SOM-Based Direct Inverse Trajectory Control System for Double-Propeller Boat Maneuvers

This paper presents the development of neural-network-based control system using self-organizing-maps (SOMs) for the maneuvers of a double-propeller boat. The performance characteristics of the developed SOM controller system are compared with a widely-used supervised learning mechanism, the backpropagation neural network (BPNN) controller. The experimental results show that the proposed unsupervised SOM controller can control the boat model with very low error, although most artificial neural network (ANN)-based controllers are usually designed using supervised learning approaches. The important characteristic of the proposed SOM controller system is that it utilizes a mapping principle instead of an error calculation such as that in the BPNN controller system; consequently, the proposed SOM controller system is not very sensitive to non-ideal training data, which produces a low control error for the generated elliptical trajectory data. It is also confirmed in these experiments that when more mapping neurons are utilized in the SOM controller, a lower control error is achieved. It is expected that in a real implementation, the SOM controller could provide more robust control than the BPNN controller in handling small disturbances such as light winds and small waves.