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Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum

Year 2015, Volume: 3 Issue: 1, 1 - 6, 13.01.2015
https://doi.org/10.18201/ijisae.87020

Abstract

This paper presents a linear quadratic optimal controller design for a nonlinear inverted pendulum. Linear Quadratic Regulator (LQR), an optimal control method, is usually used for control of the dynamical systems. Main design parameters in LQR are the weighting matrices; however there is no relevant systematic techniques presented to choose these matrices. Generally, selecting weighting matrices is performed by trial and error method since there is no direct relation between weighting matrices and time domain specifications like overshoot percentage, settling time, and steady state error. Also it is time consuming and highly depends on designer’s experience. In this paper LQR is used to control an inverted pendulum as a nonlinear dynamical system and the Artificial Bee Colony (ABC) algorithm is used for selecting weighting matrices to overcome LQR design difficulties. The ABC algorithm is a swarm intelligence based optimization algorithm and it can be used for multivariable function optimization efficiently.  The simulation results justify that the ABC algorithm is a very efficient way to determine LQR weighting matrices in comparison with trial and error method.

References

  • K. J. Aström and K. Furuta, "Swinging up a pendulum by energy control," Automatica, vol. 36, no. 2, pp. 287-295, 2000.
  • J. Anderson, "Learning to control of an inverted pendulum using neural networks," IEEE Control Systems Magazine, vol. 9, no. 3, pp. 31-37, 1989.
  • A. Kuo, "The six determinants of gaint and the inverted pendulum analogy: Adynamic walking perspective," Human Movement, vol. 26, pp. 617-656, 2007.
  • S. Jeong and T. Takahashi, "Wheeled inverted pendulum type asistant robot: inverted mobile, standing and sitting motions," in IEEE/RSJ International Conferance on Intelligent Robots and Systems, 2007, 2007.
  • D. E. Kirk, Optimal control theory: an introduction, New Jersey: Prentice-Hall, Inc., 1970.
  • M. Athans, "The status of optimal control theory and applications for for deterministic systems," IEEE Transactions on Automatic Control, vol. 11, no. 3, pp. 580-596, 1966.
  • R. E. Kalman, "When is a linear control system optimal?," Journal of Basic Engineering, vol. 86, pp. 51-56, 1964.
  • H. Kwakernaak and R. Sivan, Linear optimal control systems, New York: Wiley-Interscience, 1972.
  • J. V. D. F. Neto, I. S. Abreu and F. N. Da Silva, "Neural-genetic synthesis for state-space controllers based on linear quadratic regulator design for eigenstructure assignment," Trans. Sys. Man Cyber. Part B, vol. 40, no. 2, pp. 266-285, 2010.
  • C. P. Bottura, J. V. da Fonseca Neto, "Parallel eigenstructure assignment via LQR design and genetic algorithms," in Proceedings of the American Control Conference 1999. Vol. 4. , San Diago, 1999.
  • S. Mobayen, A. Rabiei, M. Moradi and B. Mohammady, "Linear quadratic optimal control system design using particle swarm optimization algorithm," International Journal of the Physical Sciences, vol. 6(30), pp. 6958-6966, 2011.
  • K. Ogata, "Quadratic Optimal Control Systems," in Discrete-Time Control Systems 2nd Edition, Englewood Cliffs, NJ, Prentice-Hall, 1995, pp. 566-633.
  • D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Erciyes University, Kayseri, Turkey, 2005.
  • D. Karaboga and B. Basturk, "An artificial bee colony (ABC) algorithm for numeric function optimization.," in IEEE Symp. Swarm Intelligence, Indianapolis, 2006.
  • D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Applied Soft Computing, vol. 8, no. 1, pp. 687-697, 2008.
  • D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, 2007.
  • D. Karaboga and B. Akay, "A comparative study of Artificial Bee Colony algorithm," Applied Mathematics and Computation, vol. 214, no. 1, p. 108–132, 2009
  • "Detailed Pseudocode of the ABC Algorithm," 14 October 2008. [Online]. Available: http://mf.erciyes.edu.tr/abc/pub/PsuedoCode.pdf. [Accessed 1 March 2014].
  • R. Coban , "A fuzzy controller design for nuclear research reactors using the particle swarm optimization algorithm," Nuclear Engineering and Design, vol. 241, no. 5, pp. 1899-1908, 2011.
Year 2015, Volume: 3 Issue: 1, 1 - 6, 13.01.2015
https://doi.org/10.18201/ijisae.87020

Abstract

References

  • K. J. Aström and K. Furuta, "Swinging up a pendulum by energy control," Automatica, vol. 36, no. 2, pp. 287-295, 2000.
  • J. Anderson, "Learning to control of an inverted pendulum using neural networks," IEEE Control Systems Magazine, vol. 9, no. 3, pp. 31-37, 1989.
  • A. Kuo, "The six determinants of gaint and the inverted pendulum analogy: Adynamic walking perspective," Human Movement, vol. 26, pp. 617-656, 2007.
  • S. Jeong and T. Takahashi, "Wheeled inverted pendulum type asistant robot: inverted mobile, standing and sitting motions," in IEEE/RSJ International Conferance on Intelligent Robots and Systems, 2007, 2007.
  • D. E. Kirk, Optimal control theory: an introduction, New Jersey: Prentice-Hall, Inc., 1970.
  • M. Athans, "The status of optimal control theory and applications for for deterministic systems," IEEE Transactions on Automatic Control, vol. 11, no. 3, pp. 580-596, 1966.
  • R. E. Kalman, "When is a linear control system optimal?," Journal of Basic Engineering, vol. 86, pp. 51-56, 1964.
  • H. Kwakernaak and R. Sivan, Linear optimal control systems, New York: Wiley-Interscience, 1972.
  • J. V. D. F. Neto, I. S. Abreu and F. N. Da Silva, "Neural-genetic synthesis for state-space controllers based on linear quadratic regulator design for eigenstructure assignment," Trans. Sys. Man Cyber. Part B, vol. 40, no. 2, pp. 266-285, 2010.
  • C. P. Bottura, J. V. da Fonseca Neto, "Parallel eigenstructure assignment via LQR design and genetic algorithms," in Proceedings of the American Control Conference 1999. Vol. 4. , San Diago, 1999.
  • S. Mobayen, A. Rabiei, M. Moradi and B. Mohammady, "Linear quadratic optimal control system design using particle swarm optimization algorithm," International Journal of the Physical Sciences, vol. 6(30), pp. 6958-6966, 2011.
  • K. Ogata, "Quadratic Optimal Control Systems," in Discrete-Time Control Systems 2nd Edition, Englewood Cliffs, NJ, Prentice-Hall, 1995, pp. 566-633.
  • D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Erciyes University, Kayseri, Turkey, 2005.
  • D. Karaboga and B. Basturk, "An artificial bee colony (ABC) algorithm for numeric function optimization.," in IEEE Symp. Swarm Intelligence, Indianapolis, 2006.
  • D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Applied Soft Computing, vol. 8, no. 1, pp. 687-697, 2008.
  • D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, 2007.
  • D. Karaboga and B. Akay, "A comparative study of Artificial Bee Colony algorithm," Applied Mathematics and Computation, vol. 214, no. 1, p. 108–132, 2009
  • "Detailed Pseudocode of the ABC Algorithm," 14 October 2008. [Online]. Available: http://mf.erciyes.edu.tr/abc/pub/PsuedoCode.pdf. [Accessed 1 March 2014].
  • R. Coban , "A fuzzy controller design for nuclear research reactors using the particle swarm optimization algorithm," Nuclear Engineering and Design, vol. 241, no. 5, pp. 1899-1908, 2011.
There are 19 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Baris Ata

Ramazan Coban

Publication Date January 13, 2015
Published in Issue Year 2015 Volume: 3 Issue: 1

Cite

APA Ata, B., & Coban, R. (2015). Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum. International Journal of Intelligent Systems and Applications in Engineering, 3(1), 1-6. https://doi.org/10.18201/ijisae.87020
AMA Ata B, Coban R. Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum. International Journal of Intelligent Systems and Applications in Engineering. January 2015;3(1):1-6. doi:10.18201/ijisae.87020
Chicago Ata, Baris, and Ramazan Coban. “Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum”. International Journal of Intelligent Systems and Applications in Engineering 3, no. 1 (January 2015): 1-6. https://doi.org/10.18201/ijisae.87020.
EndNote Ata B, Coban R (January 1, 2015) Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum. International Journal of Intelligent Systems and Applications in Engineering 3 1 1–6.
IEEE B. Ata and R. Coban, “Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum”, International Journal of Intelligent Systems and Applications in Engineering, vol. 3, no. 1, pp. 1–6, 2015, doi: 10.18201/ijisae.87020.
ISNAD Ata, Baris - Coban, Ramazan. “Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum”. International Journal of Intelligent Systems and Applications in Engineering 3/1 (January 2015), 1-6. https://doi.org/10.18201/ijisae.87020.
JAMA Ata B, Coban R. Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum. International Journal of Intelligent Systems and Applications in Engineering. 2015;3:1–6.
MLA Ata, Baris and Ramazan Coban. “Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum”. International Journal of Intelligent Systems and Applications in Engineering, vol. 3, no. 1, 2015, pp. 1-6, doi:10.18201/ijisae.87020.
Vancouver Ata B, Coban R. Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum. International Journal of Intelligent Systems and Applications in Engineering. 2015;3(1):1-6.

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