Active Vibration Control of a Modular Robot Combining a Back-Propagation Neural Network with a Genetic Algorithm

Abstract

In this paper, a genetic algorithm based back-propagation neural network suboptimal controller is developed to control the vibration of a nine-degrees-of-freedom modular robot. A finite-element method is used to model the modules of the robot, and the entire system dynamic equation is established using the substructure synthesis method. Then the joint stiffness parameters are identified based on the experimental modal analysis experiment. After modeling the whole structure with the models of the robotic modules and the joint parameters, simulations of the vibration control for the modular robot in several configurations are carried out. It is shown that the control method presented in this paper is effective at suppressing the residual vibrations of the modular robot.

Keywords

Active vibration control finite-element analysis modular robot neural network

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References

Bathe, K. J. , 1996, Finite Element Procedures, Prentice-Hall, Englewood Cliffs, NJ .

Davies, L. , 1991, Handbook of Genetic Algorithms, Van Nostrand-Reinhold, NY .

Fei, Y. , Zhao, X. , and Song, L. , 2001, “A method for modular robots generating dynamics automatically,” Robotica 19, 59-66 .

Fukuda, T. , Nakagawa, S. , Kawauchi, Y. , and Buss, M. , 1989, “Structure decision method for self-organizing robots based on cell structures - CEBOT,” in Proceedings of the IEEE International Conference on Robotics and Automation, Scottsdale, AZ, pp. 695-700 .

Gamarra-Rosado, V. O. and Yuhara, E. A. O. , 1999, “Dynamic modeling and simulation of a flexible robotic manipulator,” Robotica 17, 523-528 .

Hashimoto, M. and Kiyosawa, Y. , 1998, “The effects of joint flexibility on robot motion control using joint torque sensors,” International Journal of Robotics and Automation 13(2), 43-47 .

Haykin, S. , 1999, Neural Network: A Comprehensive Foundation, Prentice-Hall, Englewood Cliffs, NJ .

Li, Y. , Liu, X. , Peng, Z. , and Liu, Y. , 2002, “The identification of joint parameters for modular robots using fuzzy theory and a genetic algorithm,” Robotica 20(5), 509-517 .

Li, Y. , Liu, Y. , Liu, X. , and Peng, Z. , 2003 “Parameter identification and vibration control for modular manipulators,” in Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan, September 14-19, pp. 3254-3259 .

10.

Liu, X. and Yang, B. , 1999, “Structure vibration control by tuned distributed vibration damper,” in ASME Design Engineering Technique Conference, Las Vegas, NV, September 12-15, pp. 1-9 .

11.

Lu, X. , Tao, Y. , and Zhou, J. , 2000, “Neural network based active vibration control of flexible structures,” International Journal of Robotic Research 13(1), 107-111 .

12.

Macnab, C. J. B. and D’Eleuterio, G.. M. T. , 2000, “Discrete-time Lyapunov design for neuroadaptive control of elastic-joint robots,” International Journal of Robotic Research 19(5), 511-525 .

13.

Ono, I. and Kobayashi, S. , 1997, “A real-coded genetic algorithm for function optimization using unimodal normal distribution crossover,” in Proceedings of the 7th International Conference on Genetic Algorithms, East Lansing, MI, July, Morgan Kauffman, San Mateo, CA, pp. 246-253 .

14.

Schmitz, D. , Khosla, P. , and Kanade, T. , 1988, “The CMU reconfigurable modular manipulator system,” Technical Report CMU-RI-TR-88-07, Robotics Institute, Carnegie Mellon University , May.

15.

Will, P. M. and Grossman, D. D. , 1975, “An experimental system for computer controlled mechanical assembly,” IEEE Transactions on Computers 24(9), 879-888 .

16.

Zhu, Y. , Qiu, J. , and Tani, J. , 2001, “Simultaneous optimization of a two-link flexible robot arm,” Journal of Robotic Systems 18(1), 29-38 .