Robotics and Control Systems

  • M. H. Fazel ZarandiEmail author
  • H. Mosadegh
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 341)


Robots are of those intelligent systems created to do a wide range of activities with the aim of human aid and productivity improvement. Besides, many different fields of studies such as engineering, healthcare, computer science, mathematics and management are involved in order to increase the efficiency and effectiveness of robots. Generally speaking, robotics and control systems is a branch of engineering science that deals with all aspects of robot’s design, operation and control. More precisely, the concept of control in this paper is knowing the techniques required for programming robot’s activities such as its physical movements, rotations, decisions and planning. In addition to mathematical modeling optimization and scheduling, there are a lot of control theory based approaches dealing with physical movement control of the robot at every moment of time. Due to the uncertainties, fuzzy set theory, applicable for all control techniques, is extensively used for robots. The role of fuzzy modeling becomes more evident when one can include human expertise and knowledge via fuzzy rules in the control system. Without loss of generality, this paper presents fuzzy control techniques as well as fuzzy mathematical scheduling model for an m-machine robotic cell with one manipulator robot. Furthermore, it proposes an integrated fuzzy robotic control system, in which the fuzzy optimization model is solved at every predetermined period of time such as beginning of shifts or days, etc. Then, based on the solutions obtained, input parameters and unpredictable disturbances, the autonomous fuzzy control is executed continuously. These two modules transfer information and feedback to each other via an intermediate collaborative module. The explanations are supported via an example.


Robotic Control Fuzzy Optimization Rule-based 


  1. 1.
    Freeman, C.T., et al.: Iterative learning control in health care: electrical stimulation and robotic-assisted upper-limb stroke rehabilitation. Control Syst. IEEE 32(1), 18–43 (2012)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Hussain, S., Xie, S.Q., Liu, G.: Robot assisted treadmill training: Mechanisms and training strategies. Med. Eng. Phys. 33(5), 527–533 (2012)CrossRefGoogle Scholar
  3. 3.
    Katić, D., Vukobratović, M.: Survey of intelligent control techniques for humanoid robots. J. Intell. Rob. Syst. 37(2), 117–141 (2003)CrossRefGoogle Scholar
  4. 4.
    Simorov, A., et al.: Review of surgical robotics user interface: what is the best way to control robotic surgery? Surg. Endosc. 26(8), 2117–2125 (2012)CrossRefGoogle Scholar
  5. 5.
    Liu, Y., Nejat, G.: Robotic urban search and rescue: a survey from the control perspective. J. Intell. Rob. Syst. 72(2), 147–165 (2013)CrossRefGoogle Scholar
  6. 6.
    Wen, L., et al.: Hydrodynamic investigation of a self-propelled robotic fish based on a force-feedback control method. Bioinspiration & Biomimentics 7 (2012)Google Scholar
  7. 7.
    Du, Y., et al.: Review on reliability in pipeline robotic control systems. Int. J. Comput. Appl. Technol. 49(1), 12–21 (2014)CrossRefGoogle Scholar
  8. 8.
    Lenarcic, J., Bajd, T., Stanisic, M.M.: Robot Mechanisms, Springer (2013)Google Scholar
  9. 9.
    Kim J.-H., et al.: Robot Intelligence Technology and Applications, vol. 2, Springer (2014)Google Scholar
  10. 10.
    Kim, J.-H., et al.: Robot Intelligence Technology and Applications, vol. 3, Springer (2015)Google Scholar
  11. 11.
    Christ, R.D., Wernli Sr R.L.: Chapter 19—Manipulators. In: Christ, R.D., Wernli, R.L. (ed.) The ROV Manual (Second Edition), pp. 503–534, Oxford, Butterworth-Heinemann (2014)Google Scholar
  12. 12.
    Sun, Y., Qian, H., Xu, Y.: Chapter 5.1—The state of the art in grasping and manipulation for household service. In: Wu, Y.X.Q. (ed.) Household Service Robotics, pp. 341–356, Oxford, Academic Press (2015)Google Scholar
  13. 13.
    Sethi, S.P., et al.: Sequencing of parts and robot moves in a robotic cell. Int. J. Flex. Manuf. Syst. 4(3), 331–358 (1992)CrossRefGoogle Scholar
  14. 14.
    Logendran, R., Sriskandarajah, C.: Sequencing of robot activities and parts in two-machine robotic cells. Int. J. Prod. Res. 34(12), 3447–3463 (1996)CrossRefzbMATHGoogle Scholar
  15. 15.
    Chen, H., Chu, C., Proth, J.-M.: Sequencing of Parts in Robotic Cells. Int. J. Flex. Manuf. Syst. 9(1), 81–104 (1997)CrossRefGoogle Scholar
  16. 16.
    Sriskandarajah, C., Hall, N.G., Kamoun, H.: Scheduling large robotic cells without buffers. Ann. Oper. Res. 76, 287–321 (1998)CrossRefzbMATHGoogle Scholar
  17. 17.
    Crama, Y., et al.: Cyclic scheduling in robotic flowshops. Ann. Oper. Res. 96(1), 97–124 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Akturk, M.S., Gultekin, H., Karasan, O.E.: Robotic cell scheduling with operational flexibility. Discrete Appl. Math. 145(3), 334–348 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Gultekin, H., Akturk, M.S., Karasan, O.E.: Scheduling in robotic cells: process flexibility and cell layout. Int. J. Prod. Res. 46(8), 2105–2121 (2008)CrossRefzbMATHGoogle Scholar
  20. 20.
    Zarandi, Fazel, MHH, Mosadegh, Fattahi, M.: Two-machine robotic cell scheduling problem with sequence-dependent setup times. Comput. Oper. Res. 40(5), 1420–1434 (2013)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Bagchi, T.P., Gupta, J.N.D., Sriskandarajah, C.: A review of TSP based approaches for flowshop scheduling. Eur. J. Oper. Res. 169(3), 816–854 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Dawande, M., et al.: Sequencing and scheduling in robotic cells: recent developments. J. Sched. 8(5), 387–426 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Cassandras Christos G., Stéphane, L.: Introduction to discrete event systems. Springer (2008)Google Scholar
  24. 24.
    Kandel, A., Langholz, G.: Fuzzy Control Systems. CRC Press (1993)Google Scholar
  25. 25.
    Sousa, J.M.C., Kaymak, U.: Fuzzy Decision Making in Modeling and Control 2002: World Scientific Publishing Co. Pte. LtdGoogle Scholar
  26. 26.
    Peng, L., Peng-Yung, W.: Neural-fuzzy control system for robotic manipulators. Cont. Syst. IEEE 22(1), 53–63 (2002)CrossRefGoogle Scholar
  27. 27.
    Nanayakkara, T., Sahin, F., Jamshidi, M.: Intelligent control systems with an introduction to system of systems engineering. CRC Press (2010)Google Scholar
  28. 28.
    Al-Hadithi, B., Matía, F., Jiménez, A.: Fuzzy controller for robot manipulators. In: Melin, P., et al. (ed.) Foundations of Fuzzy Logic and Soft Computing, pp. 688–697, Springer, Berlin, HeidelbergGoogle Scholar
  29. 29.
    Siciliano, B., et al.: Advances in Control of Articulated and Mobile Robots. Springer (2004)Google Scholar
  30. 30.
    Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 1973. SMC-3(1), 28–44Google Scholar
  31. 31.
    Mamdani, E.H.: application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans. Comput. C-26(12), 1182–1191 (1977)Google Scholar
  32. 32.
    Featherstone, R., Orin, D.: Robot dynamics: equations and algorithms. in Robotics and Automation. In: Proceedings IEEE International Conference on ICRA ‘00 (2000)Google Scholar
  33. 33.
    Spong, M.W., Vidyasagar, M.: Robot Dynamics and Control. Wiley (1989)Google Scholar
  34. 34.
    Durkin, J.: Expert systems: design and development (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Industrial Engineering and Management SystemsAmirkabir University of TechnologyTehranIran

Personalised recommendations