Starts with an overview of a classical PID controller design. An account of how neural networks may be incorporated to provide control in such a setup is given. The example used in the paper is the problem of controlling a high frequency acoustics platform (HFAP) in-flight. The HFAP is towed by a ship and flown in the water behind the ship to acquire acoustic data reflected from the sea floor. The stability of such a platform is of prime importance to the accuracy of data collected. Using flight data from previous runs of the platform, a neural network is trained. The trained network is then used to predict the behavior of the platform. These predictions may then be directly translated to control signals minimizing the platform's spatial deviations. In the paper results form the trained neural network on predicting the behavior of the platform are displayed. Network prediction results illustrating the ability of the network to operate with partial input are displayed. Displaying these results in contrast with conventional controller results given the same input parameters emphasizes the importance of such a feature. Finally the use of different network architectures and the cost of using these network, in terms of computing power is investigated.
A neural network approach to digital control (high frequency acoustics platform)
1998
11 Seiten, 11 Quellen
Aufsatz (Konferenz)
Englisch
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