Penalaan Mandiri Full State Feedback dengan LQR dan JST Pada Kendali Quadrotor

https://doi.org/10.22146/ijeis.37212

Faisal Fajri Rahani(1*), Tri Kuntoro Priyambodo(2)

(1) Program Pascasarjana Ilmu Komputer, DIKE, FMIPA UGM, Yogyakarta
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


Quadrotor is one type of unmanned aerial vehicle that has the ability to vertical takeoff and landing. In this research, a system designed to stabilize quadrotor during flight condition by maintaining at angle of roll, pitch, yaw, and x, y, and z axis position using LQR full state feedback with artificial neural network (ANN).

The LQR full state feedback method uses 12 states with each K constant being tuned with ANN. This research implements ANN method to change feedback constant at angle of roll, pitch, and yaw and x, y, and z axis. The artificial neural network method uses 12 input layers, 12 hidden layers, and 1 output layer.

Testing with ANN improved the rise time to ± 2.18 seconds at the roll angle, ± 1.23 seconds at the pitch angle, and ± 0.31 seconds at the yaw angle. Improved settling time value up to ± 2.41 seconds at roll angle, ± 1.23 seconds at pitch angle, and ± 1.07 seconds at yaw angle. Improved steady state eror value of ± 0.61% at roll angle, ± 4.88% at pitch angle, and ± 0.82% at the yaw angle.


Keywords


unmanned aerial vehicle;neural networks;UAV

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References

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DOI: https://doi.org/10.22146/ijeis.37212

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