MPID Control Tuning for a Flexible Manipulator Using a Neural Network

Tamer Mansour; Atsushi Konno; Masaru Uchiyama

doi:10.20965/jrm.2010.p0082

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JRM Vol.22 No.1 pp. 82-90

doi: 10.20965/jrm.2010.p0082

(2010)

Paper:

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MPID Control Tuning for a Flexible Manipulator Using a Neural Network

Tamer Mansour, Atsushi Konno, and Masaru Uchiyama

Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, 6-6-01 Aramaki-aza, Aoba-Ku, Sendai 980-8579, Japan

Received:

September 15, 2009

Accepted:

December 15, 2009

Published:

February 20, 2010

Keywords:

robot vision, network, JPEG 2000, communication, data size

Abstract

This paper studies the use of neural networks as a tuning tool for the gain in Modified Proportional-Integral-Derivative (MPID) control used to control a flexible manipulator. The vibration control gain in the MPID controller has been determined in an empirical way so far. It is a considerable time consuming process because the vibration control performance depends not only on the vibration control gain but also on the other parameters such as the payload, references and PD joint servo gains. Hence, the vibration control gain must be tuned considering the other parameters. In order to find optimal vibration control gain for the MPID controller, a neural network based approach is proposed in this paper. The proposed neural network finds an optimum vibration control gain that minimizes a criteria function. The criteria function is selected to represent the effect of the vibration of the end effector in addition to the speed of response. The scaled conjugate gradient algorithm is used as a learning algorithm for the neural network. Tuned gain response results are compared to results for other types of gains. The effectiveness of using the neural network appears in the reduction of the computational time and the ability to tune the gain with different loading condition.

Cite this article as:

T. Mansour, A. Konno, and M. Uchiyama, “MPID Control Tuning for a Flexible Manipulator Using a Neural Network,” J. Robot. Mechatron., Vol.22 No.1, pp. 82-90, 2010.

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References

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[3] D. Sun, J. Shan, Y. Su, H. Liu, and C. Lam, “Hybrid Control of a Rotational Flexible Beam Using Enhanced PD Feedback with a Non-Linear Differentiator and PZT Actuators,” Smart Mater. Struct., Vol.14, pp. 69-78, 2005.
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[8] H. A. Talebi, K. Khorasani, and R. V. Patel, “Neural Network Based Control Schemes for Flexible Link Manipulators: Simulations and Experiments,” Neural Networks, Vol.11, pp. 1357-1377, 1998.
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[10] M. Isogai, F. Arai, and T. Fukuda, “Intelligent Sensor Fault Detection of Vibration Control for Flexible Structures,” J. of Robotics and Mechatronics, Vol.11, No.6, pp. 524-530, 1999.
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[14] T. Mansour, A. Konno, and M. Uchiyama, “Modified PID Control of a Single-Link Flexible Robot,” Advanced Robotics, Vol.22, pp. 433-449, 2008.
[15] S. S. Ge, T. H. Lee, and G. Zhu, “Genetic Algorithm Tuning of Lyapunov-Based Controllers” An Application to a Single-Link Flexible Robot System,” IEEE Trans. On Industrial Electronics, Vol.43, No.5, pp. 567-573, 1996.
[16] J. Principe, N. Euliano, and W. Lefebvre, “Neural and Adaptive Systems: Fundamentals Through Simulations,” John Wiley and Sons, New York, pp. 100-172, 2000.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] R. H. Jr., Cannon and E. Schmitz, “Initial Experiments on the End Point Control of a Flexible One-Link Robot,” Int. J. of Robotics Research, Vol.3, No.4, pp. 62-75, 1984.

[2] [2] S. S. Ge, T. H. Lee, and J. Q. Gong, “A Robust Distributed Controller of a Single-Link SCARA /Cartesian Smart Materials Robot,” Mechatronics, Vol.9, No.1, 1999, pp. 65-93, 1999.

[3] [3] D. Sun, J. Shan, Y. Su, H. Liu, and C. Lam, “Hybrid Control of a Rotational Flexible Beam Using Enhanced PD Feedback with a Non-Linear Differentiator and PZT Actuators,” Smart Mater. Struct., Vol.14, pp. 69-78, 2005.

[4] [4] V. Etxebarria, A. Sanz, and I. Lizarraga, “Control of a Lightweight Flexible Robotic Arm Using Sliding Modes,” Int. J. of Advanced Robotic Systems, Vol.2, No.2, pp. 103-110, 2005.

[5] [5] H. G. Lee, S. Arimoto, and F. Miyazaki, “Liapunov Stability Analysis for PDS Control of Flexible Multi-link Manipulators,” Proc. of the Conf. on Decision and Control, Austin, pp. 75-80, 1988.

[6] [6] T. Maruyama, C. Xu, A. Ming, and M. Shimojo, “Motion Control of Ultra-High-Speed Manipulator with a Flexible Link Based on Dynamically Coupled Driving,” J. of Robotics and Mechatronics, Vol.18, No.5, pp. 598-607, 2006.

[7] [7] F.Matsuno and A. Hayashi, “PDS Cooperative Control of Two Onelink Flexible Arms,” Proc. of the 2000 IEEE Int. Conf. on Robotics and Automation, San Francisco, pp. 1490-1495, 2000.

[8] [8] H. A. Talebi, K. Khorasani, and R. V. Patel, “Neural Network Based Control Schemes for Flexible Link Manipulators: Simulations and Experiments,” Neural Networks, Vol.11, pp. 1357-1377, 1998.

[9] [9] M. Kawato, K. Furukawa, and R. Suzuki, “A Hierarchical Neural Network Model for Control and Learning of Voluntary Movement,” Biological Cybernetics, Vol.57, pp. 169-185, 1987.

[10] [10] M. Isogai, F. Arai, and T. Fukuda, “Intelligent Sensor Fault Detection of Vibration Control for Flexible Structures,” J. of Robotics and Mechatronics, Vol.11, No.6, pp. 524-530, 1999.

[11] [11] T. Lianfang, J. Wang, and Z. Mao, “Constrained Motion Control of Flexible Robot Manipulators Based on Recurrent Neural Networks,” IEEE Trans. On Systems, Man, And Cybernetics Part B: Cybernetics, Vol.34, No.3, pp. 1541-1552, 2004.

[12] [12] X. P. Cheng and R. V. Patel, “Neural Network Based Tracking Control of a Flexible MacroMicro Manipulator System,” Neural Networks, Vol.16, pp. 271-286, 2003.

[13] [13] A. Yazdizadeh, K. Khorasani, and R. V. Patel, “Identification of a Two-Link Flexible Manipulator Using Adaptive Time Delay Neural Networks,” IEEE Trans. On Systems, Man, And Cybernetics Part B: Cybernetics, Vol.30, No.1, pp. 165-172, 2000.

[14] [14] T. Mansour, A. Konno, and M. Uchiyama, “Modified PID Control of a Single-Link Flexible Robot,” Advanced Robotics, Vol.22, pp. 433-449, 2008.

[15] [15] S. S. Ge, T. H. Lee, and G. Zhu, “Genetic Algorithm Tuning of Lyapunov-Based Controllers” An Application to a Single-Link Flexible Robot System,” IEEE Trans. On Industrial Electronics, Vol.43, No.5, pp. 567-573, 1996.

[16] [16] J. Principe, N. Euliano, and W. Lefebvre, “Neural and Adaptive Systems: Fundamentals Through Simulations,” John Wiley and Sons, New York, pp. 100-172, 2000.