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A Direct Adaptive Neural Command Controller Design for an Unstable Helicopter by M. Vijaya Kumar; S. Suresh; S.N. Omkar; Ranjan Ganguli; Prasad Sampath is a Computer Science article available to read on EtoBox.

What is A Direct Adaptive Neural Command Controller Design for an Unstable Helicopter about?

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).

Who reads A Direct Adaptive Neural Command Controller Design for an Unstable Helicopter?

Author

M. Vijaya Kumar; S. Suresh; S.N. Omkar; Ranjan Ganguli; Prasad Sampath

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0952-1976)

Published

2009

Language

EN

Field

Computer Science (Physical Sciences)