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|Title:||Identification and predictive control of a multistage evaporator||Authors:||Atuonwu, J.C.
Nonlinear model predictive control
Nonlinear system identification
Recurrent neural networks
|Issue Date:||Dec-2010||Citation:||Atuonwu, J.C., Cao, Y., Rangaiah, G.P., Tadé, M.O. (2010-12). Identification and predictive control of a multistage evaporator. Control Engineering Practice 18 (12) : 1418-1428. ScholarBank@NUS Repository. https://doi.org/10.1016/j.conengprac.2010.08.002||Abstract:||A recurrent neural network-based nonlinear model predictive control (NMPC) scheme in parallel with PI control loops is developed for a simulation model of an industrial-scale five-stage evaporator. Input-output data from system identification experiments are used in training the network using the Levenberg-Marquardt algorithm with automatic differentiation. The same optimization algorithm is used in predictive control of the plant. The scheme is tested with set-point tracking and disturbance rejection problems on the plant while control performance is compared with that of PI controllers, a simplified mechanistic model-based NMPC developed in previous work and a linear model predictive controller (LMPC). Results show significant improvements in control performance by the new parallel NMPC-PI control scheme. © 2010 Elsevier Ltd.||Source Title:||Control Engineering Practice||URI:||http://scholarbank.nus.edu.sg/handle/10635/64049||ISSN:||09670661||DOI:||10.1016/j.conengprac.2010.08.002|
|Appears in Collections:||Staff Publications|
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