Application-Oriented Adaptive Neural Networks Design for Ship’s Linear-Tracking Control
- 3.2k Downloads
By employing Radial Basis Function (RBF) Neural Networks (NN) to approximate uncertain functions, an application-oriented adaptive neural networks design for ship linear-tracking control was brought in based on dynamic surface control (DSC) and minimal-learning-parameter (MLP) algorithm. With less learning parameters and reduced computation load, the proposed algorithm can avoid the possible controller singularity problem and the trouble caused by ”explosion of complexity” in traditional backstepping methods is removed, so it is convenient to be implemented in applications. In addition, the boundedness stability of the closed-loop system is guaranteed and the tracking error can be made arbitrarily small. Simulation results on ocean-going training ship ’YULONG’ are shown to validate the effectiveness and the performance of the proposed algorithm.
KeywordsRBF Neural Networks DSC MLP Linear-Tracking Control Backstepping
Unable to display preview. Download preview PDF.
- 1.Pettersen, K.Y., Lefeber, E.: Way-point tracking control of ships. In: Proc. of 40th IEEE CDC, Orlando, USA, pp. 940–945 (December 2001)Google Scholar
- 4.Xu, J.H., Liu, Y.J.: The application of Hybrid intelligence system for ship-tracking control, pp. 53–61. Master dissertation of Shanghai Maritime University (2006)Google Scholar
- 5.Li, T.S., Yu, B., Hong, B.G.: A Novel Adaptive Fuzzy Design for Path Following for Underactuated Ships with Actuator Dynamics. In: ICIEA 2009, pp. 2796–2800 (2009)Google Scholar