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Application of Fractional Order Controllers on Experimental and Simulation Model of Hydraulic Servo System

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Fractional Order Control and Synchronization of Chaotic Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 688))

Abstract

Hydraulic Servo System (HSS) plays an important role in industrial applications and other fields such as plastic injection machine, material testing machines, flight simulator and landing gear system of the aircraft. The main reason of using hydraulic systems in many applications is that, it can provide a high torque and high force. The hydraulic control problems can be classified into force, position, acceleration and velocity problems. This chapter presents a study of using fractional order controllers for a simulation model and experimental position control of hydraulic servo system. It also presents an implementation of a non-linear simulation model of Hydraulic Servo System (HSS) using MATLAB/SIMULINK based on the physical laws that govern the studied system. A simulation model and experimental hardware of hydraulic servo system have been implemented to give an acceptable closed loop control system. This control system needs; for example, a conventional controller or fractional order controller to make a hydraulic system stable with acceptable steady state error. The utilized optimization techniques for tuning the proposed fractional controller are Genetic Algorithm (GA). The utilized simulation model in this chapter describes the behavior of BOSCH REXROTH of Hydraulic Servo System (HSS). Furthermore the fractional controllers and conventional controllers will be tuned by Genetic Algorithm. In addition, the hydraulic system has a nonlinear effect due to the friction between cylinders and pistons, fluid compressibility and valve dynamics. Due to these effects, the simulation and experimental results show that using fractional order controllers will give better response, minimum performance indices values, better disturbance rejection, and better sinusoidal trajectory than the conventional PID/PI controllers. It also shows that the fractional controller based on Genetic Algorithm has the desired robustness to system uncertainties such as the perturbation of the viscous friction, Coulomb friction, and supply pressure.

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References

  1. Astrom, K. J., & Hägglund, T. (1995). PID controllers: Theory, design and tuning (2nd ed.). Research Triangle Park: International Society of Automation. ISBN 1-55617-516-7.

    Google Scholar 

  2. Ahmed, O. A. (2009). Control of fluid power system-nonlinear fuzzy control for force tracking of electro-hydraulic servo system. Master Thesis, Faculty of Engineering (at Shoubra), Benha University, Egypt.

    Google Scholar 

  3. Aly, A. A. (2011). PID parameters optimization using genetic algorithm technique for electro hydraulic servo control system. Intelligent Control and Automation, 2(02), 69–76‏.

    Google Scholar 

  4. Azar, A. T., & Serrano, F. E. (2014). Robust IMC-PID tuning for cascade control systems with gain and phase margin specifications. Neural Computing and Applications, 25(5), 983–995. doi:10.1007/s00521-014-1560-x.

  5. Azar, A. T., & Serrano, F. E. (2015). Design and modeling of anti wind up PID controllers. In Q. Zhu & A. T Azar (Eds.), Complex system modelling and control through intelligent soft computations. Studies in Fuzziness and Soft Computing (Vol. 319, pp. 1–44). Germany: Springer. doi:10.1007/978-3-319-12883-2_1.

  6. Azar, A. T., & Vaidyanathan, S. (2015). Chaos modeling and control systems design. Studies in Computational Intelligence (Vol. 581). Germany: Springer. ISBN 978-3-319-13131-3.

    Google Scholar 

  7. Bonchis, A., Corke, P. I., Rye, D. C., & Ha, Q. P. (2001). Variable structure methods in hydraulic servo systems control. Automatica, 37(4), 589–595.

    Google Scholar 

  8. Chen, H. M., Shen, C. S., & Lee, T. E. (2013). Implementation of precision force control for an electro-hydraulic servo press system. In Proceeding of Third International Conference on Intelligent System Design and Engineering Applications (ISDEA) (pp. 854–857).‏ IEEE.

    Google Scholar 

  9. Das, S. (2008). Functional fractional calculus for system identification and controls. Berlin, Heidelberg: Springer.

    MATH  Google Scholar 

  10. Edvard, D., & Uroš, Ž. (2011). An intelligent electro-hydraulic servo drive positioning. Strojniškivestnik-Journal of Mechanical Engineering, 57(5), 394–404.

    Google Scholar 

  11. Elbayomy, K. M., Zongxia, J., & Huaqing, Z. (2008). PID controller optimization by GA and its performances on the electro-hydraulic servo control system. Chinese Journal of Aeronautics, 21(4), 378–384.

    Google Scholar 

  12. Essa, M., Aboelela M. A. S., & Hassan, M. A. M. (2014a). Position control of hydraulic servo system using proportional-integral-derivative controller tuned by some evolutionary techniques. Journal of Vibration and Control, 1077546314551445.

    Google Scholar 

  13. Essa, M., Aboelela M. A. S., & Hassan, M. A. M. (2014b). Position control of hydraulic servo systems using evolutionary techniques. Master thesis, Faculty of Engineering (Cairo University), Egypt.

    Google Scholar 

  14. Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95–99.‏ doi:10.1023/A:1022602019183.

  15. Jelali, M., & Kroll, A. (2012). Hydraulic servo-systems: Modeling, identification and control. Springer, London Ltd. ISBN 978-1-4471-1123-8.

    Google Scholar 

  16. Kao, C. C. (2009). Applications of particle swarm optimization in mechanical design.‏ Journal of Gaoyuan University, 15, 93–116.

    Google Scholar 

  17. Kim, J.-S., Kim, J.-H., Park, J-M., Park, S.-M., Choe, W.-Y., & Heo, H. (2008). Auto tuning PID controller based on improved genetic algorithm for reverse osmosis plant. World Academy of Science, Engineering and Technology, 47(2), 384–389.

    Google Scholar 

  18. Ljung, L. (2010). System Identification Toolbox 7 Getting started guide. http://www.mathworks.com.

  19. Maneetham, D., & Afzulpurkar, N. (2010). Modeling, simulation and control of high speed nonlinear hydraulic servo system. Journal of Automation Mobile Robotics and Intelligent Systems, 4, 94–103.

    Google Scholar 

  20. Mahdi, S. M. (2012). Controlling a nonlinear servo hydraulic system using PID controller with a genetic algorithm tool. IJCCCE, 12(1), 42–52.

    Google Scholar 

  21. Mathwork. (2014). Simulink Design Optimization Toolbox. http://www.mathworks.com.

  22. Mannesmann Rexroth. (2005) 4/3-way high response directional valve direct actuated, with electrical position feedback Type 4WRSE. http://www.boschrexroth.com.

  23. Mahony, T. O., Downing, C. J., & Fatla, K. (2000). Genetic algorithm for PID parameter optimization: Minimizing error criteria. Process Control and Instrumentation, 148–153.

    Google Scholar 

  24. Machado, J. T. (1997). Analysis and design of fractional-order digital control systems. Systems Analysis Modeling Simulation, 27(2–3), 107–122.

    Google Scholar 

  25. Md Rozali, S., Rahmat, M. F. A., Husain, A. R., & Kamarudin, M. N. (2014). Design of adaptive back stepping with gravitational search algorithm for nonlinear system. Journal of Theoretical and Applied Information Technology (JATIT), 59(2), 460–468.

    Google Scholar 

  26. Merritt, H. E. (1967). Hydraulic control systems. New York: Wiley.‏ ISBN 0-471-59617-5.

    Google Scholar 

  27. Mihajlov, M., Nikolić, V., & Antić, D. (2002). Position control of an electro-hydraulic servo system using sliding mode control enhanced by fuzzy PI controller. Facta Universitatis-Series: Mechanical Engineering, 1(9), 1217–1230.

    Google Scholar 

  28. National Instrument. (2002). DAQ NI 6013/6014 User Manual. http://www.ni.com.

  29. Nelles, O. (2013). Nonlinear system identification: From classical approaches to neural networks and fuzzy models. Berlin, Heidelberg: Springer. ISBN 978-3-642-08674-8.

    Google Scholar 

  30. Olranthichachat, P., & Kaitwanidvilai, S. (2011). Design of optimal robust PI controller for electro-hydraulic servo system. Engineering Letters, 19(3), 197–203.

    Google Scholar 

  31. Olranthichachat, P., & Kaitwanidvilai, S. (2011b). Structure specified robust H∞ loop shaping control of a MIMO electro-hydraulic servo system using particle swarm optimization. In Proceedings of the International Multi Conference of Engineers and Computer Scientists (Vol. 2).

    Google Scholar 

  32. Podlubny, I. (1999). Fractional-order systems and PIλDδ controllers. IEEE Transactions on Automatic Control, 44(1), 208–214.

    Article  MathSciNet  MATH  Google Scholar 

  33. Puangdownreong, D., & Sukulin, A. (2012). Obtaining an optimum PID controllers for unstable systems using current search. International Journal of Systems Engineering, Applications & Development 6(2), 188–195.

    Google Scholar 

  34. Rydberg, K. E. (2008). Hydraulic servo systems. TMHP51 fluid and mechanical engineering systems. Linköping University, 1–45.

    Google Scholar 

  35. Roozbahani, H., Wu, H., & Handroos, H. (2011). Real-time simulation based robust adaptive control of hydraulic servo system. In Proceeding of IEEE International Conference on Mechatronics (ICM), 779–784.

    Google Scholar 

  36. Saad, M. S., Jamaluddin, H., & Darus, I. Z. (2012). PID controller tuning using evolutionary algorithms. ‏WSEAS Transactions on Systems and Control, 7(4), 139–149.

    Google Scholar 

  37. Sirouspour, M. R., & Salcudean, S. E. (2000). On the nonlinear control of hydraulic servo-systems. In IEEE International Conference on Robotics and Automation Proceedings. ICRA’00 (Vol. 2, pp. 1276–1282).‏ IEEE.

    Google Scholar 

  38. Sivanandam, S. N., & Deepa, S. N. (2007). Introduction to genetic algorithms. Berlin, Heideberg, New York: Springer. ISBN 978-3-540-73189-4.

    Google Scholar 

  39. Sohl, G. A., & Bobrow J. E. (1999). Experiments and simulations on the nonlinear control of a hydraulic servo system. IEEE Transactions on Control Systems Technology, 7(2), 238–247.

    Google Scholar 

  40. Tooby, J., & Cosmides, L. (1989). Adaption versus phylogeny: The role of animal psychology in the study of human behavior. International Journal of Comparative Psychology, 2(3), 175–188.

    Google Scholar 

  41. Tian, J. (2013). Study on control strategy of electro-hydraulic servo loading system. Indonesian Journal of Electrical Engineering, 11(9), 5044–5047.

    Google Scholar 

  42. Truong, D. Q., Kwan, A. K., Hung, H. T., & Yoon II, J. (2008). A study on hydraulic load simulator using self tuning grey predictor-fuzzy PID. In Proceedings of the 17th World Congress the International Federation of Automatic Control (IFAC) (Vol. 41, No. 2, pp. 13791–13796).

    Google Scholar 

  43. Xue, D., Atherton, D. P., & Chen, Y. (2007). Linear feedback control: Analysis and design with MATLAB (Vol. 14). Siam.

    Google Scholar 

  44. Yao, J. J., Di, D., Jiang, G., & Liu, S. (2012). High precision position control of electro-hydraulic servo system based on feed-forward compensation. Research Journal of Applied Sciences, Engineering and Technology., 4(4), 289–298.

    Google Scholar 

  45. Yu, M., & Lichen, G. (2013). Fuzzy immune PID control of hydraulic system based on PSO algorithm. Indonesian Journal of Electrical Engineering, 11(2), 890–895.

    Google Scholar 

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Author information

Authors and Affiliations

  1. IAET, Imbaba Airport, Giza, Egypt

    M. El-Sayed M. Essa

  2. Faculty of Engineering, Electric Power and Machines Department, Cairo University, Giza, Egypt

    Magdy A. S. Aboelela & M. A. M. Hassan

Authors
  1. M. El-Sayed M. Essa
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  2. Magdy A. S. Aboelela
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  3. M. A. M. Hassan
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Corresponding author

Correspondence to M. El-Sayed M. Essa .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Benha University Faculty of Computers and Information, AND Nanoelectronics Integrated Systems Center (NISC), Nile University, Egypt. , Benha, Egypt

    Ahmad Taher Azar

  2. Research and Development Centre, Vel Tech University Research and Development Centre, Chennai, Tamil Nadu, India

    Sundarapandian Vaidyanathan

  3. Department of Mathematics and CS, University of Tebessa Department of Mathematics and CS, Tebessa, Algeria

    Adel Ouannas

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Essa, M.ES.M., Aboelela, M.A.S., Hassan, M.A.M. (2017). Application of Fractional Order Controllers on Experimental and Simulation Model of Hydraulic Servo System. In: Azar, A., Vaidyanathan, S., Ouannas, A. (eds) Fractional Order Control and Synchronization of Chaotic Systems. Studies in Computational Intelligence, vol 688. Springer, Cham. https://doi.org/10.1007/978-3-319-50249-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-50249-6_10

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