JRM Vol.28 No.5 pp. 722-729
doi: 10.20965/jrm.2016.p0722


Design and Experimental Evaluation of a Data-Oriented Generalized Predictive PID Controller

Zhe Guan*, Shin Wakitani**, and Toru Yamamoto**

*Graduate School of Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan

**Institute of Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan

March 24, 2016
July 25, 2016
October 20, 2016
data-oriented technique, generalized predictive control (GPC), proportional-integral-derivative (PID) tuning, injection molding
This paper presents a data-oriented technique for designing a proportional-integral-derivative (PID) controller based on a generalized predictive control law for linear unknown systems. In several control design approaches, a model-based control theory, which requires accurate modeling and identification of the plant, is used to calculate the control parameters. However, in higher-order systems and/or systems with an unknown time delay such as chemical industries and thermal industries, it is difficult to model or identify the plant accurately. Over the last decade, data-oriented techniques in which the online or offline data are utilized have been attracting considerable attention. Designing the controllers for unknown plants based on only the input/output data is the main feature of this technique. In this study, controller parameters are first obtained by using a generalized predictive control law with the data-oriented technique, and are converted to PID parameters from the practical point of view. The proposed method is validated experimentally using a real injection-molding machine. The results demonstrate the efficiency of the proposed method.
Schematic figure of data-oriented GPC-PID controller

Schematic figure of data-oriented GPC-PID controller

Cite this article as:
Z. Guan, S. Wakitani, and T. Yamamoto, “Design and Experimental Evaluation of a Data-Oriented Generalized Predictive PID Controller,” J. Robot. Mechatron., Vol.28 No.5, pp. 722-729, 2016.
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Last updated on Jun. 03, 2024