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A neural network based robust control system design for the trajectory of Autonomous Underwater Vehicles (AUVs) is presented in this paper. Two types of control structure were used to control prescribed trajectories of an AUV. The vehicle was tested with random disturbances while taxiing under water. The results of the simulation showed that the proposed neural network based robust control system has superior performance in adapting to large random disturbances such as underwater flow. It is proved that this kind of neural predictor could be used in real-time AUV applications.

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An experimental design method for noise and vibration analysis of two car engines by feedforward and radial basis neural networks is presented. Two types of car engines are experimentally analyzed by using intelligent data acquisition card with software. Measured vibration and noise parameters of two car engines are used as desired values of the neural networks. The effectiveness of using Radial Basis Neural Network (RBNN) with backpropagation algorithm is demonstrated for predicting the vibrations and noises of two car engines. The robustness of the proposed RBNN predictor to parameters of vibration and noise as well measurement disturbances is investigated. The result of experiments and simulation show that the proposed RBNN is able to adapt effectively under disturbances.