Fuzzy-neural net based control strategy for robot manipulator trajectory tracking

A novel scheme with a neural network feed-forward controller and a fuzzy feedback controller is proposed for trajectory tracking of robot manipulators with unknown dynamic model. In the scheme, an improved BP network is used as feed-forward controller, which approximates to expected torque. The feedback controller is constructed based on T-S fuzzy model. The fuzzy rules are initialized according to the experiments of experts and experienced operators, which can provide better training data than that supplied by conventional feedback controller. Simulation results show that the presented scheme has good tracking performance and disturbance rejection ability.

[1]  Frank L. Lewis,et al.  Optimal design of CMAC neural-network controller for robot manipulators , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[2]  Ricardo O. Carelli,et al.  Neural networks for advanced control of robot manipulators , 2002, IEEE Trans. Neural Networks.

[3]  Ken Tomiyama,et al.  Two adaptive robust sliding mode controllers for robot manipulators , 1994, Proceedings of 1994 9th IEEE International Symposium on Intelligent Control.

[4]  Kamran Iqbal,et al.  PID controller design for the human arm-robot manipulator coordination problem , 2002, Proceedings of the IEEE Internatinal Symposium on Intelligent Control.

[5]  Zhang Suzhen,et al.  Variable-step BP training algorithm with an adaptive momentum term , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[6]  Chen Hu,et al.  Neural network output feedback control for uncertain robot , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[7]  Edgar N. Sánchez,et al.  Takagi-Sugeno fuzzy scheme for real-time trajectory tracking of an underactuated robot , 2002, IEEE Trans. Control. Syst. Technol..

[8]  Shuzhi Sam Ge,et al.  Neural network based adaptive impedance control of constrained robots , 2002, Proceedings of the IEEE Internatinal Symposium on Intelligent Control.

[9]  A. M. Fayaz.  Analysis of fuzzy T-S observers and controllers from the interpolation perspective , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).