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Structural Load Alleviation Control Systems (SLACS) are one of the principal components of Active Control Technology, which allows aerospace structures to operate more efficiently by making them lighter, yet still capable of sustaining the high operational loads experienced in flight. A SLACS alleviates the bending and torsional moments caused by the forces and moments acting upon the aircraft wings. A SLACS must be designed to achieve a well distributed alleviation across the entire span of the wing. SLACS designed using deterministic methods, such as linear optimal, H(infinity ), or quantitative methods have had only limited success, owing to controller complexities, and the unsteady effects of flight through turbulence. The main objective of this paper is to provide an alternative solution for a high dimensional aircraft control problem using artificial neural networks. These networks have nonlinear modelling capabilities, and can potentially be used to adapt on-line to account for time-varying aircraft characteristics. To address problems of persistent input excitation and slow convergence commonly faced when synthesizing large neural controllers, this neural solution requires that the control architecture be decomposed into a hybrid combination of smaller sub-networks, and linear quadratic regulators, employed together in parallel. The modelling technique is based on the traditional backpropagating multi-layered perceptron and the B-Spline associative memory networks. The B-Spline network generally has faster parameter convergence with minimal learning interference, and is therefore potentially more robust in on-line implementations. The results of digital simulations are used to demonstrate the effectiveness of such neural SLACS controlling the wing structure of a large transport aircraft in flight. The performance is assessed for both clear air and turbulent conditions.
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