Evaluation performance 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 thr...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/68747/ http://irep.iium.edu.my/68747/ http://irep.iium.edu.my/68747/1/68747_Evaluation%20Performance%20of%20Linear%20-program%20book.pdf http://irep.iium.edu.my/68747/2/68747_Evaluation%20Performance%20of%20Linear%20-slide.pdf |
| 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 like integral part and Kalman filter in 1 DOF and 2 DOF LQG make the controllers more robust comparing to LQR. Circular and Helical trajectories have been adopted in order to investigate the performance of the controllers in MATLAB/Simulink environment. Remarkably 2 DOF LQG ensures its highly robust performance when system was considered under noise and disturbances. Root Mean Square Error (RMSE) method is adopted to investigate the tracking performances of the controllers. |
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