Stable nonlinear adaptive controller for an autonomous underwater vehicle using neural networks

Abstract

In general, the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions. For this reason, high performance control system for an AUV usually should have the capacities of learning and adaptation to the time-varying dynamics of the vehicle. In this article, we present a robust adaptive nonlinear control scheme for an AUV, where a linearly parameterized neural network (LPNN) is introduced to approximate the uncertainties of the vehicle's dynamics, and the basis function vector of the network is constructed according to the vehicle's physical properties. The proposed control scheme can guarantee that all of the signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

Keywords:

• Nonlinear systems
• Neural networks
• Functional approximation
• Uncertainties
• Robustness
• AUV

Acknowledgments

This work was supported in part by the Ministry of Maritime Affairs and Fisheries and the Ministry of Science and Technology, Korea.