Performance evaluation of linear quadratic regulator and linear quadratic Gaussian controllers on quadrotor platform
The purpose of this article is to evaluate the performances of the three different controllers such as Linear Quadratic Regulator (LQR), 1-DOF (Degree of Freedom) Linear Quadratic Gaussian (LQG) and 2-DOF LQG based on Quadrotor trajectory tracking and control effort. The basic algorithm of these th...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Blue Eyes Intelligence Engineering & Sciences Publication
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/74027/7/74027%20Performance%20evaluation%20of%20linear%20quadratic.pdf http://irep.iium.edu.my/74027/8/74027%20Performance%20evaluation%20of%20linear%20quadratic%20SCOPUS.pdf http://irep.iium.edu.my/74027/ https://www.ijrte.org/wp-content/uploads/papers/v7i6s/F02380376S19.pdf |
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| Summary: | The purpose of this article is to evaluate the performances of the three different controllers such as Linear Quadratic Regulator (LQR), 1-DOF (Degree of Freedom) Linear Quadratic Gaussian (LQG) and 2-DOF LQG based on Quadrotor trajectory tracking and control effort. The basic algorithm of
these three controllers are almost same but arrangement of some additional features, such as integral part and Kalman filter in the 1-DOF and 2-DOF LQG, make these two LQG controllers more robust comparing to LQR. Circular and Helical trajectories have been adopted in order to investigate the performances of the controllers in MATLAB/Simulink environment. Remarkably the 2-DOF LQG ensures its highly robust performance when system
was considered under uncertainties. In order to investigate the tracking performance of the controllers, Root Mean Square Error (RMSE) method is adopted. The 2-DOF LQG significantly ensures that the error is less than 5% RMSE and maintains stable control input continuously. |
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