Adaptive fuzzy sliding control for a three-link passive robotic manipulator

An adaptive fuzzy sliding control (AFSC) scheme is proposed to control a passive robotic manipulator. The motivation for the design of the adaptive fuzzy sliding controller is to eliminate the chattering and the requirement of pre-knowledge on the bounds of the errors associated with the conventional sliding control. The stability and convergence of the adaptive fuzzy sliding controller are proven both theoretically and practically by simulations. A three-link passive manipulator model with two unactuated joints is derived to be used in the simulations. Simulation results demonstrate that the proposed system is robust against structured and unstructured uncertainties.

[1]  Fuchun Sun,et al.  The adaptive sliding mode control based on a fuzzy neural network for manipulators , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[2]  Miss A.O. Penney (b) , 1974, The New Yale Book of Quotations.

[3]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[4]  Gang Feng,et al.  Design of adaptive fuzzy sliding mode controller for robot manipulators , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[5]  Lei Pan,et al.  PD manipulator controller with fuzzy adaptive gravity compensation , 2000 .

[6]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[7]  Subashini Elangovan,et al.  Adaptive fuzzy sliding control for a three-link passive robotic manipulator , 2004 .

[8]  Yangsheng Xu,et al.  Optimal control of manipulators with any number of passive joints , 1998 .

[9]  Li-Xin Wang,et al.  Stable adaptive fuzzy control of nonlinear systems , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[10]  Peng-Yung Woo,et al.  Neural-fuzzy control system for robotic manipulators , 2002 .

[11]  Susumu Tachi,et al.  Position control of manipulator with passive joints using dynamic coupling , 1991, IEEE Trans. Robotics Autom..

[12]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[13]  Paul I. Ro,et al.  Robust control of passive-jointed robot and experimental validation using sliding mode , 1996 .

[14]  L. Wang,et al.  Fuzzy systems are universal approximators , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[15]  Peng-Yung Woo,et al.  An adaptive fuzzy sliding mode controller for robotic manipulators , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[16]  Chai Tianyou,et al.  Adaptive fuzzy sliding mode control for nonlinear systems , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[17]  G. Feng,et al.  An adaptive fuzzy controller based on sliding mode for robot manipulators , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .