Volume 17, Issue 7 (9-2017)                   Modares Mechanical Engineering 2017, 17(7): 413-420 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Parhizkar N, Naghash A. Comparison of Back Stepping Optimized via PSO Algorithm and LQR Controllers for a Quadrotor. Modares Mechanical Engineering 2017; 17 (7) :413-420
URL: http://mme.modares.ac.ir/article-15-9899-en.html
1- Amirkabir University of technology, Aerospace engineering department
2-
Abstract:   (4901 Views)
Comparison of Back stepping method optimized via particle swarm optimization algorithm and LQR method for hovering control of a quadrotor is presented in this paper. Quadrotor is not a stable dynamical system and development of high performance controllers for it is important. First the dynamic model of a quadrotor is introduced and state-space equations are presented in order to simulate the dynamic model. Then two Back stepping and LQR controllers are designed to control Euler angles and height of the quadrotor. In order to optimize back stepping controller, its parameters are determined using particle swarm optimization algorithm to minimize cost function considered for LQR controller. Also commands to the motors are calculated and plotted to show the feasibility of the controller. To obtain better comparison, the cost function is calculated for different weighting matrices of Q and R for two controllers and the results are compared. The results show that Back stepping controller has more ability to minimize the cost function in comparison to LQR and the cost function in Back stepping has less values for several choices of weighting matrices.
Full-Text [PDF 1085 kb]   (6515 Downloads)    
Article Type: Research Article | Subject: Control
Received: 2017/04/6 | Accepted: 2017/05/26 | Published: 2017/08/4

Add your comments about this article : Your username or Email:
CAPTCHA

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.