle as actuators. Two rule bases...

full text

Estimation of Vehicle Mass Using an Extended Kalman Filter

Vehicle mass is an important parameter when developing features which improve drivability and performance feel for passenger cars. A vehicle’s mass naturally depends on the load and the number of passengers. Therefore it is desired to have a fast, accurate and robust mass estimation algorithm. In this thesis an extended Kalman filter is used to estimate the mass of a passenger car. The filter u...

full text

My Resources

Save resource for easier access later


Journal title:
international journal of robotics

جلد ۴، شماره ۱، صفحات ۲۲-۲۸

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023