Aerospace Mechanics

Aerospace Mechanics

Velocity of Control of Electro Hydraulic Servo System by Using Fuzzy Coupled Method

Abstract
Electro hydraulic control systems are important in their ability to handle large torque loads and quick response. Velocity, position and torque control are the most important controlling methods for the systems. The control systems suggested before including uncertainty parameters such as: internal friction, external noises, and non-linearity in the model behavior. Parallel distributed compensator method based on Takagi-Sugeno and fuzzy controller are used in this paper by feedback error learning's idea. In addition velocity control and identification of the model is carried out by applying regulation scheme. Results indicate that the controller presents better performance and response to the other controllers Also stability of controller is validated by linear matrix inequality method
Keywords

1.   H. E. Merritt, “Hydraulic Control Systems”, New York, John Wiley & Sons, Inc, 1967.##
2. Watton, J. “Fluid Power Systems, Modelling Simulation and Microcomputer Control”, Englewood Cliffs, N. J. Prentice-Hall, pp. 323-345, 1988.##
3. Cloy, D. Mc. and Martin, H.R. “The Control of Fluid Power”, New York, John Wiley & Sons, 1973.##
4. Rui, Liu. “Nonlinear Control of Electro-Hydraulic Servo Systems: Theory and Experiment”, Master Thesis, University of Illinois at Urbana-Champaign, 1998.##
5. Jovanovic, M. “Nonlinear Control of an Electro Hydraulic Velocity Servo System”, American Control Conference, Anchorage, Alaska, USA, Vol. 1, pp. 588-593, 2002.##
6. Mohseni, S. A., Aliyari, M., and Teshnehlab, M. “EHSS Velocity Control by Fuzzy Neural Networks”, IEEE Conf, North American Fuzzy Information Processing, Society, pp 13-18, 2006.##
7. Azimian, H. Adlgostar, R., and Teshnehlab, M. “Velocity Control of an Electro Hydraulic Servomotor by Neural Networks”, International Conference Physcon, Saint Petersburg, RUSSIA, pp. 677-682, 2005.##
8. Chan, L. C. Y. and Asokanthan, S. F. “CMAC Based Controller for Hydro Mechanical Systems”, American Control Conference, Arlington, USA, Vol. 6, pp. 4496-4501, 2001.##
9. Mohseni, S. A. Aliyari Shooredeli, M. Teshnehlab, M. “Decoupled Sliding-Mode with Fuzzy Neural Network Controller for EHSS Velocity Control”, International Conference on Intelligent, .Malaysia, pp. 7-11, 2007.##
10. M. Kawato, “Computational Schemes and Neural Network Models for Formation and Control of Multijoint Arm Trajectory”, Massachusets, MIT university, 1990.##
11. Miyamoto, H., Kawato, M., Setoyama, T., and Suzuki, R. “Feedback Error Learning Neural Network for Trajectory Control of a Robotic Manipulator Neural Networks”, Journal of Neural Networks of Elsevier, Vol. 1, pp. 251-265, 1988.##
12. Asadi Asad Abad, M.R., Zare Bidaki, A.R., and Jahanshahi, M. “Velocity Control Of Electro-Hydraulic Servo System by Tracking Method”. International Journal of Smart Electrical Engineering, Vol. 1, No. 3. pp 1-6. 2014.##
13. Wang, H. O., Tanaka, K. Griffin, M. “Parallel Distributed Compensation of Nonlinear Systems by Takagi-Sugeno Fuzzy Model”, Proc. American Control Conference, Seattle, USA, Vol. 2, pp. 531-538, 1995.##
14. Wang, H. O., Tanaka, K. “Fuzzy Control Systems Design and Analysis”, New York, Wiley, pp. 5-81, 2001.##
15. Akbari, A., Zare Bidaki, A.R., Hosseini, A.R., Aliyari Shhooredeli, M. “Investigation of Stability of Electro-Hydraulic Servo System Using Fuzzy Intelligent Controller”. Journal of Mechanical of Modares, Vol. 14, No. 3, pp. 36-42, 2014.##
16. Moetakef Imani, B, Ghorashi Khalil S.H, “Implementation of Adaptive and Proportional-Integral Algorithms in Servo Hydraulic Fatigue Testing Machin”, Journal of Mechanic of Emam Hossein, Vol 4, pp. 1-9, 1395.##
17. Kashefi, S, “Predictive Adaptive Autopilot for STT Missile Based on Model Reference Method and Lyapanov Theorem”, Journal of Mechanic of Emam Hossein, Vol 3, pp. 91-100, 1391.##

  • Receive Date 25 April 2017
  • Revise Date 19 February 2019
  • Accept Date 19 September 2018
  • Publish Date 21 April 2019