A direct adaptive neural command controller design for an unstable helicopter

Authors:
M. Vijaya Kumar;S. Suresh;S. N. Omkar;Ranjan Ganguli;Prasad Sampath
Affiliations:
Rotary Wing Research and Design Centre, Hindustan Aeronautics Limited, Bangalore 560017, India;Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560012, India;Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560012, India;Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560012, India;Rotary Wing Research and Design Centre, Hindustan Aeronautics Limited, Bangalore 560017, India
Venue:
Engineering Applications of Artificial Intelligence
Year:
2009

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Abstract

This paper presents an off-line (finite time interval) and on-line learning direct adaptive neural controller for an unstable helicopter. The neural controller is designed to track pitch rate command signal generated using the reference model. A helicopter having a soft inplane four-bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is used for the simulation studies. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using backpropagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval) network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller (DANC) is compared with feedback error learning neural controller (FENC).