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Research Papers

Modeling and Identification of Real-Time Processes Based on Nonzero Setpoint Autotuning Test

[+] Author and Article Information
Prasenjit Ghorai

Department of Electronics
and Instrumentation Engineering,
NIT Agartala, Tripura 799046, India
e-mail: pghorai@gmail.com

Somanath Majhi

Department of Electronics
and Electrical Engineering,
IIT Guwahati, Assam 781039, India
e-mail: smajhi@iitg.ernet.in

Saurabh Pandey

Department of Electronics
and Electrical Engineering,
IIT Guwahati, Assam 781039, India
e-mail: p.saurabh@iitg.ernet.in

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 29, 2016; final manuscript received September 14, 2016; published online November 11, 2016. Assoc. Editor: Shankar Coimbatore Subramanian.

J. Dyn. Sys., Meas., Control 139(2), 021010 (Nov 11, 2016) (8 pages) Paper No: DS-16-1063; doi: 10.1115/1.4034802 History: Received January 29, 2016; Revised September 14, 2016

The paper presents a real-time system modeling and identification scheme for estimation of plant model parameters using a single asymmetrical relay test. A modified set of analytical expressions for unknown plant models under nonzero setpoint and non-negative relay settings is derived. Thereafter, the unknown parameters of three different stable plant models are identified as first-order plus dead time, overdamped, and critically damped second-order plus dead time. The well-known examples from literature are included to show the accuracy of the proposed method through computer simulations. Yokogawa distributed control system centum CS3000 is considered as a design platform for an experimental setup for the realization of asymmetrical relay feedback test. Finally, the transfer function models derived from successive identification of plant dynamics are compared with the literature through Nyquist plots.

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References

Figures

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Fig. 1

Real-time relay-based control system with nonzero setpoint

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Fig. 2

Plant input and output information

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Fig. 3

Nyquist frequency response plots for example 1: (a) actual plant, (b) proposed model, (c) model by Liu and Gao [19], and (d) model by Vivek and Chidambaram [14]

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Fig. 4

Limit cycle output for example 2: (a) noisy output with 20 dB SNR and (b) reconstructed output

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Fig. 5

Nyquist frequency response plots for example 2: (a) actual plant, (b) proposed model, (c) model by Bajarangbali et al. [17], and (d) model by Liu and Gao [18]

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Fig. 6

Nyquist frequency response plots for example 2: (a) actual plant, (b) proposed model with 10 dB noise, (c) proposed model with 20 dB noise, and (d) proposed model with 30 dB noise

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Fig. 7

Nyquist frequency response plots for example 3: (a) actual plant, (b) proposed model, (c) model by Vivek and Chidambaram [26], and (d) model by Srinivasan and Chidambaram [13]

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Fig. 8

Experimental setup of a real-time water level control system

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Fig. 9

Response of level control system using an asymmetrical relay: (a) limit cycle around setpoint and (b) relay output

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Fig. 10

Comparison between simulation and experimental plant output: (a) setpoint, (b) plant output, (c) FOPDT model output, (d) overdamped SOPDT model output, and (e) ZN test-based model output

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Fig. 11

Nyquist frequency response plots for level control system: (a) proposed FOPDT process model, (b) proposed overdamped SOPDT model, (c) proposed critically damped SOPDT model, and (d) model by ZN test

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