5. Simulation Results and Conclusion
We use the cargo model given in (9) to emulate the ship dynamics. Fig.1 shows the heading and Fig.2 shows the rudder angle for self-learning fuzzy controller. Simulation program is designed in C++ program language. In C++Builder5.0 integration development environment, Matlab6.0 plot instructions are applied. NO.1 is the first period response curve and No.2 is the second period response curve respectively in Fig.1 and Fig.2. In the first period, there is bigger steady error. After self-learning the steady error is eliminated completely in the second period.
Considering the change of cargo dynamics, a pure delay is added to model (9). Fig.3 shows the heading and Fig.4 shows the rudder angle for self-learning fuzzy controller. After three times self-learning the steady error is also eliminated.
Simulation results show that the self-studying fuzzy control arithmetic is effective in generating the appropriate modifying quantity for good process response. Especially in eliminating steady error it is quite successful. The paper's work lays a foundation in modeling and analysis of fuzzy learning control system.
Fig. 1 the cargo's heading curves
Fig.2 the cargo's rudder angles curves
Fig.3 the cargo's heading curves
Fig.4 the cargo's rudder angle curves
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Liu Qing: female, 38 years old, Professor, received her PH.D, in Ship and Ocean Structure Object's Design and Manufeature from Wuhuan University of Technology in 2002, received her M.S. in Electric Drive and Automation from Wu huan Transportion University in 1988, received her B.S in electric automation from Wuhan Institute of Water Transportation and Engineering in 1985. Her researching intrests in intelligent control theory and technology, computer process control and ship steering and control. Working in Automation school. Wuhan University of Technology now.
Address: 1040 He Ping Road
Wu Han. P.R.China.