{"title":"Design of Optimal Proportional Integral Derivative Attitude Controller for an Uncoupled Flexible Satellite Using Particle Swarm Optimization","authors":"Martha C. Orazulume, Jibril D. Jiya","volume":110,"journal":"International Journal of Mechanical and Mechatronics Engineering","pagesStart":341,"pagesEnd":347,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10003824","abstract":"
Flexible satellites are equipped with various appendages which vibrate under the influence of any excitation and make the attitude of the satellite to be unstable. Therefore, the system must be able to adjust to balance the effect of these appendages in order to point accurately and satisfactorily which is one of the most important problems in satellite design. Proportional Integral Derivative (PID) Controller is simple to design and computationally efficient to implement which is used to stabilize the effect of these flexible appendages. However, manual turning of the PID is time consuming, waste energy and money. Particle Swarm Optimization (PSO) is used to tune the parameters of PID Controller. Simulation results obtained show that PSO tuned PID Controller is able to re-orient the spacecraft attitude as well as dampen the effect of mechanical resonance and yields better performance when compared with manually tuned PID Controller.<\/p>\r\n","references":"[1]\tK. J. Walchko, \u201cRobust Nonlinear Attitude Control with Disturbance compensation\u201d, 2003. \r\n[2]\tC. D Brown, \u201cElements of Spacecraft Design\u201d, AIAA Education Series, 2002.\r\n[3]\tO. Montenbruck and E. Gill, \u201cSatellite Orbits: Models, Methods and Applications\u201d, Spring-Verlag, 2000. \r\n[4]\tD. A. Vallado, \u201cFundamentals of Astrodynamics and Applications\u201d, Microcosm and press and Kluwer Academic publishers, 2001. \r\n[5]\tP. C. Hughes, \u201cSpacecraft Attitude Dynamics\u201d, John Willey & Sons, Inc, 1986. \r\n[6]\tK. J. Astrom and T. Hagglund, \u201cPID controllers. Theory design and tuning, NC: \tInstrument Society of America, Research Triangle Park\u201d, 1995.\r\n[7]\tZ. L. Gaing, \u201cA Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System\u201d, IEEE Transaction on Energy conversion, vol. 19, no. 2, p. 284.\r\n[8]\tZ. Shafei and A. T. Shenton, \u201cTuning PIDtype controllers for stable and unstable systems \twith time delay\u201d, Automatica, Vol. 30, p. 1609-1615, 1994.\r\n[9]\tZ. Shafei and A. T. Shenton, A.T, \u201cFrequency domain Design of PID Controllers for Stable and \tUnstable Systems with Time Delay\u201d, Automatica, vol 33, p. 2223-2232, 1997.\r\n[10]\tJ. Ackerman, D. Kaesbauer, \"Stable polyhedra in parameter space\u201d, Automatica, vol. 39, p. \t937-943, 2003.\r\n[11]\tJ. Ackerman and D. Kaesbauer, and R. Muench, \u201cRobust gamma-stability analysis in a plant parameter space\u201d, Automatica, vol. 27, 75-85, 1991. \r\n[12]\tL. C. Smith, \u201cFundamentals of control theory, Chemical Engineering\u201d, vol. 86, no. 22, p. 11 39, 1979.\r\n[13]\tJ. G. Ziegler and N. B. Nichols, \u201cOptimum settings for Automatic Controllers\u201d, Trans. ASME, Vol. 64, P. 759-768, 1942.\r\n[14]\tE. E. Omizegba, M. I. Onogu and O. U. O Okereke, \u201cFuzzy Attitude Control of Flexible Satellite with Uncoupled Axes\u201d Nigerian Journal of Rresearch and Development, Vol. 3, No. 3, 2004.\r\n[15]\tJ. Kennedy and R. C. Eberhart, \u201cParticle Swarm Optimization\u201d, Conference Proc. IEEE International \tConference on Neural Networks (Perth, Australia), IEEE Services Center, Piscataway, \tNJ, pp IV:1942-1948, 1995.\r\n[16]\tY. Shi and R. C. Eberhart, \u201cA Modified Particle Swarm Optimizer\u201d, Conference Proceedings, IEEE Congress on Evolutionary Computation, p. 69\u201373, 1998.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 110, 2016"}