Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks
نویسنده
چکیده
SUMMARY A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNs) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off-nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear virtual reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.
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تاریخ انتشار 1997