Dynamic Tanker Steering Control Using Generalized Ellipsoidal-Basis-Function-Based Fuzzy Neural Networks

This paper deals with tanker steering control based on a novel multiple-input multiple-output generalized ellipsoidal-basis-function-based fuzzy neural network (GEBF-FNN) with online updating of system structure and parameters. The main contributions of this paper are as follows. 1) A GEBF-FNN-based nonlinear steering model incorporating the nonlinearity underlying tanker dynamics is proposed. 2) The static local controller (SLC), whose controller gains are locally fixed with the initial forward speed and the desired heading for individual steering commands, is implemented. 3) The dynamic local controller (DLC) is further realized by employing adaptive controller gains pertaining to time-varying forward speed and heading dynamics. 4) The GEBF-FNN-based steering controller is developed by identifying a nonlinear mapping from the heading error, acceleration and forward speed to dynamic controller gains, and thereby contributing to a model-free adaptive control scheme. Simulation results and comprehensive studies on benchmark problems demonstrate that the GEBF-FNN-based model can capture the essential tanker dynamics, and the proposed SLC, DLC, and GEBF-FNN-based schemes achieve superior performance in terms of heading regulation and forward speed loss. In comparison with the SLC and traditional fuzzy controllers, the DLC and GEBF-FNN-based controllers achieve higher accuracy of heading regulation with less rudder efforts and minimal forward speed losses.