Abstract
This paper discussed the operation of Particle Swarm Optimization (PSO) to optimize a Fuzzy Model Reference Learning Controller (FMRLC) for ship. FMRLC is developed by synthesizing several basic ideas from fuzzy set and control theory. It can achieve the heading regulation of ship exposed to plant changes and disturbances by adjusting the rules in a direct fuzzy controller so that the overall system behaves like a “reference model”. It is shown that PSO can provide a very promising technique for the design of FMRLC for its simplicity and ease of use. The promising results from the experiment provide direct evidence for the feasibility and effectiveness of PSO for the optimization of FMRL controller for ship heading regulation.
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