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|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.
|Appears in Collections:||Department of Electrical Engineering|
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