International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 4, Issue 8  (August 2017), Pages:  139-148


Title: Improving the performance indices of a dynamic system using adaptive learning controllers

Author(s):  Srinibash Swain 1, *, Partha Sarathi Khuntia 2

Affiliation(s):

1Faculty of Electrical Engineering, Bijupattnaik University of Technology, Bhubaneswar, India
2Faculty of Electronics and Telecommunications Engineering, Bijupattnaik University of Technology, Bhubaneswar, India

https://doi.org/10.21833/ijaas.2017.08.020

Full Text - PDF          XML

Abstract:

In this paper, the angle of attack of an aircraft is controlled by using soft computing techniques like Genetic Algorithm (GA), Fuzzy Model Reference Learning Controller (FMRLC) and Radial Basis Function Neural Controller (RBFNC) and the performance indices like Mean Square Error (MSE), Integral Square Error (ISE), and Integral Absolute Time Error (IATE) etc. of the dynamic system is improved. The result is compared with the conventional techniques like Tyreus-Luyben (TL), Ziegler-Nichols (ZN) and Interpolation Rule (IR) for tuning the PID controller. It was established that the errors by using soft computing techniques are very less as compared to the conventional techniques thereby improving the performance indices of the dynamic system. 

© 2017 The Authors. Published by IASE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Angle of attack performance indices, Adaptive controller, Learning controller, Radial basis function

Article History: Received 13 February 2017, Received in revised form 12 July 2017, Accepted 17 July 2017

Digital Object Identifier: 

https://doi.org/10.21833/ijaas.2017.08.020

Citation:

Swain S and Khuntia PS (2017). Improving the performance indices of a dynamic system using adaptive learning controllers. International Journal of Advanced and Applied Sciences, 4(8): 139-148

http://www.science-gate.com/IJAAS/V4I8/Swain.html


References:

  1. Alfaro-Cid E, McGookin EW, and Murray-Smith DJ (2006). GA-optimized PID and pole-placement real and simulated performance when controlling the dynamics of a supply ship. IEEE Proceedings on Control Theory and Applications, 153(2): 228–236. 
  2. Ali A and Majhi S (2009). PI/PID controller design based on IMC and percentage overshoot specification to controller set point change. ISA Transactions, 48(1):10-15. https://doi.org/10.1016/j.isatra.2008.09.002            PMid:18848321 
  3. Baruch IS and Hernandez S (2011). Decentralized direct I-Term fuzzy neural control of an anaerobic digestion bioprocess plant. Computational intelligence in control and automation. In the IEEE Conference on Computational Intelligence in Control and Automation, IEEE, Paris, France: 36-43. https://doi.org/10.1109/CICA.2011.5945753 
  4. Chang PH and Jung JH (2009). A systematic method for gain selection of robust PI control for nonlinear plants of second-order controller canonical form. IEEE Transactions on Control Systems Technology, 17(2):473-483. https://doi.org/10.1109/TCST.2008.2000989 
  5. Devaraj D and Selvabala B (2009). Real coded genetic algorithm and fuzzy logic approach for real time tuning of proportional-integral-derivative controller in automatic voltage regulator system. IET Generation, Transmission and Generation, 3(7):641-649. https://doi.org/10.1049/iet-gtd.2008.0287 
  6. Di Ruscio D (2010). On tuning PI controllers for integrating plus time delay systems. Modeling, Identification and Control, 31(4):145-164. https://doi.org/10.4173/mic.2010.4.3 
  7. Dimeas F and Aspragathos N (2014). Fuzzy learning variable admittance control for human-robot cooperation. In the IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Chicago, USA: 4770-4775. https://doi.org/10.1109/IROS.2014.6943240 
  8. Gracey W (1985). Summary of methods of measuring angle of attack on aircraft. NACA Technical Note 4351. Available online at: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19930085167.pdf 
  9. Grimholt C (2010). Verification and improvement of SIMC method for PI control. Technical report 4550, Department of Chemical Engineering, Norwegian University of Science and Technology, Trondheim, Norway. 
  10. Haugen F (2010). Comparing PI tuning methods in a real benchmark temperature control system. Modeling, Identification and Control, 31(3):79–91. https://doi.org/10.4173/mic.2010.3.1 
  11. Kwong WA, Passino KM, Laukonen EG, and Yurkovich S (1995). Expert supervision of fuzzy learning systems for fault tolerant aircraft control. Proceedings of IEEE, 83(3): 466–483. https://doi.org/10.1109/5.364491 
  12. Lian RJ (2014). Adaptive self-organizing fuzzy sliding mode Radial basis function neural network controller for robotic systems. IEEE Transactions on Industrial Electronics, 61(3):1493–1503. https://doi.org/10.1109/TIE.2013.2258299 
  13. Lin FJ, Chen SY, Teng LT, and Chu H (2009). Recurrent functional link based fuzzy neural network controller with improved partcle swarm optimization for a linear synchronous motor drive. IEEE Transactions on Magnetics, 45(8): 3151-3165. https://doi.org/10.1109/TMAG.2009.2017530 
  14. Neath MJ, Swain AK, Madawala UK, and Thrimawithana DJ (2014). An optimal PID controller for a bidirectional inductive power transfer systemusing multi-objctive genetic algorithm. IEEE Transactions on Power Electronics, 29(3):1523–1531. https://doi.org/10.1109/TPEL.2013.2262953 
  15. Seng TL, Khalid MB, and Yusof R (1999). Tuning of a neuro-fuzzy controller by genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(2): 226-236. https://doi.org/10.1109/3477.752795     PMid:18252294 
  16. Shamsuzzoha M and Skogestad S (2010). The Setpoint overshoot method: a simple and fast method for closed loop PID tuning. Journal Process Control, 20(10):1220-1234. https://doi.org/10.1016/j.jprocont.2010.08.003 
  17. Skogestad S (2010). Tuning for smooth PID control with acceptable disturbance rejection. Industrial and Engineering Chemistry Research, 45(23): 7817-7822. https://doi.org/10.1021/ie0602815 
  18. Whidborne JF and Istepanian RSH (2001). Genetic Algorithm approach to designing finite precision controller structures. IEEE Proceedings Control Theory and Applications, 148(5): 377–382. https://doi.org/10.1049/ip-cta:20010604 
  19. Xia Y and Wang J (2004). A recurrent neural network for nonlinear convex optimization subject to nonlinear inequality constraints. IEEE Transactions on Circuits and Systems I, 51(7):1385-1394. https://doi.org/10.1109/TCSI.2004.830694 
  20. Yordanova S and Haralanova E (2011). Design and implementation of robust multivariable PI like fuzzy logic controller for aerodynamic plant. International Journal of Advanced Intelligence Paradigms, 3(3-4): 257-272. https://doi.org/10.1504/IJAIP.2011.043430