Fuzzy model reference learning control for cargo ship steering

Abstract

The use of a learning control system to maintain adequate performance of a cargo ship autopilot when there are process disturbances or variations is examined. The objective is to make an initial assessment of what advantages a fuzzy learning control approach has over conventional adaptive control approaches. The simulation results indicate that the fuzzy model reference learning controller (FMRLC) has several potential advantages over model reference adaptive control (MRAC), including improved convergence rates, use of less control energy, enhanced disturbance rejection properties, and lack of dependence on a mathematical model. Using the comparative analysis, the authors discuss how the well-developed concepts in conventional adaptive control can be used to evaluate fuzzy learning control techniques. >