Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/66777
Title: Process identification from closed-loop response using optimization methods
Authors: Viswanathan, P.K.
Rangaiah, G.P. 
Keywords: Closed-loop process identification
Optimization
Relay response
Second order plus dead time model
Step response
Issue Date: May-2000
Citation: Viswanathan, P.K., Rangaiah, G.P. (2000-05). Process identification from closed-loop response using optimization methods. Chemical Engineering Research and Design 78 (4) : 528-541. ScholarBank@NUS Repository.
Abstract: Existing methods employ different approaches and analyses for closed-loop identification of a suitable model such as a second order plus dead time (SOPDT) model for a single-input single-output (SISO) process. In this study, an optimization approach for identifying a SOPDT model from closed-loop response is proposed and its performance is extensively evaluated mainly via simulation. Typical examples, test conditions, controller and response types, step and relay responses as well as measurement noise are considered. Three optimization methods, namely, the multi-pass LJ method (which finds the global minimum), modified Simplex method and a quasi-Newton method are tried. The results show that the recovered model is more accurate than those obtained by other methods, and that a reliable and consistent model can be obtained even from the response with measurement noise. At least two minima are likely when step response is used and several minima are probable when relay response is employed. The optimization approach is independent of the nature of the closed-loop response and controller used in the test, be it a PID controller or a relay. Importantly, the test duration can be considerably shortened because the step test need not be conducted until steady state, and in the relay test, steady oscillations are not required.
Source Title: Chemical Engineering Research and Design
URI: http://scholarbank.nus.edu.sg/handle/10635/66777
ISSN: 02638762
Appears in Collections:Staff Publications

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