Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 410428
Title Nonlinear model predictive control of a multistage evaporator system using recurrent neural networks
Author(s) Atuonwu, J.C.; Cao, Y.; Rangaiah, G.P.; Tade, M.O.
Source In: Proceedings of the 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, Xian, China, 25-27 May 2009. - - p. 1662 - 1667.
Event EEE conference on Industrial Electronics and Applications, Xian, China, 2009-05-25/2009-05-27
Department(s) Systems and Control Group
Publication type Contribution in proceedings
Publication year 2011
Abstract The use of multistage evaporators, motivated by the energy economy from reusing the flashed steam is common in a wide range of process industries. Such evaporators however present several control problems which manifest in the form of strong interactions among the many process variables, significant dead times, tendency to open-loop instability and severe nonlinearities. In this paper, a nonlinear model predictive control (NMPC) scheme utilizing a proportional-integral (PI) controller in its inner loop is developed for a simulated industrial-scale five-stage evaporator using a continuous-time recurrent neural network in state space as its internal model. Input-output data obtained from closed-loop system identification experiments are used in training the network by the Levenberg-Marquardt algorithm with automatic differentiation. A similar approach is used in developing an optimal control law for the plant based on the model predictions. The effectiveness of this scheme is tested by simulating various control problem scenarios involving set-point tracking and disturbance rejection and comparing performance with that of decentralized PI controllers developed earlier. Results show significant improvements in control performance, particularly in terms of settling time.
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