Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/829
Title: State Estimation Based Optimal Control and NARMA-L2 Controllers of a Scaled-Model Helicopter
Authors: Khaldi, Mohamad 
Abiad, Hassan El
Affiliations: Department of Electrical Engineering 
Issue Date: 2009
Part of: 2nd IFAC International Conference on Intelligent Control Systems and Signal Processing (ICONS 2009)
Start page: 55
End page: 60
Conference: IFAC International Conference on Intelligent Control Systems and Signal Processing (ICONS 2009) (2nd : 21-23 Sept 2009 : Istanbul,Turkey) 
Abstract: 
Scaled-model helicopters are highly nonlinear, coupled, and unstable machines. They have fast response and controlling them is very complicated and need high degree of precision. In this paper, a detailed nonlinear model is derived. An optimal controller that is based upon state estimation is designed and implemented for each of the control inputs using a linearized model around an unaccelerated hovering motion. The optimal linear controller was then applied to the nonlinear model. In addition, input/output measurements of the nonlinear model were used to train the Multi-Layer Neural Networks (MLNNs) of the Nonlinear AutoRegressive with Moving Average (NARMA-L2) controller. Then, NARMA-L2 was applied to the nonlinear model and the results were compared with both the classical, namely PI-D, and the optimal control.
URI: https://scholarhub.balamand.edu.lb/handle/uob/829
Type: Conference Paper
Appears in Collections:Department of Electrical Engineering

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