Adaptive neural flight control system for helicopter

Authors:
Sundaram Suresh
Affiliations:
Department of Electrical Engineering, Indian Institute of Technology, Delhi
Venue:
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Year:
2009

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Abstract

This paper presents an adaptive neural flight control design for helicopters performing nonlinear maneuver. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller uses a real-time learning dynamic radial basis function network, which uses Lyapunov based on-line update rule integrated with the neuron growth criterion. The real-time learning dynamic radial basis function network does not require a priori training and also find a compact network for implementation. The proposed adaptive law provide necessary global stability and better tracking performance. The simulation studies are carried-out using a nonlinear desktop simulation model. The performances of the proposed adaptive control mechanism clearly show that it is very effective when the helicopter is performing nonlinear maneuver.