Integrated design of an adaptive neurocontroller for a 2-D aeroelastic system

The design of control configured structures has been considered in a number of recent studies. Both active and passive measures for structural vibration control have been examined in this context. The present paper addresses issues related to the use of neural network based control systems in such applications. A simplified 2-D representation of an aeroelastic system, consisting of an airfoil with a trailing-edge flap, comprises the test bed for the present study. With a proper selection of structural spring characteristics, and choice of unsteady aerodynamic forces and moments, the system provides a rudimentary 2-D model of a helicopter rotor blade that includes both structural and aerodynamic nonlinearities. The integrated optimal design of the plant and its control system for optimized response under disturbance loading is the principle objective of the design exercise. The focus of the paper is three-fold - it establishes the justification for replacing traditional control systems with neurocontrollers in such problems, examines issues related to an integrated structural-control design strategy, and discusses a detailed implementation of the approach in a linearized 2-D aeroelastic system. The design problem contains multiple relative optima, and the use of a genetic algorithm (GA) based optimization procedure is shown to be an effective tool to locate the optimal design. Results from numerical experiments are presented in support of the proposed design approach.