MRAC for A Launch Vehicle Actuation System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 425)

Abstract

Actuators are used to provide Thrust Vector Control (TVC) for lifting the launch vehicle and its payload to reach its space orbits. The TVC comprises actuation and power components for controlling the engines direction and thrust to steer the vehicle. Here the actuator considered is a linear Electromechanical Actuator (EMA) system. The design is based on the Model Reference Adaptive Controller (MRAC) technique. It calculates an error value as the difference between actual system and desired model and attempts to minimize the error by adjusting the controller parameters. Adaptive control laws are established with the gradient method. The experimental result shows that the MRAC has better tracking performance than that with the traditional compensated system when both controllers are subjected to the same parameter variations. The simulation is done using MATLAB/SIMULINK software.

Keywords

EMA Compensator MRAC MIT rule 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Balaban, E., Saxsena, A.: A diagnostic approach for electro mechanical actuators in aerospace system. In: IEEE Aerospace Conference (2009)Google Scholar
  2. 2.
    Cowan, J.R., Myers, W.N.: Design and test of a high power electromechanical actuator for thrust vector control. In: AIAA/SAE/ASME/ASEE 28th Joint Propulsion Conference and EXhibit, AIAA 92-3851 (1992)Google Scholar
  3. 3.
    Sumathi, R.,Usha, M.: Pitch and Yaw Attitude Control of a Rocket Engine Using Hybrid Fuzzy- PID Controller. The Open Automation and Control Systems Journal, 29–39 (2014)Google Scholar
  4. 4.
    Swarnkar, P, Jain, S, Nema, R.K.: Effect of Adaptation Gain on System Performance for Model Reference Adaptive Control Scheme Using MIT Rule. World Academy of Science, Engineering and Technology, Paris, 545–550 (2010)Google Scholar
  5. 5.
    Joseph, A., Isaac, J.S.: Real Time Implementation of Model Reference Adaptive Controller for a Conical Tank. International Journal on Theoretical and Applied Research in Mechanical Engineering 2(1), 57–62 (2013)Google Scholar
  6. 6.
    Jain, P., Nigam, M.J.: Real time control of ball and beam system with model reference adaptive control strategy using MIT rule. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, pp. 305–308 (2013)Google Scholar
  7. 7.
    Duka, A.-V., Oltean, S.E., Dulău, M.: Model reference adaptive vs. learning control for the inverted pendulum. In: International Conference on Control Engineering and Applied Informatics (CEAI), vol. 9, pp. 67–75 (2007)Google Scholar
  8. 8.
    Li, Y., Lu, H., Tian, S., et al.: Posture Control of Electromechanical-Actuator-Based Thrust Vector System for Aircraft Engine. IEEE Trans. Ind. Electron. 59(9), 3561–3571 (2012)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Oh, C.-S., Sun, B.-C., Park, Y.-K.: Modeling & simulation of a launch vehicle thrust vehicle control system. In: 12th International Conference on Control, Automation & System, pp. 2088–2092 (2012)Google Scholar
  10. 10.
    Ogatta, K.: Modern control Engineering, 4th edn. Prentice Hall, New Jersey publications (2002)Google Scholar
  11. 11.
    Astrom, K.J., Wittenmark, B.: Adaptive Control, Englewood Cliff, 2nd edn. Prentice Hall, NJ (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Electronics and InstrumentationVimal Jyothi Engineering CollegeChemperi, KannurIndia
  2. 2.Scientist/EngineerCECG, VSSC, ISROTrivandrumIndia

Personalised recommendations