Neural network identification and control of an underwater vehicle

Real-time predictive control requires a forward model that is both accurate and fast. This paper introduces two nonlinear internal memory network architectures and compares their performance with a Multi-layer Perceptron (MLP) augmented with the use of spread encoding. The test plant is a single component from an Underwater Robotic Vehicle (URV), comprising a thruster encased in a steel frame and provided with buoyancy. This assemblv is free to move under water and is controlled for depth. The internal memory networks are of comparable accuracy to the MLP but more parsimonious, resulting, in a faster response which makes them better suited for on-line control. Although a particular case study is presented as the focus of this paper, the algorithms and methods developed have generic applicability.

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