This paper developed a control scheme of neural network based on feedforward and PD (proportional and derivative) control for high-precision flight simulator. A radial basis-function neural network (RBFNN) controller was used to learn and to compensate the unknown model dynamics, parameter variation and disturbance of the system on-line. The iterative algorithm of RBFNN parameters is got by Lyapunov stability theory. The effectiveness of the proposed control scheme is evaluated by simulation results and a real-time flight simulator system experiment. It is found that the proposed scheme can reduce the plant's sensitivity to parameter variation and disturbance and high precision performance of flight simulator can be obtained.
Neural network adaptive control of high-precision flight simulator: theory and experiments
2009
5 Seiten, 19 Quellen
Aufsatz (Konferenz)
Englisch
Nonlinear flight control using adaptive neural network
British Library Online Contents | 1998
|Flight control law synthesis using neural network theory
AIAA | 1992
|Adaptive Structures Flight Experiments
NTRS | 1992
|