Optimal Design of LQR Controller Based on Improved Artificial Bee Colony Optimization Algorithm

Huai De Yang

doi:10.4028/www.scientific.net/AMR.971-973.1272

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 971-973Optimal Design of LQR Controller Based on Improved...

Optimal Design of LQR Controller Based on Improved Artificial Bee Colony Optimization Algorithm

Abstract:

The inverted pendulum system is characterized as a typical nonlinear, fast multi-variable, essentially unstable system. It is difficult to control because of its instability .In order to improve balance control, the mathematical model of the single inverted pendulum is established, a LQR controller is designed which is based on improved artificial bee colony. Experiments show that the improved algorithm has better performance than standard artificial bee colony algorithm on convergence and rate balance control to meet the requirements of the single inverted pendulum.

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Periodical:

Advanced Materials Research (Volumes 971-973)

Pages:

1272-1275

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.971-973.1272

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Cite this paper

Online since:

June 2014

Authors:

Huai De Yang*

Keywords:

Artificial Bee Colony Algorithm, Inverted Pendulum, LQR Controller, Optimal Design

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* - Corresponding Author

References

[1] Zhang J, Zhang L, Xie J. Application of memetic algorithm in control of linear inverted pendulum[C]/Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on. IEEE, 2011: 103-107.

DOI: 10.1109/ccis.2011.6045041

Google Scholar

[2] Hammett K D. Control of Nonlinear Systems via State Feedback State-Dependent Riccati Equation Techniques[R]. AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH, (1997).

Google Scholar

[3] D. Karaboga , B. Basturk , On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing 8, 2008, pp, 687-697.

DOI: 10.1016/j.asoc.2007.05.007

Google Scholar

[4] Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm[J]. Journal of global optimization, 2007, 39(3): 459-471.

DOI: 10.1007/s10898-007-9149-x

Google Scholar

[5] Shokouhifar M, Abkenar G S. An Artificial Bee Colony Optimization for MRI Fuzzy Segmentation of Brain Tissue[J]. International Proceedings of Economics Development & Research, 2011, 6.

Google Scholar

[6] Lee C Y, Yao X. Evolutionary programming using mutations based on the Lévy probability distribution[J]. Evolutionary Computation, IEEE Transactions on, 2004, 8(1): 1-13.

DOI: 10.1109/tevc.2003.816583

Google Scholar

[7] Yang, Xin-She, et al., eds. Swarm intelligence and bio-inspired computation: Theory and applications. Newnes, (2013).

Google Scholar

[8] Zhang qian, Yaoquan Yang. Research PID controller parameters optimization method based on genetic algorithms [J] Electric Power Science and Engineering, 2012, 27 (11): 53-57.

Google Scholar