An efficient neural network based tracking controller for autonomous underwater vehicles subject to unknown dynamics

2014 ◽  
Author(s):  
Chang-Zhong Pan ◽  
Simon X. Yang ◽  
Xu-Zhi Lai ◽  
Lan Zhou
Keyword(s):  
Neural Network ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Tracking Controller

A New Tracking Controller Design for Underwater Vehicles Using Quadratic Stabilization

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
R. Prasanth Kumar ◽  
Anirvan Dasgupta ◽  
C. S. Kumar
Keyword(s):  
Linear Time ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Reference Trajectory ◽  
Unknown Parameters ◽  
Adaptation Algorithm ◽  
Stabilizing Controller ◽  
Uncertain Dynamics ◽  
Quadratic Stabilization ◽  
Tracking Controller

This paper proposes a new tracking controller for autonomous underwater vehicles (AUVs) using the concept of simultaneous quadratic stabilization. The nonlinear underwater vehicle system is viewed as a set of locally linear time invariant systems obtained by linearizing the system equations on the reference trajectory about some discrete points. A single stabilizing controller is then designed for the set of systems so obtained. However, this controller requires the exact parameters of the system. Since the hydrodynamic parameters of AUVs are generally not known with sufficient accuracy, the proposed controller is used for the known part of the dynamics and an adaptation algorithm is used to estimate the unknown parameters online and compensate for the rest of the plant dynamics. The proposed controller can thus adaptively handle the complete nonlinear uncertain dynamics of the plant. Simulation results are presented and discussed for a typical AUV.

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