Abstract:

A Radial Basis Function (RBF) neural network is applied to plant identification for active control of laminar boundary layer transition by suction. A suitable RBF structure is selected and its optimal parameters are obtained by training a network with real experimental data from a two channel suction system. The plant model from the trained network, which represents the plant response, can be used successfully to solve the optimal suction flow rates instead of using an assumed input/output function. Simulation results show that the RBF neural network is an effective tool in plant identification for the nonlinearly constrained optimization problem of laminar flow control.

Key words: suction, laminar-turbulence transition, radial basis function neural network