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Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator

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Abstract

In this paper an ANFIS-PD+I (AFSPD+I) based hybrid force/position controller has been proposed which works effectively with unspecified robot dynamics in the presence of external disturbances. A constraint is put to limit the movement of manipulator in XY Cartesian coordinates. The validity of the proposed controller has been tested using a 6-degree of freedom PUMA robot manipulator. The performance comparison have been done with the fuzzy proportional derivative plus integral, fuzzy proportional integral derivative and conventional proportional integral derivative controllers subjected to the same data set with proposed controller. The projected AFSPD+I controller adhered to the desired path closer and smoother than the other mentioned controllers.

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References

  • Alavandar, S., & Nigam, M. (2008). Fuzzy PD+ I control of a six DOF robot manipulator. Industrial Robot, Emerald. doi:10.1108/01439910810854610.

  • Armstrong, B., Khatib, O., & Burdick, J. (1986). The explicit dynamic model and inertial parameters of the PUMA 560 arm. In IEEE international conference on robotics and automation (vol. 3, pp. 510–518). doi:10.1109/ROBOT.1986.1087644.

  • Bezine, H., Derbel, N., & Alimi, A. M. (2002). Fuzzy control of robot manipulators: Some issues on design and rule base size reduction. Engineering Applications of Artificial Intelligence, 15(5), 401–416. doi:10.1016/S0952-1976(02)00075-1.

    Article  Google Scholar 

  • Burlacu, A., Copot, C., & Lazar, C. (2013). Predictive control architecture for real-time image moments based servoing of robot manipulators. Journal of Intelligent Manufacturing, 1–10. doi:10.1007/s10845-013-0743-0.

  • Carvajal, J., Chen, G., & Ogmen, H. (2000). Fuzzy PID controller: Design, performance evaluation, and stability analysis. Information Sciences, 123(3–4), 249–270. doi:10.1016/S0020-0255(99)00127-9.

    Article  Google Scholar 

  • Castillo, O., & Melin, P. (2003). Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach. Applied Soft Computing, 3(4), 363–378. doi:10.1016/j.asoc.2003.05.007.

    Article  Google Scholar 

  • Daoud, S., Chehade, H., Yalaoui, F., & Amodeo, L. (2014). Efficient metaheuristics for pick and place robotic systems optimization. Journal of Intelligent Manufacturing, 25(1), 27–41. doi:10.1007/s10845-012-0668-z.

    Article  Google Scholar 

  • Durmuş, B., Temurtaş, H., Yumuşak, N., & Temurtaş, F. (2009). A study on industrial robotic manipulator model using model based predictive controls. Journal of Intelligent Manufacturing, 20(2), 233–241. doi:10.1007/s10845-008-0221-2.

    Article  Google Scholar 

  • Eldukhri, E. E., & Kamil, H. G. (2013). Optimisation of swing-up control parameters for a robot gymnast using the Bees Algorithm. Journal of Intelligent Manufacturing, 1–9. doi:10.1007/s10845-013-0848-5.

  • Fanaei, A., & Farrokhi, M. (2006). Robust adaptive neuro-fuzzy controller for hybrid position/force control of robot manipulators in contact with unknown environment. Journal of Intelligent and Fuzzy Systems, 17(2), 125–144.

    Google Scholar 

  • Feng-Yi, H., & Li-Chen, F. (2000). Intelligent robot deburring using adaptive fuzzy hybrid position/force control. IEEE Transactions on Robotics and Automation, 16(4), 325–335. doi:10.1109/70.864223.

    Article  Google Scholar 

  • Fisher, W. D., & Mujtaba, M. S. (1992). Hybrid position/force control: A correct formulation. The International Journal of Robotics Research. doi:10.1177/027836499201100403.

  • Goldenberg, A. A., & Song, P. (1997). Principles for design of position and force controllers for robot manipulators. Robotics and Autonomous Systems. doi:10.1016/S0921-8890(96)00079-6.

  • Haklidir, M., & Tasdelen, I. (2009a). Modeling, simulation and fuzzy control of an anthropomorphic robot arm by using Dymola. Journal of Intelligent Manufacturing. doi:10.1007/s10845-008-0227-9.

  • Haklidir, M., & Tasdelen, I. (2009b). Modeling, simulation and fuzzy control of an anthropomorphic robot arm by using Dymola. Journal of Intelligent Manufacturing, 20(2), 177–186. doi:10.1007/s10845-008-0227-9.

    Article  Google Scholar 

  • Jakovljevic, Z., Petrovic, P., Mikovic, V., & Pajic, M. (2014). Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly. Journal of Intelligent Manufacturing, 25(3), 571–587. doi:10.1007/s10845-012-0706-x.

    Article  Google Scholar 

  • Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665–685. doi:10.1109/21.256541.

    Article  Google Scholar 

  • Jatta, F., Legnani, G., Visioli, A., & Ziliani, G. (2006). On the use of velocity feedback in hybrid force/velocity control of industrial manipulators. Control Engineering Practice, 14(9), 1045–1055. doi:10.1016/j.conengprac.2005.06.005.

    Article  Google Scholar 

  • Kaganami, H. G., Ali, S. K., & Zou, B. (2011). Optimal approach for texture analysis and classification based on wavelet transform and neural network. Journal of Information Hiding and Multimedia Signal Processing, 2(1), 33–40.

    Article  Google Scholar 

  • Karayiannidis, Y., Rovithakis, G., & Doulgeri, Z. (2007). Force/position tracking for a robotic manipulator in compliant contact with a surface using neuro-adaptive control. Automatica, 43(7), 1281–1288. doi:10.1016/j.automatica.2006.12.019.

    Article  Google Scholar 

  • Kazemian, H. B. (2001). Comparative study of a learning fuzzy PID controller and a self-tuning controller. ISA Transaction. doi:10.1016/S0019-0578(00)00056-2.

  • Khoury, G. M., Saad, M., Kanaan, H. Y., & Asmar, C. (2004). Fuzzy PID control of a five DOF robot arm. Journal of Intelligent and Robotic Systems, 40(3), 299–320. doi:10.1023/B:JINT.0000038947.97195.22.

    Article  Google Scholar 

  • Kiguchi, K., & Fukuda, T. (2000). Position/force control of robot manipulators for geometrically unknown objects using fuzzy neural networks. IEEE Transactions on Industrial Electronics, 47(3), 641–649. doi:10.1109/41.847905.

    Article  Google Scholar 

  • Kumar, N., Panwar, V., Sukavanam, N., Sharma, S., & Borm, J.-H. (2011). Neural network based hybrid force/position control for robot manipulators. International Journal of Precision Engineering and Manufacturing, 12(3), 419–426. doi:10.1007/s12541-011-0054-3.

    Article  Google Scholar 

  • Kwan, C. M. (1995). Hybrid force/position control for manipulators with motor dynamics using a sliding-adaptive approach. IEEE Transactions on Automatic Control, 963–968. doi:10.1109/9.384241.

  • Latif, A. (2013). An adaptive digital image watermarking scheme using fuzzy logic and tabu search. Journal of Information Hiding and Multimedia Signal Processing, 4(4), 250–271.

    Google Scholar 

  • Lewis, F. L., Jagannathan, S., & Yesildirek, A. (1999). Neural network control of robot manipulators and non-linear systems (Ist ed.). UK: Taylor & Francis.

    Google Scholar 

  • Li, W. (1998). Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller. IEEE Transactions on Fuzzy Systems. doi:10.1109/91.728430.

  • Li, W., Chang, X. G., Farrell, J., & Wahl, F. M. (2001a). Design of an enhanced hybrid fuzzy P+ID controller for a mechanical manipulator. IEEE Transactions on Systems, Man, and Cybernetics, 31(6), 938–945. doi:10.1109/3477.969497.

  • Li, W., Chang, X., Wahl, F., & Farrell, J. (2001b). Tracking control of a manipulator under uncertainty by FUZZY P+ ID controller. Fuzzy Sets Systems. doi:10.1016/S0165-0114(00)00019-1.

  • Lin, J., Lin, C. C., & Lo, H. (2010). Hybrid position/force control of robot manipulators mounted on oscillatory bases using adaptive fuzzy control. In IEEE International Symposium on Intelligent Control. doi:10.1109/ISIC.2010.5612905.

  • Misir, D., Malki, H. A., & Guanrong, C. (1996). Design and analysis of a fuzzy proportional-integral-derivative controller. Fuzzy Sets Systems. doi:10.1016/0165-0114(95)00149-2.

  • Patel, R. V., Talebi, H. A., Jayender, J., & Shadpey, F. (2009). A robust position and force control strategy for 7-DOF redundant manipulators. IEEE/ASME Transactions on Mechatronics, 14(5), 575–589. doi:10.1109/TMECH.2008.2009637.

    Article  Google Scholar 

  • Qingjiu, H., & Enomoto, R. (2009). Hybrid position, posture, force and moment control of robot manipulators. In IEEE international conference on robotics and biomimetics, 2008. doi:10.1109/ROBIO.2009.4913213.

  • Song, Z., Yi, J., Zhao, D., & Li, X. (2005). A computed torque controller for uncertain robotic manipulator systems: Fuzzy approach. Fuzzy Sets Systems. doi:10.1016/j.fss.2005.03.007.

  • Su, Y. X., Yang, S. X., Sun, D., & Duan, B. Y. (2004). A simple hybrid fuzzy PD controller. Mechatronics, 14(8), 877–890. doi:10.1016/j.mechatronics.2004.05.002.

    Article  Google Scholar 

  • Sun, Y. L., & Er, M. J. (2004). Hybrid fuzzy control of robotics systems. IEEE Transactions on Fuzzy Systems, 12(6), 755–765. doi:10.1109/TFUZZ.2004.836097.

    Article  Google Scholar 

  • Wang, D., Soh, Y. C., & Cheah, C. C. (1995). Robust motion and force control of constrained manipulators by learning. Automatica. doi:10.1016/0005-1098(94)00066-R.

  • Zhang, H., & Paul, R. P. (1985). Hybrid control of robot manipulators. In IEEE international conference on robotics and automation (vol. 2, pp. 1481–1484). doi:10.1109/ROBOT.1985.1087304.

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Correspondence to Himanshu Chaudhary.

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Chaudhary, H., Panwar, V., Prasad, R. et al. Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator. J Intell Manuf 27, 1299–1308 (2016). https://doi.org/10.1007/s10845-014-0952-1

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  • DOI: https://doi.org/10.1007/s10845-014-0952-1

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