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 thes...
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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/73129/7/73129%20Performance%20evaluation%20of%20linear%20quadratic.pdf http://irep.iium.edu.my/73129/8/73129%20Performance%20evaluation%20of%20linear%20quadratic%20SCOPUS.pdf http://irep.iium.edu.my/73129/ 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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