Abstract
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.
Highlights
In recent years, the autonomous control system of unmanned surface vehicles (USVs) has gained significant attention due to the importance of USVs in various applications, such as military, marine mapping, sea surveillance and aquatic data acquisition
The validity of this proposed controller was confirmed in a test with real boat model experimental data despite the fact that most artificial neural network (ANN)-based controllers cannot be designed via unsupervised learning approaches
One of the important characteristics of the proposed SOM controller is that it utilizes a mapping principle instead of an error calculation such as that used in the backpropagation (BPNN) controller
Summary
The autonomous control system of unmanned surface vehicles (USVs) has gained significant attention due to the importance of USVs in various applications, such as military, marine mapping, sea surveillance and aquatic data acquisition. The authors have developed an SOM controller employing direct inverse control (DIC) schemes for the velocity and course control of a double-propeller boat model, and its performance characteristics have been compared with those of the backpropagation neural network controller (BPNN controller) [36] and [37]. The inverse transfer function of the plant, f −1, is replaced by the artificial neural networks
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