Neuro-fuzzy-based Smith predictor for FOPLDT process control
by Hao Chen; Zoubir Zouaoui; Zheng Chen
International Journal of Mechatronics and Automation (IJMA), Vol. 2, No. 3, 2012

Abstract: In this paper, a novel approach for a first-order-plus-long-dead-time (FOPLDT) process control has been developed. The Smith predictor (SP) is a well-known compensation scheme to deal with the problem of long dead time in process control. However, in the presence of process modelling errors in gain, time constant or time delay, the Smith predictor appears to have poor performance, as even instability may be caused as soon as model mismatch becomes significant. An application of neuro-fuzzy-based Smith predictor method is introduced to improve the robustness to modelling errors for FOPLDT process control. From simulation evaluation, this new proposed approach provides superior performance to the original Smith predictor and robustness to modelling errors.

Online publication date: Thu, 27-Nov-2014

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