JRM Vol.22 No.1 pp. 82-90
doi: 10.20965/jrm.2010.p0082


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

September 15, 2009
December 15, 2009
February 20, 2010
robot vision, network, JPEG 2000, communication, data size

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:
Tamer Mansour, Atsushi Konno, and Masaru 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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