Research Papers

Performance-Adaptive Generalized Predictive Control-Based Proportional-Integral-Derivative Control System and Its Application

[+] Author and Article Information
Takao Sato

Associate Professor
Graduate School of Engineering,
University of Hyogo,
2167 Shosha,
Himeji 671-2280, Japan
e-mail: tsato@eng.u-hyogo.ac.jp

Toru Yamamoto

Institute of Engineering,
Hiroshima University,
1-4-1 Kagamiyama,
Hiroshima 739-8527, Japan
e-mail: yama@hiroshima-u.ac.jp

Nozomu Araki

Assistant Professor
Graduate School of Engineering,
University of Hyogo,
2167 Shosha,
Himeji 671-2280, Japan
e-mail: araki@eng.u-hyogo.ac.jp

Yasuo Konishi

Graduate School of Engineering,
University of Hyogo,
2167 Shosha,
Himeji 671-2280, Japan
e-mail: konishi@eng.u-hyogo.ac.jp

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 13, 2012; final manuscript received June 24, 2014; published online August 8, 2014. Assoc. Editor: Gregory Shaver.

J. Dyn. Sys., Meas., Control 136(6), 061003 (Aug 08, 2014) (9 pages) Paper No: DS-12-1419; doi: 10.1115/1.4027923 History: Received December 13, 2012; Revised June 24, 2014

In the present paper, we discuss a new design method for a proportional-integral-derivative (PID) control system using a model predictive approach. The PID compensator is designed based on generalized predictive control (GPC). The PID parameters are adaptively updated such that the control performance is improved because the design parameters of GPC are selected automatically in order to attain a user-specified control performance. In the proposed scheme, the estimated plant parameters are updated only when the prediction error increases. Therefore, the control system is not updated frequently. The control system is updated only when the control performance is sufficiently improved. The effectiveness of the proposed method is demonstrated numerically. Finally, the proposed method is applied to a weigh feeder, and experimental results are presented.

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Fig. 1

Image of trade-off curves

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Fig. 3

Difference in the control input

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Fig. 9

Trade-off curves at step 766

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Fig. 6

Identified β0, β1

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Fig. 8

Trade-off curves at step 200

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Fig. 20

Proportional gain

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Fig. 12

Block diagram of the experimental setup

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Fig. 13

Mass flow rate (kg/step)

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Fig. 14

Difference in the control input

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Fig. 15

Input voltage (V)

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Fig. 17

Trade-off curves plotted on 2D surface

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Fig. 19

Identified β0, β1



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