Total views : 872

Iterative Linear Quadratic Regulator (ILQR) Controller for Trolley Position Control of Quanser 3DOF Crane

Affiliations

  • Department of Robotics and Artificial Intelligence, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, H-12 Main Campus, Islamabad, Pakistan
  • University of Central Punjab, Lahore, Pakistan
  • University of Lahore, Pakistan

Abstract


In this paper, we have investigated the performance of Iterative Linear Quadratic Regulator (ILQR) on trolley position of 3DOF crane. In ILQR, we select optimum parameters Q and R automatically instead of hit and trial method. Algorithm chooses the parameters Q and R which results in minimum trolley’s settling time of the jib system. A number of simulations have carried out using Matlab/Simulink. The results show that the optimized LQR results reduce settling time of trolley along with smaller overshoot with less rise time.

Keywords

Iterative Linear Quadratic Regulator (ILQR), Proportional Integral Derivative (PID), Three Degree of Freedom (3DOF)

Full Text:

 |  (PDF views: 982)

References


  • Skogestad S. Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control. 2004 June 13; 14(4):465.
  • BLevine KJ, Mullhaupt, P. A simple output feedback PDcontroller for nonlinear cranes. IEEE Conference on Decision and Control; Sydney. 2000 Dec 12–15. p. 5097–101.
  • Al-Mousa AA. Control of rotary cranes using fuzzy logic and time-delayed position feedback control. Virginia Polytechnic Institute and State University; 2000.
  • Antic D, Jovanovic Z, Peric S, Nikolic, Milojkovic M, Milosevic M. Anti-swing fuzzy controller applied in a 3D crane system. Engineering Technology and Applied Science Research. 2012; 2(2).
  • Lee HH, Cho SK. A new fuzzy-logic anti-swing control for industrial three-dimensional overhead cranes. Proceedings of the IEEE International Conference on Robototics and Automation (ICRA); South Korea. 2001 Mar. p. 2956–61.
  • Liang Y, Koh K. Concise anti-swing approach for fuzzy crane control. Electronics Letters. 1997 Jan; 33(2):167–8.
  • Wu T S, Karkoub M, Yu W S, Chen CT, Her MG, Wu KW. Anti-sway tracking control of tower cranes with delayed uncertainty using a robust adaptive fuzzy control. Fuzzy Sets and Systems. 2015 Jan.
  • Jafari J, Ghazal M, Nazemizadeh M. A LQR optimal method to control the position of an overhead crane. International Journal of Robotics and Automation (IJRA). 201; 3(4):252–7.
  • Zhang W, Hu J, Abate A. Infinite-horizon switched LQR problems in discrete time: a suboptimal algorithm with performance analysis. IEEE Transactions on Automatic Control. 2015; 51(7):1815–21.
  • Abdullah J, Ruslee. R, Jalani J. Performance comparison between LQR and FLC for Automatic 3DOF Crane Systems. International Journal of Control and Automation. 2011; 4(4):163–78.
  • Mendez J, Acosta L, Moreno L, Hamilton A, and Marichal G. Design of a neural network based self-tuning controller for an overhead crane. Proceedings of the 1998 IEEE International Conference on Control Applications; Trieste. 1998 Sep 1–4. p. 168–71.
  • Nakazono K, Ohnishi K, Kinjo H, Yamamoto T. Vibration control of load for rotary crane system using neural network with GA-based training. Artificial Life and Robotics. 2008; 13(1):98–101.
  • Panuncio F, Yu W, Li X. Stable neural PID anti-swing control for an overhead crane. 2013 IEEE International Symposium on Intelligent Control (ISIC); Hyderabad. 2013 Aug 28–30. p. 53–8.
  • Smoczek J. Fuzzy logic and neural network approach to identification and adaptive control of an overhead traveling crane. Logistyka. 2009.
  • Toxqui R, Yu W, Li X. Anti-swing control for overhead crane with neural compensation. 2006 Internatiional Joint Conference on Neural Network (IJCNN’06); Canada. 2006. p. 4697–703.
  • Yildirim S and Uzmay IB, Neural network applications to vehicle’s vibration analysis. Mechanism and Machine Theory. 2003; 38(1):27–41.
  • Yu W, Li X, Panuncio F. Stable Neural PID Anti-Swing Control for an Overhead Crane. Intelligent Automation and Soft Computing. 2014; 20(4):145–58.
  • Lin J, Chao WS. Vibration suppression control of beam-cart system with piezoelectric transducers by decomposed parallel adaptive neuro-fuzzy control. Journal of Vibration and Control. 2009 Dec; 15(12):1885–906.
  • Nakazono K, Ohnisihi K, Kinjo H. Load swing suppression in jib crane systems using a genetic algorithm-trained neuro-controller. 4th IEEE International Conference on Mechatronics (ICM); Kumamoto, Japan. 2007 May 8–10. p. 1–4.
  • Quan-Yi H Application of adaptive neuro-fuzzy inference system in anti-swing control to crane hook. Hoisting and Conveying Machinery. 2007; 1(9):21–5.
  • Rekik C, Djemel M, Derbel N On the neuro-genetic approach for determining optimal control of a rotary crane. Proceedings. of 2003 IEEE Conference on Control Application (CCA); 2003 Jun 23–25. p. 124–8.
  • Bertsekas DP. Constrained optimization and Lagrange multiplier methods. Academic Press; 2014.
  • Everett H, III. Generalized Lagrange multiplier method for solving problems of optimum allocation of resources, Operations Research. 1963; 11(3):399–417.
  • Quanser 3DOF Crane user manual. Available from: www.quanser.com/products/3DOF_crane
  • Borrelli F, Keviczky T. Distributed LQR design for identical dynamically decoupled systems. IEEE Transactions on Automatic Control. 2008 Sep; 53(8):1901–12.
  • Leung FH, Tam PK, Li C. An improved LQR-based controller for switching DC-DC converters. IEEE Trans on Industrial Electronics. 1993 Oct; 40(5):521–8.
  • Lincoln B, Bernhardsson B. LQR optimization of linear system switching. IEEE Transactions on Automatic Control. 2002 Oct; 47(10):1701–5.
  • Olalla C, Leyva R, El A Aroudi, Queinnec I, Robust LQR control for PWM converters: an LMI approach. IEEE Transactions on Industrial Electronics. 2009; 56(7):2548–58.
  • Sam YM, Ghani MRHA, Ahmad N. LQR controller for active car suspension. Proceedings in TENCON; Kuala Lumpur. 2000. p. 441–4.
  • Yu R, Hwang RC. Optimal PID speed control of brush less DC motors using LQR approach. 2004 IEEE International Conference on Systems, Man and Cybernetics; Taiwan. 2004 Oct 10–13. p. 473–8.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.