Particle swarm optimization of neural controller for tanker ship steering
This paper discussed the implementation of Particle Swarm Optimization (PSO) to optimize a Radial Basis Function (RBF) neural controller for tanker ship control. The RBF neural controller uses reinforcement learning strategy to achieve the heading regulation of tanker ship exposed to plant changes a...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
2007
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| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/3236/ |
| Summary: | This paper discussed the implementation of Particle Swarm Optimization (PSO) to optimize a Radial Basis Function (RBF) neural controller for tanker ship control. The RBF neural controller uses reinforcement learning strategy to achieve the heading regulation of tanker ship exposed to plant changes and disturbances. However, the tuning of the neural controller design parameters are considered to be difficult and tedious due to the high nonlinearity of the ship dynamic model and the external disturbances. It is shown that PSO can provide a very promising technique for its simplicity and ease of use. Moreover, Centroidal Voronoi Tessellation (CVT) is implemented to select the starting positions of the particles strategically. The promising results from the experiment provide direct evidence for the feasibility and effectiveness of PSO for the optimization of neural controller for tanker ship heading regulation. |
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