Robust Neural Network Control of Electrically Driven Robot Manipulator Using Backstepping Approach

A novel approach to neural network based tracking-control of robot manipulator including actuator dynamics is proposed by using of backstepping method. A simple two-step backstepping is considered for an n-link robotic system, and a feedforward neural controller is designed at second step where structured and unstructured uncertainties in robot dynamics and actuator model are approximated by this neural controller. Bounds of network reconstruction error and other imprecisions are estimated adaptively and for compensating them, a robust control signal is added and modified. Stability analysis is performed by the Lyapunov direct method and performance efficiency of the proposed controller is justified by the simulations.

[1]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[2]  Withit Chatlatanagulchai,et al.  Intelligent control of a two-link flexible-joint robot, using backstepping, neural networks, and direct method , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[4]  Kok Kiong Tan,et al.  Adaptive neural network algorithm for control design of rigid-link electrically driven robots , 2008, Neurocomputing.

[5]  Shuzhi Sam Ge,et al.  Adaptive neural network control for strict-feedback nonlinear systems using backstepping design , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[6]  Bin Yao,et al.  Neural network adaptive robust control of nonlinear systems in semi-strict feedback form , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[7]  Frank L. Lewis,et al.  Neural Network Control Of Robot Manipulators And Non-Linear Systems , 1998 .

[8]  Frank L. Lewis,et al.  Design and implementation of industrial neural network controller using backstepping , 2003, IEEE Trans. Ind. Electron..

[9]  Shuzhi Sam Ge,et al.  Adaptive neural control of uncertain MIMO nonlinear systems , 2004, IEEE Transactions on Neural Networks.

[10]  M. Fateh.  On the Voltage-Based Control of Robot Manipulators , 2008 .

[11]  Fuchun Sun,et al.  Neural network control of flexible-link manipulators using sliding mode , 2006, Neurocomputing.

[12]  Mohammad Reza Soltanpour,et al.  Robust Backstepping Control of Robot Manipulator in Task Space with Uncertainties in Kinematics and Dynamics , 2009 .

[13]  Mark W. Spong,et al.  Robot dynamics and control , 1989 .

[14]  Okyay Kaynak,et al.  Robust and adaptive backstepping control for nonlinear systems using RBF neural networks , 2004, IEEE Transactions on Neural Networks.

[15]  Dan Wang,et al.  Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form , 2002, Autom..

[16]  Y.-C. Chang,et al.  Robust tracking control for a class of uncertain electrically driven robots , 2009 .

[17]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[18]  Zhong-Ping Jiang,et al.  Stable neural controller design for unknown nonlinear systems using backstepping , 2000, IEEE Trans. Neural Networks Learn. Syst..

[19]  Rong-Jong Wai,et al.  Robust Neural-Fuzzy-Network Control for Robot Manipulator Including Actuator Dynamics , 2006, IEEE Transactions on Industrial Electronics.

[20]  Yeong-Chan Chang,et al.  An intelligent robust tracking control for electrically-driven robot systems , 2008, Int. J. Syst. Sci..

[21]  Frank L. Lewis,et al.  Robust backstepping control of nonlinear systems using neural networks , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[22]  Panos J. Antsaklis,et al.  Neural networks for control systems , 1990, IEEE Trans. Neural Networks.