Abstract
This study presents a theoretical–experimental scheme to control a redundant robot manipulator in the presence of unmodeled dynamics and discontinuous friction. The proposed control scheme does not require a priori knowledge of upper bounds, robot’s parameters, and external disturbance. The advantage of a feed-forward neural network (FFNN) controller is its robustness and ability to handle the model uncertainties. The virtual experimental results are carried out for a three-link planar redundant manipulator to show the effectiveness of the controller.
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References
Lewis, F.L., Jagannathan, S., Yesildirek, A.: Neural Network Control of Robot Manipulators. Taylor and Francis (1999)
Khatib, O.: Dynamic control of manipulators in operational space. In: Proceedings of the 6th IFTOMM Congress on Theory of Machines and Mechanisms, pp. 1–10 (1983)
Hsu, P., Hauser, J., Sastry, S.: Dynamic control of redundant manipulators. J. Robotic Syst. 6, 133–148 (1989)
Xian, B., de Quieroz, M.S., Dawson, D., Walkar, I.: Task space tracking control of redundant robot manipulators via quaternion feedback. In: Proceedings of the IEEE International conference on control applications, pp. 363–368 (2001)
Li, Z., Sinha, N.K., Elbestawi, M.: Simulation of robust controller for redundant manipulators. In: IEEE 34th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 521–524 (1991)
Zergeroglu, E., Dawson, D.M., Walker, I.W., Setlur, P.: Nonlinear tracking control of kinematically redundant robot manipulators. IEEE/ASME Trans. Mechatron. 9, 129–132 (2004)
Ozbay, U., Sahin, H.T., Zergeroglu, E.: Robust tracking control of kinematically redundant robot manipulators subject to multiple self-motion criteria. Robotica 26, 711–728 (2008)
Tee, K.P., Yan, R.: Adaptive operational space control of redundant robot manipulators. In: American Control Conference on O’Farrell Street, San Francisco, CA, USA, pp. 1742–1747 (2011)
Maaroof, O.W., Gezgin, E., Dede, M.I.C.: General subtask controller for redundant robot manipulators. In: 12th International Conference on Control, Automation and Systems, pp. 1352–1357 (2012)
Soto, I., Campa, R.: Two-loop control of redundant manipulators: analysis and experiments on a 3-DOF planar arm. Int. J. Adv. Rob. Syst. 10, 1–8 (2013)
Madania, T., Daachib, B., Benalleguea, A.: Adaptive variable structure controller of redundant robots with mobile/fixed obstacles avoidance. Robot. Auton. Syst. 61, 55–564 (2013)
Galicki, M.: Robust task space finite-time chattering-free control of robotic manipulators. J. Intell. Rob. Syst. (2016). doi:10.1007/s10846-016-0387-3
Lewis, F.L., Yesildirek, A., Liu, K.: Multilayer neural-net robot controller with guaranteed tracking performance. IEEE Trans. Neural Networks 7, 388–399 (1996)
Wai, R.J.: Tracking control based on neural network strategy for robot manipulator. Neurocomputing 51, 425–445 (2003)
Chien, T.L., Chen, C.C., Huang, Y.C., Lin, W.J.: Stability and almost disturbance decoupling analysis of nonlinear system subject to feedback linearization and feedforward neural network controller. IEEE Trans. Neural Networks 19, 1220–1230 (2008)
Kumar, N., Panwar, V., Sukavanam, N., Sharma, S.P., Borm, J.H.: Neural network based nonlinear tracking control of kinematically redundant robot manipulators. Math. Comput. Model. 53, 1889–1901 (2011)
Singh, H.P., Sukavanam, N.: Neural network based control scheme for redundant robot manipulators subject to multiple self-motion criteria. Math. Comput. Model. 55, 1275–1300 (2012)
Jasour, A.M., Farrokhi, M.: Adaptive neuro-predictive control for redundant robot manipulators in presence of static and dynamic obstacles: a Lyapunov-based approach. Adapt. Control Signal Process. 28, 386–411 (2014)
Shoushtari, A.L., Mazzoleni, S., Dario, P.: Bio-inspired kinematical control of redundant robotic manipulators. Assembly Autom. 36, 200–215 (2016)
Waibel, B.J.E., Kazerooni, H.: Theory and experiments on the stability of robot compliance control. IEEE J. Robot. Autom. 7, 95–104 (1991)
Singh, H.P., Sukavanm, N.: Simulation and stability analysis of neural network based control scheme for switched linear systems. ISA Trans. 51, 105–110 (2012)
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This work is financially supported by research and development grant, University of Delhi, New Delhi, India.
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Singh, H.P., Kumar, S., Kumar, P., Mahajan, A. (2018). Virtual Experimental Analysis of Redundant Robot Manipulators Using Neural Networks. In: Pant, M., Ray, K., Sharma, T., Rawat, S., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 584. Springer, Singapore. https://doi.org/10.1007/978-981-10-5699-4_3
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DOI: https://doi.org/10.1007/978-981-10-5699-4_3
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