Designing an Optimal Pid Controller for Control the Plans Height, Based on Control of Autopilot by Using Evolutionary Algorithms

Volume 6, Issue 4, pp 260 - 271 http://dx.doi.org/10.22436/jmcs.06.04.02

Download PDF

Download XML

• Export citations
  • CITATIONS & REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • ARTICLE CITATION
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)

• 3695 Downloads
• 4430 Views

Authors

Mohammad Fiuzy - Bioelectrical Department, Electrical and Computer Faculty, Hakim Sabzevari University, Sabzevar, Iran. Javad Haddadnia - Bioelectrical Department, Electrical and Computer Faculty, Hakim Sabzevari University, Sabzevar, Iran. Seyed Kamaleddin Mousavi Mashhadi - Control Department, Electrical and Computer Faculty, Iran University of Science & Technology, Tehran, Iran.

Abstract

The dangers of poor pilot performance as well as time and place conditions, and such as low altitude and climate, damage critical aircraft control system. Mentioned factors, caused damage in control of sensitive military and research aircraft. So researchers had to find a solution for this problem. Therefore, the use of Unmanned Aircraft Vehicle or (UAV) in sensitive and important Operation is required. Furthermore, designing the controller system is one of the main discussed Dynamic issues in Flying Objects. Here, has been attempted to determine the optimal coefficients of PID1 controller in Autopilot based on Optimization Algorithm such as Evolutionary Algorithms in order to regulate and desired control on height of an Unmanned Aircraft Vehicle (UAV). The proposed Cost Function simultaneously optimized the system performance specifications. Optimization done by using Evolutionary Algorithms such as \(GA^2\), \(PSO^3\) and Social Policy Optimization Algorithm or \(ICA^4\). Finally, the system response based on Evolutionary Algorithms compared with the advantages of Classic method Optimization based on Constraints design, then you can easily decide on the superiority and being optimized of the proposed system.

Share and Cite

Keywords

• Unmanned Aircraft Vehicle (UAV)
• Autopilots
• Height Control
• Controller
• Evolutionary
• Biological and Social Optimization.

MSC

•  90C59

References

• [1] B. Alireza, M. Mortazavi, Designning Fuzzy Autopilot for U. A.V. Aerospace Mechanic , Journal of I.R.of Iran, 6 (2011), 1-10


• [2] K. poulos, I. Kokotovic, P. Morse, Systematic Design of Adaptive Controllers for Feedback Linearizable Systems, IEEE Transaction on Automatic Control Journal, 36 (1991), 1241-1253
  • View Article
  • MathSciNet
  • MATH


• [3] I. Barkana, Classical and Simple Adaptive Control for Non-minimum Phase Autopilot Design , AIAA, Journal of Guidance, Control and Dynamics , 28 (2005), 631-638
  • View Article
  • Google Scholar


• [4] J. E. Slotine, W. Li , Applied Non-linear Control. Printice-Hall Publisher, Englewood Cliffs, New Jersey (1991)
  • Google Scholar


• [5] P. K. A. Menon, M. E. Badget, R. A. Walker, E. L. Duke., Non-linear Flight Test Trajectory Controllers for Aircraft, AIAA. Journal of Guidance Control and Dynamics, 10 (1987), 67-72
  • View Article
  • Google Scholar


• [6] M. Tahk, M. Briggs, P. K. A Menon, Application of Plant Inversion via State Feedback to Missile Autopilot Design, Proceeding IEEE Conf, Decision Control, Austin, Texas, (1986), 730-735
  • View Article
  • Google Scholar


• [7] M. Azam, S. N. Singh, Inevitability and Trajectory Control for Non-linear Maneuvers of Aircraft, AIAA Journal of Guidance Control and Dynamics, 17 (1994), 192-200


• [8] D. J. Bugajski, D. F. Enns, Non-linear Control Law with Application to High Angle-of-Attack Flight, AIAA Journal of Guidance Control and Dynamics, 15 (1992), 761-769
  • View Article
  • Google Scholar


• [9] L. X. Wang, A Course in Fuzzy Systems and Control, Upper Saddle River, Prentice-Hall, New Jersey (1997)
  • Google Scholar


• [10] S. F. Wu, C. J. H. Engelen, R. Babuska, Q. P. Chu, J. A. Mulder, Fuzzy Logic Based Full- Envelope Autonomous Flight Control for an Atmospheric Re-Entry Spacecraft, Journal of Control Eng. , 11 (2003), 11-25
  • View Article
  • Google Scholar


• [11] B. Kadmiry, D. Driankov, A Fuzzy Flight Controller Combining Linguistic and Model- Based Fuzzy Control, Journal of Fuzzy Sets and System, 146 (2004), 313-347
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [12] K. Cohen, D. E. Bossert, Fuzzy Logic Non- minimum Phase Autopilot Design , AIAA Journal of Guidance, Navigation, and Control Conf and Exhibit. 11-14 August, Austin, Texas (2003)
  • View Article
  • Google Scholar


• [13] D. E. Bossert, K. Cohen, PID and Fuzzy Logic Pitch Attitude Hold Systems for a Fighter Jet, AIAA Journal of Guidance, Navigation, and Control Conf and Exhibit, 5-8 August, Monterey, California (2002)
  • View Article
  • Google Scholar


• [14] T. H. S. Li, M. Y. Shieh, Design of a GA- based PID Controller for Non minimum Phase Systems, Journal of Fuzzy Sets and Systems, 111 (2000), 183-197
  • View Article
  • MathSciNet
  • Google Scholar


• [15] A. Tsourds, E. J. Hughes, B. A. White, Fuzzy Multi-Objective Design for a Lateral Missile Autopilot, Journal of Control Eng, Practice, 14 (2006), 547-561
  • View Article
  • Google Scholar


• [16] V. Rajinikanth, K. Latha, Tuning and Retuning of PID Controller for Unstable Systems Using Evolutionary Algorithm, International Scholarly Research Network ISRN Chemical Engineering , 5 (2012), 10 -21
  • Google Scholar


• [17] C. Moon, J. Kim, G. Choi, Y. Seo, An efficient genetic algorithm for the traveling salesman problem with precedence constraints, European Journal of Operational Research , 14 (2002), 606–617
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [18] H. Vahed, S. Soleimanpoor, Improved genetic algorithm with local search procedures in the allocation of weapons, First Joint Conference on Fuzzy Systems and Intelligence Artificial, ferdowsi UNIVERSITY of I.R of Iran, (2007), 31-37


• [19] R. Shelley, Roulette Wheel Study, MIT Publisher, (1998). P. Galiasso and Roger Wainwright. A Hybrid Genetic Algorithm for the Point to Multipoint Routing Problem with Single Split Paths, Proceedings of ACM/SIGAPP Symposium on Applied Computing, (2001)
  • View Article
  • Google Scholar


• [20] M. Yaghini, M. A. Kazemzade, Optimizatin algoritm fractal, Journal of Jahad Daneshgahiee branch of Isfahan University (Especial Issue), (2010), 65-72


• [21] Ron Shelly, Roulette Wheel Study, MIT Publisher , Second Edition (2001)


• [22] P. Galiasso, R. Wainwright, A Hybrid Genetic Algorithm by Particle Swarm Optimization for the Point to Multipoint Routing Problem White Single Splite Paths, Proceedings of ACM/SIGAPP Symposium on Applied Computing, SAC'01, March 11-14, (2001), 327-332
  • View Article
  • Google Scholar


• [23] M. R. S. Babaki , Generalized algorithm GCBPSO, Proceeding of 12Th Conference of Computer, Isfahan, (1380), 102-108


• [24] Y. Shi, R. Eberhart, A Modified Particle Swarm Optimizer, IEEE International Conference on Evolutionary Computation, USA, (1998), 255- 262
  • View Article
  • Google Scholar


• [25] X. Cui, Document clustering using particle swarm optimization, IEEE Journal of soft Computing, (2005), 185-191
  • View Article
  • Google Scholar


• [26] M. Sheybani, M. meibody, PSO-LA: A Novel of Optimization, 12th Conference on Computer Engineering, I.R.of Iran, (2006), 1162


• [27] M. Norozie Beyrami, Improve the convergence of the algorithm of particle swarm, Osco IAU Branch, Conference on industrial engineering, (2006), 21-28


• [28] E. Atashpaz, F. Hashemzadeh, R. Rajabioun, C. Lucas, Colonial competitive algorithm: a novel approach for PID controller designin MIMO distillation column process, International, Journal of Intelligent Computing and Cybernetics, 1 (2008), 337 – 355
  • View Article
  • MathSciNet
  • Google Scholar


• [29] R. Rajabioun, E. Atashpaz, C. Lucas, Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement, Journal of Lecture Notes In Computer Science, 5 (2008), 680-695
  • View Article
  • Google Scholar
  • MATH


• [30] B. Oskouyi, E. Atashpaz-Gargari, N. Soltani, C. Lucas, Application of Imperialist Competitive Algorithm for Materials Property Characterization from Sharp Indentation Test , International Journal of Engineering Simulation, Vol 8, No 4 (2009)
  • Google Scholar


• [31] A. Khabbazi, E. Atashpaz, C. Lucas, Imperialist Competitive Algorithm for Minimum Bit Error Rate Beamforming, International Journal of Bio-Inspired Computation (IJBIC), Vol 10, No4 (2009)
  • View Article
  • Google Scholar


• [32] A. M. Jasour, E. Atashpaz, C. Lucas, Vehicle Fuzzy Controller Design Using Imperialist Competitive Algorithm, Proceeding of Second Iranian Joint Congress on Fuzzy and Intelligent Systems, Tehran ,I.R.of Iran, (2008), 105-111
  • Google Scholar


• [33] M. Kohansal, M. J. Sanjari, G. B. Gharehpetian, A novel approach to frequency control in an islanded microgrid by load shedding scheduling, Proceedings of the International Conference on Renewable Energies and Power Quality (ICREPQ'13), Madrid, (2013), 454-460
  • Google Scholar