Information Technology Reference
In-Depth Information
Chapter 10
Neuro-Fuzzy Modeling and Fuzzy Control
of a Fast Ferry
Matilde Santos
10.1 Introduction
Intelligent control achieves automation via the emulation of biological intelligence.
It either seeks to replace a human who performs a control task (e.g., a chemical
process operator) or it borrows ideas from how biological systems solve problems
and applies them to the solution of control problems (e.g., the use of neural networks
for control) (Passino 2001 ; Santos 2011 ).
In this chapter we present an example of the application of intelligent techniques
to a challenging real system, a fast ferry, where these methods have proved to provide
useful solutions. The chapter brings together some previous works and develops the
topic as a comprehensive case study.
We have focused on the application of soft computing methodologies, mainly
fuzzy logic but with the complement of neural networks and genetic algorithms.
These techniques aim to exploit tolerance for imprecision, uncertainty and partial
truth to achieve tractability, robustness and low cost solutions (Zadeh 1994 ).
The system we are dealing with is quite complex. It is a TF-120 fast ferry called
“Silvia Ana” (average speed 40 knots). The ferry still works in La Plata and in
the Baltic Sea. The craft has an aluminium-made deep V hull, and the following
characteristics: 119m length, 14.696mbeam, 2.405mdraught, 475 tons deadweight,
1,250 passengers, 250 cars (Anonymous 1996 , 1998 ).
The goal of dealing with fast marine systems is to stabilize the motion of the craft
for some purposes such as to improve the comfort of the passengers and the safety,
while maintaining the speed. The main impact on the behavior regarding this aspect
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