×

ANFIS-based an adaptive continuous sliding-mode controller for robot manipulators in operational space. (English) Zbl 1423.70034

Summary: This paper addresses the task-space robust trajectory tracking control problem for robot manipulators in the presence of uncertainties and external disturbances. First, a discontinuous sliding-mode controller-based inverse dynamics control strategy (IDSMC) with discontinuous robust control action is synthesized. Second, an adaptive inverse dynamics controller based on continuous sliding-mode control (AIDCSMC) is designed, in which the adaptation laws are addressed to compensate for the unknown parameters of the dynamical model of robot manipulators. The global stability of the closed-loop control system is proven using the Lyapunov theorem and the proposed AIDCSMC controller is further proven to guarantee convergence to zero of both trajectory tracking error and error rate. Finally, a hybrid intelligent neuro-fuzzy adaptive fuzzy inference system (ANFIS)-based adaptive inverse dynamics controller with continuous sliding-mode control (ANFIS-AIDCSMC) is adopted. Numerical simulations using the dynamic model of rigid robot manipulators with uncertainties show the effectiveness of the presented approach in simple and complex trajectory tracking problems. The simulation results indicate that the control performance of the robot system is satisfactory, and the proposed approach can achieve favorable tracking performance and it is robust with regard to uncertainties.

MSC:

70E60 Robot dynamics and control of rigid bodies
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Alavandar, S., Nigam, M.J.: Comparative analysis of conventional and soft computing based control strategies for robot manipulators with uncertainties. Int. J. Comput. Cogn. 7(1), 52-61 (2009)
[2] Barambones, O., Etxebarria, V.: Robust adaptive control for robot manipulator with un-modeled dynamics. Int. J. Cybern. Syst. 31(1), 67-86 (2000) · Zbl 1017.93071
[3] Barambones, O., Etxebarria, V.: Robust neural control of robot manipulators. Automatica 38(2), 235-242 (2002) · Zbl 0991.93080
[4] Capisani, L.; Ferrara, A.; Magnani, L., Second order sliding mode motion control of rigid robot manipulators, New Orleans, LA, USA
[5] Cheah, C., Kawamura, S., Arimoto, S.: Stability of hybrid position and force control for robotic manipulator with kinematics and dynamics uncertainties. Automatica 39(5), 847-855 (2003) · Zbl 1033.93048
[6] Cheah, C.; Liu, C.; Slotine, J., Approximate Jacobian adaptive control for robot manipulators, New Orleans, LA
[7] Cheah, C., Liaw, H.: Inverse Jacobian regulator with gravity compensation: stability and experiment. IEEE Trans. Robot. 21(4), 741-747 (2005)
[8] Cheah, C., Hirano, M., Kawamura, S., Arimoto, S.: Approximate Jacobian control for robots with uncertain kinematics and dynamics. IEEE Trans. Robot. Autom. 19(4), 692-702 (2003)
[9] Cheng, L., Hou, Z., Tan, M.: Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model. Automatica 45(10), 2312-2318 (2009) · Zbl 1179.93110
[10] Edwards, C., Spurgeon, S.: Sliding Mode Control: Theory and Applications, 1st edn. Taylor & Francis, London (1998) · Zbl 0964.93019
[11] Hacioglu, Y., Arslan, Y., Yazig, N.: MIMO fuzzy sliding mode controlled dual arm robot in load transportation. J. Franklin Inst. 348(8), 1886-1902 (2011) · Zbl 1231.93019
[12] Ho, H., Wong, Y., Rad, A.: Robust fuzzy tracking control for robotic manipulators. Simul. Model. Pract. Theory 15(7), 801-816 (2007)
[13] Kara, S., Dasb, S., Ghosh, P.: Applications of neuro fuzzy systems: a brief review and future outline. Appl. Soft Comput. 15(20), 243-259 (2014)
[14] Lian, R.: Enhanced adaptive grey-prediction self-organizing fuzzy sliding-mode controller for robotic systems. Inf. Sci. 236(14), 186-204 (2013) · Zbl 1284.93164
[15] Liu, H., Zhang, T.: Adaptive neural network finite-time control for uncertain robotic manipulators. J. Intell. Robot. Syst. 134(6), 1-15 (2012)
[16] Liu, H., Zhang, T.: Fuzzy sliding mode control of robotic manipulators with kinematic and dynamic uncertainties. J. Dyn. Syst. Meas. Control 134(6), 061007 (2012)
[17] Luca, M.; Antonella, F.; Lorenza, M., Second order sliding mode motion control of rigid robot manipulators, New Orleans, LA, USA, December 12-14, 2007
[18] Melin, P., Castillo, O.: Intelligent control of a stepping motor drive using an adaptive neuro-fuzzy inference system. Inf. Sci. 170(8), 133-150 (2005)
[19] Moreno-Valenzuela, J., Gonzlez-Hernndez, L.: Operational space trajectory tracking control of robot manipulators endowed with a primary controller of synthetic joint velocity. ISA Trans. 50(1), 131-140 (2010)
[20] Siciliano, B., Sciavicco, L., Khatib, O.: Springer Handbook of Robotics. Springer, Berlin/Heidelberg (2008) · Zbl 1171.93300
[21] Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics Modelling, Planning and Control. Springer, London (2009)
[22] Sun, T., Pei, H., Pan, Y., Zhou, H., Zhang, C.: Neural network-based sliding mode adaptive control for robot manipulators. Neurocomputing 74(14), 2377-2384 (2011)
[23] Torres, S., Méndez, J., Acosta, L., Becerra, V.: On improving the performance in robust controllers for robot manipulators with parametric disturbances. Control Eng. Pract. 15(5), 557-566 (2007)
[24] Utkin, V., Guldner, J., Shi, J.: Sliding Mode Control in Electro-Mechanical Systems, 2nd edn. CRC press/Taylor & Francis, Boca Raton/London (2017)
[25] Wang, W., Xie, Y.: Adaptive inverse dynamics control of robots with uncertain kinematics and dynamics. Automatica 45(9), 2114-2119 (2009) · Zbl 1175.93117
[26] Yazarel, Y., Cheah, C.: Task-space adaptive control of robotic manipulators with uncertainties in gravity regressor matrix and kinematics. IEEE Trans. Autom. Control 47(9), 1580-1585 (2002) · Zbl 1364.93527
[27] Yuzheng, G., Peng-Yung, W.: An adaptive fuzzy sliding mode controller for robotic manipulators. IEEE Trans. Syst. Man Cybern., Part A, Syst. Hum. 33(2), 149-159 (2003)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.