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Neural control of helicopter blade-vortex interaction noise

Significant reduction of helicopter blade-vortex interaction (BVI) noise is currently one of the most advanced research topics in the helicopter industry. This is due to the complex flow, the close aerodynamic and structural coupling, and the interaction of the blades with the trailing edge vortices. Analytical and numerical modeling techniques are therefore currently still far from a sufficient degree of accuracy to obtain satisfactory results using classical model based control concepts. Neural networks with a proven potential to learn nonlinear relationships implicitly encoded in a training data set are therefore an appropriate and complementary technique for the alternative design of a nonlinear controller for BVI noise reduction. For nonlinear and adaptive control different neural control strategies have been proposed. Two possible approaches, a direct and an indirect neural controller are described. In indirect neural control, the plant has to be identified first by training a network with measured data. The plant network is then used to train the controller network. On the other hand the direct control approach does not rely on an explicit plant model, instead a specific training algorithm (like reinforcement learning) uses the information gathered from interactions with the environment. In the investigation of the BVI noise phenomena, helicopter developers have undertaken substantial efforts in full scale flight tests and wind tunnel experiments. Data obtained in these experiments have been adequately preprocessed using wavelet analysis and filtering techniques and are then used in the design of a neural controller. Neural open-loop control and neural closed-loop control concepts for the BVI noise reduction problem are conceived, simulated and compared against each other in this work in the above mentioned framework.