Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98926
Title: An on-line learning neural controller for helicopters performing highly nonlinear maneuvers
Authors: Suresh, Sundaram
Sundararajan, Narasimhan
Issue Date: 2011
Source: Suresh, S., & Sundararajan, N. (2012). An on-line learning neural controller for helicopters performing highly nonlinear maneuvers. Applied soft computing, 12(1), 360-371.
Series/Report no.: Applied soft computing
Abstract: This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.
URI: https://hdl.handle.net/10356/98926
http://hdl.handle.net/10220/12551
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2011.08.036
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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