Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model
Jianqiang Yi*, Naoyoshi Yubazaki* and Kaoru Hirota**
*Technology Research Center, Mycom, Inc. 12, S. Shimobano, Saga Hirosawa, Ukyo, Kyoto 616-8303, Japan
**Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori, Yokohama 226-8502, Japan
Received:November 2, 1999Accepted:August 15, 2000Published:July 20, 2000
Keywords:Fuzzy control, Importance degree, Single input rule module, Trajectory tracking, Unconstrained object
A trajectory tracking experiment system taking an unconstrained table-tennis ball as the control object is constructed, and a fuzzy controller based on the SIRMs dynamically connected fuzzy inference model is proposed. For each of the three input items of the fuzzy controller, a SIRM (Single Input Rule Module) is established and an importance degree is defined. Especially for the input item corresponding to ball velocity, its importance degree is tuned dynamically according to moving conditions. The summation of the products of the importance degree and the fuzzy inference result of the SIRMs is calculated to control the angles of a table, making the ball on the table move along a desired trajectory. A virtual spiral asymptotic trajectory is also introduced to give the object an adequate desired position at each sampling time. Tracking experiment results for three kinds of circles and one kind of ellipses show that in more than 80% of the experiments performed under the SIRMs dynamically connected fuzzy inference model, the maximum tracking error is smaller than 0.05m and the unevenness of the sampling steps necessary for each round is very small. Compared with conventional fuzzy controller, the SIRMs dynamically connected fuzzy inference model is proved to be effective in tracking control of unconstrained objects.
Cite this article as:J. Yi, N. Yubazaki, and K. Hirota, “Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.4 No.4, pp. 302-312, 2000.Data files: