Abstract
The aim of this paper is to present an artificial neural network (ANN) based adaptive nonlinear control approach of a robot arm, with highlight on its capability as a compliant control scheme. The approach is based on a computed torque law and consists of two main components: a feedforward controller (approximated by the ANN) and a proportional-derivative (PD) feedback loop. Here, the feedforward controller is used to approximate the nonlinear system dynamics and can also adapt to the long-term dynamics of the arm while the PD feedback loop can be tuned to obtain proper compliant behaviour to deal with instantaneous disturbances (e.g., collisions). The employed controller structure makes it possible to decouple these two components for individual parameter adjustments. The performance of the control approach is evaluated and demonstrated in physical simulation which shows promising results.
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References
Haddadin, S., Albu-Schäffer, A., Luca, A.D., Hirzinger, G.: Collision detection and reaction: a contribution to safe physical human-robot interaction. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 3356–3363. IEEE (2008)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989)
Hunt, K.J., Sbarbaro, D., Zbikowski, R., Gawthrop, P.J.: Neural networks for control systems - a survey. Automatica 28(6), 1083–1112 (1992)
Isidori, A.: Nonlinear Control Systems. Springer Science & Business Media, New York (2013)
Kröse, B., van der Smagt, P.: An Introduction to Neural Networks, 8th edn. The University of Amsterdam, The Netherlands (1996)
Krstic, M., Kokotovic, P.V., Kanellakopoulos, I.: Nonlinear and Adaptive Control Design. Wiley, New York (1995)
Lewis, F.L., Dawson, D.M., Abdallah, C.T.: Robot Manipulator Control. Marcel Dekker Inc., New York (2004)
Lewis, F.L., Yegildirek, A., Liu, K., Member, S.: Multilayer neural-net robot controller with guaranteed tracking performance. IEEE Trans. Neural Netw. 7(2), 388–399 (1996)
Lewis, F., Jagannathan, S., Yesildirak, A.: Neural Network Control of Robot Manipulators and Non-linear Systems. CRC Press, Boca Raton (1998)
Luca, A., Albu-Schaffer, A., Haddadin, S., Hirzinger, G.: Collision detection and safe reaction with the DLR-III lightweight manipulator arm. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1623–1630. IEEE, October 2006
Martius, G., Hesse, F., Güttler, F., Der, R.: LPZROBOTS: a free and powerful robot simulator (2010). http://robot.informatik.uni-leipzig.de/software
Miller, W.T., Werbos, P.J., Sutton, R.S.: Neural Networks for Control. MIT Press, Cambridge (1995)
Omidvar, O., Elliott, D.L.: Neural Systems for Control. Elsevier, Amsterdam (1997)
Schiavi, R., Grioli, G., Sen, S., Bicchi, A.: VSA-II: a novel prototype of variable stiffness actuator for safe and performing robots interacting with humans. In: 2008 IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 2171–2176. IEEE (2008)
Selmic, R.R., Lewis, F.L.: Neural-network approximation of piecewise continuous functions: application to friction compensation. IEEE Trans. Neural Netw. 13(3), 745–751 (2002)
Sisbot, E.A., Marin-Urias, L.F., Alami, R., Simeon, T.: A human aware mobile robot motion planner. IEEE Trans. Robot. 23(5), 874–883 (2007)
Spall, J.C., Cristion, J.A.: Model-free control of nonlinear stochastic systems with discrete-time measurements. IEEE Trans. Autom. Control 43(9), 1198–1210 (1998)
Tonietti, G., Schiavi, R., Bicchi, A.: Design and control of a variable stiffness actuator for safe and fast physical human/robot interaction. In: 2005 Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, pp. 526–531. IEEE (2005)
Xiong, X., Wörgötter, F., Manoonpong, P.: Neuromechanical control for hexapedal robot walking on challenging surfaces and surface classification. Robot. Auton. Syst. 62(12), 1777–1789 (2014)
Xiong, X., Wörgötter, F., Manoonpong, P.: Virtual agonist-antagonist mechanisms produce biological muscle-like functions: an application for robot joint control. Ind. Robot Int. J. 41(4), 340–346 (2014)
Yang, W., Hammoudi, N., Herrmann, G., Lowenberg, M., Chen, X.: Dynamic gain-scheduled control and extended linearisation: extensions, explicit formulae and stability. Int. J. Control 88(1), 163–179 (2015)
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Jankovics, V., Mátéfi-Tempfli, S., Manoonpong, P. (2016). Artificial Neural Network Based Compliant Control for Robot Arms. In: Tuci, E., Giagkos, A., Wilson, M., Hallam, J. (eds) From Animals to Animats 14. SAB 2016. Lecture Notes in Computer Science(), vol 9825. Springer, Cham. https://doi.org/10.1007/978-3-319-43488-9_9
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DOI: https://doi.org/10.1007/978-3-319-43488-9_9
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