Online self-constructing fuzzy neural identification for ship motion dynamics based on MMG model
- Resource Type
- Conference
- Authors
- Wang, Ning; Niu, Xiaobing; Liu, Yudong
- Source
- Proceedings of the 10th World Congress on Intelligent Control and Automation Intelligent Control and Automation (WCICA), 2012 10th World Congress on. :458-463 Jul, 2012
- Subject
- Bioengineering
Robotics and Control Systems
Signal Processing and Analysis
Components, Circuits, Devices and Systems
Communication, Networking and Broadcast Technologies
Computing and Processing
Mathematical model
Marine vehicles
Fuzzy neural networks
Vectors
Fuzzy control
Hydrodynamics
MMG Ship Motion Model
Fuzzy Neural Network
Generalized Ellipsoidal Basis Function
Online Self-constructing
- Language
In this paper, an online self-constructing fuzzy neural identification for MMG ship motion model is clearly identified by using the promising Generalized Ellipsoidal Function Based Fuzzy Neural Network (GEBF-FNN) method. Nonlinear differential equations of MMG-type ship motion dynamics are used to establish the reference model implicating essential nonlinearities for GEBF-FNN based ship motion model (GEBF-FNN-SMM) identification. The GEBF-FNN-SMM starts without fuzzy rules and online recruits efficient fuzzy rules via rule node generation criteria and parameter estimation. The resultant GEBF-FNN-SMM reasonably captures essential dynamics since the checking process validates the prediction performance with high accuracy. Finally, in order to demonstrate that the GEBF-FNN-SMM scheme is effective, simulation studies are conducted on zig-zag maneuvers. Moreover, comprehensive comparisons are carefully presented. Simulation results indicate that the GEBF-FNN-SMM achieves promising performance in terms of approximation and prediction.