Neuro-fuzzy approach to mode transitioning in aerospace applications

A real-time adaptation scheme is proposed for the online customization of mode transition controllers designed off-line via blending local mode controllers. It consists of the desired transition trajectory model, the active plant model and the mode transition controller. The active plant model, which incorporates local mode information, is initially trained off-line to capture the desired transition trajectory and controls. Afterwards, the active plant model is adapted online via structure and parameter learning to capture the input/output relationship of the nonlinear system to be controlled. The proposed adaptation scheme is illustrated for a hover to forward flight mode transition control of a helicopter encountering parametric changes and wind disturbances