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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Mechatronics and Automation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
