Skip to main content
SpringerLink
Log in

An optimal fuzzy-PI force/motion controller to increase industrial robot autonomy

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper presents a method for robot self-recognition and self-adaptation through the analysis of the contact between the robot end effector and its surrounding environment. Often, in off-line robot programming, the idealized robotic environment (the virtual one) does not reflect accurately the real one. In this situation, we are in the presence of a partially unknown environment (PUE). Thus, robotic systems must have some degree of autonomy to overcome this situation, especially when contact exists. The proposed force/motion control system has an external control loop based on forces and torques exerted on the robot end effector and an internal control loop based on robot motion. The external control loop is tested with an optimal proportional integrative (PI) and a fuzzy-PI controller. The system performance is validated with real-world experiments involving contact in PUEs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Pan Z, Polden J, Larkin N, Duin SV, Norrish J (2012) Recent progress on programming methods for industrial robots. Robot Comput Integrated Manuf 28(2):87–94. doi:10.1016/j.rcim.2011.08.004

    Article  Google Scholar 

  2. Biggs G, MacDonald B (2003) A survey of robot programming systems. In: Proc Australasian Conf Robot Automat 2003 (ACRA 2003), 1 pp 1–10, Brisbane, Australia

  3. Kim JY (2004) CAD-based automated robot programming in adhesive spray systems for shoe outsoles and uppers. J Robotic Syst 21(11):625–634. doi:10.1002/rob.20040

    Article  Google Scholar 

  4. Chen H, Fuhlbrigge T, Li X (2008) Automated industrial robot path planning for spray painting process: a review. In: Proc 4th IEEE Conf Automation Science Engineering (CASE 2008), Washington DC, USA. doi:10.1109/COASE.2008.4626515

  5. Soron M, Kalaykov I (2007) Generation of continuous tool paths based on CAD models for friction stir welding in 3D. In: Proc 2007 Mediterranean Conf Control Automation (MED 2007), Athens, Greece. doi:10.1109/MED.2007.4433944

  6. Neto P, Pires JN, Moreira AP (2010) CAD-based off-line robot programming. In: Proc 2010 IEEE Conf Robotics Automation Mechatronics (RAM 2010), Singapore. doi:10.1109/RAMECH.2010.5513141

  7. Neto P, Mendes N (2013) Direct off-line robot programming via a common CAD package. Robot Auton Syst, in press

  8. Neto P, Pires JN, Moreira AP (2013) Robot simulation: an approach based on CAD-autodesk inventor. IEEE Robot Autom Mag, in press

  9. Nubiola A, Bonev IA (2013) Absolute calibration of an ABB IRB 1600 robot using laser tracker. Robot Comput Integrated Manuf 29(1):236–245. doi:10.1016/j.rcim.2012.06.004

    Article  Google Scholar 

  10. Neto P, Mendes N, Araújo R, Pires JN, Moreira AP (2012) High-level programming based on CAD: dealing with unpredictable environments. Ind Robot 39(3):294–303. doi:10.1108/01439911211217125

    Article  Google Scholar 

  11. Bolmsjö G, Olsson M (2005) Sensors in robotic arc welding to support small series production. Ind Robot 32(4):341–345. doi:10.1108/01439910510600218

    Article  Google Scholar 

  12. Fridenfalk M, Bolmsjö G (2002) Design and validation of a sensor guided robot control system for welding in shipbuilding. Int J Joining Mater 14(3/4):44–55

    Google Scholar 

  13. Brink K, Olsson M, Bolmsjö G (1997) Increased autonomy in industrial robotic systems: a framework. J Intell Robotic Syst 19:357–373. doi:10.1023/A:1007909120189

    Article  Google Scholar 

  14. Johansson R, Robertsson A, Nilsson K, Brogardh T, Cederberg P, Olsson M, Olsson T, Bolmsjö G (2004) Sensor integration in task-level programming and industrial robotic task execution control. Ind Robot 31(3):284–296. doi:10.1108/01439910410532369

    Article  Google Scholar 

  15. Kenney J, Buckley T, Brock O (2009) Interactive segmentation for manipulation in unstructured environments. In: Proc 2009 IEEE Int Conf Robot Autom (ICRA 2009), pp. 1337–1382, Kobe, Japan. doi:10.1109/ROBOT.2009.5152393

  16. Lopez-Juarez I, Corona-Castuera J, Peña-Cabrera M, Ordaz-Hernandez K (2005) On the design of intelligent robotic agents for assembly. Inf Sci 171(4):377–402. doi:10.1016/j.ins.2004.09.011

    Article  Google Scholar 

  17. Zeng G, Hemami A (1997) An overview of robot force control. Robotica 15(5):473–482. doi:10.1017/S026357479700057X

    Article  Google Scholar 

  18. Siciliano B, Villani L (1999) Robot force control. Kluwer, Boston

    Book  MATH  Google Scholar 

  19. Nagata F, Kusumoto Y, Fujimoto Y, Watanabe K (2007) Robotic sanding system for new designed furniture with free-formed surface. Robot Comput Integrated Manuf 23(4):371–379. doi:10.1016/j.rcim.2006.04.004

    Article  Google Scholar 

  20. Pires JN, Ramming J, Rauch S, Araujo R (2002) Force/torque sensing applied to industrial robotic deburring. Sens Rev 22(3):232–241. doi:10.1108/02602280210433070

    Article  Google Scholar 

  21. Lin ST, Huang AK (1998) Hierarchical fuzzy force control for industrial robots. IEEE Trans Ind Electron 45(4):646–653. doi:10.1109/41.704894

    Article  Google Scholar 

  22. Li HX, Gatland HR (1995) A new methodology for designing a fuzzy logic controller. IEEE Trans Syst Man Cybern 25(3):505–512. doi:10.1109/21.364863

    Article  Google Scholar 

  23. Tang KS, Man KF, Chen G, Kwong S (2001) An optimal fuzzy PID controller. IEEE Trans Ind Electron 48(4):757–765. doi:10.1109/41.937407

    Article  Google Scholar 

  24. Mamdani EH (1974) Application of fuzzy algorithms for control of simple dynamic plant. Proc Inst Electr Eng 121(12):1585–1588. doi:10.1049/piee.1974.0328

    Article  Google Scholar 

  25. Hsieh CH, Chou JH, Wu YJ (2002) Optimal predicted fuzzy PI gain scheduling controller of constant turning force systems with fixed metal removal rate. Int J Adv Manuf Technol 19(10):714–721. doi:10.1007/s001700200081

    Article  Google Scholar 

  26. Mendes N, Neto P, Pires JN, Loureiro A (2013) Discretization and fitting of nominal data for autonomous robots. Expert Syst Appl 40(4):1143–1151. doi:10.1016/j.eswa.2012.08.023

    Article  Google Scholar 

  27. Mendes N, Neto P, Pires JN, Moreira AP (2010) Fuzzy-PI force control for industrial robotics. In: Vadakkepat P et al (eds) Trends in intelligent robotics. Springer, Heidelberg, pp 322–329. doi:10.1007/978-3-642-15810-0_41

    Chapter  Google Scholar 

  28. Lopes A, Almeida F (2008) A force-impedance controlled industrial robot using an active robotic auxiliary device. Robot Comput Integrated Manuf 24(3):299–309. doi:10.1016/j.rcim.2007.04.002

    Article  Google Scholar 

  29. Gudur PP, Dixit US (2009) An application of fuzzy inference for studying the dependency of roll force and roll torque on process variables in cold flat rolling. Int J Adv Manuf Technol 42(1–2):41–52. doi:10.1007/s00170-008-1574-6

    Article  Google Scholar 

  30. Ho WH, Chou JH (2007) Design of optimal controllers for Takagi-Sugeno fuzzy-model-based systems. IEEE Trans Syst Man Cybern Syst Hum 37(3):329–339. doi:10.1007/s00170-008-1574-6

    Article  Google Scholar 

  31. Fanaei A, Farrokhi M (2006) Robust adaptive neuro-fuzzy controller for hybrid position/force control of robot manipulators in contact with unknown environment. J Intell Fuzzy Syst 17(2):125–144

    MATH  Google Scholar 

  32. Kiguchi K, Fukuda T (1997) Intelligent position/force controller for industrial robot manipulators—application of fuzzy neural networks. IEEE Trans Ind Electron 44(6):753–761. doi:10.1109/41.649935

    Article  Google Scholar 

  33. Kucukdemiral IB, Cansever G (2006) Sugeno based robust adaptive fuzzy sliding mode controller for SISO nonlinear systems. J Intell Fuzzy Syst 17(2):113–124

    MATH  Google Scholar 

  34. Wai RJ, Yang ZW (2008) Adaptive fuzzy neural network control design via a T-S fuzzy model for a robot manipulator including actuator dynamics. IEEE Trans Syst Man Cybern B Cybern 38(5):1326–1346. doi:10.1109/TSMCB.2008.925749

    Article  Google Scholar 

  35. MacVicar-Whelan PJ (1977) Fuzzy sets for man–machine interactions. Int J Man Mach Stud 8(6):687–697

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering (CEMUC)-POLO II, University of Coimbra, Rua Luís Reis Santos, 3030-788, Coimbra, Portugal

    Nuno Mendes, Pedro Neto, J. Norberto Pires & Altino Loureiro

Authors
  1. Nuno Mendes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedro Neto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Norberto Pires
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Altino Loureiro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nuno Mendes.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mendes, N., Neto, P., Pires, J.N. et al. An optimal fuzzy-PI force/motion controller to increase industrial robot autonomy. Int J Adv Manuf Technol 68, 435–441 (2013). https://doi.org/10.1007/s00170-013-4741-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-013-4741-3

Keywords

Search

Navigation